{escape(title)}
- '
+ + "".join(rows)
+ + "
{escape(LABELS[field])}
'
+ f'{escape(str(record[field]))}
' + f'{name}' + f'{date}{amount_html}' + "
" + f'- {"".join(rows)}
Showing the first {shown} of {total}.
' + if total is not None and total > shown + else "" + ) + + return ( + f'{escape(heading)}
' + + "".join(_subaward_card(subaward) for subaward in subawards) + + note + + "No award detail was returned.
' + ) + tokens = { + "__TITLE__": escape(str(title)), + "__SUBTITLE__": escape(str(record.get("award_id") or "")), + "__BADGES__": _badges_html(record), + "__DESCRIPTION__": ( + f'{escape(str(description))}
' if description else ""
+ ),
+ "__KPIS__": _kpis_html(record),
+ "__BODY__": body,
+ "__SUBAWARDS__": _subawards_html(subawards or [], subaward_total),
+ }
+
+ return re.sub(
+ "|".join(tokens),
+ lambda match: tokens[match.group(0)],
+ template,
+ )
diff --git a/openbb_platform/providers/government_us/openbb_government_us/treasury/utils/fiscal_data.py b/openbb_platform/providers/government_us/openbb_government_us/treasury/utils/fiscal_data.py
new file mode 100644
index 00000000000..ebdba6c2029
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/treasury/utils/fiscal_data.py
@@ -0,0 +1,148 @@
+"""FiscalData API client."""
+
+from datetime import date as dateType
+from typing import Any
+
+BASE_URL = "https://api.fiscaldata.treasury.gov/services/api/fiscal_service/"
+MAX_PAGE_SIZE = 10000
+MAX_RETRIES = 3
+
+
+def default_start_date(
+ start: dateType | None, end: dateType | None, lookback_days: int
+) -> dateType:
+ """Return the effective start date, defaulting to a trailing window.
+
+ Parameters
+ ----------
+ start : date | None
+ Requested start date; returned as-is when provided.
+ end : date | None
+ Requested end date, used as the window anchor; today when None.
+ lookback_days : int
+ Length of the trailing window applied when start is None, so the
+ default query is bounded instead of returning the full history.
+
+ Returns
+ -------
+ date
+ The requested start date, or the anchor minus the trailing window.
+ """
+ from datetime import timedelta
+
+ if start is not None:
+ return start
+ anchor = end or dateType.today()
+ return anchor - timedelta(days=lookback_days)
+
+
+def build_filters(conditions: list[tuple[str, str, Any]]) -> str | None:
+ """Build a FiscalData filter string from (field, operator, value) conditions.
+
+ Parameters
+ ----------
+ conditions : list[tuple[str, str, Any]]
+ Conditions as (field, operator, value); entries with a None value are
+ skipped. Operators are eq, lt, lte, gt, gte, or in.
+
+ Returns
+ -------
+ str | None
+ The comma-joined filter string, or None when no condition applies.
+ """
+ parts = [
+ f"{field}:{operator}:{value}"
+ for field, operator, value in conditions
+ if value is not None
+ ]
+ return ",".join(parts) if parts else None
+
+
+async def get_fiscal_data(
+ endpoint: str,
+ filters: str | None = None,
+ sort: str | None = None,
+ page_size: int = MAX_PAGE_SIZE,
+ **kwargs: Any,
+) -> list[dict]:
+ """Fetch every page of a FiscalData API table.
+
+ Parameters
+ ----------
+ endpoint : str
+ Table path under the fiscal_service base, e.g.
+ 'v2/accounting/od/debt_to_penny'.
+ filters : str | None
+ FiscalData filter string, e.g. 'record_date:gte:2024-01-01'.
+ sort : str | None
+ Sort key, e.g. 'record_date' or '-record_date'.
+ page_size : int
+ Rows per page, capped by the API at 10000.
+
+ Returns
+ -------
+ list[dict]
+ All rows across pages, with the literal string 'null' and empty or
+ whitespace-only strings mapped to None.
+
+ Raises
+ ------
+ EmptyDataError
+ If the query returns no rows.
+ OpenBBError
+ If the API responds with an error payload or an invalid body.
+ """
+ import asyncio
+
+ from openbb_core.app.model.abstract.error import OpenBBError
+ from openbb_core.provider.utils.errors import EmptyDataError
+ from openbb_core.provider.utils.helpers import amake_request
+
+ params = [f"page[size]={page_size}"]
+ if filters:
+ params.append(f"filter={filters}")
+ if sort:
+ params.append(f"sort={sort}")
+ base = f"{BASE_URL}{endpoint}?{'&'.join(params)}"
+
+ async def fetch_page(page_number: int) -> dict:
+ url = f"{base}&page[number]={page_number}"
+ last_error: Exception | None = None
+ for attempt in range(MAX_RETRIES):
+ try:
+ response = await amake_request(url, **kwargs)
+ except Exception as e: # noqa: BLE001
+ last_error = e
+ await asyncio.sleep(2**attempt)
+ continue
+ if isinstance(response, dict) and "data" in response:
+ return response
+ if isinstance(response, dict) and "error" in response:
+ raise OpenBBError(
+ f"FiscalData API error -> {response.get('error')}:"
+ f" {response.get('message')}"
+ )
+ last_error = OpenBBError(f"Unexpected FiscalData response -> {response}")
+ await asyncio.sleep(2**attempt)
+ raise OpenBBError(
+ f"FiscalData request failed after {MAX_RETRIES} attempts -> {url}"
+ ) from last_error
+
+ first_page = await fetch_page(1)
+ rows: list[dict] = list(first_page.get("data", []))
+ total_pages = int(first_page.get("meta", {}).get("total-pages", 1) or 1)
+
+ for page_number in range(2, total_pages + 1):
+ page = await fetch_page(page_number)
+ rows.extend(page.get("data", []))
+
+ if not rows:
+ raise EmptyDataError("The request was returned empty.")
+
+ def clean(value: Any) -> Any:
+ """Map the literal 'null' and empty or whitespace-only strings to None."""
+ if isinstance(value, str) and (value == "null" or not value.strip()):
+ return None
+ return value
+
+ return [{key: clean(value) for key, value in row.items()} for row in rows]
diff --git a/openbb_platform/providers/government_us/openbb_government_us/utils/helpers.py b/openbb_platform/providers/government_us/openbb_government_us/treasury/utils/helpers.py
similarity index 100%
rename from openbb_platform/providers/government_us/openbb_government_us/utils/helpers.py
rename to openbb_platform/providers/government_us/openbb_government_us/treasury/utils/helpers.py
diff --git a/openbb_platform/providers/government_us/openbb_government_us/treasury/utils/recipient.py b/openbb_platform/providers/government_us/openbb_government_us/treasury/utils/recipient.py
new file mode 100644
index 00000000000..0e5de0cdfd2
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/treasury/utils/recipient.py
@@ -0,0 +1,249 @@
+"""USAspending recipient helpers."""
+
+REQUEST_TIMEOUT = 90
+
+EARLIEST_FISCAL_YEAR = 2008
+
+EARLIEST_SEARCH_DATE = "2007-10-01"
+
+RECIPIENT_LEVELS = {"P", "C", "R"}
+
+TOP_CATEGORIES: dict[str, str] = {
+ "awarding_agency": "Awarding Agencies",
+ "awarding_subagency": "Awarding Sub-Agencies",
+ "federal_account": "Federal Accounts",
+ "cfda": "Assistance Listings",
+ "country": "Countries",
+ "state_territory": "U.S. States or Territories",
+}
+
+
+def normalize_recipient_id(recipient_id: str) -> str:
+ """Normalize a recipient id to the casing the source requires.
+
+ Parameters
+ ----------
+ recipient_id : str
+ A recipient id of the form '{escape(title)}
- {body}
{escape(LABELS[field])}
'
+ + _number_cell(field, profile[field], tag="div").replace(
+ ""
+ for field in KPI_FIELDS
+ if profile.get(field) is not None
+ )
+
+
+def _badges_html(profile: dict) -> str:
+ """Render the identifying badges shown under the title."""
+ location = profile.get("location") or {}
+ values = [
+ LEVEL_LABELS.get(profile.get("recipient_level") or ""),
+ profile.get("uei"),
+ profile.get("duns"),
+ location.get("state_code"),
+ *(profile.get("business_types") or []),
+ ]
+
+ return "".join(
+ f'{escape(str(value).replace("_", " "))}'
+ for value in values
+ if value
+ )
+
+
+def _top_table(title: str, rows: list[dict], denominator: float | None) -> str:
+ """Render one top-category table, deriving each row's share of the total."""
+ if not rows:
+ return ""
+
+ cells = []
+
+ for row in rows:
+ amount = row.get("amount")
+ share = (
+ f'{amount / denominator * 100} '
+ if amount is not None and denominator
+ else '{escape(str(row.get('name') or ''))} "
+ + (
+ f'{escape(str(amount))} '
+ if amount is not None
+ else ''
+ '
"
+ )
+
+
+def _tops_html(tops: dict[str, list[dict]], denominator: float | None) -> str:
+ """Render the top-category tables inside a collapsible container."""
+ from openbb_government_us.treasury.utils.recipient import TOP_CATEGORIES
+
+ cards = "".join(
+ _top_table(TOP_CATEGORIES[category], rows, denominator)
+ for category, rows in tops.items()
+ if category in TOP_CATEGORIES
+ )
+
+ if not cards:
+ return ""
+
+ return (
+ ''
+ )
+
+
+def _child_card(child: dict) -> str:
+ """Render one child recipient as a collapsible card."""
+ name = escape(str(child.get("name") or "Unnamed recipient"))
+ state = escape(str(child.get("state_province") or ""))
+ amount = child.get("amount")
+ amount_html = (
+ f'{escape(str(amount))}'
+ if amount is not None
+ else ""
+ )
+
+ return (
+ ''
+ ""
+ )
+
+
+def _children_html(children: list[dict], shown: int | None = None) -> str:
+ """Render the child recipients inside a collapsible container."""
+ if not children:
+ return ""
+
+ total = shown if shown is not None else len(children)
+ note = (
+ f'"
+ )
+
+
+def render_recipient_info(
+ profile: dict,
+ tops: dict[str, list[dict]] | None = None,
+ children: list[dict] | None = None,
+ denominator: float | None = None,
+ child_total: int | None = None,
+) -> str:
+ """Render a recipient profile as a standalone HTML page.
+
+ Parameters
+ ----------
+ profile : dict
+ The recipient profile payload.
+ tops : dict[str, list[dict]] | None
+ Top-category rows keyed by category name.
+ children : list[dict] | None
+ Child recipient rows, rendered as collapsible cards.
+ denominator : float | None
+ Total obligations over the same window, used to derive each category
+ row's share. Shares are omitted when it is absent or zero.
+ child_total : int | None
+ Total child recipients, used to disclose a truncated render.
+
+ Returns
+ -------
+ str
+ The rendered HTML document.
+ """
+ import re
+
+ template = TEMPLATE.read_text(encoding="utf-8")
+ location = profile.get("location") or {}
+ body = (
+ _section_html("Identity", _rows_html(IDENTITY_FIELDS, profile))
+ + _section_html("Location", _rows_html(LOCATION_FIELDS, location))
+ ) or ':' drill token into its parts.
+
+ Parameters
+ ----------
+ scope : str | None
+ A token emitted by a breakdown row, e.g. 'agency:1173'.
+
+ Returns
+ -------
+ tuple[str, str] | None
+ The dimension and id, or None when no token was given.
+
+ Raises
+ ------
+ OpenBBError
+ If the token is malformed or names an unknown dimension.
+ """
+ from openbb_core.app.model.abstract.error import OpenBBError
+
+ value = (scope or "").strip()
+
+ if not value:
+ return None
+
+ dimension, separator, identifier = value.partition(":")
+
+ if not separator or not identifier.strip():
+ raise OpenBBError(
+ f"Invalid scope: '{scope}'. It must read ':', e.g."
+ " 'agency:1173', as emitted by a breakdown row."
+ )
+
+ dimension = dimension.strip()
+
+ if dimension not in SCOPE_DIMENSIONS:
+ raise OpenBBError(
+ f"Invalid scope dimension: '{dimension}'. Valid dimensions are: "
+ + ", ".join(SCOPE_DIMENSIONS)
+ )
+
+ return dimension, identifier.strip()
+
+
+DIMENSION_OPTIONS = (
+ "object_class",
+ "budget_function",
+ "budget_subfunction",
+ "agency",
+ "federal_account",
+)
+
+
+async def dimension_options(
+ dimension: str, agency: str | None = None
+) -> list[dict[str, str]]:
+ """List the selectable entries of one spending dimension.
+
+ Parameters
+ ----------
+ dimension : str
+ One of DIMENSION_OPTIONS.
+ agency : str | None
+ Narrow the entries to one agency. Federal accounts number ~1,900
+ government-wide but only ~160 within an agency, so the list is only
+ practical once scoped.
+
+ Returns
+ -------
+ list[dict[str, str]]
+ Widget options pairing each entry's name with its id, by name.
+ """
+ from openbb_government_us.treasury.utils.recipient import REQUEST_TIMEOUT
+ from openbb_government_us.treasury.utils.usaspending import post_usaspending
+
+ if dimension not in DIMENSION_OPTIONS:
+ return []
+
+ fiscal_year, period = await latest_submission()
+ filters: dict[str, str] = {"fy": str(fiscal_year), "period": period}
+
+ if agency:
+ filters["agency"] = agency
+
+ response = await post_usaspending(
+ "spending/",
+ {"type": dimension, "filters": filters},
+ timeout=REQUEST_TIMEOUT,
+ )
+ options = [
+ {"label": str(row["name"]), "value": str(row["id"])}
+ for row in response.get("results") or []
+ if row.get("id") is not None and row.get("name")
+ ]
+
+ return sorted(options, key=lambda option: option["label"])
+
+
+async def awarding_agency_options() -> list[dict[str, str]]:
+ """List the top-tier awarding agencies, by name."""
+ from openbb_government_us.treasury.utils.recipient import REQUEST_TIMEOUT
+ from openbb_government_us.treasury.utils.usaspending import get_usaspending
+
+ response = await get_usaspending(
+ "references/toptier_agencies/", timeout=REQUEST_TIMEOUT
+ )
+ options = [
+ {"label": str(row["agency_name"]), "value": str(row["agency_name"])}
+ for row in response.get("results") or []
+ if row.get("agency_name")
+ ]
+
+ return sorted(options, key=lambda option: option["label"])
+
+
+def explorer_type_options(scoped: bool) -> list[dict[str, str]]:
+ """List the breakdowns that work for the current scope.
+
+ Parameters
+ ----------
+ scoped : bool
+ Whether any scoping filter is set.
+
+ Returns
+ -------
+ list[dict[str, str]]
+ The offered breakdowns. The four that are too large to enumerate
+ government-wide are offered only once a scope narrows them, so the
+ control never presents a choice that would fail.
+ """
+ offered = STANDALONE_TYPES + (SCOPE_ONLY_TYPES if scoped else ())
+
+ return [{"label": TYPE_LABELS[name], "value": name} for name in offered]
diff --git a/openbb_platform/providers/government_us/openbb_government_us/treasury/utils/usaspending.py b/openbb_platform/providers/government_us/openbb_government_us/treasury/utils/usaspending.py
new file mode 100644
index 00000000000..1223f76986c
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/treasury/utils/usaspending.py
@@ -0,0 +1,140 @@
+"""USAspending API client."""
+
+from typing import Any, Literal
+
+BASE_URL = "https://api.usaspending.gov/api/v2/"
+
+
+async def _request(
+ url: str,
+ method: Literal["GET", "POST"] = "GET",
+ payload: dict | None = None,
+ **kwargs: Any,
+) -> Any:
+ """Send a single request to the USAspending API.
+
+ Parameters
+ ----------
+ url : str
+ Absolute request URL.
+ method : Literal["GET", "POST"]
+ HTTP method.
+ payload : dict | None
+ JSON body to send with a POST.
+
+ Returns
+ -------
+ Any
+ The decoded JSON response.
+ """
+ from openbb_core.provider.utils.helpers import amake_request
+
+ if payload is not None:
+ kwargs["json"] = payload
+ headers = dict(kwargs.pop("headers", None) or {})
+ headers.setdefault("Content-Type", "application/json")
+ kwargs["headers"] = headers
+
+ return await amake_request(url, method=method, **kwargs)
+
+
+def _check_response(response: Any, url: str) -> Any:
+ """Validate a USAspending response body.
+
+ Parameters
+ ----------
+ response : Any
+ The decoded response body.
+ url : str
+ The request URL, used in error messages.
+
+ Returns
+ -------
+ dict | list
+ The validated response body. A few endpoints, such as
+ 'recipient/children//', return a bare array rather than an object.
+
+ Raises
+ ------
+ OpenBBError
+ If the body is neither a JSON object nor array, or carries an API
+ error message.
+ """
+ from openbb_core.app.model.abstract.error import OpenBBError
+
+ if isinstance(response, list):
+ return response
+
+ if not isinstance(response, dict):
+ raise OpenBBError(f"Unexpected USAspending response -> {url} -> {response}")
+
+ if "detail" in response:
+ raise OpenBBError(f"USAspending API error -> {response['detail']}")
+
+ return response
+
+
+async def get_usaspending(path: str, **kwargs: Any) -> Any:
+ """Send a GET request to a USAspending endpoint.
+
+ Parameters
+ ----------
+ path : str
+ Endpoint path under the v2 base, e.g. 'awards/CONT_IDV_X_1549/'.
+
+ Returns
+ -------
+ Any
+ The decoded JSON response, an object or a bare array.
+
+ Raises
+ ------
+ OpenBBError
+ If the request fails or the API returns an error.
+ """
+ from openbb_core.app.model.abstract.error import OpenBBError
+
+ url = f"{BASE_URL}{path}"
+
+ try:
+ response = await _request(url, method="GET", **kwargs)
+ except Exception as e: # noqa: BLE001
+ raise OpenBBError(
+ f"USAspending request failed -> {url} -> {e.__class__.__name__}: {e}"
+ ) from e
+
+ return _check_response(response, url)
+
+
+async def post_usaspending(path: str, payload: dict, **kwargs: Any) -> dict:
+ """Send a POST request with a JSON body to a USAspending endpoint.
+
+ Parameters
+ ----------
+ path : str
+ Endpoint path under the v2 base, e.g. 'subawards/'.
+ payload : dict
+ The JSON request body.
+
+ Returns
+ -------
+ dict
+ The decoded JSON response.
+
+ Raises
+ ------
+ OpenBBError
+ If the request fails or the API returns an error.
+ """
+ from openbb_core.app.model.abstract.error import OpenBBError
+
+ url = f"{BASE_URL}{path}"
+
+ try:
+ response = await _request(url, method="POST", payload=payload, **kwargs)
+ except Exception as e: # noqa: BLE001
+ raise OpenBBError(
+ f"USAspending request failed -> {url} -> {e.__class__.__name__}: {e}"
+ ) from e
+
+ return _check_response(response, url)
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/__init__.py b/openbb_platform/providers/government_us/openbb_government_us/usda/__init__.py
new file mode 100644
index 00000000000..24992a0339c
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/__init__.py
@@ -0,0 +1,276 @@
+"""USDA provider module."""
+
+from importlib.util import find_spec
+
+from openbb_core.provider.abstract.provider import Provider
+
+from openbb_government_us.usda.models.adoption_of_genetically_engineered_crops_in_the_united_states import (
+ AdoptionOfGeneticallyEngineeredCropsInTheUnitedStatesFetcher,
+)
+from openbb_government_us.usda.models.agricultural_and_food_research_and_development_expenditures_in_the_united_states import (
+ AgriculturalAndFoodRdExpendituresFetcher,
+)
+from openbb_government_us.usda.models.agricultural_exchange_rates import (
+ AgriculturalExchangeRatesFetcher,
+)
+from openbb_government_us.usda.models.agricultural_productivity import (
+ AgriculturalProductivityFetcher,
+)
+from openbb_government_us.usda.models.agricultural_trade_multipliers import (
+ AgriculturalTradeMultipliersFetcher,
+)
+from openbb_government_us.usda.models.area_and_road_ruggedness_scales import (
+ AreaAndRoadRuggednessScalesFetcher,
+)
+from openbb_government_us.usda.models.bell_report import FasBellReportFetcher
+from openbb_government_us.usda.models.commodity_costs_and_returns import (
+ CommodityCostsAndReturnsFetcher,
+)
+from openbb_government_us.usda.models.commodity_psd_data import (
+ UsdaCommodityPsdDataFetcher,
+)
+from openbb_government_us.usda.models.commodity_psd_report import (
+ UsdaCommodityPsdReportFetcher,
+)
+from openbb_government_us.usda.models.commuting_zones_and_labor_market_areas import (
+ CommutingZonesAndLaborMarketAreasFetcher,
+)
+from openbb_government_us.usda.models.cost_estimates_of_foodborne_illnesses import (
+ CostEstimatesOfFoodborneIllnessesFetcher,
+)
+from openbb_government_us.usda.models.cotton_wool_and_textile_data import (
+ CottonWoolAndTextileDataFetcher,
+)
+from openbb_government_us.usda.models.county_typology_codes import (
+ CountyTypologyCodesFetcher,
+)
+from openbb_government_us.usda.models.dairy_data import DairyDataFetcher
+from openbb_government_us.usda.models.eating_and_health_module_atus import (
+ EatingAndHealthModuleAtusFetcher,
+)
+from openbb_government_us.usda.models.ers_publication_download import (
+ ErsPublicationDownloadFetcher,
+)
+from openbb_government_us.usda.models.ers_publications import ErsPublicationsFetcher
+from openbb_government_us.usda.models.farm_household_income_and_characteristics import (
+ FarmHouseholdIncomeAndCharacteristicsFetcher,
+)
+from openbb_government_us.usda.models.farm_income_and_wealth_statistics import (
+ FarmIncomeAndWealthStatisticsFetcher,
+)
+from openbb_government_us.usda.models.feed_grains_database import (
+ FeedGrainsDatabaseFetcher,
+)
+from openbb_government_us.usda.models.fertilizer_use_and_price import (
+ FertilizerUseAndPriceFetcher,
+)
+from openbb_government_us.usda.models.food_at_home_monthly_area_prices import (
+ FoodAtHomeMonthlyAreaPricesFetcher,
+)
+from openbb_government_us.usda.models.food_availability_per_capita_data_system import (
+ FoodAvailabilityPerCapitaDataSystemFetcher,
+)
+from openbb_government_us.usda.models.food_consumption_nutrient_intakes_and_diet_quality import (
+ FoodConsumptionNutrientIntakesAndDietQualityFetcher,
+)
+from openbb_government_us.usda.models.food_dollar_series import (
+ FoodDollarSeriesFetcher,
+)
+from openbb_government_us.usda.models.food_expenditure_series import (
+ FoodExpenditureSeriesFetcher,
+)
+from openbb_government_us.usda.models.food_price_outlook import (
+ FoodPriceOutlookFetcher,
+)
+from openbb_government_us.usda.models.food_security_in_the_united_states import (
+ FoodSecurityInTheUnitedStatesFetcher,
+)
+from openbb_government_us.usda.models.frontier_and_remote_area_codes import (
+ FrontierAndRemoteAreaCodesFetcher,
+)
+from openbb_government_us.usda.models.fruit_and_tree_nuts_data import (
+ FruitAndTreeNutsDataFetcher,
+)
+from openbb_government_us.usda.models.fruit_and_vegetable_prices import (
+ FruitAndVegetablePricesFetcher,
+)
+from openbb_government_us.usda.models.global_food_assessment import (
+ GlobalFoodAssessmentFetcher,
+)
+from openbb_government_us.usda.models.international_agricultural_productivity import (
+ InternationalAgriculturalProductivityFetcher,
+)
+from openbb_government_us.usda.models.international_baseline_data import (
+ InternationalBaselineDataFetcher,
+)
+from openbb_government_us.usda.models.international_macroeconomic_data_set import (
+ InternationalMacroeconomicDataSetFetcher,
+)
+from openbb_government_us.usda.models.livestock_and_meat_domestic_data import (
+ LivestockAndMeatDomesticDataFetcher,
+)
+from openbb_government_us.usda.models.livestock_and_meat_international_trade_data import (
+ LivestockAndMeatInternationalTradeDataFetcher,
+)
+from openbb_government_us.usda.models.major_land_uses import MajorLandUsesFetcher
+from openbb_government_us.usda.models.meat_price_spreads import (
+ MeatPriceSpreadsFetcher,
+)
+from openbb_government_us.usda.models.milk_cost_of_production import (
+ MilkCostOfProductionFetcher,
+)
+from openbb_government_us.usda.models.natural_amenities_scale import (
+ NaturalAmenitiesScaleFetcher,
+)
+from openbb_government_us.usda.models.normalized_prices import (
+ NormalizedPricesFetcher,
+)
+from openbb_government_us.usda.models.oil_crops_yearbook import (
+ OilCropsYearbookFetcher,
+)
+from openbb_government_us.usda.models.poverty_area_measures import (
+ PovertyAreaMeasuresFetcher,
+)
+from openbb_government_us.usda.models.price_spreads import (
+ FarmToConsumerPriceSpreadsFetcher,
+)
+from openbb_government_us.usda.models.purchase_to_plate import (
+ PurchaseToPlateFetcher,
+)
+from openbb_government_us.usda.models.report_calendar import FasReportCalendarFetcher
+from openbb_government_us.usda.models.resource_requirements_of_food_demand import (
+ ResourceRequirementsOfFoodDemandFetcher,
+)
+from openbb_government_us.usda.models.rice_yearbook import RiceYearbookFetcher
+from openbb_government_us.usda.models.rural_urban_commuting_area_codes import (
+ RuralUrbanCommutingAreaCodesFetcher,
+)
+from openbb_government_us.usda.models.rural_urban_continuum_codes import (
+ RuralUrbanContinuumCodesFetcher,
+)
+from openbb_government_us.usda.models.season_average_price_forecasts import (
+ SeasonAveragePriceForecastsFetcher,
+)
+from openbb_government_us.usda.models.snap_policy_data_sets import (
+ SnapPolicyDataSetsFetcher,
+)
+from openbb_government_us.usda.models.state_agricultural_trade import (
+ StateAgriculturalTradeFetcher,
+)
+from openbb_government_us.usda.models.sugar_sweeteners_yearbook import (
+ SugarSweetenersYearbookFetcher,
+)
+from openbb_government_us.usda.models.urban_influence_codes import (
+ UrbanInfluenceCodesFetcher,
+)
+from openbb_government_us.usda.models.us_agricultural_trade import (
+ UsAgriculturalTradeFetcher,
+)
+from openbb_government_us.usda.models.us_bioenergy_statistics import (
+ UsBioenergyStatisticsFetcher,
+)
+from openbb_government_us.usda.models.us_food_imports import UsFoodImportsFetcher
+from openbb_government_us.usda.models.vegetables_and_pulses import (
+ VegetablesAndPulsesFetcher,
+)
+from openbb_government_us.usda.models.weather_bulletin import (
+ UsdaWeatherBulletinFetcher,
+)
+from openbb_government_us.usda.models.weather_bulletin_download import (
+ UsdaWeatherBulletinDownloadFetcher,
+)
+from openbb_government_us.usda.models.wheat_data import WheatDataFetcher
+
+COMMODITY_INSTALLED = find_spec("openbb_commodity") is not None
+
+usda_provider = Provider(
+ name="usda",
+ website="https://www.usda.gov",
+ description="""Data published by the U.S. Department of Agriculture -
+Production, Supply and Distribution (PSD) reports and time series from the
+Foreign Agricultural Service, and the Weekly Weather and Crop Bulletin from
+the Economics, Statistics and Market Information System. All sources are
+public and keyless.""",
+ fetcher_dict={
+ "AdoptionOfGeneticallyEngineeredCropsInTheUnitedStates": (
+ AdoptionOfGeneticallyEngineeredCropsInTheUnitedStatesFetcher
+ ),
+ "AgriculturalAndFoodRdExpenditures": AgriculturalAndFoodRdExpendituresFetcher,
+ "AgriculturalExchangeRates": AgriculturalExchangeRatesFetcher,
+ "AgriculturalProductivity": AgriculturalProductivityFetcher,
+ "AgriculturalTradeMultipliers": AgriculturalTradeMultipliersFetcher,
+ "AreaAndRoadRuggednessScales": AreaAndRoadRuggednessScalesFetcher,
+ "CommodityCostsAndReturns": CommodityCostsAndReturnsFetcher,
+ "CommodityPsdData": UsdaCommodityPsdDataFetcher,
+ "CommodityPsdReport": UsdaCommodityPsdReportFetcher,
+ "CommutingZonesAndLaborMarketAreas": (CommutingZonesAndLaborMarketAreasFetcher),
+ "CostEstimatesOfFoodborneIllnesses": CostEstimatesOfFoodborneIllnessesFetcher,
+ "CottonWoolAndTextileData": CottonWoolAndTextileDataFetcher,
+ "CountyTypologyCodes": CountyTypologyCodesFetcher,
+ "DairyData": DairyDataFetcher,
+ "EatingAndHealthModuleAtus": EatingAndHealthModuleAtusFetcher,
+ "ErsPublicationDownload": ErsPublicationDownloadFetcher,
+ "ErsPublications": ErsPublicationsFetcher,
+ "FarmHouseholdIncomeAndCharacteristics": (
+ FarmHouseholdIncomeAndCharacteristicsFetcher
+ ),
+ "FarmIncomeAndWealthStatistics": FarmIncomeAndWealthStatisticsFetcher,
+ "FarmToConsumerPriceSpreads": FarmToConsumerPriceSpreadsFetcher,
+ "FasBellReport": FasBellReportFetcher,
+ "FasReportCalendar": FasReportCalendarFetcher,
+ "FeedGrainsDatabase": FeedGrainsDatabaseFetcher,
+ "FertilizerUseAndPrice": FertilizerUseAndPriceFetcher,
+ "FoodAtHomeMonthlyAreaPrices": FoodAtHomeMonthlyAreaPricesFetcher,
+ "FoodAvailabilityPerCapitaDataSystem": (
+ FoodAvailabilityPerCapitaDataSystemFetcher
+ ),
+ "FoodConsumptionNutrientIntakesAndDietQuality": (
+ FoodConsumptionNutrientIntakesAndDietQualityFetcher
+ ),
+ "FoodDollarSeries": FoodDollarSeriesFetcher,
+ "FoodExpenditureSeries": FoodExpenditureSeriesFetcher,
+ "FoodPriceOutlook": FoodPriceOutlookFetcher,
+ "FoodSecurityInTheUnitedStates": FoodSecurityInTheUnitedStatesFetcher,
+ "FrontierAndRemoteAreaCodes": FrontierAndRemoteAreaCodesFetcher,
+ "FruitAndTreeNutsData": FruitAndTreeNutsDataFetcher,
+ "FruitAndVegetablePrices": FruitAndVegetablePricesFetcher,
+ "GlobalFoodAssessment": GlobalFoodAssessmentFetcher,
+ "InternationalAgriculturalProductivity": (
+ InternationalAgriculturalProductivityFetcher
+ ),
+ "InternationalBaselineData": InternationalBaselineDataFetcher,
+ "InternationalMacroeconomicDataSet": InternationalMacroeconomicDataSetFetcher,
+ "LivestockAndMeatDomesticData": LivestockAndMeatDomesticDataFetcher,
+ "LivestockAndMeatInternationalTradeData": (
+ LivestockAndMeatInternationalTradeDataFetcher
+ ),
+ "MajorLandUses": MajorLandUsesFetcher,
+ "MeatPriceSpreads": MeatPriceSpreadsFetcher,
+ "MilkCostOfProduction": MilkCostOfProductionFetcher,
+ "NaturalAmenitiesScale": NaturalAmenitiesScaleFetcher,
+ "NormalizedPrices": NormalizedPricesFetcher,
+ "OilCropsYearbook": OilCropsYearbookFetcher,
+ "PovertyAreaMeasures": PovertyAreaMeasuresFetcher,
+ "PurchaseToPlate": PurchaseToPlateFetcher,
+ "ResourceRequirementsOfFoodDemand": ResourceRequirementsOfFoodDemandFetcher,
+ "RiceYearbook": RiceYearbookFetcher,
+ "RuralUrbanCommutingAreaCodes": RuralUrbanCommutingAreaCodesFetcher,
+ "RuralUrbanContinuumCodes": RuralUrbanContinuumCodesFetcher,
+ "SeasonAveragePriceForecasts": SeasonAveragePriceForecastsFetcher,
+ "SnapPolicyDataSets": SnapPolicyDataSetsFetcher,
+ "StateAgriculturalTrade": StateAgriculturalTradeFetcher,
+ "SugarSweetenersYearbook": SugarSweetenersYearbookFetcher,
+ "UrbanInfluenceCodes": UrbanInfluenceCodesFetcher,
+ "UsAgriculturalTrade": UsAgriculturalTradeFetcher,
+ "UsBioenergyStatistics": UsBioenergyStatisticsFetcher,
+ "UsFoodImports": UsFoodImportsFetcher,
+ "VegetablesAndPulses": VegetablesAndPulsesFetcher,
+ "WeatherBulletin": UsdaWeatherBulletinFetcher,
+ "WeatherBulletinDownload": UsdaWeatherBulletinDownloadFetcher,
+ "WheatData": WheatDataFetcher,
+ },
+ repr_name="U.S. Department of Agriculture",
+ instructions="""All endpoints use public, keyless data from USDA FAS PSD
+Online and the USDA ESMIS publication service, so no credentials are
+required.""",
+)
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/assets/apps.json b/openbb_platform/providers/government_us/openbb_government_us/usda/assets/apps.json
new file mode 100644
index 00000000000..4745d8eff51
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/assets/apps.json
@@ -0,0 +1,811 @@
+[
+ {
+ "name": "US Department of Agriculture",
+ "img": "https://upload.wikimedia.org/wikipedia/commons/thumb/5/52/Corn_Fields%2C_Iowa_Farm_7-13_%2815277889101%29.jpg/1280px-Corn_Fields%2C_Iowa_Farm_7-13_%2815277889101%29.jpg",
+ "img_dark": "https://upload.wikimedia.org/wikipedia/commons/thumb/5/52/Corn_Fields%2C_Iowa_Farm_7-13_%2815277889101%29.jpg/1280px-Corn_Fields%2C_Iowa_Farm_7-13_%2815277889101%29.jpg",
+ "img_light": "https://upload.wikimedia.org/wikipedia/commons/thumb/5/52/Corn_Fields%2C_Iowa_Farm_7-13_%2815277889101%29.jpg/1280px-Corn_Fields%2C_Iowa_Farm_7-13_%2815277889101%29.jpg",
+ "description": "Agricultural production, export sales, prices, costs, land use, and trade from the USDA's Foreign Agricultural Service and Economic Research Service.",
+ "allowCustomization": true,
+ "tabs": {
+ "charts": {
+ "id": "charts",
+ "name": "ERS Charts",
+ "layout": [
+ {
+ "i": "usda_ers_chart_usda_obb",
+ "x": 0,
+ "y": 0,
+ "w": 40,
+ "h": 30
+ }
+ ]
+ },
+ "publications": {
+ "id": "publications",
+ "name": "Publications",
+ "layout": [
+ {
+ "i": "usda_publication_documents_custom_obb",
+ "x": 0,
+ "y": 0,
+ "w": 40,
+ "h": 30
+ }
+ ]
+ },
+ "trade": {
+ "id": "trade",
+ "name": "Trade",
+ "layout": [
+ {
+ "i": "usda_agricultural_trade_usda_obb",
+ "x": 0,
+ "y": 0,
+ "w": 40,
+ "h": 15,
+ "state": {
+ "params": {
+ "table": "exports_ytd",
+ "value_type": "value",
+ "period_basis": "Monthly"
+ }
+ }
+ },
+ {
+ "i": "usda_us_food_imports_usda_obb",
+ "x": 0,
+ "y": 15,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "table": "by_food_group",
+ "measure": "value"
+ }
+ }
+ },
+ {
+ "i": "usda_agricultural_exchange_rates_usda_obb",
+ "x": 0,
+ "y": 29,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "table": "real_index_annual"
+ }
+ }
+ },
+ {
+ "i": "usda_state_agricultural_trade_usda_obb",
+ "x": 0,
+ "y": 43,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "table": "exports_by_commodity",
+ "state": "California"
+ }
+ }
+ },
+ {
+ "i": "usda_agricultural_trade_multipliers_usda_obb",
+ "x": 0,
+ "y": 57,
+ "w": 40,
+ "h": 14
+ }
+ ]
+ },
+ "psd": {
+ "id": "psd",
+ "name": "Production, Supply & Distribution",
+ "layout": [
+ {
+ "i": "usda_psd_data_usda_obb",
+ "x": 0,
+ "y": 0,
+ "w": 40,
+ "h": 14
+ },
+ {
+ "i": "usda_psd_report_custom_obb",
+ "x": 0,
+ "y": 14,
+ "w": 40,
+ "h": 20
+ }
+ ]
+ },
+ "prices_and_costs": {
+ "id": "prices_and_costs",
+ "name": "Prices & Costs",
+ "layout": [
+ {
+ "i": "usda_price_spreads_usda_obb",
+ "x": 0,
+ "y": 0,
+ "w": 20,
+ "h": 14
+ },
+ {
+ "i": "usda_milk_cost_of_production_usda_obb",
+ "x": 20,
+ "y": 0,
+ "w": 20,
+ "h": 14
+ },
+ {
+ "i": "usda_dairy_data_usda_obb",
+ "x": 0,
+ "y": 14,
+ "w": 40,
+ "h": 14
+ },
+ {
+ "i": "usda_meat_price_spreads_usda_obb",
+ "x": 0,
+ "y": 28,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "table": "choice_beef"
+ }
+ }
+ },
+ {
+ "i": "usda_commodity_costs_and_returns_usda_obb",
+ "x": 0,
+ "y": 42,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "commodity": "corn",
+ "region": "U.S. total"
+ }
+ }
+ },
+ {
+ "i": "usda_food_price_outlook_usda_obb",
+ "x": 0,
+ "y": 56,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "table": "cpi_forecast"
+ }
+ }
+ },
+ {
+ "i": "usda_fertilizer_use_and_price_usda_obb",
+ "x": 0,
+ "y": 70,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "table": "us_plant_nutrient_consumption"
+ }
+ }
+ },
+ {
+ "i": "usda_normalized_prices_usda_obb",
+ "x": 0,
+ "y": 84,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "table": "table1_national_prices"
+ }
+ }
+ },
+ {
+ "i": "usda_food_at_home_monthly_area_prices_usda_obb",
+ "x": 0,
+ "y": 98,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "table": "monthly_area_prices",
+ "group": "Dairy",
+ "item": "40000"
+ }
+ }
+ }
+ ]
+ },
+ "farm_income": {
+ "id": "farm_income",
+ "name": "Farm Income & Food Economy",
+ "layout": [
+ {
+ "i": "usda_farm_income_and_wealth_statistics_usda_obb",
+ "x": 0,
+ "y": 0,
+ "w": 40,
+ "h": 20,
+ "state": {
+ "params": {
+ "table": "income_statement",
+ "state": "US"
+ }
+ }
+ },
+ {
+ "i": "usda_food_dollar_series_usda_obb",
+ "x": 0,
+ "y": 20,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "series": "nominal",
+ "table": 1
+ }
+ }
+ },
+ {
+ "i": "usda_food_expenditure_series_usda_obb",
+ "x": 0,
+ "y": 34,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "table": "food_and_alcohol"
+ }
+ }
+ },
+ {
+ "i": "usda_food_availability_per_capita_usda_obb",
+ "x": 0,
+ "y": 48,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "data_system": "food_availability",
+ "food_group": "eggs"
+ }
+ }
+ },
+ {
+ "i": "usda_food_security_in_the_united_states_usda_obb",
+ "x": 0,
+ "y": 62,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "table": "national_trend"
+ }
+ }
+ },
+ {
+ "i": "usda_cost_estimates_of_foodborne_illnesses_usda_obb",
+ "x": 0,
+ "y": 76,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "table": "cases_and_cost"
+ }
+ }
+ },
+ {
+ "i": "usda_snap_policy_data_sets_usda_obb",
+ "x": 0,
+ "y": 90,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "state": "CA"
+ }
+ }
+ },
+ {
+ "i": "usda_farm_household_income_and_characteristics_usda_obb",
+ "x": 0,
+ "y": 104,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "table": "finances_2021_26f"
+ }
+ }
+ }
+ ]
+ },
+ "export_sales": {
+ "id": "export_sales",
+ "name": "Export Sales",
+ "layout": [
+ {
+ "i": "usda_report_calendar_usda_obb",
+ "x": 0,
+ "y": 0,
+ "w": 40,
+ "h": 9,
+ "state": {
+ "params": {
+ "report_type": "export_sales"
+ }
+ }
+ },
+ {
+ "i": "usda_bell_report_custom_obb",
+ "x": 0,
+ "y": 9,
+ "w": 40,
+ "h": 21,
+ "state": {
+ "params": {
+ "commodity_code": "801"
+ }
+ }
+ }
+ ]
+ },
+ "diet_and_nutrition": {
+ "id": "diet_and_nutrition",
+ "name": "Diet & Nutrition",
+ "layout": [
+ {
+ "i": "usda_food_consumption_nutrient_intakes_and_diet_quality_usda_obb",
+ "x": 0,
+ "y": 0,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "table": "2_nutrient_intake",
+ "demographics": "US consumers aged 2 and above",
+ "statistic": "mean"
+ }
+ }
+ },
+ {
+ "i": "usda_eating_and_health_module_atus_usda_obb",
+ "x": 0,
+ "y": 14,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "table": "eating_and_drinking_time",
+ "year": "2023"
+ }
+ }
+ },
+ {
+ "i": "usda_purchase_to_plate_usda_obb",
+ "x": 0,
+ "y": 28,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "food_group": "milk"
+ }
+ }
+ },
+ {
+ "i": "usda_resource_requirements_of_food_demand_usda_obb",
+ "x": 0,
+ "y": 42,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "table": "Xf0000"
+ }
+ }
+ }
+ ]
+ },
+ "productivity": {
+ "id": "productivity",
+ "name": "Productivity & Global",
+ "layout": [
+ {
+ "i": "usda_agricultural_productivity_usda_obb",
+ "x": 0,
+ "y": 0,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "table": "national_indices"
+ }
+ }
+ },
+ {
+ "i": "usda_international_agricultural_productivity_usda_obb",
+ "x": 0,
+ "y": 14,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "grouping": "north_america",
+ "country": 179
+ }
+ }
+ },
+ {
+ "i": "usda_international_macroeconomic_data_set_usda_obb",
+ "x": 0,
+ "y": 28,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "variable": "real_gdp",
+ "measure": "level"
+ }
+ }
+ },
+ {
+ "i": "usda_agricultural_and_food_rd_expenditures_usda_obb",
+ "x": 0,
+ "y": 42,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "measure": "real"
+ }
+ }
+ },
+ {
+ "i": "usda_international_baseline_data_usda_obb",
+ "x": 0,
+ "y": 56,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "commodity": "Wheat",
+ "attribute": "Production"
+ }
+ }
+ },
+ {
+ "i": "usda_global_food_assessment_usda_obb",
+ "x": 0,
+ "y": 70,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "element": "Total grain demand"
+ }
+ }
+ }
+ ]
+ },
+ "wheat": {
+ "id": "wheat",
+ "name": "Wheat",
+ "layout": [
+ {
+ "i": "usda_wheat_data_usda_obb",
+ "x": 0,
+ "y": 0,
+ "w": 40,
+ "h": 20,
+ "state": {
+ "params": {
+ "table": "us_supply_and_disappearance"
+ }
+ }
+ }
+ ]
+ },
+ "grains_and_oilseeds": {
+ "id": "grains_and_oilseeds",
+ "name": "Grains & Oilseeds",
+ "layout": [
+ {
+ "i": "usda_feed_grains_database_usda_obb",
+ "x": 0,
+ "y": 0,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "table": "corn_supply_and_use"
+ }
+ }
+ },
+ {
+ "i": "usda_oil_crops_yearbook_usda_obb",
+ "x": 0,
+ "y": 14,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "table": "soybeans_supply_disappearance_price",
+ "frequency": "annual"
+ }
+ }
+ },
+ {
+ "i": "usda_rice_yearbook_usda_obb",
+ "x": 0,
+ "y": 28,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "table": "us_supply_disappearance_price",
+ "frequency": "Annual"
+ }
+ }
+ },
+ {
+ "i": "usda_season_average_price_forecasts_usda_obb",
+ "x": 0,
+ "y": 42,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "commodity": "Corn"
+ }
+ }
+ },
+ {
+ "i": "usda_us_bioenergy_statistics_usda_obb",
+ "x": 0,
+ "y": 56,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "table": "ethanol_supply_marketing_year"
+ }
+ }
+ },
+ {
+ "i": "usda_adoption_of_genetically_engineered_crops_usda_obb",
+ "x": 0,
+ "y": 70,
+ "w": 40,
+ "h": 14,
+ "state": {
+ "params": {
+ "crop": "corn",
+ "state": "United States"
+ }
+ }
+ }
+ ]
+ },
+ "sugar_fruit_and_vegetables": {
+ "id": "sugar_fruit_and_vegetables",
+ "name": "Sugar, Fruit & Vegetables",
+ "layout": [
+ {
+ "i": "usda_sugar_sweeteners_yearbook_usda_obb",
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\ No newline at end of file
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/assets/ers_chart.html b/openbb_platform/providers/government_us/openbb_government_us/usda/assets/ers_chart.html
new file mode 100644
index 00000000000..08fd88358d6
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/assets/ers_chart.html
@@ -0,0 +1,272 @@
+
+
+
+
+
+USDA ERS Interactive Charts
+
+
+
+
+
+
+
+
+
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/assets/ers_viz_catalog.json b/openbb_platform/providers/government_us/openbb_government_us/usda/assets/ers_viz_catalog.json
new file mode 100644
index 00000000000..7d46caa236e
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/assets/ers_viz_catalog.json
@@ -0,0 +1,436 @@
+[
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+ "workbook": "2014TenureOwnershipandTransitionofAgriculturalLandTOTALsurvey",
+ "default_view": "TOTALStory",
+ "description": "2014 Tenure, Ownership, and Transition of Agricultural Land (TOTAL) survey",
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+ "title": "Annual Value of State Exports",
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+ "title": "Average share of exports in U.S. agricultural production",
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+ "description": "Average share of exports in U.S. agricultural production",
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+ "last_updated": "2025-07-22"
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+ "title": "Changes in farm values can affect retail prices",
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+ "description": "Changes in farm values can affect retail prices",
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+ "description": "Commodity Costs and Returns: Livestock and Milk",
+ "last_updated": "2026-05-28"
+ },
+ {
+ "title": "Consecutive decades of high poverty since 1960 in U.S. counties",
+ "workbook": "Consecutivedecadesofhighpovertysince1960inU_S_counties",
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+ "description": "Consecutive decades of high poverty since 1960 in U.S. counties",
+ "last_updated": "2024-10-17"
+ },
+ {
+ "title": "Dietary Recommendations and Calorie Consumption",
+ "workbook": "DietaryRecommendationsandCalorieConsumption",
+ "default_view": "DietaryRecomandCalorieCon",
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+ },
+ {
+ "title": "Digging Into the U.S. Farm Balance Sheet",
+ "workbook": "BalanceSheetStoryPoint2",
+ "default_view": "BalanceSheet",
+ "description": "Digging Into the U.S. Farm Balance Sheet",
+ "last_updated": "2026-02-05"
+ },
+ {
+ "title": "Estimated U.S. poultry and red meat disappearance per capita by year",
+ "workbook": "EstimatedU_S_poultryandredmeatdisappearancepercapitabyyear",
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+ "description": "Estimated U.S. poultry and red meat disappearance per capita by year",
+ "last_updated": "2024-10-18"
+ },
+ {
+ "title": "Exports of bulk and high-value commodities",
+ "workbook": "Exportsofbulkandhigh-valuecommodities",
+ "default_view": "XPBlkvsHVP",
+ "description": "Exports of bulk and high-value commodities",
+ "last_updated": "2025-07-22"
+ },
+ {
+ "title": "Farm share of retail price varies across food products",
+ "workbook": "Farmshareofretailpricevariesacrossfoodproducts",
+ "default_view": "Farmshareofretailpricevariesacrossfoodproducts",
+ "description": "Farm share of retail price varies across food products",
+ "last_updated": "2026-02-17"
+ },
+ {
+ "title": "Food Security in the United States: How Do States Compare?",
+ "workbook": "FoodSecurityintheUnitedStatesHowDoStatesCompare",
+ "default_view": "mainstory",
+ "description": "Food Security in the United States: How Do States Compare?",
+ "last_updated": "2026-02-18"
+ },
+ {
+ "title": "Fruit and Vegetable Availability",
+ "workbook": "FruitandVegetableAvailability",
+ "default_view": "FruitVegetableStory",
+ "description": "Fruit and Vegetable Availability, 2017.",
+ "last_updated": "2024-10-18"
+ },
+ {
+ "title": "Fruit and vegetable costs per cup equivalent: Cost ranges",
+ "workbook": "FruitandvegetablecostspercupequivalentCostranges",
+ "default_view": "BoxPlotsStory3",
+ "description": "Fruit and vegetable costs per cup equivalent: Cost ranges",
+ "last_updated": "2025-12-09"
+ },
+ {
+ "title": "Fruit and vegetable retail prices: Most and least expensive",
+ "workbook": "FruitandvegetableretailpricesMostandleastexpensive",
+ "default_view": "MostLeastStory2",
+ "description": "Fruit and vegetable retail prices: Most and least expensive",
+ "last_updated": "2025-12-09"
+ },
+ {
+ "title": "Fruits and Vegetable Costs per Edible Cup Equivalent",
+ "workbook": "FruitsandVegetableCostsperEdibleCupEquivalent",
+ "default_view": "CostsStory1",
+ "description": "Average retail prices for fresh and processed fruits and vegetables on a per-pound basis/per-pint basis and per edible cup equivalent.",
+ "last_updated": "2025-12-09"
+ },
+ {
+ "title": "Get to know your State",
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+ "default_view": "State-leveldashboard",
+ "description": "Get to know your state",
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+ },
+ {
+ "title": "Global cotton supply and use",
+ "workbook": "Globalcottonsupplyanduse",
+ "default_view": "Dashboard3",
+ "description": "Global cotton supply and use",
+ "last_updated": "2024-10-18"
+ },
+ {
+ "title": "Imports of bulk and high-value commodities",
+ "workbook": "Importsofbulkandhigh-valuecommodities",
+ "default_view": "IMPBlkvsHVP",
+ "description": "Imports of bulk and high-value commodities",
+ "last_updated": "2025-07-22"
+ },
+ {
+ "title": "International Baseline Projections",
+ "workbook": "InternationalBaselineProjections",
+ "default_view": "Internationalbaselinevisualization",
+ "description": "International Baseline Projections",
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+ },
+ {
+ "title": "Major Uses of Land in the United States, 2012",
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+ "description": "Major Uses of Land in the United States, 2012",
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+ },
+ {
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+ "description": "Market baskets",
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+ },
+ {
+ "title": "Meeting honey demand in the United States",
+ "workbook": "MeetinghoneydemandintheUnitedStates",
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+ "description": "Meeting honey demand in the United States",
+ "last_updated": "2026-06-18"
+ },
+ {
+ "title": "National Food Expenditure Series",
+ "workbook": "NationalFoodExpenditureSeries",
+ "default_view": "National",
+ "description": "National Food Expenditure Series",
+ "last_updated": "2026-06-01"
+ },
+ {
+ "title": "National Household Food Acquisition and Purchase Survey Food Expenditures",
+ "workbook": "NationalHouseholdFoodAcquisitionandPurchaseSurveyFoodExpenditures",
+ "default_view": "FoodAPSDataViz",
+ "description": "National Household Food Acquisition and Purchase Survey Food Expenditures",
+ "last_updated": "2024-10-18"
+ },
+ {
+ "title": "Percentage change in UK agricultural exports and imports",
+ "workbook": "PercentagechangeinUKagriculturalexportsandimports",
+ "default_view": "Growthrate",
+ "description": "Percentage change in UK agricultural exports and imports",
+ "last_updated": "2024-10-18"
+ },
+ {
+ "title": "Persistent poverty and high poverty U.S. counties",
+ "workbook": "PersistentpovertyandhighpovertyU_S_counties",
+ "default_view": "PersistentandHighPovertyDashboard",
+ "description": "Persistent poverty and high poverty U.S. counties",
+ "last_updated": "2022-12-06"
+ },
+ {
+ "title": "Prevalence and distribution of food insecurity by education status",
+ "workbook": "Prevalenceanddistributionoffoodinsecuritybyeducationstatus",
+ "default_view": "Story2",
+ "description": "Prevalence and distribution of food insecurity by education status",
+ "last_updated": "2026-02-18"
+ },
+ {
+ "title": "Price spreads for individual food products",
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+ "default_view": "Pricespreadsforindividualfoodproducts",
+ "description": "Price spreads for individual food products",
+ "last_updated": "2026-02-17"
+ },
+ {
+ "title": "Shares of different regions in U.S. agricultural exports",
+ "workbook": "SharesofdifferentregionsinU_S_agriculturalexports",
+ "default_view": "SharesofdifferentregionsinUSagriculturalexports",
+ "description": "Shares of different regions in U.S. agricultural exports",
+ "last_updated": "2025-07-22"
+ },
+ {
+ "title": "SNAP Policy Index: Eligibility",
+ "workbook": "SNAPPolicyIndexEligibility",
+ "default_view": "Eligibilty",
+ "description": "SNAP Policy Index: Eligibility",
+ "last_updated": "2026-06-15"
+ },
+ {
+ "title": "SNAP Policy Index: Outreach",
+ "workbook": "SNAPPolicyIndexOutreach",
+ "default_view": "Outreach",
+ "description": "SNAP Policy Index: Outreach",
+ "last_updated": "2024-10-18"
+ },
+ {
+ "title": "SNAP Policy Index: Overview",
+ "workbook": "SNAPPolicyIndexOverview",
+ "default_view": "Overview",
+ "description": "SNAP Policy Index: Overview",
+ "last_updated": "2024-10-18"
+ },
+ {
+ "title": "SNAP Policy Index: Stigma",
+ "workbook": "SNAPPolicyIndexStigma",
+ "default_view": "Stigma",
+ "description": "SNAP Policy Index: Stigma",
+ "last_updated": "2024-10-18"
+ },
+ {
+ "title": "SNAP Policy Index: Transaction costs",
+ "workbook": "SNAPPolicyIndexTransactioncosts",
+ "default_view": "Transaction",
+ "description": "SNAP Policy Index: Transaction costs",
+ "last_updated": "2024-10-18"
+ },
+ {
+ "title": "State Food Expenditure Series",
+ "workbook": "StateFoodExpenditureSeries",
+ "default_view": "State",
+ "description": "State Food Expenditure Series",
+ "last_updated": "2026-07-10"
+ },
+ {
+ "title": "State-Level Estimates of Low Income and Low Access Populations",
+ "workbook": "State-LevelEstimatesofLowIncomeandLowAccessPopulations",
+ "default_view": "FinalWide",
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+ {
+ "title": "Statistical summary of the poultry and eggs sector",
+ "workbook": "StatisticalsummaryofthePoultryandEggsSector",
+ "default_view": "tableAndLineGraph",
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+ "last_updated": "2024-10-18"
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+ "title": "The Farm Income Atlas",
+ "workbook": "IncomeStatementStoryPoint3",
+ "default_view": "Incomestatement",
+ "description": "The Farm Income Atlas",
+ "last_updated": "2026-02-05"
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+ "description": "Trends in agricultural outputs, inputs, and total factor productivity (TFP) by country income group, 1961–2023",
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+ "description": "Trends in food insecurity in U.S. households with children",
+ "last_updated": "2026-02-18"
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+ "last_updated": "2026-02-18"
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+ "title": "U.S. agricultural exports supported jobs outside farming",
+ "workbook": "U_S_agriculturalexportssupportedjobsoutsidefarming",
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+ "description": "U.S. agricultural exports supported jobs outside farming",
+ "last_updated": "2025-07-22"
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+ "title": "U.S. agricultural exports to the UK by product",
+ "workbook": "U_S_agriculturalexportstotheUKbyproduct",
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+ "last_updated": "2024-10-18"
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+ {
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+ "workbook": "U_S_exportvaluesperregion",
+ "default_view": "XPbyregionbar",
+ "description": "U.S. export values per region",
+ "last_updated": "2025-07-22"
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+ {
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+ "workbook": "U_S_FarmerHouseholdWellbeing",
+ "default_view": "OVERVIEW-FullWidth",
+ "description": "U.S. Farmer Household Wellbeing",
+ "last_updated": "2026-02-24"
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+ {
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+ "workbook": "U_S_foodsystemresourceusebysupplychainstage",
+ "default_view": "RRFDFigure2",
+ "description": "U.S. food system resource use by supply chain stage",
+ "last_updated": "2026-04-08"
+ },
+ {
+ "title": "U.S. food system’s share of national resource use",
+ "workbook": "U_S_foodsystemsshareofnationalresourceuse",
+ "default_view": "RRFDFigure1",
+ "description": "U.S. food system’s share of national resource use",
+ "last_updated": "2026-03-17"
+ },
+ {
+ "title": "U.S. food-at-home resource use by category",
+ "workbook": "U_S_food-at-homeresourceusebycategory",
+ "default_view": "RRFDFigure3",
+ "description": "U.S. food-at-home resource use by category",
+ "last_updated": "2026-03-17"
+ },
+ {
+ "title": "U.S. per capita food availability for additional commodities",
+ "workbook": "U_S_percapitafoodavailabilityforadditionalcommodities",
+ "default_view": "PerCapitaStory2",
+ "description": "U.S. per capita food availability for additional commodities",
+ "last_updated": "2024-10-18"
+ },
+ {
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+ "workbook": "U_S_percapitafoodavailabilityforselectedcommodities",
+ "default_view": "PerCapitaStory1",
+ "description": "U.S. per capita food availability for selected commodities",
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+ {
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+ },
+ {
+ "title": "U.S. State-level poultry sales for major commodities",
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+ "default_view": "Map",
+ "description": "U.S. State-level poultry sales for major commodities",
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+ {
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+ "description": "UK agricultural imports and exports",
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+ },
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+ "workbook": "Vegetablesandpulsescommodityhighlights",
+ "default_view": "Story1",
+ "description": "Vegetables and pulses: commodity highlights",
+ "last_updated": "2025-09-25"
+ },
+ {
+ "title": "Wheat Data Visualization",
+ "workbook": "WheatDataVisualization",
+ "default_view": "U_S_Overview",
+ "description": "Wheat Data Visualization",
+ "last_updated": "2026-07-15"
+ },
+ {
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+ "workbook": "WorldAgriculturalSupplyandDemandEstimatesataGlance",
+ "default_view": "MAIN_STORY",
+ "description": "World Agricultural Supply and Demand Estimates at a Glance",
+ "last_updated": "2026-07-15"
+ }
+]
\ No newline at end of file
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/__init__.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/__init__.py
new file mode 100644
index 00000000000..88cceda6502
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/__init__.py
@@ -0,0 +1 @@
+"""USDA provider models."""
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/adoption_of_genetically_engineered_crops_in_the_united_states.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/adoption_of_genetically_engineered_crops_in_the_united_states.py
new file mode 100644
index 00000000000..dfa5309dde2
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/adoption_of_genetically_engineered_crops_in_the_united_states.py
@@ -0,0 +1,243 @@
+"""USDA ERS Adoption of Genetically Engineered Crops Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.app.service.system_service import SystemService
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_adoption_of_genetically_engineered_crops_in_the_united_states import ( # noqa: E501
+ CROP_STATES,
+ CROPS,
+ DEFAULT_CROP,
+ DEFAULT_STATE,
+ STATE_CANON,
+ STATES_ALL,
+ crop_options,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+api_prefix = SystemService().system_settings.api_settings.prefix
+
+
+class AdoptionOfGeneticallyEngineeredCropsInTheUnitedStatesQueryParams(QueryParams):
+ """USDA ERS Adoption of Genetically Engineered Crops Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/adoption-of-genetically-engineered-crops-in-the-united-states
+ """
+
+ __json_schema_extra__ = {
+ "crop": {
+ "x-widget_config": {
+ "label": "Crop",
+ "value": DEFAULT_CROP,
+ "multiSelect": False,
+ "multiple": False,
+ "options": crop_options(),
+ },
+ },
+ "state": {
+ "multiple_items_allowed": True,
+ "x-widget_config": {
+ "label": "State",
+ "type": "endpoint",
+ "multiSelect": False,
+ "multiple": False,
+ "value": DEFAULT_STATE,
+ "optionsEndpoint": f"{api_prefix}/usda/ge_crop_states",
+ "optionsParams": {"crop": "$crop"},
+ "style": {"popupWidth": 300},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ crop: str = Field(
+ default=DEFAULT_CROP,
+ description="Crop whose sheet is retrieved. The crop selects the trait"
+ + " columns and the state option list. Corn and cotton publish four"
+ + " trait columns (Bt only, HT only, Stacked, All GE); soybeans publish"
+ + " only HT only and All GE. Valid crops are:\n "
+ + ", ".join(CROPS)
+ + "\n",
+ )
+ state: str | None = Field(
+ default=DEFAULT_STATE,
+ description="State(s) to retrieve, as a comma-separated list of names."
+ + " The published estimating-program states vary by crop; 'United"
+ + " States' is the national total and 'Other States' is the residual"
+ + " aggregate. If None, defaults to 'United States'.",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data, compared against the"
+ + " observation year. If None, returns from 2000, the first year of the"
+ + " series.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data, compared against the"
+ + " observation year. If None, returns up to the most recent year.",
+ )
+
+ @field_validator("crop", mode="before", check_fields=False)
+ @classmethod
+ def _validate_crop(cls, v):
+ """Validate crop."""
+ if not v:
+ return DEFAULT_CROP
+ crop = v.strip() if isinstance(v, str) else v
+ if crop not in CROPS:
+ raise OpenBBError(
+ f"Invalid crop: {crop}. Valid crops are: " + ", ".join(CROPS)
+ )
+ return crop
+
+ @field_validator("state", mode="before", check_fields=False)
+ @classmethod
+ def _validate_state(cls, v):
+ """Validate and canonicalize the state filter."""
+ if not v:
+ return DEFAULT_STATE
+ tokens = v.split(",") if isinstance(v, str) else list(v)
+ canonical: list[str] = []
+ unknown: list[str] = []
+ for token in tokens:
+ text = str(token).strip()
+ if not text:
+ continue
+ match = STATE_CANON.get(text.casefold())
+ if match is None:
+ unknown.append(text)
+ else:
+ canonical.append(match)
+ if unknown:
+ raise OpenBBError(
+ f"Invalid state(s): {', '.join(unknown)}. Valid states are: "
+ + ", ".join(STATES_ALL)
+ )
+ return ",".join(canonical) if canonical else DEFAULT_STATE
+
+
+class AdoptionOfGeneticallyEngineeredCropsInTheUnitedStatesData(NullTokenMixin, Data):
+ """USDA ERS Adoption of Genetically Engineered Crops Data.
+
+ One crop's genetically engineered planting shares pivoted to a wide layout:
+ the years stay in the rows while the crop's GE trait categories spread into
+ percent columns, whose set is four for corn and upland cotton and two for
+ soybeans.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Adoption of Genetically Engineered Crops",
+ "$.description": "Annual share of planted acres in genetically"
+ " engineered varieties of corn, upland cotton, and soybeans, by"
+ " trait and by State, published by the USDA Economic Research"
+ " Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ state: str = Field(
+ description="State of the observation, or 'United States' for the"
+ + " national total and 'Other States' for the residual aggregate.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "State",
+ "cellDataType": "text",
+ "pinned": "left",
+ }
+ },
+ )
+ year: int = Field(
+ description="Year of the observation.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Year",
+ "cellDataType": "number",
+ "pinned": "left",
+ "maxWidth": 90,
+ }
+ },
+ )
+
+
+class AdoptionOfGeneticallyEngineeredCropsInTheUnitedStatesFetcher(
+ Fetcher[
+ AdoptionOfGeneticallyEngineeredCropsInTheUnitedStatesQueryParams,
+ list[AdoptionOfGeneticallyEngineeredCropsInTheUnitedStatesData],
+ ]
+):
+ """Fetch USDA ERS Adoption of Genetically Engineered Crops in the United States."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> AdoptionOfGeneticallyEngineeredCropsInTheUnitedStatesQueryParams:
+ """Transform the query params."""
+ return AdoptionOfGeneticallyEngineeredCropsInTheUnitedStatesQueryParams(
+ **params
+ )
+
+ @staticmethod
+ async def aextract_data(
+ query: AdoptionOfGeneticallyEngineeredCropsInTheUnitedStatesQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected crop's long-format rows."""
+ from openbb_government_us.usda.utils import (
+ ers_adoption_of_genetically_engineered_crops_in_the_united_states as ers,
+ )
+
+ return await ers.afetch_crop(query.crop)
+
+ @staticmethod
+ def transform_data(
+ query: AdoptionOfGeneticallyEngineeredCropsInTheUnitedStatesQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[AdoptionOfGeneticallyEngineeredCropsInTheUnitedStatesData]:
+ """Pivot the long-format rows into the wide trait-by-year layout."""
+ traits = CROPS[query.crop]["traits"]
+ selected = (
+ set(query.state.split(",")) if query.state else set(CROP_STATES[query.crop])
+ )
+ grouped: dict[tuple, dict] = {}
+ for record in data:
+ state = record["state"]
+ if state not in selected:
+ continue
+ year = record["year"]
+ if query.start_year is not None and year < query.start_year:
+ continue
+ if query.end_year is not None and year > query.end_year:
+ continue
+ key = (state, year)
+ cell = grouped.get(key)
+ if cell is None:
+ cell = {}
+ grouped[key] = cell
+ cell[record["trait"]] = record["value"]
+ results: list[AdoptionOfGeneticallyEngineeredCropsInTheUnitedStatesData] = []
+ for state, year in sorted(grouped):
+ row = {"state": state, "year": year}
+ for trait in traits:
+ row[trait] = grouped[(state, year)].get(trait)
+ results.append(
+ AdoptionOfGeneticallyEngineeredCropsInTheUnitedStatesData.model_validate(
+ row
+ )
+ )
+ return results
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/agricultural_and_food_research_and_development_expenditures_in_the_united_states.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/agricultural_and_food_research_and_development_expenditures_in_the_united_states.py
new file mode 100644
index 00000000000..2d47a7ad8a4
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/agricultural_and_food_research_and_development_expenditures_in_the_united_states.py
@@ -0,0 +1,218 @@
+"""USDA ERS Agricultural and Food R&D Expenditures Model."""
+
+from typing import Any, Literal
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from openbb_core.provider.utils.errors import EmptyDataError
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_agricultural_and_food_research_and_development_expenditures_in_the_united_states import ( # noqa: E501
+ MEASURES,
+ SERIES_ORDER,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+Measure = Literal["nominal", "real"]
+
+DEFAULT_MEASURE = "real"
+
+
+def _number(header: str) -> dict:
+ """Build a numeric column config for one series field."""
+ return {"x-widget_config": {"headerName": header, "cellDataType": "number"}}
+
+
+class AgriculturalAndFoodRdExpendituresQueryParams(QueryParams):
+ """USDA ERS Agricultural and Food R&D Expenditures Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/agricultural-and-food-research-and-development-expenditures-in-the-united-states
+ """
+
+ __json_schema_extra__ = {
+ "measure": {
+ "x-widget_config": {
+ "label": "Measure",
+ "multiSelect": False,
+ "multiple": False,
+ "value": DEFAULT_MEASURE,
+ "options": [
+ {"label": "Real (constant 2022 dollars)", "value": "real"},
+ {"label": "Nominal (current dollars)", "value": "nominal"},
+ ],
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ measure: Measure = Field(
+ default=DEFAULT_MEASURE,
+ description="Measure to retrieve, selecting which values fill the R&D"
+ + " series columns. 'real' is inflation-adjusted constant 2022 U.S."
+ + " dollars; 'nominal' is current U.S. dollars. Both are in millions."
+ + " The R&D price index column is the same under either measure.",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data, compared against the"
+ + " observation year. If None, returns from 1970.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data, compared against the"
+ + " observation year. If None, returns up to the most recent year.",
+ )
+
+ @field_validator("measure", mode="before", check_fields=False)
+ @classmethod
+ def _validate_measure(cls, v):
+ """Validate measure."""
+ if not v:
+ return DEFAULT_MEASURE
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip().lower()
+ if value not in MEASURES:
+ raise OpenBBError(
+ f"Invalid measure: {value}. Valid measures are: " + ", ".join(MEASURES)
+ )
+ return value
+
+
+class AgriculturalAndFoodRdExpendituresData(NullTokenMixin, Data):
+ """USDA ERS Agricultural and Food R&D Expenditures Data.
+
+ One row per year, with the public and private agricultural and food R&D
+ series spread into a fixed set of value columns filled by the selected
+ measure, alongside the measure-agnostic R&D price index.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Agricultural and Food R&D Expenditures",
+ "$.description": "Public and private agricultural and food"
+ " research and development expenditures in the United States,"
+ " in current and constant dollars with the R&D price index,"
+ " published by the USDA Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ year: int = Field(
+ description="Year of the observation.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Year",
+ "cellDataType": "text",
+ "pinned": "left",
+ "maxWidth": 90,
+ }
+ },
+ )
+ public_total: float | None = Field(
+ default=None,
+ description="Total public agricultural and food R&D expenditures, in"
+ + " millions of dollars under the selected measure.",
+ json_schema_extra=_number("Public total"),
+ )
+ public_usda_intramural: float | None = Field(
+ default=None,
+ description="Public agricultural and food R&D performed by USDA"
+ + " intramural agencies, in millions of dollars under the selected"
+ + " measure.",
+ json_schema_extra=_number("USDA intramural"),
+ )
+ public_state_universities: float | None = Field(
+ default=None,
+ description="Public agricultural and food R&D performed by State"
+ + " universities and cooperating institutions, in millions of dollars"
+ + " under the selected measure.",
+ json_schema_extra=_number("State universities"),
+ )
+ private_input_industries: float | None = Field(
+ default=None,
+ description="Private agriculture input industries R&D, in millions of"
+ + " dollars under the selected measure. Discontinued after 2014.",
+ json_schema_extra=_number("Private input industries"),
+ )
+ private_food_industry: float | None = Field(
+ default=None,
+ description="Private food industry R&D, in millions of dollars under"
+ + " the selected measure.",
+ json_schema_extra=_number("Private food industry"),
+ )
+ rd_price_index: float | None = Field(
+ default=None,
+ description="R&D price index (2022 = 100) used to deflate the nominal"
+ + " series, the same under either measure.",
+ json_schema_extra=_number("R&D price index"),
+ )
+
+
+class AgriculturalAndFoodRdExpendituresFetcher(
+ Fetcher[
+ AgriculturalAndFoodRdExpendituresQueryParams,
+ list[AgriculturalAndFoodRdExpendituresData],
+ ]
+):
+ """Fetch USDA ERS Agricultural and Food R&D Expenditures."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> AgriculturalAndFoodRdExpendituresQueryParams:
+ """Transform the query params."""
+ return AgriculturalAndFoodRdExpendituresQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: AgriculturalAndFoodRdExpendituresQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the long-format R&D expenditure records."""
+ from openbb_government_us.usda.utils import (
+ ers_agricultural_and_food_research_and_development_expenditures_in_the_united_states as util,
+ )
+
+ return await util.afetch_records()
+
+ @staticmethod
+ def transform_data(
+ query: AgriculturalAndFoodRdExpendituresQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[AgriculturalAndFoodRdExpendituresData]:
+ """Pivot the selected measure's series into columns, one row per year."""
+ pivoted: dict[int, dict] = {}
+ for record in data:
+ if record["measure"] not in (query.measure, None):
+ continue
+ year = record["year"]
+ if query.start_year is not None and year < query.start_year:
+ continue
+ if query.end_year is not None and year > query.end_year:
+ continue
+ row = pivoted.setdefault(year, {"year": year})
+ row[record["series"]] = record["value"]
+ if not pivoted:
+ raise EmptyDataError("No records match the given filters.")
+ results: list[AgriculturalAndFoodRdExpendituresData] = []
+ for year in sorted(pivoted):
+ row = pivoted[year]
+ ordered = {"year": year}
+ for series in SERIES_ORDER:
+ if series in row:
+ ordered[series] = row[series]
+ results.append(
+ AgriculturalAndFoodRdExpendituresData.model_validate(ordered)
+ )
+ return results
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/agricultural_exchange_rates.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/agricultural_exchange_rates.py
new file mode 100644
index 00000000000..3bc2b2bdf75
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/agricultural_exchange_rates.py
@@ -0,0 +1,302 @@
+"""Agricultural Exchange Rates Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from openbb_core.provider.utils.errors import EmptyDataError
+from pydantic import ConfigDict, Field, field_validator, model_validator
+
+from openbb_government_us.usda.utils.ers_agricultural_exchange_rates import (
+ BILATERAL_REGIONS,
+ EXCHANGE_RATE_FILES,
+ INDEX_WEIGHTS,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+DEFAULT_TABLE = "real_index_annual"
+DEFAULT_WEIGHTS = "U.S. markets (U.S. export weights)"
+
+
+class AgriculturalExchangeRatesQueryParams(QueryParams):
+ """Agricultural Exchange Rates Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/agricultural-exchange-rate-data-set
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "x-widget_config": {
+ "label": "Table",
+ "value": DEFAULT_TABLE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": config["label"], "value": key}
+ for key, config in EXCHANGE_RATE_FILES.items()
+ ],
+ "style": {"popupWidth": 420},
+ },
+ },
+ "weights": {
+ "x-widget_config": {
+ "label": "Weight scheme",
+ "value": DEFAULT_WEIGHTS,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": scheme, "value": scheme} for scheme in INDEX_WEIGHTS
+ ],
+ "style": {"popupWidth": 360},
+ },
+ },
+ "region": {
+ "x-widget_config": {
+ "label": "Region",
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": region, "value": region} for region in BILATERAL_REGIONS
+ ],
+ "style": {"popupWidth": 260},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ table: str = Field(
+ default=DEFAULT_TABLE,
+ description="Exchange-rate table to retrieve. Each table pivots to a wide"
+ + " layout with time in the rows and the commodity (index tables) or"
+ + " partner country (bilateral tables) in the value columns."
+ + " Valid tables are:\n "
+ + ", ".join(EXCHANGE_RATE_FILES)
+ + "\n",
+ )
+ weights: str | None = Field(
+ default=DEFAULT_WEIGHTS,
+ description="Trade-weight scheme to keep for the index tables. Defaults to"
+ + " the U.S. export-weighted index. Set to None to stack all three schemes"
+ + " as a pinned row dimension. Ignored for the bilateral tables."
+ + " Valid schemes are:\n "
+ + ", ".join(INDEX_WEIGHTS)
+ + "\n",
+ )
+ region: str | None = Field(
+ default=None,
+ description="Region to keep for the bilateral tables, filtering which"
+ + " partner-country columns appear. If None, all partner countries are"
+ + " returned. Ignored for the index tables. Valid regions are:\n "
+ + ", ".join(BILATERAL_REGIONS)
+ + "\n",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data."
+ + " If None, returns from the beginning of the series.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data."
+ + " If None, returns up to the most recent year.",
+ )
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate table."""
+ if not v:
+ return DEFAULT_TABLE
+ table = v[0] if isinstance(v, (list, tuple)) else v
+ table = str(table).strip()
+ if table not in EXCHANGE_RATE_FILES:
+ raise OpenBBError(
+ f"Invalid table: {table}. Valid tables are: "
+ + ", ".join(EXCHANGE_RATE_FILES)
+ )
+ return table
+
+ @field_validator("weights", "region", mode="before", check_fields=False)
+ @classmethod
+ def _validate_optional_filters(cls, v):
+ """Normalize an optional filter to a stripped string or None."""
+ if not v:
+ return None
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ return str(value).strip() or None
+
+ @model_validator(mode="after")
+ def _validate_filter_scope(self):
+ """Validate an applied filter's value against the table's dimension set."""
+ kind = EXCHANGE_RATE_FILES[self.table]["kind"]
+ if (
+ kind == "index"
+ and self.weights is not None
+ and (self.weights not in INDEX_WEIGHTS)
+ ):
+ raise OpenBBError(
+ f"Invalid weight scheme: {self.weights}. Valid schemes are: "
+ + ", ".join(INDEX_WEIGHTS)
+ )
+ if (
+ kind == "bilateral"
+ and self.region is not None
+ and (self.region not in BILATERAL_REGIONS)
+ ):
+ raise OpenBBError(
+ f"Invalid region: {self.region}. Valid regions are: "
+ + ", ".join(BILATERAL_REGIONS)
+ )
+ return self
+
+
+class AgriculturalExchangeRatesData(NullTokenMixin, Data):
+ """Agricultural Exchange Rates Data.
+
+ One selected exchange-rate table pivoted to a wide layout: time stays in
+ the rows while the commodity (index tables) or partner country (bilateral
+ tables) spreads into value columns, whose set varies by table.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Agricultural Exchange Rates",
+ "$.description": "Real and nominal agricultural exchange rates,"
+ " as commodity trade-weighted indexes and bilateral local-currency"
+ " rates per U.S. dollar, published by the USDA Economic Research"
+ " Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ period: str = Field(
+ description="Row label of the observation: the year for annual tables"
+ + " ('1970'), the year and month for monthly tables ('1970 January'),"
+ + " with the trade-weight scheme prefixed when more than one scheme is"
+ + " shown at once.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Period",
+ "pinned": "left",
+ "maxWidth": 320,
+ }
+ },
+ )
+
+
+class AgriculturalExchangeRatesFetcher(
+ Fetcher[
+ AgriculturalExchangeRatesQueryParams,
+ list[AgriculturalExchangeRatesData],
+ ]
+):
+ """Fetch USDA ERS Agricultural Exchange Rate Data Set."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> AgriculturalExchangeRatesQueryParams:
+ """Transform the query params."""
+ return AgriculturalExchangeRatesQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: AgriculturalExchangeRatesQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected table's long-format rows."""
+ from openbb_government_us.usda.utils import ers_agricultural_exchange_rates
+
+ return await ers_agricultural_exchange_rates.afetch_table(query.table)
+
+ @staticmethod
+ def _period_label(
+ weights: str | None, year: int, month: str | None, weights_vary: bool
+ ) -> str:
+ """Fold the weight scheme, year, and month into one readable label."""
+ time_label = f"{year} {month}" if month else str(year)
+ if weights_vary and weights:
+ return f"{weights} — {time_label}"
+ return time_label
+
+ @staticmethod
+ def transform_data(
+ query: AgriculturalExchangeRatesQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[AgriculturalExchangeRatesData]:
+ """Filter, then pivot into period rows with the series as columns."""
+ kind = EXCHANGE_RATE_FILES[query.table]["kind"]
+ filtered: list[dict] = []
+ for record in data:
+ year = record["year"]
+ if query.start_year is not None and year < query.start_year:
+ continue
+ if query.end_year is not None and year > query.end_year:
+ continue
+ if (
+ kind == "index"
+ and query.weights is not None
+ and record["weights"] != query.weights
+ ):
+ continue
+ if (
+ kind == "bilateral"
+ and query.region is not None
+ and record["region"] != query.region
+ ):
+ continue
+ filtered.append(record)
+ if not filtered:
+ raise EmptyDataError("No records match the given filters.")
+ weights_vary = len({r["weights"] for r in filtered if r["weights"]}) > 1
+ pivoted: dict[tuple, dict] = {}
+ column_order: list[str] = []
+ seen_columns: set[str] = set()
+ for record in filtered:
+ weights = record["weights"]
+ key = (weights, record["year"], record["month"])
+ row = pivoted.get(key)
+ if row is None:
+ row = {
+ "_weights_order": (
+ INDEX_WEIGHTS.index(weights)
+ if weights in INDEX_WEIGHTS
+ else len(INDEX_WEIGHTS)
+ ),
+ "_year": record["year"],
+ "_month_order": record["month_order"] or 0,
+ "period": AgriculturalExchangeRatesFetcher._period_label(
+ weights, record["year"], record["month"], weights_vary
+ ),
+ }
+ pivoted[key] = row
+ if record["series"] not in seen_columns:
+ seen_columns.add(record["series"])
+ column_order.append(record["series"])
+ row[record["series"]] = record["value"]
+ results = sorted(
+ pivoted.values(),
+ key=lambda row: (
+ row["_weights_order"],
+ -row["_year"],
+ -row["_month_order"],
+ ),
+ )
+ validated: list[AgriculturalExchangeRatesData] = []
+ for row in results:
+ payload: dict[str, Any] = {"period": row["period"]}
+ for column in column_order:
+ payload[column] = row.get(column)
+ validated.append(AgriculturalExchangeRatesData.model_validate(payload))
+ return validated
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/agricultural_productivity.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/agricultural_productivity.py
new file mode 100644
index 00000000000..ae8696f84d2
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/agricultural_productivity.py
@@ -0,0 +1,241 @@
+"""USDA ERS Agricultural Productivity Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.app.service.system_service import SystemService
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from openbb_core.provider.utils.errors import EmptyDataError
+from pydantic import ConfigDict, Field, field_validator, model_validator
+
+from openbb_government_us.usda.utils.ers_agricultural_productivity import (
+ AGRICULTURAL_PRODUCTIVITY_FILES,
+ NATIONAL_STATE,
+ allowed_states,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+api_prefix = SystemService().system_settings.api_settings.prefix
+
+DEFAULT_TABLE = "national_indices"
+DEFAULT_STATE = NATIONAL_STATE
+
+
+class AgriculturalProductivityQueryParams(QueryParams):
+ """USDA ERS Agricultural Productivity Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/agricultural-productivity-in-the-united-states
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "x-widget_config": {
+ "label": "Table",
+ "value": DEFAULT_TABLE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": config["label"], "value": key}
+ for key, config in AGRICULTURAL_PRODUCTIVITY_FILES.items()
+ ],
+ "style": {"popupWidth": 520},
+ },
+ },
+ "state": {
+ "x-widget_config": {
+ "label": "State",
+ "type": "endpoint",
+ "value": DEFAULT_STATE,
+ "multiSelect": False,
+ "multiple": False,
+ "optionsEndpoint": f"{api_prefix}/usda/agricultural_productivity_states",
+ "optionsParams": {"table": "$table"},
+ "style": {"popupWidth": 280},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ table: str = Field(
+ default=DEFAULT_TABLE,
+ description="Productivity table to retrieve. Each table is a set of"
+ + " named index series indexed by year; the series become the wide"
+ + " value columns while year, and the State for the relative-levels"
+ + " table, stay as pinned row columns. The national tables cover the"
+ + " United States; the relative-levels table covers the 48 contiguous"
+ + " States. Valid tables are:\n "
+ + ", ".join(AGRICULTURAL_PRODUCTIVITY_FILES)
+ + "\n",
+ )
+ state: str = Field(
+ default=DEFAULT_STATE,
+ description="State to retrieve for the relative-levels table, as a"
+ + " two-letter code, or 'US' for the national tables. The national"
+ + " tables are published for the United States only; a State value is"
+ + " ignored for them.",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data, compared against the"
+ + " observation year. If None, returns from the beginning of the"
+ + " series.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data, compared against the"
+ + " observation year. If None, returns up to the most recent year.",
+ )
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate table."""
+ if not v:
+ return DEFAULT_TABLE
+ table = v[0] if isinstance(v, (list, tuple)) else v
+ table = str(table).strip()
+ if table not in AGRICULTURAL_PRODUCTIVITY_FILES:
+ raise OpenBBError(
+ f"Invalid table: {table}. Valid tables are: "
+ + ", ".join(AGRICULTURAL_PRODUCTIVITY_FILES)
+ )
+ return table
+
+ @field_validator("state", mode="before", check_fields=False)
+ @classmethod
+ def _validate_state(cls, v):
+ """Normalize the state to a two-letter code, defaulting to 'US'."""
+ if not v:
+ return DEFAULT_STATE
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ return str(value).strip().upper() or DEFAULT_STATE
+
+ @model_validator(mode="after")
+ def _reconcile_state(self):
+ """Coerce the state to one published for the selected table."""
+ codes = allowed_states(self.table)
+ if self.state not in codes:
+ self.state = codes[0]
+ return self
+
+
+class AgriculturalProductivityData(NullTokenMixin, Data):
+ """USDA ERS Agricultural Productivity.
+
+ One selected productivity table pivoted to a wide layout: year stays in
+ the rows while the table's series dimension spreads into value columns,
+ whose set varies by table.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Agricultural Productivity",
+ "$.description": "Farm output, input, and total factor"
+ " productivity indices for the United States, and the relative"
+ " productivity levels of the 48 contiguous States, published by"
+ " the USDA Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ year: int = Field(
+ description="Year of the observation.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Year",
+ "cellDataType": "text",
+ "pinned": "left",
+ "maxWidth": 90,
+ }
+ },
+ )
+ state: str | None = Field(
+ default=None,
+ description="State of the observation, or 'United States' for the"
+ + " national tables.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "State",
+ "pinned": "left",
+ "hide": True,
+ }
+ },
+ )
+
+
+class AgriculturalProductivityFetcher(
+ Fetcher[
+ AgriculturalProductivityQueryParams,
+ list[AgriculturalProductivityData],
+ ]
+):
+ """Fetch USDA ERS Agricultural Productivity."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(params: dict[str, Any]) -> AgriculturalProductivityQueryParams:
+ """Transform the query params."""
+ return AgriculturalProductivityQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: AgriculturalProductivityQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected table's long-format rows."""
+ from openbb_government_us.usda.utils import ers_agricultural_productivity
+
+ return await ers_agricultural_productivity.afetch_table(query.table)
+
+ @staticmethod
+ def transform_data(
+ query: AgriculturalProductivityQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[AgriculturalProductivityData]:
+ """Pivot the series into columns, one row per year, oldest year first."""
+ from openbb_government_us.usda.utils.ers_agricultural_productivity import (
+ STATE_LABELS,
+ )
+
+ state_label = STATE_LABELS.get(query.state, query.state)
+ series_order: list[str] = []
+ seen_series: set[str] = set()
+ pivoted: dict[int, dict] = {}
+ for record in data:
+ if record["state"] is not None and record["state"] != query.state:
+ continue
+ year = record["year"]
+ if query.start_year is not None and year < query.start_year:
+ continue
+ if query.end_year is not None and year > query.end_year:
+ continue
+ row = pivoted.get(year)
+ if row is None:
+ row = {"year": year, "state": state_label}
+ pivoted[year] = row
+ series = record["series"]
+ if series not in seen_series:
+ seen_series.add(series)
+ series_order.append(series)
+ row[series] = record["value"]
+ if not pivoted:
+ raise EmptyDataError("No records match the given filters.")
+ results: list[AgriculturalProductivityData] = []
+ for year in sorted(pivoted):
+ row = pivoted[year]
+ ordered = {"year": row["year"], "state": row["state"]}
+ for series in series_order:
+ if series in row:
+ ordered[series] = row[series]
+ results.append(AgriculturalProductivityData.model_validate(ordered))
+ return results
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/agricultural_trade_multipliers.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/agricultural_trade_multipliers.py
new file mode 100644
index 00000000000..d9cb586154d
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/agricultural_trade_multipliers.py
@@ -0,0 +1,188 @@
+"""USDA ERS Agricultural Trade Multipliers Model."""
+
+from typing import Any
+
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from pydantic import ConfigDict, Field
+
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+
+class AgriculturalTradeMultipliersQueryParams(QueryParams):
+ """USDA ERS Agricultural Trade Multipliers Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/agricultural-trade-multipliers
+ """
+
+
+class AgriculturalTradeMultipliersData(NullTokenMixin, Data):
+ """USDA ERS Agricultural Trade Multipliers Data.
+
+ One wide row per commodity, with the USDA ERS agricultural trade multiplier
+ measures spread across columns: producer- and port-level output and
+ employment multipliers and the commodity's export value.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Agricultural Trade Multipliers",
+ "$.description": "Producer- and port-level output and employment"
+ " multipliers estimating the U.S. economic output and jobs"
+ " supported by agricultural exports, by commodity, published by"
+ " the USDA Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ commodity_id: int | None = Field(
+ default=None,
+ description="Numeric commodity identifier, as published (COMMID)."
+ + " None on the aggregate all-exports row.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Commodity ID",
+ "cellDataType": "number",
+ "hide": True,
+ "maxWidth": 120,
+ }
+ },
+ )
+ commodity: str = Field(
+ description="Commodity name, as published, or the aggregate"
+ + " 'All agricultural exports'.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Commodity",
+ "cellDataType": "text",
+ "pinned": "left",
+ }
+ },
+ )
+ year: int = Field(
+ description="Reference year of the multipliers and export value.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Year",
+ "cellDataType": "number",
+ "maxWidth": 90,
+ }
+ },
+ )
+ producer_output_multiplier: float | None = Field(
+ default=None,
+ description="Producer output multiplier: total U.S. dollar economic"
+ + " output supported per U.S. dollar of the commodity's export value.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Producer Output Multiplier (USD output per USD export)",
+ "cellDataType": "number",
+ }
+ },
+ )
+ producer_employment_multiplier: float | None = Field(
+ default=None,
+ description="Producer employment multiplier: jobs supported per 1"
+ + " billion U.S. dollars of the commodity's export value.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Producer Employment Multiplier (jobs per $1B export)",
+ "cellDataType": "number",
+ }
+ },
+ )
+ port_output_multiplier: float | None = Field(
+ default=None,
+ description="Port output multiplier: total U.S. dollar economic output"
+ + " supported per U.S. dollar of the commodity's export value.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Port Output Multiplier (USD output per USD export)",
+ "cellDataType": "number",
+ }
+ },
+ )
+ port_employment_multiplier: float | None = Field(
+ default=None,
+ description="Port employment multiplier: jobs supported per 1 billion"
+ + " U.S. dollars of the commodity's export value.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Port Employment Multiplier (jobs per $1B export)",
+ "cellDataType": "number",
+ }
+ },
+ )
+ export_value: float | None = Field(
+ default=None,
+ description="Commodity export value, in thousands of U.S. dollars.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Export Value ($1,000)",
+ "cellDataType": "number",
+ }
+ },
+ )
+
+
+class AgriculturalTradeMultipliersFetcher(
+ Fetcher[
+ AgriculturalTradeMultipliersQueryParams,
+ list[AgriculturalTradeMultipliersData],
+ ]
+):
+ """Fetch USDA ERS Agricultural Trade Multipliers."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> AgriculturalTradeMultipliersQueryParams:
+ """Transform the query params."""
+ return AgriculturalTradeMultipliersQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: AgriculturalTradeMultipliersQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the long CSV rows."""
+ from openbb_government_us.usda.utils import ers_agricultural_trade_multipliers
+
+ return await ers_agricultural_trade_multipliers.afetch_table()
+
+ @staticmethod
+ def transform_data(
+ query: AgriculturalTradeMultipliersQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[AgriculturalTradeMultipliersData]:
+ """Pivot the long rows into one wide row per commodity."""
+ pivoted: dict[tuple, dict] = {}
+ for order, record in enumerate(data):
+ commodity = record["commodity"]
+ key = (record["commodity_id"], commodity, record["year"])
+ row = pivoted.get(key)
+ if row is None:
+ row = {
+ "_order": order,
+ "commodity_id": record["commodity_id"],
+ "commodity": commodity,
+ "year": record["year"],
+ }
+ pivoted[key] = row
+ row[record["field"]] = record["value"]
+ results = sorted(pivoted.values(), key=lambda r: r["_order"])
+ return [
+ AgriculturalTradeMultipliersData.model_validate(
+ {k: v for k, v in row.items() if not k.startswith("_")}
+ )
+ for row in results
+ ]
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/area_and_road_ruggedness_scales.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/area_and_road_ruggedness_scales.py
new file mode 100644
index 00000000000..49735fe3a12
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/area_and_road_ruggedness_scales.py
@@ -0,0 +1,559 @@
+"""USDA ERS Area and Road Ruggedness Scales Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.app.service.system_service import SystemService
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from openbb_core.provider.utils.errors import EmptyDataError
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_area_and_road_ruggedness_scales import (
+ ALL_STATES,
+ DEFAULT_STATE,
+ DEFAULT_VINTAGE,
+ VINTAGE_OPTIONS,
+ VINTAGES,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+api_prefix = SystemService().system_settings.api_settings.prefix
+
+
+class AreaAndRoadRuggednessScalesQueryParams(QueryParams):
+ """USDA ERS Area and Road Ruggedness Scales Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/area-and-road-ruggedness-scales
+ """
+
+ __json_schema_extra__ = {
+ "vintage": {
+ "x-widget_config": {
+ "label": "Vintage",
+ "value": DEFAULT_VINTAGE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": VINTAGE_OPTIONS,
+ },
+ },
+ "state": {
+ "x-widget_config": {
+ "label": "State",
+ "type": "endpoint",
+ "multiSelect": False,
+ "multiple": False,
+ "value": DEFAULT_STATE,
+ "optionsEndpoint": f"{api_prefix}/usda/area_road_ruggedness_states",
+ "optionsParams": {"vintage": "$vintage"},
+ "style": {"popupWidth": 280},
+ },
+ },
+ }
+
+ vintage: str = Field(
+ default=DEFAULT_VINTAGE,
+ description="Census-tract vintage to retrieve, selecting the source"
+ + " file. Each vintage is a separate census-tract lookup carrying the"
+ + " Area Ruggedness Scale (ARS) and Road Ruggedness Scale (RRS)"
+ + " classifications and their terrain statistics. Valid vintages"
+ + " are:\n "
+ + ", ".join(VINTAGES)
+ + "\n",
+ )
+ state: str = Field(
+ default=DEFAULT_STATE,
+ description="State to retrieve, as a two-letter postal abbreviation."
+ + " The census-tract lookup carries tens of thousands of rows, so the"
+ + " state is the primary scope filter. If None, defaults to West"
+ + " Virginia.",
+ )
+
+ @field_validator("vintage", mode="before", check_fields=False)
+ @classmethod
+ def _validate_vintage(cls, v):
+ """Validate the census-tract vintage."""
+ if not v:
+ return DEFAULT_VINTAGE
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip()
+ if value not in VINTAGES:
+ raise OpenBBError(
+ f"Invalid vintage: {value}. Valid vintages are: " + ", ".join(VINTAGES)
+ )
+ return value
+
+ @field_validator("state", mode="before", check_fields=False)
+ @classmethod
+ def _validate_state(cls, v):
+ """Validate and canonicalize the state filter."""
+ if not v:
+ return DEFAULT_STATE
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip().upper()
+ if value not in ALL_STATES:
+ raise OpenBBError(
+ f"Invalid state: {value}. Valid states are: "
+ + ", ".join(sorted(ALL_STATES))
+ )
+ return value
+
+
+class AreaAndRoadRuggednessScalesData(NullTokenMixin, Data):
+ """USDA ERS Area and Road Ruggedness Scales Data.
+
+ One row per census tract for the selected vintage, a geographic
+ classification lookup carrying the Area Ruggedness Scale (ARS) and Road
+ Ruggedness Scale (RRS) classifications with their terrain ruggedness index
+ statistics, the tract's rurality and Ruggedness-Scale region, and its
+ population, land area, and population density.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Area and Road Ruggedness Scales",
+ "$.description": "Census-tract Area Ruggedness Scale (ARS) and"
+ " Road Ruggedness Scale (RRS) classifications, with the"
+ " underlying terrain ruggedness index statistics, rurality,"
+ " and population, published in vintages by the USDA Economic"
+ " Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ tract_fips: str = Field(
+ description="Eleven-digit census-tract FIPS code, with leading zeros"
+ + " preserved.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Tract FIPS",
+ "cellDataType": "text",
+ "pinned": "left",
+ "maxWidth": 140,
+ }
+ },
+ )
+ tract_name: str | None = Field(
+ default=None,
+ description="Census-tract name, as published.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Tract Name",
+ "cellDataType": "text",
+ "hide": True,
+ }
+ },
+ )
+ county_fips: str | None = Field(
+ default=None,
+ description="Five-digit county FIPS code, with leading zeros preserved.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "County FIPS",
+ "cellDataType": "text",
+ "hide": True,
+ "maxWidth": 120,
+ }
+ },
+ )
+ county_name: str | None = Field(
+ default=None,
+ description="County or county-equivalent area name, as published.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "County",
+ "cellDataType": "text",
+ "pinned": "left",
+ "minWidth": 180,
+ }
+ },
+ )
+ state_fips: str | None = Field(
+ default=None,
+ description="Two-digit state FIPS code, with a leading zero preserved.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "State FIPS",
+ "cellDataType": "text",
+ "hide": True,
+ "maxWidth": 110,
+ }
+ },
+ )
+ state: str = Field(
+ description="Two-letter state or district postal code.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "State",
+ "cellDataType": "text",
+ "pinned": "left",
+ "maxWidth": 90,
+ }
+ },
+ )
+ state_name: str | None = Field(
+ default=None,
+ description="Full state or district name.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "State Name",
+ "cellDataType": "text",
+ "pinned": "left",
+ "minWidth": 150,
+ }
+ },
+ )
+ rs_region: str | None = Field(
+ default=None,
+ description="Ruggedness-Scale region code, 1 to 12.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "RS Region",
+ "cellDataType": "text",
+ "hide": True,
+ "maxWidth": 110,
+ }
+ },
+ )
+ rs_region_name: str | None = Field(
+ default=None,
+ description="Ruggedness-Scale region name, one of the twelve regions"
+ + " the ARS and RRS thresholds are calibrated within.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "RS Region Name",
+ "cellDataType": "text",
+ "minWidth": 180,
+ }
+ },
+ )
+ primary_ruca: str | None = Field(
+ default=None,
+ description="Primary Rural-Urban Commuting Area (RUCA) code, 1 to 10,"
+ + " with 99 marking a water tract.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Primary RUCA",
+ "cellDataType": "text",
+ "hide": True,
+ "maxWidth": 120,
+ }
+ },
+ )
+ rurality_code: str | None = Field(
+ default=None,
+ description="Rurality code, 1 to 3, with 99 marking a tract without a"
+ + " rurality code.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Rurality Code",
+ "cellDataType": "text",
+ "hide": True,
+ "maxWidth": 120,
+ }
+ },
+ )
+ rurality_name: str | None = Field(
+ default=None,
+ description="Rurality name: Urbanized area, Urban commuting, Rural, or"
+ + " no rurality code.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Rurality",
+ "cellDataType": "text",
+ "minWidth": 150,
+ }
+ },
+ )
+ population: int | None = Field(
+ default=None,
+ description="Census-tract population, as a full count.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Population",
+ "cellDataType": "number",
+ "maxWidth": 130,
+ }
+ },
+ )
+ land_area: float | None = Field(
+ default=None,
+ description="Census-tract land area, in square miles.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Land Area (sq mi)",
+ "cellDataType": "number",
+ }
+ },
+ )
+ pop_density: float | None = Field(
+ default=None,
+ description="Population density, in persons per square mile.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Pop. Density",
+ "cellDataType": "number",
+ }
+ },
+ )
+ area_tri_count: int | None = Field(
+ default=None,
+ description="Count of terrain ruggedness index (TRI) grid cells summed"
+ + " over the tract's area.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Area TRI Count",
+ "cellDataType": "number",
+ "hide": True,
+ }
+ },
+ )
+ area_tri_mean: float | None = Field(
+ default=None,
+ description="Mean area terrain ruggedness index over the tract, in"
+ + " meters.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Area TRI Mean",
+ "cellDataType": "number",
+ }
+ },
+ )
+ area_tri_std_dev: float | None = Field(
+ default=None,
+ description="Standard deviation of the area terrain ruggedness index"
+ + " over the tract, in meters.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Area TRI Std Dev",
+ "cellDataType": "number",
+ "hide": True,
+ }
+ },
+ )
+ area_tri_median: float | None = Field(
+ default=None,
+ description="Median area terrain ruggedness index over the tract, in"
+ + " meters.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Area TRI Median",
+ "cellDataType": "number",
+ }
+ },
+ )
+ area_tri_min: float | None = Field(
+ default=None,
+ description="Minimum area terrain ruggedness index over the tract, in"
+ + " meters.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Area TRI Min",
+ "cellDataType": "number",
+ "hide": True,
+ }
+ },
+ )
+ area_tri_max: float | None = Field(
+ default=None,
+ description="Maximum area terrain ruggedness index over the tract, in"
+ + " meters.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Area TRI Max",
+ "cellDataType": "number",
+ "hide": True,
+ }
+ },
+ )
+ area_tri_range: float | None = Field(
+ default=None,
+ description="Range of the area terrain ruggedness index over the tract,"
+ + " in meters.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Area TRI Range",
+ "cellDataType": "number",
+ "hide": True,
+ }
+ },
+ )
+ ars: str | None = Field(
+ default=None,
+ description="Area Ruggedness Scale code, 1 to 6, from 1 Level to 6"
+ + " Extremely rugged.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "ARS",
+ "cellDataType": "text",
+ "maxWidth": 90,
+ }
+ },
+ )
+ ars_description: str | None = Field(
+ default=None,
+ description="Area Ruggedness Scale description for the code.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "ARS Description",
+ "cellDataType": "text",
+ "minWidth": 160,
+ }
+ },
+ )
+ road_tri_count: int | None = Field(
+ default=None,
+ description="Count of terrain ruggedness index (TRI) grid cells sampled"
+ + " along the tract's roads.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Road TRI Count",
+ "cellDataType": "number",
+ "hide": True,
+ }
+ },
+ )
+ road_tri_mean: float | None = Field(
+ default=None,
+ description="Mean road terrain ruggedness index over the tract, in"
+ + " meters.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Road TRI Mean",
+ "cellDataType": "number",
+ }
+ },
+ )
+ road_tri_std_dev: float | None = Field(
+ default=None,
+ description="Standard deviation of the road terrain ruggedness index"
+ + " over the tract, in meters.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Road TRI Std Dev",
+ "cellDataType": "number",
+ "hide": True,
+ }
+ },
+ )
+ road_tri_median: float | None = Field(
+ default=None,
+ description="Median road terrain ruggedness index over the tract, in"
+ + " meters.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Road TRI Median",
+ "cellDataType": "number",
+ }
+ },
+ )
+ road_tri_min: float | None = Field(
+ default=None,
+ description="Minimum road terrain ruggedness index over the tract, in"
+ + " meters.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Road TRI Min",
+ "cellDataType": "number",
+ "hide": True,
+ }
+ },
+ )
+ road_tri_max: float | None = Field(
+ default=None,
+ description="Maximum road terrain ruggedness index over the tract, in"
+ + " meters.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Road TRI Max",
+ "cellDataType": "number",
+ "hide": True,
+ }
+ },
+ )
+ road_tri_range: float | None = Field(
+ default=None,
+ description="Range of the road terrain ruggedness index over the tract,"
+ + " in meters.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Road TRI Range",
+ "cellDataType": "number",
+ "hide": True,
+ }
+ },
+ )
+ rrs: str | None = Field(
+ default=None,
+ description="Road Ruggedness Scale code, 1 to 5, from 1 Level to 5"
+ + " Highly rugged.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "RRS",
+ "cellDataType": "text",
+ "maxWidth": 90,
+ }
+ },
+ )
+ rrs_description: str | None = Field(
+ default=None,
+ description="Road Ruggedness Scale description for the code.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "RRS Description",
+ "cellDataType": "text",
+ "minWidth": 160,
+ }
+ },
+ )
+
+
+class AreaAndRoadRuggednessScalesFetcher(
+ Fetcher[
+ AreaAndRoadRuggednessScalesQueryParams,
+ list[AreaAndRoadRuggednessScalesData],
+ ]
+):
+ """Fetch USDA ERS Area and Road Ruggedness Scales for census tracts."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> AreaAndRoadRuggednessScalesQueryParams:
+ """Transform the query params."""
+ return AreaAndRoadRuggednessScalesQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: AreaAndRoadRuggednessScalesQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected vintage's census-tract lookup records."""
+ from openbb_government_us.usda.utils import (
+ ers_area_and_road_ruggedness_scales as ers,
+ )
+
+ return await ers.afetch_vintage(query.vintage)
+
+ @staticmethod
+ def transform_data(
+ query: AreaAndRoadRuggednessScalesQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[AreaAndRoadRuggednessScalesData]:
+ """Scope the lookup to the selected state and sort by tract FIPS."""
+ rows = [record for record in data if record.get("state") == query.state]
+ if not rows:
+ raise EmptyDataError("No records match the given filters.")
+ rows.sort(key=lambda record: record.get("tract_fips") or "")
+ return [
+ AreaAndRoadRuggednessScalesData.model_validate(record) for record in rows
+ ]
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/bell_report.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/bell_report.py
new file mode 100644
index 00000000000..28e0f8a3ff6
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/bell_report.py
@@ -0,0 +1,125 @@
+"""FAS Bell Report Model."""
+
+from typing import Any
+
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from pydantic import Field
+
+
+class FasBellReportQueryParams(QueryParams):
+ """FAS Bell Report Query Parameters.
+
+ Source: https://apps.fas.usda.gov/esrqs/#/reports/bellreport
+ """
+
+ __json_schema_extra__ = {
+ "urls": {
+ "multiple_items_allowed": True,
+ }
+ }
+
+ urls: str | list[str] | dict[str, list[str]] = Field(
+ description="Report viewer URLs to download, as returned by the"
+ + " bell_report_urls endpoint.",
+ kw_only=True,
+ )
+
+
+class FasBellReportData(Data):
+ """FAS Bell Report Data.
+
+ Weekly export sales commitments by commodity and marketing year. The
+ source embargoes each report until its release time, so only published
+ weeks are offered.
+ """
+
+ error_type: str | None = Field(
+ default=None,
+ description="Error type, if the report could not be downloaded.",
+ )
+ content: str = Field(
+ description="Base64-encoded PDF document.",
+ )
+ filename: str | None = Field(
+ default=None,
+ description="The filename of the downloaded PDF.",
+ )
+ data_format: dict[str, str] | None = Field(
+ default=None,
+ description="Data format information, including data type and filename.",
+ )
+
+
+class FasBellReportFetcher(Fetcher[FasBellReportQueryParams, list[FasBellReportData]]):
+ """Fetch USDA FAS Bell Reports as PDFs."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(params: dict[str, Any]) -> FasBellReportQueryParams:
+ """Transform the query params."""
+ return FasBellReportQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: FasBellReportQueryParams,
+ credentials: dict[str, Any] | None = None,
+ **kwargs: Any,
+ ) -> list:
+ """Download each report from the FAS report viewer."""
+ import base64
+
+ from openbb_government_us.usda.utils.fas_bell_report import (
+ afetch_report,
+ report_file_name,
+ )
+
+ urls = (
+ query.urls.get("urls", [])
+ if isinstance(query.urls, dict)
+ else (query.urls if isinstance(query.urls, list) else query.urls.split(","))
+ )
+ results: list = []
+
+ for url in urls:
+ target = url.strip()
+ try:
+ filename = report_file_name(target)
+ except Exception as exc: # noqa: BLE001
+ results.append(
+ {
+ "error_type": "invalid_url",
+ "content": f"{exc.__class__.__name__}: {exc}",
+ "filename": None,
+ }
+ )
+ continue
+ try:
+ content = await afetch_report(target)
+ except Exception as exc: # noqa: BLE001
+ results.append(
+ {
+ "error_type": "download_error",
+ "content": f"{exc.__class__.__name__}: {exc}",
+ "filename": filename,
+ }
+ )
+ continue
+ results.append(
+ {
+ "content": base64.b64encode(content).decode("utf-8"),
+ "filename": filename,
+ "data_format": {"data_type": "pdf", "filename": filename},
+ }
+ )
+
+ return results
+
+ @staticmethod
+ def transform_data(
+ query: FasBellReportQueryParams, data: list, **kwargs: Any
+ ) -> list[FasBellReportData]:
+ """Transform the data."""
+ return [FasBellReportData(**item) for item in data]
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/commodity_costs_and_returns.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/commodity_costs_and_returns.py
new file mode 100644
index 00000000000..4645a6b4a0b
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/commodity_costs_and_returns.py
@@ -0,0 +1,322 @@
+"""Commodity Costs and Returns Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.app.service.system_service import SystemService
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from openbb_core.provider.utils.errors import EmptyDataError
+from pydantic import ConfigDict, Field, field_validator, model_validator
+
+from openbb_government_us.usda.utils.ers_commodity_costs_and_returns import (
+ CATEGORY_CHOICES,
+ COMMODITY_FILES,
+ COMMODITY_REGIONS,
+ category_order,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+api_prefix = SystemService().system_settings.api_settings.prefix
+
+
+class CommodityCostsAndReturnsQueryParams(QueryParams):
+ """Commodity Costs and Returns Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/commodity-costs-and-returns
+ """
+
+ __json_schema_extra__ = {
+ "commodity": {
+ "x-widget_config": {
+ "label": "Commodity",
+ "multiSelect": False,
+ "multiple": False,
+ "value": "corn",
+ "options": [
+ {"label": entry["label"], "value": key}
+ for key, entry in COMMODITY_FILES.items()
+ ],
+ "style": {"popupWidth": 320},
+ },
+ },
+ "region": {
+ "x-widget_config": {
+ "label": "Region",
+ "type": "endpoint",
+ "multiSelect": False,
+ "multiple": False,
+ "value": "U.S. total",
+ "optionsEndpoint": f"{api_prefix}/usda/commodity_cost_regions",
+ "optionsParams": {"commodity": "$commodity"},
+ "style": {"popupWidth": 320},
+ },
+ },
+ "category": {
+ "x-widget_config": {
+ "label": "Budget section",
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": label, "value": key}
+ for key, label in CATEGORY_CHOICES.items()
+ ],
+ "style": {"popupWidth": 300},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ commodity: str = Field(
+ default="corn",
+ description="Commodity budget to retrieve; selects the per-commodity"
+ + " cost-and-return file. Valid commodities are:\n "
+ + ", ".join(COMMODITY_FILES)
+ + "\n",
+ )
+ region: str | None = Field(
+ default=None,
+ description="ERS Farm Resource Region to retrieve, as the published"
+ + " region label. The region set varies per commodity. If None, every"
+ + " region in the commodity's budget is returned, with 'U.S. total'"
+ + " the national aggregate.",
+ )
+ category: str | None = Field(
+ default=None,
+ description="Budget section to retrieve. If None, all sections are"
+ + " returned. Valid sections are:\n "
+ + ", ".join(CATEGORY_CHOICES)
+ + "\n",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data."
+ + " None returns from the commodity's first available year.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data."
+ + " If None, returns up to the most recent year.",
+ )
+
+ @field_validator("commodity", mode="before", check_fields=False)
+ @classmethod
+ def _validate_commodity(cls, v):
+ """Validate commodity."""
+ if not v:
+ return "corn"
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip()
+ if value not in COMMODITY_FILES:
+ raise OpenBBError(
+ f"Invalid commodity: {value}. Valid commodities are: "
+ + ", ".join(COMMODITY_FILES)
+ )
+ return value
+
+ @field_validator("region", mode="before", check_fields=False)
+ @classmethod
+ def _validate_region(cls, v):
+ """Validate region."""
+ if not v:
+ return None
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip()
+ return value or None
+
+ @field_validator("category", mode="before", check_fields=False)
+ @classmethod
+ def _validate_category(cls, v):
+ """Validate category."""
+ if not v:
+ return None
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip()
+ if value not in CATEGORY_CHOICES:
+ raise OpenBBError(
+ f"Invalid category: {value}. Valid categories are: "
+ + ", ".join(CATEGORY_CHOICES)
+ )
+ return value
+
+ @model_validator(mode="after")
+ def _validate_region_for_commodity(self):
+ """Validate that the region belongs to the selected commodity."""
+ if self.region is not None and self.region not in COMMODITY_REGIONS.get(
+ self.commodity, []
+ ):
+ raise OpenBBError(
+ f"Invalid region '{self.region}' for commodity"
+ f" '{self.commodity}'. Valid regions are: "
+ + ", ".join(COMMODITY_REGIONS.get(self.commodity, []))
+ )
+ return self
+
+
+class CommodityCostsAndReturnsData(NullTokenMixin, Data):
+ """Commodity Costs and Returns Data.
+
+ Annual per-acre or per-unit cost-and-return budgets for major U.S. field
+ crops and livestock enterprises, published by USDA ERS. Each row is one
+ budget line for a region, with one value column per year.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Commodity Costs & Returns",
+ "$.description": "Per-acre and per-unit cost-and-return budgets for"
+ " major U.S. field crops and livestock enterprises, published by"
+ " the USDA Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ commodity: str = Field(
+ description="Commodity name, as published.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Commodity", "hide": True}
+ },
+ )
+ region: str = Field(
+ description="ERS Farm Resource Region of the budget, or 'U.S. total'"
+ + " for the national aggregate.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Region", "pinned": "left"}
+ },
+ )
+ category: str = Field(
+ description="Budget section of the line, as published, e.g. 'Operating"
+ + " costs' or 'Gross value of production'.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Budget section", "pinned": "left"}
+ },
+ )
+ item: str = Field(
+ description="Budget line item, as published, with footnote"
+ + " superscripts and stray whitespace stripped.",
+ json_schema_extra={"x-widget_config": {"headerName": "Item", "pinned": "left"}},
+ )
+ units: str = Field(
+ description="Unit of the line's values, as published, e.g. 'dollars"
+ + " per planted acre' or 'bushels per planted acre'.",
+ json_schema_extra={"x-widget_config": {"headerName": "Units"}},
+ )
+ survey_base_year: str | None = Field(
+ default=None,
+ description="Survey base of the most recent year in the row, as"
+ + " published, e.g. 'Base survey of 2021'.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Survey base", "hide": True}
+ },
+ )
+
+
+class CommodityCostsAndReturnsFetcher(
+ Fetcher[
+ CommodityCostsAndReturnsQueryParams,
+ list[CommodityCostsAndReturnsData],
+ ]
+):
+ """Fetch USDA ERS Commodity Costs and Returns."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> CommodityCostsAndReturnsQueryParams:
+ """Transform the query params."""
+ return CommodityCostsAndReturnsQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: CommodityCostsAndReturnsQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected commodity's tidy CSV rows."""
+ from openbb_government_us.usda.utils import ers_commodity_costs_and_returns
+
+ return await ers_commodity_costs_and_returns.afetch_commodity(query.commodity)
+
+ @staticmethod
+ def transform_data(
+ query: CommodityCostsAndReturnsQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[CommodityCostsAndReturnsData]:
+ """Transform the data by pivoting each budget line's years into columns."""
+ category = CATEGORY_CHOICES[query.category] if query.category else None
+ region_order = COMMODITY_REGIONS.get(query.commodity, [])
+ pivoted: dict[tuple, dict] = {}
+ for record in data:
+ if query.start_year is not None and record["year"] < query.start_year:
+ continue
+ if query.end_year is not None and record["year"] > query.end_year:
+ continue
+ if query.region is not None and record["region"] != query.region:
+ continue
+ if category is not None and record["category"] != category:
+ continue
+ key = (
+ record["region"],
+ record["category"],
+ record["item"],
+ record["units"],
+ )
+ row = pivoted.get(key)
+ if row is None:
+ region_index = (
+ region_order.index(record["region"])
+ if record["region"] in region_order
+ else len(region_order)
+ )
+ row = {
+ "_region_index": region_index,
+ "_category_order": category_order(record["category"]),
+ "_total_last": 1 if record["item"].startswith("Total,") else 0,
+ "_max_year": -1,
+ "commodity": record["commodity"],
+ "region": record["region"],
+ "category": record["category"],
+ "item": record["item"],
+ "units": record["units"],
+ "survey_base_year": None,
+ }
+ pivoted[key] = row
+ row[str(record["year"])] = record["value"]
+ if record["year"] > row["_max_year"]:
+ row["_max_year"] = record["year"]
+ row["survey_base_year"] = record["survey_base_year"]
+ if not pivoted:
+ raise EmptyDataError("No records match the given filters.")
+ results = sorted(
+ pivoted.values(),
+ key=lambda row: (
+ row["_region_index"],
+ row["_category_order"],
+ row["_total_last"],
+ row["item"],
+ ),
+ )
+ all_years = sorted(
+ {int(k) for row in pivoted.values() for k in row if k.isdigit()}
+ )
+ validated: list = []
+ for row in results:
+ dimensions = {
+ k: v
+ for k, v in row.items()
+ if not k.startswith("_") and not k.isdigit()
+ }
+ years = {str(year): row.get(str(year)) for year in all_years}
+ validated.append(
+ CommodityCostsAndReturnsData.model_validate({**dimensions, **years})
+ )
+ return validated
diff --git a/openbb_platform/providers/government_us/openbb_government_us/models/commodity_psd_data.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/commodity_psd_data.py
similarity index 78%
rename from openbb_platform/providers/government_us/openbb_government_us/models/commodity_psd_data.py
rename to openbb_platform/providers/government_us/openbb_government_us/usda/models/commodity_psd_data.py
index 3c1d7c5764e..a279cc7a466 100644
--- a/openbb_platform/providers/government_us/openbb_government_us/models/commodity_psd_data.py
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/commodity_psd_data.py
@@ -1,24 +1,27 @@
"""US Government Production Supply & Distribution Report Data & Time Series Model."""
-# pylint: disable=unused-argument
-
from datetime import datetime
from typing import Any
from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.app.service.system_service import SystemService
from openbb_core.provider.abstract.fetcher import Fetcher
from openbb_core.provider.standard_models.commodity_psd_data import (
CommodityPsdData,
CommodityPsdDataQueryParams,
)
-from openbb_government_us.utils.psd_codes import (
+from pydantic import ConfigDict, Field, field_validator, model_validator
+
+from openbb_government_us.usda.utils.psd_codes import (
ATTRIBUTES,
COMMODITIES,
COUNTRIES,
PSD_REPORT_NAMES,
REGIONS,
)
-from pydantic import ConfigDict, Field, field_validator, model_validator
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+api_prefix = SystemService().system_settings.api_settings.prefix
COUNTRY_CHOICES = {
**{k: v for k, v in COUNTRIES.items() if k != "world"},
@@ -26,7 +29,7 @@ COUNTRY_CHOICES = {
}
-class GovernmentUsCommodityPsdDataQueryParams(CommodityPsdDataQueryParams):
+class UsdaCommodityPsdDataQueryParams(CommodityPsdDataQueryParams):
"""US Government Commodity PSD Data Query Params.
Source: https://apps.fas.usda.gov/psdonline/app/index.html#/app/downloads
@@ -68,33 +71,30 @@ class GovernmentUsCommodityPsdDataQueryParams(CommodityPsdDataQueryParams):
"attribute": {
"multiple_items_allowed": True,
"x-widget_config": {
+ "multiSelect": False,
+ "multiple": False,
"description": "Attribute to filter the data. Ignored when commodity is Report Mode. "
- + "If None, retrieves all available attributes for the commodity.",
- "options": [{"label": "All Attributes", "value": None}]
- + [
- {
- "label": attr.replace("_", " ")
- .title()
- .replace("Us", "US")
- .replace("Ty", "TY")
- .replace("Fsi", "FSI"),
- "value": attr,
- }
- for attr in sorted(list(ATTRIBUTES))
- ],
+ + "If None, retrieves all available attributes for the commodity. "
+ + "The choices are scoped to the selected commodity.",
+ "type": "endpoint",
+ "optionsEndpoint": f"{api_prefix}/usda/psd_data_attributes",
+ "optionsParams": {"commodity": "$commodity"},
+ "style": {"popupWidth": 320},
"row": 1,
},
},
"country": {
"multiple_items_allowed": True,
"x-widget_config": {
+ "multiSelect": False,
+ "multiple": False,
"description": "Country code(s) to filter the data. Ignored when commodity is Report Mode. "
- + "If None, retrieves data for all countries.",
- "options": [{"label": "All Countries", "value": None}]
- + [
- {"label": name.replace("_", " ").title(), "value": code}
- for name, code in COUNTRY_CHOICES.items()
- ],
+ + "If None, retrieves data for all countries. "
+ + "The choices are scoped to the selected commodity.",
+ "type": "endpoint",
+ "optionsEndpoint": f"{api_prefix}/usda/psd_data_countries",
+ "optionsParams": {"commodity": "$commodity"},
+ "style": {"popupWidth": 320},
"row": 1,
},
},
@@ -321,7 +321,7 @@ class GovernmentUsCommodityPsdDataQueryParams(CommodityPsdDataQueryParams):
return self
-class GovernmentUsCommodityPsdData(CommodityPsdData):
+class UsdaCommodityPsdData(NullTokenMixin, CommodityPsdData):
"""US Government Commodity PSD Data."""
model_config = ConfigDict(
@@ -335,14 +335,32 @@ class GovernmentUsCommodityPsdData(CommodityPsdData):
"$.subCategory": "Agriculture",
"$.runButton": True,
},
+ "region": {"x-widget_config": {"headerName": "Region", "pinned": "left"}},
+ "country": {"x-widget_config": {"headerName": "Country", "pinned": "left"}},
+ "commodity": {"x-widget_config": {"headerName": "Commodity"}},
+ "attribute": {"x-widget_config": {"headerName": "Attribute"}},
+ "unit": {"x-widget_config": {"headerName": "Unit"}},
+ "marketing_year": {"x-widget_config": {"exclude": True}},
+ "value": {"x-widget_config": {"exclude": True}},
}
)
+ marketing_year: str | None = Field(
+ default=None,
+ description="Marketing year for the commodity.",
+ exclude=True,
+ )
+ value: float | int | None = Field(
+ default=None,
+ description="Value for the commodity attribute in the given marketing year.",
+ exclude=True,
+ )
-class GovernmentUsCommodityPsdDataFetcher(
+
+class UsdaCommodityPsdDataFetcher(
Fetcher[
- GovernmentUsCommodityPsdDataQueryParams,
- list[GovernmentUsCommodityPsdData],
+ UsdaCommodityPsdDataQueryParams,
+ list[UsdaCommodityPsdData],
]
):
"""US Government Commodity PSD Data Fetcher."""
@@ -350,25 +368,23 @@ class GovernmentUsCommodityPsdDataFetcher(
@staticmethod
def transform_query(
params: dict[str, Any],
- ) -> GovernmentUsCommodityPsdDataQueryParams:
+ ) -> UsdaCommodityPsdDataQueryParams:
"""Transform the query parameters into the model."""
- return GovernmentUsCommodityPsdDataQueryParams(**params)
+ return UsdaCommodityPsdDataQueryParams(**params)
@staticmethod
async def aextract_data(
- query: GovernmentUsCommodityPsdDataQueryParams,
+ query: UsdaCommodityPsdDataQueryParams,
credentials: dict[str, Any] | None,
**kwargs: Any,
) -> list:
"""Extract data from the provider."""
- # pylint: disable=import-outside-toplevel
- from openbb_government_us.utils.psd_data_downloader import (
+ from openbb_government_us.usda.utils.psd_data_downloader import (
get_psd_report_data,
get_timeseries,
)
if query.commodity is not None:
- # Time series data
try:
timeseries_data = get_timeseries(
commodity=query.commodity,
@@ -403,9 +419,38 @@ class GovernmentUsCommodityPsdDataFetcher(
@staticmethod
def transform_data(
- query: GovernmentUsCommodityPsdDataQueryParams,
+ query: UsdaCommodityPsdDataQueryParams,
data: list,
**kwargs: Any,
- ) -> list[GovernmentUsCommodityPsdData]:
- """Transform and validate the data."""
- return [GovernmentUsCommodityPsdData.model_validate(item) for item in data]
+ ) -> list[UsdaCommodityPsdData]:
+ """Pivot the marketing-year observations into columns and validate."""
+ pivoted: dict[tuple, dict] = {}
+ for order, item in enumerate(data):
+ key = (
+ item.get("region"),
+ item.get("country"),
+ item.get("commodity"),
+ item.get("attribute"),
+ item.get("unit"),
+ )
+ row = pivoted.get(key)
+ if row is None:
+ row = {
+ "_order": order,
+ "region": item.get("region"),
+ "country": item.get("country"),
+ "commodity": item.get("commodity"),
+ "attribute": item.get("attribute"),
+ "unit": item.get("unit"),
+ }
+ pivoted[key] = row
+ marketing_year = item.get("marketing_year")
+ if marketing_year is not None:
+ row[str(marketing_year)] = item.get("value")
+ results = sorted(pivoted.values(), key=lambda row: row["_order"])
+ return [
+ UsdaCommodityPsdData.model_validate(
+ {k: v for k, v in row.items() if k != "_order"}
+ )
+ for row in results
+ ]
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/commodity_psd_report.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/commodity_psd_report.py
new file mode 100644
index 00000000000..3eb4e597b37
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/commodity_psd_report.py
@@ -0,0 +1,136 @@
+"""US Government Production Supply & Distribution Circular Model."""
+
+from typing import Any
+
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from pydantic import Field
+
+from openbb_government_us.utils.serializers import OmitNoneMixin
+
+
+class UsdaCommodityPsdReportQueryParams(QueryParams):
+ """US Government Commodity PSD Report Query Params.
+
+ Source: https://apps.fas.usda.gov/psdonline/app/index.html#/app/downloads
+ """
+
+ __json_schema_extra__ = {
+ "urls": {
+ "multiple_items_allowed": True,
+ }
+ }
+
+ urls: str | list[str] | dict[str, list[str]] = Field(
+ description="Circular URLs to download, as returned by the"
+ + " psd_report_urls endpoint.",
+ kw_only=True,
+ )
+
+
+class UsdaCommodityPsdReportData(OmitNoneMixin, Data):
+ """US Government Commodity PSD Report Data.
+
+ Monthly world markets and trade circulars by commodity. The source carries
+ the most recent circular forward into every later month folder, so only
+ the month that actually published a circular is offered.
+ """
+
+ error_type: str | None = Field(
+ default=None,
+ description="Error type, if the circular could not be downloaded.",
+ )
+ content: str = Field(
+ description="Base64-encoded PDF document.",
+ )
+ filename: str | None = Field(
+ default=None,
+ description="The filename of the downloaded PDF.",
+ )
+ data_format: dict[str, str] | None = Field(
+ default=None,
+ description="Data format information, including data type and filename.",
+ )
+
+
+class UsdaCommodityPsdReportFetcher(
+ Fetcher[
+ UsdaCommodityPsdReportQueryParams,
+ list[UsdaCommodityPsdReportData],
+ ]
+):
+ """US Government Commodity PSD Report Fetcher."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> UsdaCommodityPsdReportQueryParams:
+ """Transform the query params."""
+ return UsdaCommodityPsdReportQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: UsdaCommodityPsdReportQueryParams,
+ credentials: dict[str, Any] | None = None,
+ **kwargs: Any,
+ ) -> list:
+ """Download each circular from the PSD Online host."""
+ import base64
+
+ from openbb_government_us.usda.utils.psd_circulars import (
+ afetch_circular,
+ circular_file_name,
+ )
+
+ urls = (
+ query.urls.get("urls", [])
+ if isinstance(query.urls, dict)
+ else (query.urls if isinstance(query.urls, list) else query.urls.split(","))
+ )
+ results: list = []
+
+ for url in urls:
+ target = url.strip()
+ try:
+ filename = circular_file_name(target)
+ except Exception as exc: # noqa: BLE001
+ results.append(
+ {
+ "error_type": "invalid_url",
+ "content": f"{exc.__class__.__name__}: {exc}",
+ "filename": None,
+ }
+ )
+ continue
+ try:
+ content = await afetch_circular(target)
+ except Exception as exc: # noqa: BLE001
+ results.append(
+ {
+ "error_type": "download_error",
+ "content": f"{exc.__class__.__name__}: {exc}",
+ "filename": filename,
+ }
+ )
+ continue
+ results.append(
+ {
+ "content": base64.b64encode(content).decode("utf-8"),
+ "filename": filename,
+ "data_format": {"data_type": "pdf", "filename": filename},
+ }
+ )
+
+ return results
+
+ @staticmethod
+ def transform_data(
+ query: UsdaCommodityPsdReportQueryParams,
+ data: list,
+ **kwargs: Any,
+ ) -> list[UsdaCommodityPsdReportData]:
+ """Transform the data."""
+ return [UsdaCommodityPsdReportData(**item) for item in data]
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/commuting_zones_and_labor_market_areas.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/commuting_zones_and_labor_market_areas.py
new file mode 100644
index 00000000000..fb7c1b36d20
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/commuting_zones_and_labor_market_areas.py
@@ -0,0 +1,354 @@
+"""Commuting Zones and Labor Market Areas Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from openbb_core.provider.utils.errors import EmptyDataError
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_commuting_zones_and_labor_market_areas import (
+ DEFAULT_VINTAGE,
+ STATE_NAMES,
+ VINTAGE_FILES,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+_STATE_LOOKUP = {name.casefold(): name for name in STATE_NAMES}
+
+
+class CommutingZonesAndLaborMarketAreasQueryParams(QueryParams):
+ """Commuting Zones and Labor Market Areas Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/commuting-zones-and-labor-market-areas
+ """
+
+ __json_schema_extra__ = {
+ "vintage": {
+ "x-widget_config": {
+ "label": "Vintage",
+ "value": DEFAULT_VINTAGE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": config["label"], "value": key}
+ for key, config in VINTAGE_FILES.items()
+ ],
+ "style": {"popupWidth": 420},
+ },
+ },
+ "state": {
+ "x-widget_config": {
+ "label": "State",
+ "multiSelect": False,
+ "multiple": False,
+ "options": [{"label": name, "value": name} for name in STATE_NAMES],
+ "style": {"popupWidth": 260},
+ },
+ },
+ }
+
+ vintage: str = Field(
+ default=DEFAULT_VINTAGE,
+ description="Classification vintage to retrieve. Each vintage is a"
+ + " separate county lookup published for a base year; the county rows"
+ + " and their commuting-zone codes replace one another across vintages"
+ + " rather than forming a time series. Valid vintages are:\n "
+ + ", ".join(VINTAGE_FILES)
+ + "\n",
+ )
+ state: str | None = Field(
+ default=None,
+ description="State to retrieve, by full name, e.g. 'Texas'."
+ + " If None, all states are returned. Puerto Rico is published for the"
+ + " 2020 and preliminary 2020 vintages only.",
+ )
+
+ @field_validator("vintage", mode="before", check_fields=False)
+ @classmethod
+ def _validate_vintage(cls, v):
+ """Validate vintage."""
+ if not v:
+ return DEFAULT_VINTAGE
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip()
+ if value not in VINTAGE_FILES:
+ raise OpenBBError(
+ f"Invalid vintage: {value}. Valid vintages are: "
+ + ", ".join(VINTAGE_FILES)
+ )
+ return value
+
+ @field_validator("state", mode="before", check_fields=False)
+ @classmethod
+ def _validate_state(cls, v):
+ """Validate state."""
+ if not v:
+ return None
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip()
+ canonical = _STATE_LOOKUP.get(value.casefold())
+ if canonical is None:
+ raise OpenBBError(
+ f"Invalid state: {value}. Valid states are: " + ", ".join(STATE_NAMES)
+ )
+ return canonical
+
+
+class CommutingZonesAndLaborMarketAreasData(NullTokenMixin, Data):
+ """Commuting Zones and Labor Market Areas Data.
+
+ One row per county for the selected vintage: the county's FIPS code, state,
+ and name, its primary commuting-zone code and name, and its commuter
+ containment. Codes published only for the older vintages are hidden by
+ default.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Commuting Zones and Labor Market Areas",
+ "$.description": "County-level commuting zone and labor market"
+ " area classifications for the 2020, preliminary 2020, 2000, and"
+ " 1980/1990 vintages, published by the USDA Economic Research"
+ " Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ fips: str = Field(
+ description="Five-digit county FIPS code.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "County FIPS",
+ "cellDataType": "text",
+ "pinned": "left",
+ "maxWidth": 120,
+ }
+ },
+ )
+ state: str | None = Field(
+ default=None,
+ description="Full state name, derived from the county FIPS code.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "State", "pinned": "left"}
+ },
+ )
+ county_name: str | None = Field(
+ default=None,
+ description="County name, as published.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "County", "pinned": "left"}
+ },
+ )
+ commuting_zone: str | None = Field(
+ default=None,
+ description="Primary commuting-zone identifier of the vintage: CZ2020"
+ + " for 2020, the preliminary CZ for preliminary 2020, the 2000"
+ + " commuting-zone id for 2000, and CZ90 for 1980/1990.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Commuting Zone",
+ "cellDataType": "text",
+ }
+ },
+ )
+ commuting_zone_name: str | None = Field(
+ default=None,
+ description="Commuting-zone name for the 2020 vintage, or the name of"
+ + " the largest place in the commuting zone for the 1980/1990 vintage.",
+ json_schema_extra={"x-widget_config": {"headerName": "Commuting Zone Name"}},
+ )
+ cz_containment: float | None = Field(
+ default=None,
+ description="Percent of the county's commuters contained within its"
+ + " commuting zone. 2020 vintage only.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Containment (%)",
+ "cellDataType": "number",
+ }
+ },
+ )
+ cz_avg_containment: float | None = Field(
+ default=None,
+ description="Average commuter containment across the counties of the"
+ + " commuting zone, in percent. 2020 vintage only.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Avg. Containment (%)",
+ "cellDataType": "number",
+ }
+ },
+ )
+ cz_1990: str | None = Field(
+ default=None,
+ description="1990 commuting-zone identifier of the county. 2000 vintage"
+ + " only.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Commuting Zone (1990)",
+ "cellDataType": "text",
+ "hide": True,
+ }
+ },
+ )
+ cz_1980: str | None = Field(
+ default=None,
+ description="1980 commuting-zone identifier of the county, published in"
+ + " the 2000 and 1980/1990 vintages.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Commuting Zone (1980)",
+ "cellDataType": "text",
+ "hide": True,
+ }
+ },
+ )
+ metro_area: str | None = Field(
+ default=None,
+ description="2003 metropolitan or micropolitan area of the county, as"
+ + " published. 2000 vintage only.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Metropolitan Area (2003)", "hide": True}
+ },
+ )
+ county_population: int | None = Field(
+ default=None,
+ description="County population: the 2000 census count for the 2000"
+ + " vintage, or the 1990 census count for the 1980/1990 vintage.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "County Population",
+ "cellDataType": "number",
+ "hide": True,
+ }
+ },
+ )
+ commuting_zone_population: int | None = Field(
+ default=None,
+ description="2000 census population of the commuting zone. 2000 vintage"
+ + " only.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Commuting Zone Population",
+ "cellDataType": "number",
+ "hide": True,
+ }
+ },
+ )
+ distance: float | None = Field(
+ default=None,
+ description="Cluster distance metric of the county's assignment to its"
+ + " commuting zone. 1980/1990 vintage only.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Distance",
+ "cellDataType": "number",
+ "hide": True,
+ }
+ },
+ )
+ beale_code: str | None = Field(
+ default=None,
+ description="1993 rural-urban continuum (Beale) code of the county."
+ + " 1980/1990 vintage only.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Beale Code (1993)",
+ "cellDataType": "text",
+ "hide": True,
+ }
+ },
+ )
+ msa_code: str | None = Field(
+ default=None,
+ description="1993 metropolitan statistical area code of the county."
+ + " 1980/1990 vintage only.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "MSA Code (1993)",
+ "cellDataType": "text",
+ "hide": True,
+ }
+ },
+ )
+ msa_name: str | None = Field(
+ default=None,
+ description="1993 metropolitan statistical area name of the county."
+ + " 1980/1990 vintage only.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "MSA Name (1993)", "hide": True}
+ },
+ )
+ place_code: str | None = Field(
+ default=None,
+ description="State place code of the largest place in the commuting"
+ + " zone. 1980/1990 vintage only.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "State Place Code",
+ "cellDataType": "text",
+ "hide": True,
+ }
+ },
+ )
+
+
+class CommutingZonesAndLaborMarketAreasFetcher(
+ Fetcher[
+ CommutingZonesAndLaborMarketAreasQueryParams,
+ list[CommutingZonesAndLaborMarketAreasData],
+ ]
+):
+ """Fetch USDA ERS Commuting Zones and Labor Market Areas."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> CommutingZonesAndLaborMarketAreasQueryParams:
+ """Transform the query params."""
+ return CommutingZonesAndLaborMarketAreasQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: CommutingZonesAndLaborMarketAreasQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected vintage's county records."""
+ from openbb_government_us.usda.utils import (
+ ers_commuting_zones_and_labor_market_areas,
+ )
+
+ return await ers_commuting_zones_and_labor_market_areas.afetch_vintage(
+ query.vintage
+ )
+
+ @staticmethod
+ def transform_data(
+ query: CommutingZonesAndLaborMarketAreasQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[CommutingZonesAndLaborMarketAreasData]:
+ """Filter the county lookup by state and order it by FIPS code."""
+ rows = [
+ record
+ for record in data
+ if query.state is None
+ or (record["state"] or "").casefold() == query.state.casefold()
+ ]
+ if not rows:
+ raise EmptyDataError("No records match the given filters.")
+ rows.sort(key=lambda record: record["fips"])
+ return [
+ CommutingZonesAndLaborMarketAreasData.model_validate(record)
+ for record in rows
+ ]
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/cost_estimates_of_foodborne_illnesses.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/cost_estimates_of_foodborne_illnesses.py
new file mode 100644
index 00000000000..8775ecdda97
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/cost_estimates_of_foodborne_illnesses.py
@@ -0,0 +1,156 @@
+"""USDA ERS Cost Estimates of Foodborne Illnesses Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_cost_estimates_of_foodborne_illnesses import (
+ COST_ESTIMATES_TABLES,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+DEFAULT_TABLE = "cases_and_cost"
+
+
+class CostEstimatesOfFoodborneIllnessesQueryParams(QueryParams):
+ """USDA ERS Cost Estimates of Foodborne Illnesses Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/cost-estimates-of-foodborne-illnesses
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "x-widget_config": {
+ "label": "Table",
+ "value": DEFAULT_TABLE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": config["label"], "value": key}
+ for key, config in COST_ESTIMATES_TABLES.items()
+ ],
+ "style": {"popupWidth": 480},
+ },
+ },
+ }
+
+ table: str = Field(
+ default=DEFAULT_TABLE,
+ description="Cost-estimates table to retrieve. Each table is already"
+ + " one row per pathogen, with its cost measures spread across the"
+ + " value columns; pathogen classification and pathogen stay as pinned"
+ + " row columns. Valid tables are:\n "
+ + ", ".join(COST_ESTIMATES_TABLES)
+ + "\n",
+ )
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate table."""
+ if not v:
+ return DEFAULT_TABLE
+ table = v.strip() if isinstance(v, str) else v
+ if table not in COST_ESTIMATES_TABLES:
+ raise OpenBBError(
+ f"Invalid table: {table}. Valid tables are: "
+ + ", ".join(COST_ESTIMATES_TABLES)
+ )
+ return table
+
+
+class CostEstimatesOfFoodborneIllnessesData(NullTokenMixin, Data):
+ """USDA ERS Cost Estimates of Foodborne Illnesses.
+
+ One selected table in its native wide layout: one row per pathogen, with
+ the table's cost measures as value columns whose set varies by table. All
+ monetary values are in 2023 U.S. dollars.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Cost Estimates of Foodborne Illnesses",
+ "$.description": "Estimated annual number of cases and the"
+ " economic burden of foodborne illnesses acquired in the United"
+ " States, by pathogen and by health outcome, in 2023 U.S."
+ " dollars, published by the USDA Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ pathogen_classification: str = Field(
+ description="Pathogen classification, as published: 'Bacteria',"
+ + " 'Parasites', 'Viruses', or an aggregate row's grouping such as"
+ + " 'Illnesses from major foodborne pathogens', 'Other foodborne"
+ + " gastroenteritis', or 'All pathogens'.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Classification",
+ "pinned": "left",
+ }
+ },
+ )
+ pathogen: str = Field(
+ description="Pathogen name, as published, e.g. 'Campylobacter spp.',"
+ + " or an aggregate row label such as 'Subtotal' or 'Total'.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Pathogen",
+ "pinned": "left",
+ }
+ },
+ )
+
+
+class CostEstimatesOfFoodborneIllnessesFetcher(
+ Fetcher[
+ CostEstimatesOfFoodborneIllnessesQueryParams,
+ list[CostEstimatesOfFoodborneIllnessesData],
+ ]
+):
+ """Fetch USDA ERS Cost Estimates of Foodborne Illnesses."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> CostEstimatesOfFoodborneIllnessesQueryParams:
+ """Transform the query params."""
+ return CostEstimatesOfFoodborneIllnessesQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: CostEstimatesOfFoodborneIllnessesQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected table's wide per-pathogen rows."""
+ from openbb_government_us.usda.utils import (
+ ers_cost_estimates_of_foodborne_illnesses,
+ )
+
+ return await ers_cost_estimates_of_foodborne_illnesses.afetch_table(query.table)
+
+ @staticmethod
+ def transform_data(
+ query: CostEstimatesOfFoodborneIllnessesQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[CostEstimatesOfFoodborneIllnessesData]:
+ """Emit the wide rows in source order, dropping temporary keys."""
+ results = sorted(data, key=lambda row: row["_order"])
+ return [
+ CostEstimatesOfFoodborneIllnessesData.model_validate(
+ {k: v for k, v in row.items() if not k.startswith("_")}
+ )
+ for row in results
+ ]
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/cotton_wool_and_textile_data.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/cotton_wool_and_textile_data.py
new file mode 100644
index 00000000000..fb08d7c3a3d
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/cotton_wool_and_textile_data.py
@@ -0,0 +1,315 @@
+"""USDA ERS Cotton, Wool, and Textile Data Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.app.service.system_service import SystemService
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_cotton_wool_and_textile_data import (
+ COLUMN_DIM_FIELDS,
+ CWT_TABLES,
+ FREQUENCY_RANK,
+ MONTH_NAMES,
+ MONTH_ORDER,
+ classify_frequency,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+api_prefix = SystemService().system_settings.api_settings.prefix
+
+DEFAULT_TABLE = "cotton_supply_and_use"
+
+
+class CottonWoolAndTextileDataQueryParams(QueryParams):
+ """USDA ERS Cotton, Wool, and Textile Data Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/cotton-wool-and-textile-data
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "x-widget_config": {
+ "label": "Table",
+ "value": DEFAULT_TABLE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": config["label"], "value": key}
+ for key, config in CWT_TABLES.items()
+ ],
+ "style": {"popupWidth": 480},
+ },
+ },
+ "frequency": {
+ "x-widget_config": {
+ "label": "Frequency",
+ "type": "endpoint",
+ "multiSelect": False,
+ "multiple": False,
+ "optionsEndpoint": f"{api_prefix}/usda/cotton_frequencies",
+ "optionsParams": {"table": "$table"},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ table: str = Field(
+ default=DEFAULT_TABLE,
+ description="Cotton, wool, and textile table to retrieve. Each table is a"
+ + " set of named supply-use, price, or trade series indexed by year; the"
+ + " series spread into the value columns while time stays in the rows,"
+ + " except the by-origin and by-destination cotton textile trade tables,"
+ + " which keep the country in the rows and spread years into columns."
+ + " Valid tables are:\n "
+ + ", ".join(CWT_TABLES)
+ + "\n",
+ )
+ frequency: str | None = Field(
+ default=None,
+ description="Reporting frequency to keep, one of the frequencies the"
+ + " selected table publishes: 'Annual', 'Quarterly', or 'Monthly'."
+ + " Filtering to one frequency yields a single aligned time series."
+ + " If None, the finest frequency the table offers is used.",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data, compared against the"
+ + " four-digit start year of the observation. If None, returns from the"
+ + " beginning of the series.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data, compared against the"
+ + " four-digit start year of the observation. If None, returns up to the"
+ + " most recent year.",
+ )
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate table."""
+ if not v:
+ return DEFAULT_TABLE
+ table = v.strip() if isinstance(v, str) else v
+ if table not in CWT_TABLES:
+ raise OpenBBError(
+ f"Invalid table: {table}. Valid tables are: " + ", ".join(CWT_TABLES)
+ )
+ return table
+
+ @field_validator("frequency", mode="before", check_fields=False)
+ @classmethod
+ def _validate_frequency(cls, v):
+ """Normalize frequency to a canonical label or None."""
+ if not v:
+ return None
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ return str(value).strip().title() or None
+
+
+class CottonWoolAndTextileDataData(NullTokenMixin, Data):
+ """USDA ERS Cotton, Wool, and Textile Data.
+
+ One selected cotton, wool, or textile table for one reporting frequency,
+ pivoted to a wide layout: the table's series and grouping dimensions spread
+ into value columns while the period stays in the rows, except the by-origin
+ and by-destination cotton textile trade tables, which keep the country in
+ the rows and spread years into columns.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Cotton, Wool, and Textile Data",
+ "$.description": "U.S. and world cotton, wool, and textile-fiber"
+ " supply, use, prices, and trade from the Cotton and Wool Yearbook"
+ " and the raw-fiber equivalents of U.S. textile trade, published by"
+ " the USDA Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ period: str = Field(
+ description="Row label of the observation: the calendar or marketing year"
+ + " ('2020'), the year and month for monthly tables ('2020 Jan'), the"
+ + " country for the by-origin and by-destination trade tables, or a"
+ + " country prefixed to the year where a table breaks a series out by"
+ + " country down the rows.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Period",
+ "pinned": "left",
+ "maxWidth": 240,
+ }
+ },
+ )
+
+
+class CottonWoolAndTextileDataFetcher(
+ Fetcher[
+ CottonWoolAndTextileDataQueryParams,
+ list[CottonWoolAndTextileDataData],
+ ]
+):
+ """Fetch USDA ERS Cotton, Wool, and Textile Data."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> CottonWoolAndTextileDataQueryParams:
+ """Transform the query params."""
+ return CottonWoolAndTextileDataQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: CottonWoolAndTextileDataQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected table's long-format rows."""
+ from openbb_government_us.usda.utils import ers_cotton_wool_and_textile_data
+
+ return await ers_cotton_wool_and_textile_data.afetch_table(query.table)
+
+ @staticmethod
+ def _in_range(year: int, query: CottonWoolAndTextileDataQueryParams) -> bool:
+ """Return whether a year falls within the query's year window."""
+ return (query.start_year is None or year >= query.start_year) and (
+ query.end_year is None or year <= query.end_year
+ )
+
+ @staticmethod
+ def _column_key(record: dict) -> str:
+ """Build a wide-column header from a record's grouping dims and series."""
+ parts = [
+ str(record[field]).replace("_", " ")
+ for field in COLUMN_DIM_FIELDS
+ if record[field]
+ ]
+ if record["series"]:
+ parts.append(str(record["series"]).replace("_", " "))
+ head = " · ".join(parts) if parts else "Value"
+ return f"{head} ({record['unit']})" if record["unit"] else head
+
+ @staticmethod
+ def _resolve_frequency(
+ query: CottonWoolAndTextileDataQueryParams, data: list[dict]
+ ) -> tuple[str, list[str]]:
+ """Resolve the requested frequency against the table's real frequencies."""
+ available = sorted(
+ {classify_frequency(record["period"]) for record in data},
+ key=lambda freq: FREQUENCY_RANK.get(freq, 9),
+ )
+ requested = query.frequency
+ if requested is not None and requested in available:
+ return requested, available
+ return available[-1], available
+
+ @staticmethod
+ def transform_data(
+ query: CottonWoolAndTextileDataQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[CottonWoolAndTextileDataData]:
+ """Filter to one frequency and pivot the long rows into the wide layout."""
+ if not data:
+ return []
+ frequency, _ = CottonWoolAndTextileDataFetcher._resolve_frequency(query, data)
+ years_as_columns = CWT_TABLES[query.table]["pivot"] == "years"
+ rows = [
+ record
+ for record in data
+ if classify_frequency(record["period"]) == frequency
+ and CottonWoolAndTextileDataFetcher._in_range(record["year"], query)
+ ]
+ if not rows:
+ return []
+ if years_as_columns:
+ return CottonWoolAndTextileDataFetcher._pivot_years(rows)
+ return CottonWoolAndTextileDataFetcher._pivot_series(rows, frequency)
+
+ @staticmethod
+ def _pivot_series(
+ rows: list[dict], frequency: str
+ ) -> list[CottonWoolAndTextileDataData]:
+ """Pivot to period rows with the series and grouping dims as columns."""
+ pivoted: dict[tuple, dict[str, Any]] = {}
+ column_order: list[str] = []
+ seen_columns: set[str] = set()
+ for source_order, record in enumerate(rows):
+ year = record["year"]
+ geography = record["geography"]
+ month = (
+ MONTH_NAMES.get(str(record["period"]))
+ if frequency == "Monthly"
+ else None
+ )
+ time_label = f"{year} {month}" if month else str(year)
+ label = f"{geography} — {time_label}" if geography else time_label
+ key = (geography or "", year, record["period"])
+ row = pivoted.get(key)
+ if row is None:
+ row = {
+ "_geography": geography or "",
+ "_year": year,
+ "_month": MONTH_ORDER.get(month or "", 0),
+ "_order": source_order,
+ "period": label,
+ }
+ pivoted[key] = row
+ column = CottonWoolAndTextileDataFetcher._column_key(record)
+ if column not in seen_columns:
+ seen_columns.add(column)
+ column_order.append(column)
+ row[column] = row.get(column, 0.0) + record["value"]
+ ordered = sorted(
+ pivoted.values(),
+ key=lambda row: (
+ row["_geography"],
+ -row["_year"],
+ -row["_month"],
+ row["_order"],
+ ),
+ )
+ return CottonWoolAndTextileDataFetcher._validate(ordered, column_order)
+
+ @staticmethod
+ def _pivot_years(rows: list[dict]) -> list[CottonWoolAndTextileDataData]:
+ """Pivot to country rows with the observation years spread into columns."""
+ pivoted: dict[str, dict[str, Any]] = {}
+ for source_order, record in enumerate(rows):
+ geography = record["geography"] or "Total"
+ row = pivoted.get(geography)
+ if row is None:
+ row = {"_order": source_order, "period": geography}
+ pivoted[geography] = row
+ column = str(record["year"])
+ row[column] = row.get(column, 0.0) + record["value"]
+ ordered = sorted(pivoted.values(), key=lambda row: row["_order"])
+ year_columns = sorted(
+ {key for row in pivoted.values() for key in row if key.isdigit()}, key=int
+ )
+ return CottonWoolAndTextileDataFetcher._validate(ordered, year_columns)
+
+ @staticmethod
+ def _validate(
+ ordered: list[dict], column_order: list[str]
+ ) -> list[CottonWoolAndTextileDataData]:
+ """Assemble each row with the full, identical column set and validate."""
+ validated: list[CottonWoolAndTextileDataData] = []
+ for row in ordered:
+ payload = {"period": row["period"]}
+ for column in column_order:
+ payload[column] = row.get(column)
+ validated.append(CottonWoolAndTextileDataData.model_validate(payload))
+ return validated
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/county_typology_codes.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/county_typology_codes.py
new file mode 100644
index 00000000000..4f5c9be0121
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/county_typology_codes.py
@@ -0,0 +1,401 @@
+"""USDA ERS County Typology Codes Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_county_typology_codes import (
+ DEFAULT_VINTAGE,
+ STATE_ABBRS,
+ VINTAGES,
+ state_options,
+ vintage_options,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+
+def _code_config(header: str, hide: bool = True) -> dict:
+ """Build a per-field widget config for a numeric classification code."""
+ return {"headerName": header, "cellDataType": "text", "hide": hide}
+
+
+class CountyTypologyCodesQueryParams(QueryParams):
+ """USDA ERS County Typology Codes Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/county-typology-codes
+ """
+
+ __json_schema_extra__ = {
+ "vintage": {
+ "x-widget_config": {
+ "label": "Edition",
+ "value": DEFAULT_VINTAGE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": vintage_options(),
+ "style": {"popupWidth": 360},
+ },
+ },
+ "state": {
+ "x-widget_config": {
+ "label": "State",
+ "multiSelect": False,
+ "multiple": False,
+ "options": state_options(),
+ "style": {"popupWidth": 260},
+ },
+ },
+ }
+
+ vintage: str = Field(
+ default=DEFAULT_VINTAGE,
+ description="Typology-codes edition to retrieve. Each edition is a"
+ + " separate published vintage of the county classification, keyed by"
+ + " county FIPS. Valid editions are:\n "
+ + ", ".join(VINTAGES)
+ + "\n",
+ )
+ state: str | None = Field(
+ default=None,
+ description="Two-letter state postal abbreviation to scope the county"
+ + " rows to a single state. If None, returns every county in the"
+ + " edition. Alaska and Hawaii are absent from the 1979/1986 editions.",
+ )
+
+ @field_validator("vintage", mode="before", check_fields=False)
+ @classmethod
+ def _validate_vintage(cls, v):
+ """Validate vintage."""
+ if not v:
+ return DEFAULT_VINTAGE
+ vintage = v.strip() if isinstance(v, str) else v
+ if vintage not in VINTAGES:
+ raise OpenBBError(
+ f"Invalid vintage: {vintage}. Valid vintages are: "
+ + ", ".join(VINTAGES)
+ )
+ return vintage
+
+ @field_validator("state", mode="before", check_fields=False)
+ @classmethod
+ def _validate_state(cls, v):
+ """Validate the optional state filter."""
+ if not v:
+ return None
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ state = str(value).strip().upper()
+ if not state:
+ return None
+ if state not in STATE_ABBRS:
+ raise OpenBBError(
+ f"Invalid state: {value}. Use a two-letter postal abbreviation,"
+ + " e.g. 'TX'."
+ )
+ return state
+
+
+class CountyTypologyCodesData(NullTokenMixin, Data):
+ """USDA ERS County Typology Codes.
+
+ One selected edition as a county lookup table: one row per county FIPS
+ carrying its economic-dependence type and the metro, specialization, and
+ policy classification codes published for that vintage.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS County Typology Codes",
+ "$.description": "County-level economic-dependence and policy"
+ " typology classifications for U.S. counties, published in"
+ " successive vintages by the USDA Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ fips: str = Field(
+ description="County FIPS code, five digits with a leading zero.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "FIPS",
+ "cellDataType": "text",
+ "pinned": "left",
+ "maxWidth": 90,
+ }
+ },
+ )
+ state: str | None = Field(
+ default=None,
+ description="Two-letter state postal abbreviation, from the FIPS prefix.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "State",
+ "cellDataType": "text",
+ "pinned": "left",
+ "maxWidth": 90,
+ }
+ },
+ )
+ county_name: str | None = Field(
+ default=None,
+ description="County name, as published in the edition.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "County",
+ "cellDataType": "text",
+ "pinned": "left",
+ "maxWidth": 240,
+ }
+ },
+ )
+ metro: int | None = Field(
+ default=None,
+ description="Metro-nonmetro status: 1 for metropolitan, 0 for"
+ + " nonmetropolitan, under the edition's own metro definition.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Metro", "cellDataType": "text"}
+ },
+ )
+ economic_type: str | None = Field(
+ default=None,
+ description="Non-overlapping economic-dependence type of the county,"
+ + " e.g. 'Farming', 'Mining', 'Manufacturing', 'Government',"
+ + " 'Recreation', 'Services', or 'Nonspecialized'. Derived from the"
+ + " edition's economic code or, for the flag-based 1989 and 1979/1986"
+ + " editions, from the set economic flag.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Economic Type", "cellDataType": "text"}
+ },
+ )
+ economic_type_code: int | None = Field(
+ default=None,
+ description="Numeric economic-dependence code as published, where the"
+ + " edition assigns one (2025, 2015, and 2004 editions).",
+ json_schema_extra={
+ "x-widget_config": _code_config("Economic Type Code", hide=False)
+ },
+ )
+ economic_type_label: str | None = Field(
+ default=None,
+ description="Economic-type label exactly as published, kept for the"
+ + " 2015 edition (which carries a 'Maufacturing' source typo).",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Economic Type (published)",
+ "cellDataType": "text",
+ "hide": True,
+ }
+ },
+ )
+ farming: int | None = Field(
+ default=None,
+ description="Farming specialization flag: a high-concentration flag in"
+ + " the 2025 edition, a dependence flag in earlier editions."
+ + " Sentinels are kept raw (99 = not available in 2025; 8/9 = metro"
+ + " county in the 1989 and 1979/1986 editions).",
+ json_schema_extra={"x-widget_config": _code_config("Farming", hide=False)},
+ )
+ mining: int | None = Field(
+ default=None,
+ description="Mining specialization flag, kept raw with the edition's"
+ + " sentinels.",
+ json_schema_extra={"x-widget_config": _code_config("Mining", hide=False)},
+ )
+ manufacturing: int | None = Field(
+ default=None,
+ description="Manufacturing specialization flag, kept raw with the"
+ + " edition's sentinels.",
+ json_schema_extra={
+ "x-widget_config": _code_config("Manufacturing", hide=False)
+ },
+ )
+ government: int | None = Field(
+ default=None,
+ description="Government specialization flag, kept raw with the"
+ + " edition's sentinels.",
+ json_schema_extra={"x-widget_config": _code_config("Government", hide=False)},
+ )
+ recreation: int | None = Field(
+ default=None,
+ description="Recreation flag: an economic specialization in the 2025"
+ + " and 2015 editions, a nonmetro-recreation policy flag in the 2004"
+ + " edition.",
+ json_schema_extra={"x-widget_config": _code_config("Recreation", hide=False)},
+ )
+ services: int | None = Field(
+ default=None,
+ description="Services specialization flag, published in the 2004 and"
+ + " 1989 editions.",
+ json_schema_extra={"x-widget_config": _code_config("Services")},
+ )
+ nonspecialized: int | None = Field(
+ default=None,
+ description="Nonspecialized flag: the county is not dependent on any"
+ + " single industry.",
+ json_schema_extra={
+ "x-widget_config": _code_config("Nonspecialized", hide=False)
+ },
+ )
+ low_education: int | None = Field(
+ default=None,
+ description="Low-education policy flag, published in the 2025, 2015,"
+ + " and 2004 editions.",
+ json_schema_extra={
+ "x-widget_config": _code_config("Low Education", hide=False)
+ },
+ )
+ low_employment: int | None = Field(
+ default=None,
+ description="Low-employment policy flag, published in the 2025, 2015,"
+ + " and 2004 editions.",
+ json_schema_extra={
+ "x-widget_config": _code_config("Low Employment", hide=False)
+ },
+ )
+ population_loss: int | None = Field(
+ default=None,
+ description="Population-loss policy flag, published in the 2025, 2015,"
+ + " and 2004 editions.",
+ json_schema_extra={
+ "x-widget_config": _code_config("Population Loss", hide=False)
+ },
+ )
+ housing_stress: int | None = Field(
+ default=None,
+ description="Housing-stress policy flag, published in the 2025 and"
+ + " 2004 editions.",
+ json_schema_extra={
+ "x-widget_config": _code_config("Housing Stress", hide=False)
+ },
+ )
+ retirement_destination: int | None = Field(
+ default=None,
+ description="Retirement-destination policy flag, kept raw with the"
+ + " edition's sentinels.",
+ json_schema_extra={
+ "x-widget_config": _code_config("Retirement Destination", hide=False)
+ },
+ )
+ persistent_poverty: int | None = Field(
+ default=None,
+ description="Persistent-poverty policy flag, kept raw with the"
+ + " edition's sentinels (-1 = not determined in the 2025 edition).",
+ json_schema_extra={
+ "x-widget_config": _code_config("Persistent Poverty", hide=False)
+ },
+ )
+ persistent_child_poverty: int | None = Field(
+ default=None,
+ description="Persistent related-child-poverty policy flag, published"
+ + " in the 2015 and 2004 editions.",
+ json_schema_extra={"x-widget_config": _code_config("Persistent Child Poverty")},
+ )
+ federal_lands: int | None = Field(
+ default=None,
+ description="Federal-lands policy flag, published in the 1989 and"
+ + " 1979/1986 editions.",
+ json_schema_extra={"x-widget_config": _code_config("Federal Lands")},
+ )
+ commuting: int | None = Field(
+ default=None,
+ description="Commuting policy flag, published in the 1989 edition.",
+ json_schema_extra={"x-widget_config": _code_config("Commuting")},
+ )
+ transfers_dependent: int | None = Field(
+ default=None,
+ description="Transfers-dependent policy flag, published in the 1989"
+ + " edition.",
+ json_schema_extra={"x-widget_config": _code_config("Transfers Dependent")},
+ )
+ rural_urban_continuum_code: int | None = Field(
+ default=None,
+ description="Rural-urban continuum (Beale) code, published in the"
+ + " 2004, 1989, and 1979/1986 editions.",
+ json_schema_extra={
+ "x-widget_config": _code_config("Rural-Urban Continuum Code")
+ },
+ )
+ urban_influence_code: int | None = Field(
+ default=None,
+ description="Urban-influence code, published in the 2004 edition.",
+ json_schema_extra={"x-widget_config": _code_config("Urban Influence Code")},
+ )
+ farming_1986: int | None = Field(
+ default=None,
+ description="Farming specialization flag for the 1986 update carried"
+ + " in the 1979/1986 editions, kept raw with sentinels.",
+ json_schema_extra={"x-widget_config": _code_config("Farming (1986)")},
+ )
+ mining_1986: int | None = Field(
+ default=None,
+ description="Mining specialization flag for the 1986 update carried in"
+ + " the 1979/1986 editions, kept raw with sentinels.",
+ json_schema_extra={"x-widget_config": _code_config("Mining (1986)")},
+ )
+ manufacturing_1986: int | None = Field(
+ default=None,
+ description="Manufacturing specialization flag for the 1986 update"
+ + " carried in the 1979/1986 editions, kept raw with sentinels.",
+ json_schema_extra={"x-widget_config": _code_config("Manufacturing (1986)")},
+ )
+ government_1986: int | None = Field(
+ default=None,
+ description="Government specialization flag for the 1986 update"
+ + " carried in the 1979/1986 editions, kept raw with sentinels.",
+ json_schema_extra={"x-widget_config": _code_config("Government (1986)")},
+ )
+ nonspecialized_1986: int | None = Field(
+ default=None,
+ description="Nonspecialized flag for the 1986 update carried in the"
+ + " 1979/1986 editions, kept raw with sentinels.",
+ json_schema_extra={"x-widget_config": _code_config("Nonspecialized (1986)")},
+ )
+
+
+class CountyTypologyCodesFetcher(
+ Fetcher[
+ CountyTypologyCodesQueryParams,
+ list[CountyTypologyCodesData],
+ ]
+):
+ """Fetch USDA ERS County Typology Codes."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(params: dict[str, Any]) -> CountyTypologyCodesQueryParams:
+ """Transform the query params."""
+ return CountyTypologyCodesQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: CountyTypologyCodesQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected edition's canonical county records."""
+ from openbb_government_us.usda.utils import ers_county_typology_codes
+
+ return await ers_county_typology_codes.afetch_vintage(query.vintage)
+
+ @staticmethod
+ def transform_data(
+ query: CountyTypologyCodesQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[CountyTypologyCodesData]:
+ """Scope the county rows to the selected state and sort by FIPS."""
+ selected = [
+ record
+ for record in data
+ if query.state is None or record.get("state") == query.state
+ ]
+ selected.sort(key=lambda record: record["fips"])
+ return [CountyTypologyCodesData.model_validate(record) for record in selected]
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/dairy_data.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/dairy_data.py
new file mode 100644
index 00000000000..36ba8a2596a
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/dairy_data.py
@@ -0,0 +1,260 @@
+"""USDA ERS Dairy Data Model."""
+
+from typing import Any, Literal
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_dairy_data import DAIRY_DATA_FILES
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+DEFAULT_TABLE = "situation_at_a_glance"
+
+Frequency = Literal["monthly", "quarterly", "annual"]
+
+
+class DairyDataQueryParams(QueryParams):
+ """USDA ERS Dairy Data Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/dairy-data
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "multiple_items_allowed": True,
+ "x-widget_config": {
+ "label": "Table",
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": slug.replace("_", " ").capitalize(), "value": slug}
+ for slug in sorted(DAIRY_DATA_FILES)
+ ],
+ "style": {"popupWidth": 340},
+ },
+ },
+ "frequency": {
+ "x-widget_config": {
+ "label": "Frequency",
+ "options": [
+ {"label": "Monthly", "value": "monthly"},
+ {"label": "Quarterly", "value": "quarterly"},
+ {"label": "Annual", "value": "annual"},
+ ],
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ table: str = Field(
+ default=DEFAULT_TABLE,
+ description="Table(s) to retrieve, as a comma-separated list of keys."
+ + " The default, situation_at_a_glance, is a rolling snapshot of the"
+ + " latest ~14 months; the other tables are full histories."
+ + " Valid tables are:\n "
+ + ", ".join(sorted(DAIRY_DATA_FILES))
+ + "\n",
+ )
+ frequency: Frequency | None = Field(
+ default=None,
+ description="Filter rows by observation frequency."
+ + " Mixed-frequency tables carry annual rows alongside monthly or"
+ + " quarterly rows; annual-only tables carry only 'annual' rows."
+ + " If None, all frequencies are included.",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data."
+ + " If None, returns from the beginning of each series.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data."
+ + " If None, returns up to the most recent year.",
+ )
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate table."""
+ if not v:
+ return DEFAULT_TABLE
+ tables = v.split(",") if isinstance(v, str) else list(v)
+ tables = [table.strip() for table in tables if table and table.strip()]
+ if not tables:
+ return DEFAULT_TABLE
+ unknown = [table for table in tables if table not in DAIRY_DATA_FILES]
+ if unknown:
+ raise OpenBBError(
+ f"Invalid table(s): {', '.join(unknown)}. Valid tables are: "
+ + ", ".join(sorted(DAIRY_DATA_FILES))
+ )
+ return ",".join(tables)
+
+
+class DairyDataData(NullTokenMixin, Data):
+ """USDA ERS Dairy Data.
+
+ Tidy long-format records from the machine-readable files of the USDA ERS
+ Dairy Data product: milk production, supply and utilization, per capita
+ consumption, fluid sales, and related industry indicators.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Dairy Data",
+ "$.description": "Milk production, supply and utilization, per-capita consumption, fluid sales, and dairy price series, published by the USDA Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ category: str | None = Field(
+ default=None,
+ description="Category grouping of the data item, as published.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Category", "pinned": "left"}
+ },
+ )
+ data_item: str = Field(
+ description="Data item name, as published.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Data item", "pinned": "left"}
+ },
+ )
+ unit: str = Field(
+ description="Unit of the value, as published; casing varies by table.",
+ json_schema_extra={"x-widget_config": {"headerName": "Unit"}},
+ )
+ sub_table: str | None = Field(
+ default=None,
+ description="Published sub-table name within the dataset, e.g."
+ + " 'Butter, monthly', 'M.E. Milk-fat basis, annual', or 'Milk cows'.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Sub-table", "hide": True}
+ },
+ )
+ product: str | None = Field(
+ default=None,
+ description="Dairy product category."
+ + " Only present for supply_utilization_products.",
+ json_schema_extra={"x-widget_config": {"headerName": "Product", "hide": True}},
+ )
+ data_item_id: str | None = Field(
+ default=None,
+ description="Hierarchical data item code, e.g. '1-3.04.00'; the same"
+ + " suffix identifies the same product across tables."
+ + " Only present for milk_fat_skim_solids_allocation.",
+ json_schema_extra={"x-widget_config": {"headerName": "Item ID", "hide": True}},
+ )
+ region: str | None = Field(
+ default=None,
+ description="ERS farm production region."
+ + " Only present for milk_cows_by_state.",
+ json_schema_extra={"x-widget_config": {"headerName": "Region", "hide": True}},
+ )
+ state: str | None = Field(
+ default=None,
+ description="State name; regional and national aggregates use"
+ + " 'Total region'. Only present for milk_cows_by_state.",
+ json_schema_extra={"x-widget_config": {"headerName": "State", "hide": True}},
+ )
+
+
+class DairyDataFetcher(
+ Fetcher[
+ DairyDataQueryParams,
+ list[DairyDataData],
+ ]
+):
+ """Fetch USDA ERS Dairy Data."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(params: dict[str, Any]) -> DairyDataQueryParams:
+ """Transform the query params."""
+ return DairyDataQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: DairyDataQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected tables' tidy CSV rows."""
+ import asyncio
+
+ from openbb_government_us.usda.utils import ers_dairy_data
+
+ selected = query.table.split(",")
+ results = await asyncio.gather(
+ *(ers_dairy_data.afetch_table(table) for table in selected)
+ )
+ records: list[dict] = []
+ for table, rows in zip(selected, results):
+ for row in rows:
+ records.append({**row, "table": table})
+ return records
+
+ @staticmethod
+ def transform_data(
+ query: DairyDataQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[DairyDataData]:
+ """Pivot the observations into chronologically ordered date columns."""
+ pivoted: dict[tuple, dict] = {}
+ observations: dict[tuple, dict[str, Any]] = {}
+ for order, record in enumerate(data):
+ if query.frequency is not None and record["frequency"] != query.frequency:
+ continue
+ if query.start_year is not None and record["year"] < query.start_year:
+ continue
+ if query.end_year is not None and record["year"] > query.end_year:
+ continue
+ key = (
+ record.get("table"),
+ record.get("category"),
+ record["data_item"],
+ record["unit"],
+ record.get("sub_table"),
+ record.get("product"),
+ record.get("region"),
+ record.get("state"),
+ record.get("data_item_id"),
+ )
+ row = pivoted.get(key)
+ if row is None:
+ observations[key] = {}
+ row = {
+ "_order": order,
+ "category": record.get("category"),
+ "data_item": record["data_item"],
+ "unit": record["unit"],
+ "sub_table": record.get("sub_table"),
+ "product": record.get("product"),
+ "region": record.get("region"),
+ "state": record.get("state"),
+ "data_item_id": record.get("data_item_id"),
+ }
+ pivoted[key] = row
+ observations[key][str(record["date"])] = record["value"]
+ ordered = sorted(pivoted.items(), key=lambda item: item[1]["_order"])
+ dates = sorted({date for values in observations.values() for date in values})
+ return [
+ DairyDataData.model_validate(
+ {
+ **{k: v for k, v in row.items() if k != "_order"},
+ **{date: observations[key].get(date) for date in dates},
+ }
+ )
+ for key, row in ordered
+ ]
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/eating_and_health_module_atus.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/eating_and_health_module_atus.py
new file mode 100644
index 00000000000..49e81e439d2
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/eating_and_health_module_atus.py
@@ -0,0 +1,230 @@
+"""USDA ERS Eating and Health Module (ATUS) Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_eating_and_health_module_atus import (
+ EATING_HEALTH_TABLES,
+ RELEASE_YEARS,
+)
+
+DEFAULT_TABLE = "eating_and_drinking_time"
+DEFAULT_YEAR = "2023"
+
+
+class EatingAndHealthModuleAtusQueryParams(QueryParams):
+ """USDA ERS Eating and Health Module (ATUS) Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/eating-and-health-module-atus
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "x-widget_config": {
+ "label": "Table",
+ "value": DEFAULT_TABLE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": config["label"], "value": key}
+ for key, config in EATING_HEALTH_TABLES.items()
+ ],
+ "style": {"popupWidth": 520},
+ },
+ },
+ "year": {
+ "x-widget_config": {
+ "label": "Release year",
+ "value": DEFAULT_YEAR,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [{"label": year, "value": year} for year in RELEASE_YEARS],
+ },
+ },
+ }
+
+ table: str = Field(
+ default=DEFAULT_TABLE,
+ description="Eating and Health Module table to retrieve. Each table is a"
+ + " single survey-year snapshot whose published measure-by-sex-by-statistic"
+ + " series ship pre-spread as the wide value columns, while the two"
+ + " leading dimensions stay as pinned row columns. Valid tables are:\n "
+ + ", ".join(EATING_HEALTH_TABLES)
+ + "\n",
+ )
+ year: str = Field(
+ default=DEFAULT_YEAR,
+ description="Release year selecting which survey-year snapshot to load."
+ + " Valid years are:\n "
+ + ", ".join(RELEASE_YEARS)
+ + "\n",
+ )
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate table."""
+ if not v:
+ return DEFAULT_TABLE
+ table = v.strip() if isinstance(v, str) else v
+ if table not in EATING_HEALTH_TABLES:
+ raise OpenBBError(
+ f"Invalid table: {table}. Valid tables are: "
+ + ", ".join(EATING_HEALTH_TABLES)
+ )
+ return table
+
+ @field_validator("year", mode="before", check_fields=False)
+ @classmethod
+ def _validate_year(cls, v):
+ """Validate year."""
+ if not v:
+ return DEFAULT_YEAR
+ year = str(v).strip()
+ if year not in RELEASE_YEARS:
+ raise OpenBBError(
+ f"Invalid year: {year}. Valid years are: " + ", ".join(RELEASE_YEARS)
+ )
+ return year
+
+
+class EatingAndHealthModuleAtusData(Data):
+ """USDA ERS Eating and Health Module (ATUS) Data.
+
+ One selected table for a single release year, kept in its published wide
+ layout: the two leading survey dimensions stay as pinned row columns while
+ the measure-by-sex-by-statistic series spread into value columns, whose set
+ varies by table.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Eating and Health Module (ATUS)",
+ "$.description": "Time spent eating and drinking, engaged in"
+ " associated and secondary eating, food preparation, grocery"
+ " shopping, and fast-food purchasing, by demographic, food"
+ " security, and body-mass-index subgroups, from the American Time"
+ " Use Survey Eating and Health Module, published by the USDA"
+ " Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ category: str = Field(
+ description="Leading survey dimension of the row, as published, e.g. the"
+ + " activity, response level, count of fast-food purchases, or BMI"
+ + " group.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Category",
+ "cellDataType": "text",
+ "pinned": "left",
+ }
+ },
+ )
+ subgroup: str = Field(
+ description="Second survey dimension of the row, as published, e.g. the"
+ + " age bracket, metropolitan status, gender, or breakdown question.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Subgroup",
+ "cellDataType": "text",
+ "pinned": "left",
+ }
+ },
+ )
+ measure_type: str | None = Field(
+ default=None,
+ description="Whether the fast-food row is an average count or a percent"
+ + " of purchasers. Populated only for the fast-food purchases table and"
+ + " null for every other table.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Number/percent",
+ "cellDataType": "text",
+ "pinned": "left",
+ "hide": True,
+ }
+ },
+ )
+
+
+class EatingAndHealthModuleAtusFetcher(
+ Fetcher[
+ EatingAndHealthModuleAtusQueryParams,
+ list[EatingAndHealthModuleAtusData],
+ ]
+):
+ """Fetch USDA ERS Eating and Health Module (ATUS)."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> EatingAndHealthModuleAtusQueryParams:
+ """Transform the query params."""
+ return EatingAndHealthModuleAtusQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: EatingAndHealthModuleAtusQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected table and release year's long-format rows."""
+ from openbb_government_us.usda.utils import ers_eating_and_health_module_atus
+
+ return await ers_eating_and_health_module_atus.afetch_table(
+ query.table, query.year
+ )
+
+ @staticmethod
+ def transform_data(
+ query: EatingAndHealthModuleAtusQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[EatingAndHealthModuleAtusData]:
+ """Pivot the long-format cells back into the published wide layout."""
+ pivoted: dict[tuple, dict] = {}
+ series_order: dict[str, int] = {}
+ for record in data:
+ key = (
+ record["category"],
+ record["subgroup"],
+ record["measure_type"],
+ )
+ row = pivoted.get(key)
+ if row is None:
+ row = {
+ "_row_ord": record["row_ord"],
+ "category": record["category"],
+ "subgroup": record["subgroup"],
+ "measure_type": record["measure_type"],
+ }
+ pivoted[key] = row
+ series = record["series"]
+ row[series] = record["value"]
+ series_order.setdefault(series, record["series_ord"])
+ ordered = sorted(series_order, key=lambda name: series_order[name])
+ results = sorted(pivoted.values(), key=lambda row: row["_row_ord"])
+ records: list[EatingAndHealthModuleAtusData] = []
+ for row in results:
+ clean = {
+ "category": row["category"],
+ "subgroup": row["subgroup"],
+ "measure_type": row["measure_type"],
+ }
+ for series in ordered:
+ clean[series] = row.get(series)
+ records.append(EatingAndHealthModuleAtusData.model_validate(clean))
+ return records
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/ers_publication_download.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/ers_publication_download.py
new file mode 100644
index 00000000000..95c182b2ff7
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/ers_publication_download.py
@@ -0,0 +1,132 @@
+"""USDA ERS Publication Download Model."""
+
+from typing import Any
+
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from pydantic import Field
+
+
+class ErsPublicationDownloadQueryParams(QueryParams):
+ """USDA ERS Publication Download Query Parameters.
+
+ Source: https://www.ers.usda.gov/publications
+ """
+
+ __json_schema_extra__ = {
+ "urls": {
+ "multiple_items_allowed": True,
+ }
+ }
+
+ urls: str | list[str] | dict[str, list[str]] = Field(
+ description="Publication page URLs to download, as returned by the"
+ + " ers_publications endpoint. Each page is resolved to its Full"
+ + " Report PDF.",
+ kw_only=True,
+ )
+
+
+class ErsPublicationDownloadData(Data):
+ """USDA ERS Publication Download Data.
+
+ The Full Report PDF of each requested publication page.
+ """
+
+ error_type: str | None = Field(
+ default=None,
+ description="Error type, if the report could not be downloaded.",
+ )
+ content: str = Field(
+ description="Base64-encoded PDF document.",
+ )
+ filename: str | None = Field(
+ default=None,
+ description="The filename of the downloaded PDF.",
+ )
+ data_format: dict[str, str] | None = Field(
+ default=None,
+ description="Data format information, including data type and filename.",
+ )
+
+
+class ErsPublicationDownloadFetcher(
+ Fetcher[
+ ErsPublicationDownloadQueryParams,
+ list[ErsPublicationDownloadData],
+ ]
+):
+ """Fetch USDA ERS Publications as PDFs."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> ErsPublicationDownloadQueryParams:
+ """Transform the query params."""
+ return ErsPublicationDownloadQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: ErsPublicationDownloadQueryParams,
+ credentials: dict[str, Any] | None = None,
+ **kwargs: Any,
+ ) -> list:
+ """Download the Full Report PDF behind each publication page."""
+ import base64
+
+ from openbb_government_us.usda.utils.ers_publications import (
+ afetch_publication,
+ validate_publication_url,
+ )
+
+ urls = (
+ query.urls.get("urls", [])
+ if isinstance(query.urls, dict)
+ else (query.urls if isinstance(query.urls, list) else query.urls.split(","))
+ )
+ results: list = []
+
+ for url in urls:
+ try:
+ target = validate_publication_url(url)
+ except Exception as exc: # noqa: BLE001
+ results.append(
+ {
+ "error_type": "invalid_url",
+ "content": f"{exc.__class__.__name__}: {exc}",
+ "filename": None,
+ }
+ )
+ continue
+ try:
+ content, filename = await afetch_publication(target)
+ except Exception as exc: # noqa: BLE001
+ results.append(
+ {
+ "error_type": "download_error",
+ "content": f"{exc.__class__.__name__}: {exc}",
+ "filename": None,
+ }
+ )
+ continue
+ results.append(
+ {
+ "content": base64.b64encode(content).decode("utf-8"),
+ "filename": filename,
+ "data_format": {"data_type": "pdf", "filename": filename},
+ }
+ )
+
+ return results
+
+ @staticmethod
+ def transform_data(
+ query: ErsPublicationDownloadQueryParams,
+ data: list,
+ **kwargs: Any,
+ ) -> list[ErsPublicationDownloadData]:
+ """Transform the data."""
+ return [ErsPublicationDownloadData(**item) for item in data]
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/ers_publications.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/ers_publications.py
new file mode 100644
index 00000000000..6965b41eedf
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/ers_publications.py
@@ -0,0 +1,173 @@
+"""USDA ERS Publications Model."""
+
+from datetime import date as dateType
+from typing import Any
+
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from pydantic import Field, field_validator
+
+from openbb_government_us.usda.utils.ers_publications import (
+ SERIES_CODES,
+ SERIES_GROUPS,
+ resolve_series,
+)
+from openbb_government_us.utils.serializers import OmitNoneMixin
+
+DEFAULT_SERIES = "outlook-reports"
+
+
+class ErsPublicationsQueryParams(QueryParams):
+ """USDA ERS Publications Query Parameters.
+
+ Source: https://www.ers.usda.gov/publications
+ """
+
+ __json_schema_extra__ = {
+ "series": {
+ "multiple_items_allowed": True,
+ "choices": sorted(SERIES_GROUPS) + sorted(SERIES_CODES),
+ },
+ }
+
+ series: str = Field(
+ default=DEFAULT_SERIES,
+ validate_default=True,
+ description="Series to retrieve, as a comma-separated list of group"
+ + " names or series codes. Groups expand to their member series."
+ + " Valid groups are:\n "
+ + ", ".join(sorted(SERIES_GROUPS))
+ + "\nValid series codes are:\n "
+ + ", ".join(f"{code} ({name})" for code, name in sorted(SERIES_CODES.items()))
+ + "\n",
+ )
+ start_date: dateType | None = Field(
+ default=None,
+ description="Earliest release date to return."
+ + " If None, returns from the first published report.",
+ )
+ end_date: dateType | None = Field(
+ default=None,
+ description="Latest release date to return."
+ + " If None, returns up to the most recent report.",
+ )
+ limit: int | None = Field(
+ default=None,
+ description="Maximum number of reports to return, newest first."
+ + " If None, returns every report in the selected series.",
+ )
+
+ @field_validator("series", mode="before", check_fields=False)
+ @classmethod
+ def _validate_series(cls, v):
+ """Validate series."""
+ return ",".join(resolve_series(v or DEFAULT_SERIES))
+
+
+class ErsPublicationsData(OmitNoneMixin, Data):
+ """USDA ERS Publications Data.
+
+ One record per published report, carrying the publication page URL that
+ the report viewer downloads.
+ """
+
+ id: str = Field(
+ description="Publication ID, unique within the ERS publication catalog.",
+ )
+ title: str = Field(
+ description="Title of the report, as published.",
+ )
+ release_date: dateType = Field(
+ description="Date the report was released.",
+ )
+ series_code: str | None = Field(
+ default=None,
+ description="Code of the series the report belongs to, e.g. 'LDPM'.",
+ )
+ series_name: str | None = Field(
+ default=None,
+ description="Name of the series the report belongs to.",
+ )
+ report_number: str | None = Field(
+ default=None,
+ description="Report number within the series, e.g. 'LDP-M-385'.",
+ )
+ url: str = Field(
+ description="URL of the publication page."
+ + " This is the value the report viewer downloads.",
+ )
+ authors: str | None = Field(
+ default=None,
+ description="Authors of the report, as a comma-separated list.",
+ )
+ short_description: str | None = Field(
+ default=None,
+ description="Plain-text summary of the report, when the source"
+ + " publishes one.",
+ )
+ topics: str | None = Field(
+ default=None,
+ description="Topics the report is filed under, as a comma-separated list.",
+ )
+
+
+class ErsPublicationsFetcher(
+ Fetcher[
+ ErsPublicationsQueryParams,
+ list[ErsPublicationsData],
+ ]
+):
+ """Fetch USDA ERS Publications."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(params: dict[str, Any]) -> ErsPublicationsQueryParams:
+ """Transform the query params."""
+ return ErsPublicationsQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: ErsPublicationsQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the listing rows of the selected series."""
+ from openbb_government_us.usda.utils.ers_publications import fetch_publications
+
+ return await fetch_publications(
+ query.series,
+ start_date=query.start_date,
+ end_date=query.end_date,
+ )
+
+ @staticmethod
+ def transform_data(
+ query: ErsPublicationsQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[ErsPublicationsData]:
+ """Transform the data."""
+ rows = data[: query.limit] if query.limit else data
+ return [
+ ErsPublicationsData.model_validate(
+ {
+ "id": row["id"],
+ "title": row["title"],
+ "release_date": row["release_date"],
+ "series_code": row["series_code"],
+ "series_name": row["series_name"],
+ "report_number": row["report_number"],
+ "url": row["url"],
+ "authors": ", ".join(
+ author["name"] for author in row["authors"] if author["name"]
+ )
+ or None,
+ "short_description": row["short_description"],
+ "topics": ", ".join(topic for topic in row["topics"] if topic)
+ or None,
+ }
+ )
+ for row in rows
+ ]
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/farm_household_income_and_characteristics.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/farm_household_income_and_characteristics.py
new file mode 100644
index 00000000000..bef7a225032
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/farm_household_income_and_characteristics.py
@@ -0,0 +1,195 @@
+"""Farm Household Income and Characteristics Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from openbb_core.provider.utils.errors import EmptyDataError
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_farm_household_income_and_characteristics import (
+ DEFAULT_TABLE,
+ TABLE_CONFIG,
+ TABLE_LABELS,
+ YEAR_TABLES,
+ parse_year,
+ year_sort_key,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+
+class FarmHouseholdIncomeAndCharacteristicsQueryParams(QueryParams):
+ """Farm Household Income and Characteristics Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/farm-household-income-and-characteristics
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "x-widget_config": {
+ "label": "Table",
+ "value": DEFAULT_TABLE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": label, "value": key}
+ for key, label in TABLE_LABELS.items()
+ ],
+ "style": {"popupWidth": 520},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ table: str = Field(
+ default=DEFAULT_TABLE,
+ description="Table to retrieve. Each table is a set of line items pivoted"
+ + " to a wide layout: the item stays as the pinned row column while the"
+ + " time-series tables spread years into value columns and the"
+ + " cross-tab tables spread a category into value columns. Valid tables"
+ + " are:\n "
+ + ", ".join(TABLE_CONFIG)
+ + "\n",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data. Applies only to the"
+ + " time-series tables ("
+ + ", ".join(sorted(YEAR_TABLES))
+ + "). If None, returns from the first published year.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data. Applies only to the"
+ + " time-series tables. If None, returns up to the most recent year"
+ + " or forecast year.",
+ )
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate table."""
+ if not v:
+ return DEFAULT_TABLE
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip()
+ if value not in TABLE_CONFIG:
+ raise OpenBBError(
+ f"Invalid table: {value}. Valid tables are: " + ", ".join(TABLE_CONFIG)
+ )
+ return value
+
+
+class FarmHouseholdIncomeAndCharacteristicsData(NullTokenMixin, Data):
+ """Farm Household Income and Characteristics Data.
+
+ One selected table pivoted to a wide layout: each row is a published line
+ item, with one value column per year for the time-series tables or per
+ category for the cross-tab tables. The value columns are dynamic and vary
+ by table; amounts are the raw published values.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Farm Household Income and Characteristics",
+ "$.description": "Income, finances, and characteristics of U.S."
+ " farm operator households, by year, farm type, occupation, age,"
+ " experience, management team, limited-resource status, and sex,"
+ " published by the USDA Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ item: str = Field(
+ description="Published line item of the selected table.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Item",
+ "pinned": "left",
+ "minWidth": 340,
+ }
+ },
+ )
+
+
+class FarmHouseholdIncomeAndCharacteristicsFetcher(
+ Fetcher[
+ FarmHouseholdIncomeAndCharacteristicsQueryParams,
+ list[FarmHouseholdIncomeAndCharacteristicsData],
+ ]
+):
+ """Fetch USDA ERS Farm Household Income and Characteristics."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> FarmHouseholdIncomeAndCharacteristicsQueryParams:
+ """Transform the query params."""
+ return FarmHouseholdIncomeAndCharacteristicsQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: FarmHouseholdIncomeAndCharacteristicsQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected table's long-format rows."""
+ from openbb_government_us.usda.utils import (
+ ers_farm_household_income_and_characteristics,
+ )
+
+ return await ers_farm_household_income_and_characteristics.afetch_table(
+ query.table
+ )
+
+ @staticmethod
+ def transform_data(
+ query: FarmHouseholdIncomeAndCharacteristicsQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[FarmHouseholdIncomeAndCharacteristicsData]:
+ """Pivot each item's series into columns, years chronological."""
+ shape = TABLE_CONFIG[query.table]["shape"]
+ item_order: dict[str, int] = {}
+ column_order: dict[str, int] = {}
+ grid: dict[tuple[str, str], Any] = {}
+ for record in data:
+ column = record["column"]
+ if shape == "year":
+ year = parse_year(column)
+ if query.start_year is not None and year < query.start_year:
+ continue
+ if query.end_year is not None and year > query.end_year:
+ continue
+ item_order.setdefault(record["item"], len(item_order))
+ column_order.setdefault(column, len(column_order))
+ grid[(record["item"], column)] = record["value"]
+ if shape == "year":
+ columns = sorted(column_order, key=year_sort_key)
+ else:
+ columns = list(column_order)
+ live_columns = [
+ column
+ for column in columns
+ if any(grid.get((item, column)) is not None for item in item_order)
+ ]
+ if not item_order or not live_columns:
+ raise EmptyDataError("No records match the given filters.")
+ results: list[FarmHouseholdIncomeAndCharacteristicsData] = []
+ for item in item_order:
+ row = {"item": item}
+ for column in live_columns:
+ row[column] = grid.get((item, column))
+ results.append(
+ FarmHouseholdIncomeAndCharacteristicsData.model_validate(row)
+ )
+ return results
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/farm_income_and_wealth_statistics.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/farm_income_and_wealth_statistics.py
new file mode 100644
index 00000000000..a2f67d302de
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/farm_income_and_wealth_statistics.py
@@ -0,0 +1,284 @@
+"""Farm Income and Wealth Statistics Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.app.service.system_service import SystemService
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from openbb_core.provider.utils.errors import EmptyDataError
+from pydantic import ConfigDict, Field, field_validator, model_validator
+
+from openbb_government_us.usda.utils.ers_farm_income_and_wealth_statistics import (
+ STATE_LABELS,
+ TABLE_LABELS,
+ TABLE_PREFIXES,
+ allowed_states,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+api_prefix = SystemService().system_settings.api_settings.prefix
+
+DEFAULT_TABLE = "income_statement"
+DEFAULT_STATE = "US"
+
+
+class FarmIncomeAndWealthStatisticsQueryParams(QueryParams):
+ """Farm Income and Wealth Statistics Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/farm-income-and-wealth-statistics
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "x-widget_config": {
+ "label": "Table",
+ "value": DEFAULT_TABLE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": label, "value": key}
+ for key, label in TABLE_LABELS.items()
+ ],
+ "style": {"popupWidth": 360},
+ },
+ },
+ "state": {
+ "x-widget_config": {
+ "label": "State",
+ "type": "endpoint",
+ "value": DEFAULT_STATE,
+ "multiSelect": False,
+ "multiple": False,
+ "optionsEndpoint": f"{api_prefix}/usda/farm_income_states",
+ "optionsParams": {"table": "$table"},
+ "style": {"popupWidth": 280},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ table: str = Field(
+ default=DEFAULT_TABLE,
+ description="Statement to retrieve. Each table is a set of line items"
+ + " keyed by year; the years become the wide columns while the line"
+ + " item and its unit stay as pinned row columns. The balance sheet,"
+ + " financial ratios, and farm business income tables are published"
+ + " for the United States only. Valid tables are:\n "
+ + ", ".join(TABLE_PREFIXES)
+ + "\n",
+ )
+ state: str = Field(
+ default=DEFAULT_STATE,
+ description="State to retrieve, as a two-letter code, or 'US' for the"
+ + " national aggregate. State detail is published for the income"
+ + " statement, production expenses, cash receipts, government payments,"
+ + " farm-related income, home consumption, and inventory change tables;"
+ + " the U.S.-only tables accept 'US'. Forecast years are published for"
+ + " 'US' only.",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data."
+ + " If None, returns from the first published year.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data."
+ + " If None, returns up to the most recent forecast year.",
+ )
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate table."""
+ if not v:
+ return DEFAULT_TABLE
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip()
+ if value not in TABLE_PREFIXES:
+ raise OpenBBError(
+ f"Invalid table: {value}. Valid tables are: "
+ + ", ".join(TABLE_PREFIXES)
+ )
+ return value
+
+ @field_validator("state", mode="before", check_fields=False)
+ @classmethod
+ def _validate_state(cls, v):
+ """Validate state."""
+ if not v:
+ return DEFAULT_STATE
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip().upper()
+ if value not in STATE_LABELS:
+ raise OpenBBError(
+ f"Invalid state: {value}. Valid states are: " + ", ".join(STATE_LABELS)
+ )
+ return value
+
+ @model_validator(mode="after")
+ def _validate_state_for_table(self):
+ """Validate that the state is published for the selected table."""
+ codes = allowed_states(self.table)
+ if self.state not in codes:
+ raise OpenBBError(
+ f"Invalid state '{self.state}' for table '{self.table}'."
+ f" This table is published for: " + ", ".join(codes)
+ )
+ return self
+
+
+class FarmIncomeAndWealthStatisticsData(NullTokenMixin, Data):
+ """Farm Income and Wealth Statistics Data.
+
+ One selected statement pivoted to a wide layout: each row is a published
+ line item and its unit, with one value column per year. Amounts are the
+ raw published values; year columns are dynamic and vary by table.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Farm Income and Wealth Statistics",
+ "$.description": "U.S. and State-level farm sector income"
+ " statements, production expenses, cash receipts, government"
+ " payments, balance sheets, financial ratios, and farm business"
+ " income, published by the USDA Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ line_item: str = Field(
+ description="Published line item of the statement, as the full"
+ + " variable description.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Line item", "pinned": "left"}
+ },
+ )
+ unit: str = Field(
+ description="Unit of the line's values, as published, e.g. '$1,000',"
+ + " '$1,000 per farm', 'Ratio', 'Percent', or '1,000 acres'.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Unit",
+ "pinned": "left",
+ "maxWidth": 130,
+ }
+ },
+ )
+ state: str = Field(
+ description="State of the observation, or 'United States' for the"
+ + " national aggregate.",
+ json_schema_extra={"x-widget_config": {"headerName": "State", "hide": True}},
+ )
+ part_1: str | None = Field(
+ default=None,
+ description="First component of the line-item description, as"
+ + " published, when the item is broken out.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Component", "hide": True}
+ },
+ )
+ part_2: str | None = Field(
+ default=None,
+ description="Second component of the line-item description, as"
+ + " published, when the item is broken out.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Subcomponent", "hide": True}
+ },
+ )
+
+
+class FarmIncomeAndWealthStatisticsFetcher(
+ Fetcher[
+ FarmIncomeAndWealthStatisticsQueryParams,
+ list[FarmIncomeAndWealthStatisticsData],
+ ]
+):
+ """Fetch USDA ERS Farm Income and Wealth Statistics."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> FarmIncomeAndWealthStatisticsQueryParams:
+ """Transform the query params."""
+ return FarmIncomeAndWealthStatisticsQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: FarmIncomeAndWealthStatisticsQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected table's rows for the selected state."""
+ from openbb_government_us.usda.utils import (
+ ers_farm_income_and_wealth_statistics,
+ )
+
+ return await ers_farm_income_and_wealth_statistics.afetch_table(
+ query.table, query.state
+ )
+
+ @staticmethod
+ def transform_data(
+ query: FarmIncomeAndWealthStatisticsQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[FarmIncomeAndWealthStatisticsData]:
+ """Pivot each line item's years into columns, oldest year first."""
+ prefix_rank = {
+ prefix: rank for rank, prefix in enumerate(TABLE_PREFIXES[query.table])
+ }
+ pivoted: dict[tuple, dict] = {}
+ for record in data:
+ year = record["year"]
+ if query.start_year is not None and year < query.start_year:
+ continue
+ if query.end_year is not None and year > query.end_year:
+ continue
+ key = (record["line_item"], record["unit"])
+ row = pivoted.get(key)
+ if row is None:
+ row = {
+ "_rank": prefix_rank.get(record["prefix"], len(prefix_rank)),
+ "_order": record["order"],
+ "line_item": record["line_item"],
+ "unit": record["unit"],
+ "state": STATE_LABELS.get(record["state"], record["state"]),
+ "part_1": record["part_1"],
+ "part_2": record["part_2"],
+ }
+ pivoted[key] = row
+ elif record["order"] < row["_order"]:
+ row["_order"] = record["order"]
+ row[str(year)] = record["amount"]
+ if not pivoted:
+ raise EmptyDataError("No records match the given filters.")
+ results = sorted(
+ pivoted.values(), key=lambda row: (row["_rank"], row["_order"])
+ )
+ validated: list[FarmIncomeAndWealthStatisticsData] = []
+ for row in results:
+ dimensions = {
+ k: v
+ for k, v in row.items()
+ if not k.startswith("_") and not k.isdigit()
+ }
+ years = {
+ year: row[year]
+ for year in sorted((k for k in row if k.isdigit()), key=int)
+ }
+ validated.append(
+ FarmIncomeAndWealthStatisticsData.model_validate(
+ {**dimensions, **years}
+ )
+ )
+ return validated
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/feed_grains_database.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/feed_grains_database.py
new file mode 100644
index 00000000000..4a961ce224c
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/feed_grains_database.py
@@ -0,0 +1,234 @@
+"""USDA ERS Feed Grains Database Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_feed_grains import FEED_GRAINS_TABLES
+from openbb_government_us.utils.serializers import NullTokenMixin, is_null_token
+
+DEFAULT_TABLE = "corn_supply_and_use"
+
+ROW_DIM_CANDIDATES = ("commodity_group", "commodity", "geography")
+
+
+class FeedGrainsDatabaseQueryParams(QueryParams):
+ """USDA ERS Feed Grains Database Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/feed-grains-database
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "x-widget_config": {
+ "label": "Table",
+ "value": DEFAULT_TABLE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": config["name"], "value": slug}
+ for slug, config in FEED_GRAINS_TABLES.items()
+ ],
+ "style": {"popupWidth": 460},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ table: str = Field(
+ default=DEFAULT_TABLE,
+ description="Feed Grains Yearbook table to retrieve. Each table is a set"
+ + " of named balance-sheet, price, or trade series indexed by year and"
+ + " period; the series become the wide value columns while any varying"
+ + " commodity, geography, and period are folded into a single pinned"
+ + " period label so each row is uniquely identified. Valid tables are:"
+ + "\n "
+ + ", ".join(FEED_GRAINS_TABLES)
+ + "\n",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data, compared against the"
+ + " integer year of the observation. If None, returns from the"
+ + " beginning of the series.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data, compared against the"
+ + " integer year of the observation. If None, returns up to the most"
+ + " recent year.",
+ )
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate table."""
+ if not v:
+ return DEFAULT_TABLE
+ table = v.strip() if isinstance(v, str) else v
+ if table not in FEED_GRAINS_TABLES:
+ raise OpenBBError(
+ f"Invalid table: {table}. Valid tables are: "
+ + ", ".join(FEED_GRAINS_TABLES)
+ )
+ return table
+
+
+class FeedGrainsDatabaseData(NullTokenMixin, Data):
+ """USDA ERS Feed Grains Database.
+
+ One selected Feed Grains Yearbook table pivoted to a wide layout: the
+ period stays in the rows while the table's series dimension spreads into
+ value columns, whose set varies by table. Any varying commodity group,
+ commodity, or geography is folded into the period label so each row is
+ uniquely labeled, and a single row unit is folded into the column headers.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Feed Grains Database",
+ "$.description": "U.S. and world corn, sorghum, barley, oats, and"
+ " hay acreage, supply, use, prices, and trade from the Feed"
+ " Grains Yearbook, published by the USDA Economic Research"
+ " Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ period: str = Field(
+ description="Row label of the observation: the year and published period"
+ + " token ('1975 Marketing year Sep-Aug', '2020 Sep', '2019 Marketing"
+ + " year Q1 Sep-Nov'), with any varying commodity group, commodity, or"
+ + " geography prefixed so each row is uniquely labeled.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Period",
+ "pinned": "left",
+ "maxWidth": 320,
+ }
+ },
+ )
+
+
+class FeedGrainsDatabaseFetcher(
+ Fetcher[
+ FeedGrainsDatabaseQueryParams,
+ list[FeedGrainsDatabaseData],
+ ]
+):
+ """Fetch USDA ERS Feed Grains Database."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(params: dict[str, Any]) -> FeedGrainsDatabaseQueryParams:
+ """Transform the query params."""
+ return FeedGrainsDatabaseQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: FeedGrainsDatabaseQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected table's long-format rows."""
+ from openbb_government_us.usda.utils import ers_feed_grains
+
+ records = await ers_feed_grains.afetch_yearbook()
+ return [record for record in records if record["table"] == query.table]
+
+ @staticmethod
+ def transform_data(
+ query: FeedGrainsDatabaseQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[FeedGrainsDatabaseData]:
+ """Pivot the long-format rows into period rows with series as columns."""
+ filtered = [
+ record
+ for record in data
+ if (query.start_year is None or record["year"] >= query.start_year)
+ and (query.end_year is None or record["year"] <= query.end_year)
+ ]
+ if not filtered:
+ return []
+ if len({record["attribute"] for record in filtered}) > 1:
+ series_field: str | None = "attribute"
+ elif len({record["commodity"] for record in filtered}) > 1:
+ series_field = "commodity"
+ else:
+ series_field = None
+
+ def series_value(record: dict) -> str:
+ return record[series_field] if series_field else record["attribute"]
+
+ series_values = {series_value(record) for record in filtered}
+ fold_dims: list[str] = []
+ for dim in ROW_DIM_CANDIDATES:
+ if dim == series_field:
+ continue
+ distinct = {
+ record[dim] for record in filtered if not is_null_token(record[dim])
+ }
+ if len(distinct) <= 1:
+ continue
+ if dim == "commodity_group" and all(
+ record["commodity_group"] == record["commodity"] for record in filtered
+ ):
+ continue
+ pairs = {(series_value(record), record[dim]) for record in filtered}
+ if len(pairs) == len(series_values):
+ continue
+ fold_dims.append(dim)
+
+ pivoted: dict[tuple, dict] = {}
+ column_order: list[str] = []
+ seen_columns: set[str] = set()
+ for order, record in enumerate(filtered):
+ dim_values = tuple(record[dim] for dim in fold_dims)
+ key = (*dim_values, record["year"], record["timeperiod"])
+ row = pivoted.get(key)
+ if row is None:
+ labels = [
+ value for value in dim_values if value and not is_null_token(value)
+ ]
+ time_label = (
+ f"{record['year']} {record['timeperiod']}"
+ if record["timeperiod"]
+ else str(record["year"])
+ )
+ row = {
+ "_sort": tuple((value or "") for value in dim_values),
+ "_year": record["year"],
+ "_order": order,
+ "period": " — ".join([*labels, time_label]),
+ }
+ pivoted[key] = row
+ column = series_value(record)
+ unit = record["unit"]
+ if unit and not is_null_token(unit) and unit not in column:
+ column = f"{column} ({unit})"
+ if column not in seen_columns:
+ seen_columns.add(column)
+ column_order.append(column)
+ row[column] = record["value"]
+ results = sorted(
+ pivoted.values(),
+ key=lambda row: (row["_sort"], row["_year"], row["_order"]),
+ )
+ validated: list[FeedGrainsDatabaseData] = []
+ for row in results:
+ payload: dict[str, Any] = {"period": row["period"]}
+ for column in column_order:
+ payload[column] = row.get(column)
+ validated.append(FeedGrainsDatabaseData.model_validate(payload))
+ return validated
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/fertilizer_use_and_price.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/fertilizer_use_and_price.py
new file mode 100644
index 00000000000..d181f8b9c00
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/fertilizer_use_and_price.py
@@ -0,0 +1,211 @@
+"""USDA ERS Fertilizer Use and Price Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from openbb_core.provider.utils.errors import EmptyDataError
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_fertilizer_use_and_price import (
+ FERTILIZER_TABLES,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin, is_null_token
+
+DEFAULT_TABLE = "us_plant_nutrient_consumption"
+
+MONTH_ORDER: dict[str, int] = {
+ "Jan.": 1,
+ "Feb.": 2,
+ "Mar.": 3,
+ "Apr.": 4,
+ "May": 5,
+ "June": 6,
+ "July": 7,
+ "Aug.": 8,
+ "Sept.": 9,
+ "Oct.": 10,
+ "Nov.": 11,
+ "Dec.": 12,
+}
+
+
+class FertilizerUseAndPriceQueryParams(QueryParams):
+ """USDA ERS Fertilizer Use and Price Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/fertilizer-use-and-price
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "x-widget_config": {
+ "label": "Table",
+ "value": DEFAULT_TABLE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": config["label"], "value": key}
+ for key, config in FERTILIZER_TABLES.items()
+ ],
+ "style": {"popupWidth": 560},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ table: str = Field(
+ default=DEFAULT_TABLE,
+ description="Fertilizer table to retrieve. National tables (1-8) spread"
+ + " their nutrient, material, or price measures into the wide columns;"
+ + " crop-by-State tables (9-32) spread the surveyed States into the wide"
+ + " columns. The period is the pinned row axis. Valid tables are:\n "
+ + ", ".join(FERTILIZER_TABLES)
+ + "\n",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data, compared against the"
+ + " integer year. If None, returns from the beginning of the series.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data, compared against the"
+ + " integer year. If None, returns up to the most recent year.",
+ )
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate table."""
+ if not v:
+ return DEFAULT_TABLE
+ table = v.strip() if isinstance(v, str) else v
+ if table not in FERTILIZER_TABLES:
+ raise OpenBBError(
+ f"Invalid table: {table}. Valid tables are: "
+ + ", ".join(FERTILIZER_TABLES)
+ )
+ return table
+
+
+class FertilizerUseAndPriceData(NullTokenMixin, Data):
+ """USDA ERS Fertilizer Use and Price Data.
+
+ One selected fertilizer table pivoted to a wide layout: the period stays in
+ the rows while the table's series dimension - nutrients, materials, price
+ measures, or surveyed States - spreads into value columns, whose set varies
+ by table. The farm-prices table reports several months per year, so the
+ month is folded into the period label to keep every row uniquely labeled.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Fertilizer Use and Price",
+ "$.description": "U.S. fertilizer consumption, farm prices, price"
+ " indexes, and crop-by-State application rates and shares,"
+ " published by the USDA Economic Research Service. National"
+ " nutrient and material tables are in 1,000 short tons or percent"
+ " shares, farm prices in dollars per material short ton, price"
+ " indexes on a 2011=100 base, and crop-by-State tables in percent"
+ " of acreage or pounds per fertilized acre.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ period: str = Field(
+ description="Row label of the observation: the year for annual tables"
+ + " ('2015'), the year and month for the sub-annual farm-prices table"
+ + " ('1977 Mar.'), so each row is uniquely labeled.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Period",
+ "pinned": "left",
+ "maxWidth": 260,
+ }
+ },
+ )
+
+
+class FertilizerUseAndPriceFetcher(
+ Fetcher[
+ FertilizerUseAndPriceQueryParams,
+ list[FertilizerUseAndPriceData],
+ ]
+):
+ """Fetch USDA ERS Fertilizer Use and Price."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> FertilizerUseAndPriceQueryParams:
+ """Transform the query params."""
+ return FertilizerUseAndPriceQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: FertilizerUseAndPriceQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected table's long-format rows."""
+ from openbb_government_us.usda.utils import ers_fertilizer_use_and_price
+
+ return await ers_fertilizer_use_and_price.afetch_table(query.table)
+
+ @staticmethod
+ def transform_data(
+ query: FertilizerUseAndPriceQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[FertilizerUseAndPriceData]:
+ """Pivot the long-format rows into period rows with series as columns."""
+ unit = FERTILIZER_TABLES[query.table].get("unit")
+ pivoted: dict[tuple, dict] = {}
+ col_order: list[str] = []
+ seen_cols: set[str] = set()
+ for record in data:
+ year = record["year"]
+ if query.start_year is not None and year < query.start_year:
+ continue
+ if query.end_year is not None and year > query.end_year:
+ continue
+ token = record["period"]
+ month = None if is_null_token(token) else token
+ key = (year, month)
+ row = pivoted.get(key)
+ if row is None:
+ row = {
+ "_year": year,
+ "_month_order": MONTH_ORDER.get(month, 0) if month else 0,
+ "period": f"{year} {month}" if month else str(year),
+ }
+ pivoted[key] = row
+ column = record["series"]
+ if unit and f"({unit})" not in column:
+ column = f"{column} ({unit})"
+ if column not in seen_cols:
+ seen_cols.add(column)
+ col_order.append(column)
+ row[column] = record["value"]
+ if not pivoted:
+ raise EmptyDataError("No records match the given filters.")
+ results = sorted(
+ pivoted.values(),
+ key=lambda row: (row["_year"], row["_month_order"]),
+ )
+ validated: list[FertilizerUseAndPriceData] = []
+ for row in results:
+ payload: dict[str, Any] = {"period": row["period"]}
+ for column in col_order:
+ payload[column] = row.get(column)
+ validated.append(FertilizerUseAndPriceData.model_validate(payload))
+ return validated
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/food_at_home_monthly_area_prices.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/food_at_home_monthly_area_prices.py
new file mode 100644
index 00000000000..5f1a8c121d1
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/food_at_home_monthly_area_prices.py
@@ -0,0 +1,365 @@
+"""USDA ERS Food-at-Home Monthly Area Prices Model."""
+
+from datetime import date as dateType
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.app.service.system_service import SystemService
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_food_at_home_monthly_area_prices import (
+ AREA_FIELDS,
+ AREAS,
+ DEFAULT_GROUP,
+ DEFAULT_ITEM,
+ DEFAULT_MEASURE,
+ DEFAULT_TABLE,
+ EFPG,
+ FMAP_TABLES,
+ GROUPS,
+ MEASURES,
+ resolve_measure,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+api_prefix = SystemService().system_settings.api_settings.prefix
+
+TABLE_OPTIONS = [
+ {"label": config["label"], "value": key} for key, config in FMAP_TABLES.items()
+]
+
+GROUP_OPTIONS = [{"label": group, "value": group} for group in GROUPS]
+
+
+def _area(header: str) -> dict:
+ """Build a numeric area column config for one area field."""
+ return {"x-widget_config": {"headerName": header, "cellDataType": "number"}}
+
+
+class FoodAtHomeMonthlyAreaPricesQueryParams(QueryParams):
+ """USDA ERS Food-at-Home Monthly Area Prices Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/food-at-home-monthly-area-prices
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "x-widget_config": {
+ "label": "Table",
+ "value": DEFAULT_TABLE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": TABLE_OPTIONS,
+ "style": {"popupWidth": 320},
+ },
+ },
+ "group": {
+ "x-widget_config": {
+ "label": "Food group",
+ "value": DEFAULT_GROUP,
+ "multiSelect": False,
+ "multiple": False,
+ "options": GROUP_OPTIONS,
+ "style": {"popupWidth": 320},
+ },
+ },
+ "item": {
+ "x-widget_config": {
+ "label": "Food item",
+ "type": "endpoint",
+ "value": DEFAULT_ITEM,
+ "multiSelect": False,
+ "multiple": False,
+ "optionsEndpoint": f"{api_prefix}/usda/fmap_food_items",
+ "optionsParams": {"group": "$group"},
+ "style": {"popupWidth": 420},
+ },
+ },
+ "measure": {
+ "x-widget_config": {
+ "label": "Measure",
+ "type": "endpoint",
+ "value": DEFAULT_MEASURE,
+ "multiSelect": False,
+ "multiple": False,
+ "optionsEndpoint": f"{api_prefix}/usda/fmap_measures",
+ "optionsParams": {"table": "$table"},
+ "style": {"popupWidth": 400},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ table: str = Field(
+ default=DEFAULT_TABLE,
+ description="Table to retrieve. The main table carries the nine F-MAP"
+ + " price, purchase, and store measures for 2012-2018; the supplemental"
+ + " table carries six alternative price-index formulas for 2016-2018."
+ + " Valid tables are:\n "
+ + ", ".join(FMAP_TABLES)
+ + "\n",
+ )
+ group: str = Field(
+ default=DEFAULT_GROUP,
+ description="Tier-1 food group that narrows the food-item options."
+ + " Ignored once an item is chosen. Valid groups are:\n "
+ + ", ".join(GROUPS)
+ + "\n",
+ )
+ item: str = Field(
+ default=DEFAULT_ITEM,
+ description="ERS Food Purchase Group (EFPG) code of the food item whose"
+ + " monthly area prices are spread across the area columns, e.g. '40000'"
+ + " for whole milk.",
+ )
+ measure: str = Field(
+ default=DEFAULT_MEASURE,
+ description="Measure spread into the 15 area columns. Only measures the"
+ + " selected table publishes are valid; an unavailable measure falls"
+ + " back to the table's default. Valid measures are:\n "
+ + ", ".join(MEASURES)
+ + "\n",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data."
+ + " If None, returns from the first year of the table.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data."
+ + " If None, returns up to the last year of the table.",
+ )
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate table."""
+ if not v:
+ return DEFAULT_TABLE
+ table = v.strip() if isinstance(v, str) else v
+ if table not in FMAP_TABLES:
+ raise OpenBBError(
+ f"Invalid table: {table}. Valid tables are: " + ", ".join(FMAP_TABLES)
+ )
+ return table
+
+ @field_validator("group", mode="before", check_fields=False)
+ @classmethod
+ def _validate_group(cls, v):
+ """Validate group."""
+ if not v:
+ return DEFAULT_GROUP
+ group = v.strip() if isinstance(v, str) else v
+ if group not in GROUPS:
+ raise OpenBBError(
+ f"Invalid group: {group}. Valid groups are: " + ", ".join(GROUPS)
+ )
+ return group
+
+ @field_validator("item", mode="before", check_fields=False)
+ @classmethod
+ def _validate_item(cls, v):
+ """Validate item."""
+ if not v:
+ return DEFAULT_ITEM
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ item = str(value).strip()
+ if item not in EFPG:
+ raise OpenBBError(
+ f"Invalid item: {item}. Valid items are EFPG codes: " + ", ".join(EFPG)
+ )
+ return item
+
+ @field_validator("measure", mode="before", check_fields=False)
+ @classmethod
+ def _validate_measure(cls, v):
+ """Validate measure."""
+ if not v:
+ return DEFAULT_MEASURE
+ measure = v.strip() if isinstance(v, str) else v
+ if measure not in MEASURES:
+ raise OpenBBError(
+ f"Invalid measure: {measure}. Valid measures are: "
+ + ", ".join(MEASURES)
+ )
+ return measure
+
+
+class FoodAtHomeMonthlyAreaPricesData(NullTokenMixin, Data):
+ """USDA ERS Food-at-Home Monthly Area Prices Data.
+
+ One selected food item and measure pivoted to a wide layout: the monthly
+ dates stay in the rows while the 15 U.S. areas spread into value columns.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Food-at-Home Monthly Area Prices",
+ "$.description": "Monthly food-at-home unit values, purchases,"
+ " store counts, and price indexes for a selected food item and"
+ " measure, spread across the national, four Census-region, and"
+ " ten metropolitan areas, published by the USDA Economic"
+ " Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ date: dateType = Field(
+ description="First day of the observation month.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Date",
+ "cellDataType": "date",
+ "pinned": "left",
+ "maxWidth": 120,
+ }
+ },
+ )
+ national: float | None = Field(
+ default=None,
+ description="Selected measure for the United States.",
+ json_schema_extra=_area("National"),
+ )
+ northeast: float | None = Field(
+ default=None,
+ description="Selected measure for Census Region 1: Northeast.",
+ json_schema_extra=_area("Northeast"),
+ )
+ midwest: float | None = Field(
+ default=None,
+ description="Selected measure for Census Region 2: Midwest.",
+ json_schema_extra=_area("Midwest"),
+ )
+ south: float | None = Field(
+ default=None,
+ description="Selected measure for Census Region 3: South.",
+ json_schema_extra=_area("South"),
+ )
+ west: float | None = Field(
+ default=None,
+ description="Selected measure for Census Region 4: West.",
+ json_schema_extra=_area("West"),
+ )
+ atlanta: float | None = Field(
+ default=None,
+ description="Selected measure for the Atlanta, GA metro area.",
+ json_schema_extra=_area("Atlanta, GA"),
+ )
+ boston: float | None = Field(
+ default=None,
+ description="Selected measure for the Boston, MA-NH metro area.",
+ json_schema_extra=_area("Boston, MA-NH"),
+ )
+ chicago: float | None = Field(
+ default=None,
+ description="Selected measure for the Chicago, IL-IN-WI metro area.",
+ json_schema_extra=_area("Chicago, IL-IN-WI"),
+ )
+ dallas: float | None = Field(
+ default=None,
+ description="Selected measure for the Dallas-Fort Worth, TX metro area.",
+ json_schema_extra=_area("Dallas-Fort Worth, TX"),
+ )
+ detroit: float | None = Field(
+ default=None,
+ description="Selected measure for the Detroit, MI metro area.",
+ json_schema_extra=_area("Detroit, MI"),
+ )
+ houston: float | None = Field(
+ default=None,
+ description="Selected measure for the Houston, TX metro area.",
+ json_schema_extra=_area("Houston, TX"),
+ )
+ los_angeles: float | None = Field(
+ default=None,
+ description="Selected measure for the Los Angeles, CA metro area.",
+ json_schema_extra=_area("Los Angeles, CA"),
+ )
+ miami: float | None = Field(
+ default=None,
+ description="Selected measure for the Miami, FL metro area.",
+ json_schema_extra=_area("Miami, FL"),
+ )
+ new_york: float | None = Field(
+ default=None,
+ description="Selected measure for the New York, NY-NJ-PA metro area.",
+ json_schema_extra=_area("New York, NY-NJ-PA"),
+ )
+ philadelphia: float | None = Field(
+ default=None,
+ description="Selected measure for the Philadelphia, PA-NJ-DE-MD metro"
+ + " area.",
+ json_schema_extra=_area("Philadelphia, PA-NJ-DE-MD"),
+ )
+
+
+class FoodAtHomeMonthlyAreaPricesFetcher(
+ Fetcher[
+ FoodAtHomeMonthlyAreaPricesQueryParams,
+ list[FoodAtHomeMonthlyAreaPricesData],
+ ]
+):
+ """Fetch USDA ERS Food-at-Home Monthly Area Prices."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> FoodAtHomeMonthlyAreaPricesQueryParams:
+ """Transform the query params."""
+ return FoodAtHomeMonthlyAreaPricesQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: FoodAtHomeMonthlyAreaPricesQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract one item and measure's monthly area observations."""
+ from openbb_government_us.usda.utils import (
+ ers_food_at_home_monthly_area_prices as fmap,
+ )
+
+ measure = resolve_measure(query.table, query.measure)
+ source = MEASURES[measure]["source"]
+ return await fmap.afetch_series(query.table, query.item, source)
+
+ @staticmethod
+ def transform_data(
+ query: FoodAtHomeMonthlyAreaPricesQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[FoodAtHomeMonthlyAreaPricesData]:
+ """Pivot the long observations into wide monthly rows by area."""
+ pivoted: dict[tuple[int, int], dict] = {}
+ for record in data:
+ year = record["year"]
+ month = record["month"]
+ if query.start_year is not None and year < query.start_year:
+ continue
+ if query.end_year is not None and year > query.end_year:
+ continue
+ key = (year, month)
+ row = pivoted.get(key)
+ if row is None:
+ row = {"date": dateType(year, month, 1)}
+ for field in AREA_FIELDS:
+ row[field] = None
+ pivoted[key] = row
+ area = AREAS.get(record["area_code"])
+ if area is not None:
+ row[area[0]] = record["value"]
+ return [
+ FoodAtHomeMonthlyAreaPricesData.model_validate(pivoted[key])
+ for key in sorted(pivoted)
+ ]
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/food_availability_per_capita_data_system.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/food_availability_per_capita_data_system.py
new file mode 100644
index 00000000000..cab3157e443
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/food_availability_per_capita_data_system.py
@@ -0,0 +1,256 @@
+"""USDA ERS Food Availability (Per Capita) Data System Model."""
+
+from collections import OrderedDict
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.app.service.system_service import SystemService
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from openbb_core.provider.utils.errors import EmptyDataError
+from pydantic import ConfigDict, Field, field_validator, model_validator
+
+from openbb_government_us.usda.utils.ers_food_availability_per_capita_data_system import (
+ CATALOG,
+ DATA_SYSTEM_LABELS,
+ DATA_SYSTEMS,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+api_prefix = SystemService().system_settings.api_settings.prefix
+
+DEFAULT_DATA_SYSTEM = "food_availability"
+
+SYSTEM_DEFAULT_GROUP: dict[str, str] = {
+ "food_availability": "eggs",
+ "loss_adjusted": "fruit",
+ "nutrient": "totals",
+}
+
+DATA_SYSTEM_OPTIONS = [
+ {"label": DATA_SYSTEM_LABELS[system], "value": system} for system in DATA_SYSTEMS
+]
+
+
+class FoodAvailabilityPerCapitaDataSystemQueryParams(QueryParams):
+ """USDA ERS Food Availability (Per Capita) Data System Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/food-availability-per-capita-data-system
+ """
+
+ __json_schema_extra__ = {
+ "data_system": {
+ "x-widget_config": {
+ "label": "Data system",
+ "value": DEFAULT_DATA_SYSTEM,
+ "multiSelect": False,
+ "multiple": False,
+ "options": DATA_SYSTEM_OPTIONS,
+ "style": {"popupWidth": 320},
+ },
+ },
+ "food_group": {
+ "x-widget_config": {
+ "label": "Food group",
+ "type": "endpoint",
+ "value": "eggs",
+ "multiSelect": False,
+ "multiple": False,
+ "optionsEndpoint": f"{api_prefix}/usda/food_availability_groups",
+ "optionsParams": {"data_system": "$data_system"},
+ "style": {"popupWidth": 360},
+ },
+ },
+ "commodity": {
+ "x-widget_config": {
+ "label": "Commodity",
+ "type": "endpoint",
+ "multiSelect": False,
+ "multiple": False,
+ "optionsEndpoint": f"{api_prefix}/usda/food_availability_commodities",
+ "optionsParams": {
+ "data_system": "$data_system",
+ "food_group": "$food_group",
+ },
+ "style": {"popupWidth": 420},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ data_system: str = Field(
+ default=DEFAULT_DATA_SYSTEM,
+ description="Data system to retrieve. Each selection downloads one"
+ + " cached file, pivoted to a wide layout with the measures spread into"
+ + " columns and the years in the rows. Valid data systems are:\n "
+ + ", ".join(DATA_SYSTEMS)
+ + "\n",
+ )
+ food_group: str | None = Field(
+ default=None,
+ description="Food group file within the data system, as a slug. The"
+ + " same slug can appear under more than one data system. If None, the"
+ + " data system's default file is used.",
+ )
+ commodity: str | None = Field(
+ default=None,
+ description="Commodity block within the file, as its published name."
+ + " If None, the file's first block is used.",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data."
+ + " If None, returns from the beginning of the series.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data."
+ + " If None, returns up to the most recent year.",
+ )
+
+ @field_validator("data_system", mode="before", check_fields=False)
+ @classmethod
+ def _validate_data_system(cls, v):
+ """Validate data_system."""
+ if not v:
+ return DEFAULT_DATA_SYSTEM
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip()
+ if value not in DATA_SYSTEMS:
+ raise OpenBBError(
+ f"Invalid data system: {value}. Valid data systems are: "
+ + ", ".join(DATA_SYSTEMS)
+ )
+ return value
+
+ @field_validator("food_group", "commodity", mode="before", check_fields=False)
+ @classmethod
+ def _normalize_optional(cls, v):
+ """Normalize an optional string filter to a stripped string or None."""
+ if not v:
+ return None
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ return str(value).strip() or None
+
+ @model_validator(mode="after")
+ def _resolve_food_group(self):
+ """Resolve the food group to the system default and validate it."""
+ if self.food_group is None:
+ self.food_group = SYSTEM_DEFAULT_GROUP[self.data_system]
+ elif self.food_group not in CATALOG[self.data_system]:
+ raise OpenBBError(
+ f"Invalid food group '{self.food_group}' for data system"
+ f" '{self.data_system}'. Valid food groups are: "
+ + ", ".join(CATALOG[self.data_system])
+ )
+ return self
+
+
+class FoodAvailabilityPerCapitaDataSystemData(NullTokenMixin, Data):
+ """USDA ERS Food Availability (Per Capita) Data System Data.
+
+ One selected file's commodity block, pivoted to a wide layout: the years
+ stay in the rows while the measures spread into value columns, whose set is
+ dynamic and varies by file and block. Amounts are the raw published values.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Food Availability (Per Capita)",
+ "$.description": "Per capita food supply, loss-adjusted"
+ " availability, and nutrient availability of the U.S. food"
+ " supply, published by the USDA Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ year: int = Field(
+ description="Calendar year of the observation.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Year",
+ "cellDataType": "number",
+ "pinned": "left",
+ "maxWidth": 90,
+ }
+ },
+ )
+
+
+class FoodAvailabilityPerCapitaDataSystemFetcher(
+ Fetcher[
+ FoodAvailabilityPerCapitaDataSystemQueryParams,
+ list[FoodAvailabilityPerCapitaDataSystemData],
+ ]
+):
+ """Fetch USDA ERS Food Availability (Per Capita) Data System."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> FoodAvailabilityPerCapitaDataSystemQueryParams:
+ """Transform the query params."""
+ return FoodAvailabilityPerCapitaDataSystemQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: FoodAvailabilityPerCapitaDataSystemQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected file's long-format records."""
+ from openbb_government_us.usda.utils import (
+ ers_food_availability_per_capita_data_system as ers,
+ )
+
+ food_group = query.food_group or SYSTEM_DEFAULT_GROUP[query.data_system]
+ return await ers.afetch_records(query.data_system, food_group)
+
+ @staticmethod
+ def transform_data(
+ query: FoodAvailabilityPerCapitaDataSystemQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[FoodAvailabilityPerCapitaDataSystemData]:
+ """Pivot the selected block's measures into columns, oldest year first."""
+ block = query.commodity or (data[0]["block"] if data else None)
+ column_order: OrderedDict[str, None] = OrderedDict()
+ populated: set[str] = set()
+ by_year: dict[int, dict] = {}
+ for record in data:
+ if record["block"] != block:
+ continue
+ year = record["year"]
+ if query.start_year is not None and year < query.start_year:
+ continue
+ if query.end_year is not None and year > query.end_year:
+ continue
+ attribute = record["attribute"]
+ value = record["value"]
+ column_order.setdefault(attribute, None)
+ if value is not None:
+ populated.add(attribute)
+ row = by_year.get(year)
+ if row is None:
+ row = {"year": year}
+ by_year[year] = row
+ row[attribute] = value
+ columns = [name for name in column_order if name in populated]
+ if not by_year:
+ raise EmptyDataError("No records match the given filters.")
+ results: list[FoodAvailabilityPerCapitaDataSystemData] = []
+ for year in sorted(by_year):
+ source = by_year[year]
+ row = {"year": year}
+ for name in columns:
+ row[name] = source.get(name)
+ results.append(FoodAvailabilityPerCapitaDataSystemData.model_validate(row))
+ return results
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/food_consumption_nutrient_intakes_and_diet_quality.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/food_consumption_nutrient_intakes_and_diet_quality.py
new file mode 100644
index 00000000000..8db4dc5e0bb
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/food_consumption_nutrient_intakes_and_diet_quality.py
@@ -0,0 +1,422 @@
+"""USDA ERS Food Consumption, Nutrient Intakes, and Diet Quality Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from openbb_core.provider.utils.errors import EmptyDataError
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_food_consumption_nutrient_intakes_and_diet_quality import (
+ DEFAULT_DEMOGRAPHIC,
+ DEFAULT_STATISTIC,
+ DEFAULT_TABLE,
+ DEMOGRAPHICS,
+ FOOD_SOURCES,
+ RECOMMENDED_COLUMN_ORDER,
+ RECOMMENDED_TABLE,
+ SAMPLE_TABLE,
+ STATISTIC_CHOICES,
+ TABLE_FILES,
+ TABLE_LABELS,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+
+def _cycle_year(cycle: str) -> int | None:
+ """Return the starting calendar year of a survey-cycle label."""
+ head = cycle[:4]
+ return int(head) if head.isdigit() else None
+
+
+def _source_index(food_source: str | None) -> int:
+ """Return the published ordering rank of a food source."""
+ if food_source in FOOD_SOURCES:
+ return FOOD_SOURCES.index(food_source)
+ return 0
+
+
+def _finalize(
+ rows: list[dict], column_order: list[str]
+) -> list["FoodConsumptionNutrientIntakesAndDietQualityData"]:
+ """Sort rows, spread the value columns rectangularly, and validate them."""
+ rows.sort(key=lambda row: (row["_item_order"], row["_source_index"], row["_order"]))
+ results: list[FoodConsumptionNutrientIntakesAndDietQualityData] = []
+ for row in rows:
+ source = row["food_source"]
+ payload = {
+ "item": f"{row['item']} — {source}" if source else row["item"],
+ "food_source": source,
+ "units": row["units"],
+ }
+ for column in column_order:
+ payload[column] = row.get(column)
+ results.append(
+ FoodConsumptionNutrientIntakesAndDietQualityData.model_validate(payload)
+ )
+ return results
+
+
+def _pivot_sample(
+ query: "FoodConsumptionNutrientIntakesAndDietQualityQueryParams", data: list[dict]
+):
+ """Pivot Table 1 sample sizes into one row per subgroup, cycles as columns."""
+ item_first: dict[str, int] = {}
+ groups: dict[str, dict] = {}
+ present: set[str] = set()
+ for record in data:
+ year = _cycle_year(record["cycle"])
+ if year is None:
+ continue
+ if query.start_year is not None and year < query.start_year:
+ continue
+ if query.end_year is not None and year > query.end_year:
+ continue
+ subgroup = record["demographic"]
+ item_first.setdefault(subgroup, record["order"])
+ row = groups.get(subgroup)
+ if row is None:
+ row = {
+ "_order": record["order"],
+ "_source_index": 0,
+ "item": subgroup,
+ "food_source": None,
+ "units": None,
+ }
+ groups[subgroup] = row
+ row[record["cycle"]] = record["value"]
+ present.add(record["cycle"])
+ if not groups:
+ raise EmptyDataError("No records match the given filters.")
+ for row in groups.values():
+ row["_item_order"] = item_first[row["item"]]
+ column_order = sorted(present, key=lambda cycle: _cycle_year(cycle) or 0)
+ return list(groups.values()), column_order
+
+
+def _pivot_intake(
+ query: "FoodConsumptionNutrientIntakesAndDietQualityQueryParams", data: list[dict]
+):
+ """Pivot an intake table (2-7) into one row per item and food source."""
+ demographic = query.demographics
+ statistic = STATISTIC_CHOICES[query.statistic]
+ item_first: dict[str, int] = {}
+ groups: dict[tuple, dict] = {}
+ present: set[str] = set()
+ for record in data:
+ if record["demographics"] != demographic:
+ continue
+ if record["statistic"] != statistic:
+ continue
+ year = _cycle_year(record["cycle"])
+ if year is None:
+ continue
+ if query.start_year is not None and year < query.start_year:
+ continue
+ if query.end_year is not None and year > query.end_year:
+ continue
+ item = record["item"]
+ source = record["food_source"]
+ item_first.setdefault(item, record["order"])
+ key = (item, source)
+ row = groups.get(key)
+ if row is None:
+ row = {
+ "_order": record["order"],
+ "_source_index": _source_index(source),
+ "item": item,
+ "food_source": source,
+ "units": record["units"],
+ }
+ groups[key] = row
+ if row["units"] is None and record["units"] is not None:
+ row["units"] = record["units"]
+ row[record["cycle"]] = record["value"]
+ present.add(record["cycle"])
+ if not groups:
+ raise EmptyDataError("No records match the given filters.")
+ for row in groups.values():
+ row["_item_order"] = item_first[row["item"]]
+ column_order = sorted(present, key=lambda cycle: _cycle_year(cycle) or 0)
+ return list(groups.values()), column_order
+
+
+def _pivot_recommended(
+ query: "FoodConsumptionNutrientIntakesAndDietQualityQueryParams", data: list[dict]
+):
+ """Pivot Table 8 into one row per item, recommended and actual densities as columns."""
+ item_first: dict[str, int] = {}
+ groups: dict[str, dict] = {}
+ for record in data:
+ item = record["item"]
+ item_first.setdefault(item, record["order"])
+ row = groups.get(item)
+ if row is None:
+ row = {
+ "_order": record["order"],
+ "_source_index": 0,
+ "item": item,
+ "food_source": None,
+ "units": None,
+ }
+ groups[item] = row
+ row[record["column"]] = record["value"]
+ if not groups:
+ raise EmptyDataError("No records match the given filters.")
+ for row in groups.values():
+ row["_item_order"] = item_first[row["item"]]
+ return list(groups.values()), list(RECOMMENDED_COLUMN_ORDER)
+
+
+class FoodConsumptionNutrientIntakesAndDietQualityQueryParams(QueryParams):
+ """USDA ERS Food Consumption, Nutrient Intakes, and Diet Quality Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/food-consumption-nutrient-intakes-and-diet-quality
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "x-widget_config": {
+ "label": "Table",
+ "value": DEFAULT_TABLE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": label, "value": key}
+ for key, label in TABLE_LABELS.items()
+ ],
+ "style": {"popupWidth": 460},
+ },
+ },
+ "demographics": {
+ "x-widget_config": {
+ "label": "Demographic subgroup",
+ "value": DEFAULT_DEMOGRAPHIC,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [{"label": label, "value": label} for label in DEMOGRAPHICS],
+ "style": {"popupWidth": 360},
+ },
+ },
+ "statistic": {
+ "x-widget_config": {
+ "label": "Statistic",
+ "value": DEFAULT_STATISTIC,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": label, "value": key}
+ for key, label in STATISTIC_CHOICES.items()
+ ],
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ table: str = Field(
+ default=DEFAULT_TABLE,
+ description="Table to retrieve. Tables 1-7 are a 1977-2018 survey-cycle"
+ + " series, pivoted with the survey cycles as columns; Table 8 pivots"
+ + " the recommended and 2017-2018 actual densities into columns."
+ + " Valid tables are:\n "
+ + ", ".join(TABLE_FILES)
+ + "\n",
+ )
+ demographics: str = Field(
+ default=DEFAULT_DEMOGRAPHIC,
+ description="Population subgroup filter for the intake tables (2-7),"
+ + " keeping the survey-cycle columns to a single subgroup. Ignored by"
+ + " the sample-size table (1), which lists every subgroup, and by the"
+ + " recommended-density table (8). Valid subgroups are:\n "
+ + ", ".join(DEMOGRAPHICS)
+ + "\n",
+ )
+ statistic: str = Field(
+ default=DEFAULT_STATISTIC,
+ description="Statistic to spread across the survey-cycle columns for"
+ + " the intake tables (2-7): the mean, or the standard error of the"
+ + " mean. Ignored by the sample-size and recommended-density tables."
+ + " Valid statistics are:\n "
+ + ", ".join(STATISTIC_CHOICES)
+ + "\n",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the survey-cycle columns,"
+ + " compared against the first year of each cycle. Ignored by the"
+ + " recommended-density table. If None, returns from 1977.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the survey-cycle columns, compared"
+ + " against the first year of each cycle. Ignored by the recommended-"
+ + "density table. If None, returns through 2017-2018.",
+ )
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate table."""
+ if not v:
+ return DEFAULT_TABLE
+ table = v[0] if isinstance(v, (list, tuple)) else v
+ table = str(table).strip()
+ if table not in TABLE_FILES:
+ raise OpenBBError(
+ f"Invalid table: {table}. Valid tables are: " + ", ".join(TABLE_FILES)
+ )
+ return table
+
+ @field_validator("demographics", mode="before", check_fields=False)
+ @classmethod
+ def _validate_demographics(cls, v):
+ """Validate demographics."""
+ if not v:
+ return DEFAULT_DEMOGRAPHIC
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip()
+ if value not in DEMOGRAPHICS:
+ raise OpenBBError(
+ f"Invalid demographics: {value}. Valid subgroups are: "
+ + ", ".join(DEMOGRAPHICS)
+ )
+ return value
+
+ @field_validator("statistic", mode="before", check_fields=False)
+ @classmethod
+ def _validate_statistic(cls, v):
+ """Validate statistic."""
+ if not v:
+ return DEFAULT_STATISTIC
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip()
+ if value not in STATISTIC_CHOICES:
+ raise OpenBBError(
+ f"Invalid statistic: {value}. Valid statistics are: "
+ + ", ".join(STATISTIC_CHOICES)
+ )
+ return value
+
+
+class FoodConsumptionNutrientIntakesAndDietQualityData(NullTokenMixin, Data):
+ """USDA ERS Food Consumption, Nutrient Intakes, and Diet Quality Data.
+
+ One selected table pivoted to a wide layout: the line-item identity stays
+ in the rows while the survey cycles (Tables 1-7) or the recommended and
+ actual densities (Table 8) spread into value columns, whose set varies by
+ table.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Food Consumption, Nutrient Intakes, and Diet"
+ " Quality",
+ "$.description": "U.S. daily nutrient and food-group intake, share,"
+ " and density by food source, with population-subgroup sample"
+ " sizes and 2020-2025 recommended densities, from 1977 to 2018,"
+ " published by the USDA Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ item: str = Field(
+ description="Row identity: the nutrient (Tables 2-4, 8), food group"
+ + " (Tables 5-8), or population subgroup (Table 1), as published. On the"
+ + " intake tables (2-7) the food source is appended, e.g. 'Energy — FAH',"
+ + " because each nutrient repeats once per food source.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Item",
+ "pinned": "left",
+ "minWidth": 260,
+ }
+ },
+ )
+ food_source: str | None = Field(
+ default=None,
+ description="Food source of the observation for the intake tables"
+ + " (2-7): Total, food at home (FAH), food away from home (FAFH), or an"
+ + " FAFH outlet. Already folded into the item label; kept as a separate"
+ + " toggleable column. None for the sample-size and recommended-density"
+ + " tables.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Food source",
+ "pinned": "left",
+ "minWidth": 150,
+ "hide": True,
+ }
+ },
+ )
+ units: str | None = Field(
+ default=None,
+ description="Unit of the row's values, as published, e.g. 'Calories',"
+ + " 'Grams', 'Percent of total', or 'Grams per 1,000 calories'. None"
+ + " for the sample-size table and where a unit is carried in the item"
+ + " label instead.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Units",
+ "pinned": "left",
+ "minWidth": 150,
+ "hide": True,
+ }
+ },
+ )
+
+
+class FoodConsumptionNutrientIntakesAndDietQualityFetcher(
+ Fetcher[
+ FoodConsumptionNutrientIntakesAndDietQualityQueryParams,
+ list[FoodConsumptionNutrientIntakesAndDietQualityData],
+ ]
+):
+ """Fetch USDA ERS Food Consumption, Nutrient Intakes, and Diet Quality."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> FoodConsumptionNutrientIntakesAndDietQualityQueryParams:
+ """Transform the query params."""
+ return FoodConsumptionNutrientIntakesAndDietQualityQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: FoodConsumptionNutrientIntakesAndDietQualityQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected table's long-format rows."""
+ from openbb_government_us.usda.utils import (
+ ers_food_consumption_nutrient_intakes_and_diet_quality,
+ )
+
+ return (
+ await ers_food_consumption_nutrient_intakes_and_diet_quality.afetch_table(
+ query.table
+ )
+ )
+
+ @staticmethod
+ def transform_data(
+ query: FoodConsumptionNutrientIntakesAndDietQualityQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[FoodConsumptionNutrientIntakesAndDietQualityData]:
+ """Pivot the long-format rows into the selected wide table."""
+ if query.table == SAMPLE_TABLE:
+ rows, column_order = _pivot_sample(query, data)
+ elif query.table == RECOMMENDED_TABLE:
+ rows, column_order = _pivot_recommended(query, data)
+ else:
+ rows, column_order = _pivot_intake(query, data)
+ return _finalize(rows, column_order)
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/food_dollar_series.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/food_dollar_series.py
new file mode 100644
index 00000000000..8557c0a6ae7
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/food_dollar_series.py
@@ -0,0 +1,287 @@
+"""Food Dollar Series Model."""
+
+from typing import Any, Literal
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.app.service.system_service import SystemService
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from openbb_core.provider.utils.errors import EmptyDataError
+from pydantic import ConfigDict, Field, field_validator, model_validator
+
+from openbb_government_us.usda.utils.ers_food_dollar_series import (
+ COMPONENT_COLUMNS,
+ FOOD_DOLLAR_FILES,
+ SERIES_COMPONENTS,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+api_prefix = SystemService().system_settings.api_settings.prefix
+
+Series = Literal["nominal", "real"]
+Units = Literal["share", "level"]
+
+
+class FoodDollarSeriesQueryParams(QueryParams):
+ """Food Dollar Series Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/food-dollar-series
+ """
+
+ __json_schema_extra__ = {
+ "series": {
+ "x-widget_config": {
+ "label": "Series",
+ "multiSelect": False,
+ "multiple": False,
+ "value": "nominal",
+ "options": [
+ {"label": "Nominal (current dollars)", "value": "nominal"},
+ {"label": "Real (2017 dollars)", "value": "real"},
+ ],
+ },
+ },
+ "units": {
+ "x-widget_config": {
+ "label": "Units",
+ "multiSelect": False,
+ "multiple": False,
+ "value": "share",
+ "options": [
+ {"label": "Share (cents per food dollar)", "value": "share"},
+ {"label": "Level (million dollars)", "value": "level"},
+ ],
+ },
+ },
+ "component": {
+ "x-widget_config": {
+ "label": "Primary factor",
+ "type": "endpoint",
+ "multiSelect": False,
+ "multiple": False,
+ "value": "total",
+ "optionsEndpoint": f"{api_prefix}/usda/food_dollar_components",
+ "optionsParams": {"series": "$series"},
+ "style": {"popupWidth": 280},
+ },
+ },
+ "table": {
+ "x-widget_config": {
+ "label": "Table",
+ "type": "endpoint",
+ "multiSelect": False,
+ "multiple": False,
+ "value": 1,
+ "optionsEndpoint": f"{api_prefix}/usda/food_dollar_tables",
+ "optionsParams": {"series": "$series"},
+ "style": {"popupWidth": 420},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ series: Series = Field(
+ default="nominal",
+ description="Series to retrieve, selecting the source dataset. 'nominal'"
+ + " is current-year dollars with the full primary-factor decomposition"
+ + " and all 22 tables; 'real' is inflation-adjusted 2017 dollars with"
+ + " only the six food-dollar aggregate tables.",
+ )
+ units: Units = Field(
+ default="share",
+ description="Units of the cell values. 'share' is cents per domestic"
+ + " food dollar; 'level' is millions of dollars. Orthogonal to series.",
+ )
+ component: str = Field(
+ default="total",
+ description="Primary-factor component that fills the cells. Valid"
+ + " components depend on series; for nominal:\n "
+ + ", ".join(SERIES_COMPONENTS["nominal"])
+ + "\nfor real:\n "
+ + ", ".join(SERIES_COMPONENTS["real"])
+ + "\n'total' is populated for every year and category.",
+ )
+ table: int = Field(
+ default=1,
+ description="Food-dollar table to retrieve, by table number. Nominal"
+ + " offers tables 1-22 (six dollar aggregates and 16 commodity groups);"
+ + " real offers only tables 1-6.",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data."
+ + " If None, returns from the series' first year.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data."
+ + " If None, returns up to the most recent year.",
+ )
+
+ @field_validator("series", mode="before", check_fields=False)
+ @classmethod
+ def _validate_series(cls, v):
+ """Validate series."""
+ if not v:
+ return "nominal"
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ return str(value).strip().lower()
+
+ @field_validator("units", mode="before", check_fields=False)
+ @classmethod
+ def _validate_units(cls, v):
+ """Validate units."""
+ if not v:
+ return "share"
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ return str(value).strip().lower()
+
+ @field_validator("component", mode="before", check_fields=False)
+ @classmethod
+ def _validate_component(cls, v):
+ """Validate component."""
+ if not v:
+ return "total"
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ return str(value).strip().lower()
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate table."""
+ if v is None or v == "":
+ return 1
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ try:
+ return int(str(value).strip())
+ except ValueError as error:
+ raise OpenBBError(
+ f"Invalid table: {value}. Expected a table number."
+ ) from (error)
+
+ @model_validator(mode="after")
+ def _validate_series_scope(self):
+ """Validate that component and table belong to the selected series."""
+ components = SERIES_COMPONENTS[self.series]
+ if self.component not in components:
+ raise OpenBBError(
+ f"Invalid component '{self.component}' for series '{self.series}'."
+ " Valid components are: " + ", ".join(components)
+ )
+ tables = FOOD_DOLLAR_FILES[self.series]["tables"]
+ if self.table not in tables:
+ raise OpenBBError(
+ f"Invalid table {self.table} for series '{self.series}'."
+ f" Valid tables are {tables[0]}-{tables[-1]}."
+ )
+ return self
+
+
+class FoodDollarSeriesData(NullTokenMixin, Data):
+ """Food Dollar Series Data.
+
+ One selected food-dollar table pivoted to a wide layout: the marketing-bill
+ industry groups stay in the rows while the years spread into value columns,
+ each cell holding the chosen primary-factor component in the chosen units.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Food Dollar Series",
+ "$.description": "The marketing-bill breakdown of the U.S. food"
+ " dollar into the industry groups and primary factors that supply"
+ " it, in nominal and real terms, published by the USDA Economic"
+ " Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ industry_group: str = Field(
+ description="Marketing-bill industry group or aggregate, as published,"
+ + " e.g. 'Farm production', 'Foodservices', or 'Farm share'.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Industry group", "pinned": "left"}
+ },
+ )
+
+
+class FoodDollarSeriesFetcher(
+ Fetcher[
+ FoodDollarSeriesQueryParams,
+ list[FoodDollarSeriesData],
+ ]
+):
+ """Fetch USDA ERS Food Dollar Series."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(params: dict[str, Any]) -> FoodDollarSeriesQueryParams:
+ """Transform the query params."""
+ return FoodDollarSeriesQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: FoodDollarSeriesQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected series' tidy CSV rows."""
+ from openbb_government_us.usda.utils import ers_food_dollar_series
+
+ return await ers_food_dollar_series.afetch_series(query.series)
+
+ @staticmethod
+ def transform_data(
+ query: FoodDollarSeriesQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[FoodDollarSeriesData]:
+ """Pivot the selected table's industry groups against the years."""
+ entry = FOOD_DOLLAR_FILES[query.series]
+ units_label = entry["units"][query.units]
+ component_column = COMPONENT_COLUMNS[query.component]
+ pivoted: dict[int, dict] = {}
+ for record in data:
+ if record["table_num"] != query.table:
+ continue
+ if record["units"] != units_label:
+ continue
+ year = record["year"]
+ if query.start_year is not None and year < query.start_year:
+ continue
+ if query.end_year is not None and year > query.end_year:
+ continue
+ row = pivoted.get(record["category_num"])
+ if row is None:
+ row = {
+ "_category_num": record["category_num"],
+ "industry_group": record["industry_group"],
+ }
+ pivoted[record["category_num"]] = row
+ row[str(year)] = record[component_column]
+ results: list[dict] = []
+ for row in sorted(pivoted.values(), key=lambda item: item["_category_num"]):
+ year_keys = [key for key in row if key.isdigit()]
+ if not any(row[key] is not None for key in year_keys):
+ continue
+ results.append(row)
+ if not results:
+ raise EmptyDataError("No records match the given filters.")
+ validated: list[FoodDollarSeriesData] = []
+ for row in results:
+ dimensions = {"industry_group": row["industry_group"]}
+ years = {
+ key: row[key]
+ for key in sorted((k for k in row if k.isdigit()), key=int)
+ }
+ validated.append(
+ FoodDollarSeriesData.model_validate({**dimensions, **years})
+ )
+ return validated
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/food_expenditure_series.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/food_expenditure_series.py
new file mode 100644
index 00000000000..ea74c614ff6
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/food_expenditure_series.py
@@ -0,0 +1,281 @@
+"""USDA ERS Food Expenditure Series Model."""
+
+from typing import Any, Literal
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_food_expenditure_series import (
+ FOOD_EXPENDITURE_FILES,
+ MONTH_ORDER,
+ STATES,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin, is_null_token
+
+DEFAULT_TABLE = "food_and_alcohol"
+
+Measure = Literal["nominal", "constant"]
+Taxes = Literal["with", "without"]
+
+
+class FoodExpenditureSeriesQueryParams(QueryParams):
+ """USDA ERS Food Expenditure Series Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/food-expenditure-series
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "x-widget_config": {
+ "label": "Table",
+ "value": DEFAULT_TABLE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": config["label"], "value": key}
+ for key, config in FOOD_EXPENDITURE_FILES.items()
+ ],
+ "style": {"popupWidth": 360},
+ },
+ },
+ "measure": {
+ "x-widget_config": {
+ "label": "Measure",
+ "value": "nominal",
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": "Nominal dollars", "value": "nominal"},
+ {"label": "Constant dollars (2025=100)", "value": "constant"},
+ ],
+ },
+ },
+ "taxes": {
+ "x-widget_config": {
+ "label": "Taxes and tips",
+ "value": "with",
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": "With taxes and tips", "value": "with"},
+ {"label": "Without taxes and tips", "value": "without"},
+ ],
+ },
+ },
+ "state": {
+ "x-widget_config": {
+ "label": "State",
+ "multiSelect": False,
+ "multiple": False,
+ "options": [{"label": state, "value": state} for state in STATES],
+ "style": {"popupWidth": 260},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ table: str = Field(
+ default=DEFAULT_TABLE,
+ description="Food Expenditure Series table to retrieve. Each table's"
+ + " category series become the wide value columns while the year, folded"
+ + " with the month or state where either varies, forms the pinned period"
+ + " label. Valid tables are:\n "
+ + ", ".join(FOOD_EXPENDITURE_FILES)
+ + "\n",
+ )
+ measure: Measure = Field(
+ default="nominal",
+ description="Dollar measure. 'nominal' returns current-dollar values;"
+ + " 'constant' returns chained 2025-dollar values. Selects the source"
+ + " file for the food_and_alcohol, state, and state_per_capita tables"
+ + " and the value columns for the others. Ignored by the percentage"
+ + " share columns of the normalized table.",
+ )
+ taxes: Taxes = Field(
+ default="with",
+ description="Sales-tax and tip treatment. 'with' includes taxes and"
+ + " tips; 'without' excludes them. Selects the source file for the"
+ + " food_and_alcohol, state, and state_per_capita tables; the other"
+ + " tables publish a single treatment and ignore this.",
+ )
+ state: str | None = Field(
+ default=None,
+ description="Filter the state and state_per_capita tables to states"
+ + " matching this case-insensitive substring, e.g. 'california'."
+ + " If None, every state is returned. Ignored by the other tables.",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data."
+ + " If None, returns from the beginning of the series.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data."
+ + " If None, returns up to the most recent year.",
+ )
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate table."""
+ if not v:
+ return DEFAULT_TABLE
+ table = v.strip() if isinstance(v, str) else v
+ if table not in FOOD_EXPENDITURE_FILES:
+ raise OpenBBError(
+ f"Invalid table: {table}. Valid tables are: "
+ + ", ".join(FOOD_EXPENDITURE_FILES)
+ )
+ return table
+
+
+class FoodExpenditureSeriesData(NullTokenMixin, Data):
+ """USDA ERS Food Expenditure Series Data, pivoted to period rows with the table's category series as value columns."""
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Food Expenditure Series",
+ "$.description": "U.S. food and alcohol expenditures by outlet,"
+ " final purchaser, month, and state, in nominal and constant"
+ " dollars, published by the USDA Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ period: str = Field(
+ description="Row label of the observation: the year for annual tables"
+ + " ('2020'), the year and month for monthly tables ('2020 March'), with"
+ + " the state prefixed when more than one state is shown at once"
+ + " ('California — 2020'), so each row is uniquely labeled.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Period",
+ "pinned": "left",
+ "maxWidth": 280,
+ }
+ },
+ )
+
+
+class FoodExpenditureSeriesFetcher(
+ Fetcher[
+ FoodExpenditureSeriesQueryParams,
+ list[FoodExpenditureSeriesData],
+ ]
+):
+ """Fetch USDA ERS Food Expenditure Series Data."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(params: dict[str, Any]) -> FoodExpenditureSeriesQueryParams:
+ """Transform the query params."""
+ return FoodExpenditureSeriesQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: FoodExpenditureSeriesQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected table's long-format rows."""
+ from openbb_government_us.usda.utils import ers_food_expenditure_series
+
+ return await ers_food_expenditure_series.afetch_table(
+ query.table, query.measure, query.taxes
+ )
+
+ @staticmethod
+ def _period_label(year: int, month: str | None, dim_values: list[str]) -> str:
+ """Fold the varying dimensions, the year, and the month into one label."""
+ time_label = f"{year} {month}" if month else str(year)
+ return " — ".join([*dim_values, time_label])
+
+ @staticmethod
+ def transform_data(
+ query: FoodExpenditureSeriesQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[FoodExpenditureSeriesData]:
+ """Filter, then pivot the long-format rows into period rows."""
+ state_filter = query.state.strip().casefold() if query.state else None
+ filtered: list[dict] = []
+ for record in data:
+ measure = record["measure"]
+ if measure is not None and measure != query.measure:
+ continue
+ year = record["year"]
+ if query.start_year is not None and year < query.start_year:
+ continue
+ if query.end_year is not None and year > query.end_year:
+ continue
+ state = record["state"]
+ if (
+ state_filter is not None
+ and state is not None
+ and state_filter not in state.casefold()
+ ):
+ continue
+ filtered.append(record)
+ state_varies = (
+ len(
+ {
+ record["state"]
+ for record in filtered
+ if record["state"] and not is_null_token(record["state"])
+ }
+ )
+ > 1
+ )
+ units = {record["unit"] for record in filtered}
+ single_unit = len(units) == 1 and "" not in units
+ unit_suffix = f" ({next(iter(units))})" if single_unit else ""
+ pivoted: dict[tuple, dict] = {}
+ col_order: list[str] = []
+ seen_columns: set[str] = set()
+ for record in filtered:
+ year = record["year"]
+ month = record["month"]
+ state = record["state"]
+ key = (state or "", year, month)
+ row = pivoted.get(key)
+ if row is None:
+ dim_values = (
+ [state]
+ if state_varies and state and not is_null_token(state)
+ else []
+ )
+ row = {
+ "_state": state or "",
+ "_year": year,
+ "_month_order": MONTH_ORDER.get(month or "", 0),
+ "period": FoodExpenditureSeriesFetcher._period_label(
+ year, month, dim_values
+ ),
+ }
+ pivoted[key] = row
+ column = f"{record['category']}{unit_suffix}"
+ if column not in seen_columns:
+ seen_columns.add(column)
+ col_order.append(column)
+ row[column] = record["value"]
+ results = sorted(
+ pivoted.values(),
+ key=lambda row: (row["_state"], row["_year"], row["_month_order"]),
+ )
+ validated: list[FoodExpenditureSeriesData] = []
+ for row in results:
+ payload: dict[str, Any] = {"period": row["period"]}
+ for column in col_order:
+ payload[column] = row.get(column)
+ validated.append(FoodExpenditureSeriesData.model_validate(payload))
+ return validated
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/food_price_outlook.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/food_price_outlook.py
new file mode 100644
index 00000000000..3037467e889
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/food_price_outlook.py
@@ -0,0 +1,342 @@
+"""USDA ERS Food Price Outlook Model."""
+
+from datetime import date
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.app.service.system_service import SystemService
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_food_price_outlook import (
+ FOOD_PRICE_OUTLOOK_FILES,
+ HIERARCHY_FIELDS,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+api_prefix = SystemService().system_settings.api_settings.prefix
+
+DEFAULT_TABLE = "cpi_forecast"
+
+TABLE_LABELS: dict[str, str] = {
+ "cpi_forecast": "CPI forecast - changes in consumer price indexes",
+ "ppi_forecast": "PPI forecast - changes in producer price indexes",
+ "cpi_annual": "CPI annual - percent changes since 1974",
+ "ppi_annual": "PPI annual - percent changes since 1974",
+ "cpi_forecast_history": "CPI forecast history - monthly vintages",
+ "ppi_forecast_history": "PPI forecast history - monthly vintages",
+}
+
+
+class FoodPriceOutlookQueryParams(QueryParams):
+ """USDA ERS Food Price Outlook Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/food-price-outlook
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "x-widget_config": {
+ "label": "Table",
+ "value": DEFAULT_TABLE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": TABLE_LABELS[key], "value": key}
+ for key in FOOD_PRICE_OUTLOOK_FILES
+ ],
+ "style": {"popupWidth": 480},
+ },
+ },
+ "item": {
+ "x-widget_config": {
+ "label": "Item",
+ "type": "endpoint",
+ "multiSelect": False,
+ "multiple": False,
+ "optionsEndpoint": f"{api_prefix}/usda/food_price_outlook_items",
+ "optionsParams": {"table": "$table"},
+ "style": {"popupWidth": 360},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ table: str = Field(
+ default=DEFAULT_TABLE,
+ description="Food Price Outlook table to retrieve. The forecast tables"
+ + " spread each price-index measure into a column, the annual tables"
+ + " spread each food category into a column with years as rows, and the"
+ + " forecast-history tables spread the prediction-interval bounds into"
+ + " columns. Valid tables are:\n "
+ + ", ".join(FOOD_PRICE_OUTLOOK_FILES)
+ + "\n",
+ )
+ item: str | None = Field(
+ default=None,
+ description="Filter by a single exact price-index item as published, e.g."
+ + " 'Beef and veal', scoped to the selected table. Filters rows for the"
+ + " forecast and forecast-history tables and value columns for the annual"
+ + " tables. If None, all items are included.",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data, applied to the annual"
+ + " calendar year and the forecast-history year being forecast. Forecast"
+ + " measures with no year, such as month-over-month changes, are always"
+ + " kept. If None, returns from the beginning of the series.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data, applied to the annual"
+ + " calendar year and the forecast-history year being forecast. Forecast"
+ + " measures with no year, such as month-over-month changes, are always"
+ + " kept. If None, returns up to the most recent year.",
+ )
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate table."""
+ if not v:
+ return DEFAULT_TABLE
+ table = v.strip() if isinstance(v, str) else v
+ if table not in FOOD_PRICE_OUTLOOK_FILES:
+ raise OpenBBError(
+ f"Invalid table: {table}. Valid tables are: "
+ + ", ".join(FOOD_PRICE_OUTLOOK_FILES)
+ )
+ return table
+
+ @field_validator("item", mode="before", check_fields=False)
+ @classmethod
+ def _validate_item(cls, v):
+ """Normalize the item filter to a single stripped name or None."""
+ if not v:
+ return None
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ return str(value).strip() or None
+
+
+class FoodPriceOutlookData(NullTokenMixin, Data):
+ """USDA ERS Food Price Outlook Data.
+
+ One selected Food Price Outlook table pivoted to a wide layout. The
+ forecast tables spread each measure into a column with one row per item,
+ the annual tables spread each food category into a column with one row per
+ year, and the forecast-history tables spread the prediction-interval bounds
+ into columns with one row per item, year, and monthly vintage.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Food Price Outlook",
+ "$.description": "USDA Economic Research Service forecasts and"
+ " history of consumer and producer food price index changes,"
+ " with prediction intervals and the annual record since 1974.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ item: str | None = Field(
+ default=None,
+ description="Row label: the price-index item on the forecast and"
+ + " forecast-history tables, e.g. 'All food' or 'Wholesale beef', or the"
+ + " calendar year on the annual tables, where each food category is a"
+ + " value column instead.",
+ json_schema_extra={"x-widget_config": {"headerName": "Item", "pinned": "left"}},
+ )
+ top_level: str | None = Field(
+ default=None,
+ description="Top level of the consumer price index hierarchy, as"
+ + " published. Only for the consumer forecast table.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Top level", "hide": True}
+ },
+ )
+ aggregate: str | None = Field(
+ default=None,
+ description="Aggregate level of the consumer price index hierarchy, as"
+ + " published. Only for the consumer forecast table.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Aggregate", "hide": True}
+ },
+ )
+ mid_level: str | None = Field(
+ default=None,
+ description="Mid level of the consumer price index hierarchy, as"
+ + " published. Only for the consumer forecast table.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Mid level", "hide": True}
+ },
+ )
+ low_level: str | None = Field(
+ default=None,
+ description="Low level of the consumer price index hierarchy, as"
+ + " published. Only for the consumer forecast table.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Low level", "hide": True}
+ },
+ )
+ disaggregate: str | None = Field(
+ default=None,
+ description="Disaggregate level of the consumer price index hierarchy,"
+ + " as published. Only for the consumer forecast table.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Disaggregate", "hide": True}
+ },
+ )
+ year: int | None = Field(
+ default=None,
+ description="Year of the row: the calendar year on the annual tables and"
+ + " the year being forecast on the forecast-history tables. Absent on the"
+ + " forecast tables, whose measures span several reference years.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Year", "maxWidth": 90, "hide": True}
+ },
+ )
+ forecast_date: date | None = Field(
+ default=None,
+ description="Vintage of the forecast, as the first day of the month the"
+ + " forecast was made. Only for the forecast-history tables.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Forecast date", "hide": True}
+ },
+ )
+ unit: str | None = Field(
+ default=None,
+ description="Unit of the value columns, as published, when a single unit"
+ + " applies to the whole row. Absent on the forecast tables, whose"
+ + " measures carry differing units per column.",
+ json_schema_extra={"x-widget_config": {"headerName": "Unit", "hide": True}},
+ )
+
+
+class FoodPriceOutlookFetcher(
+ Fetcher[
+ FoodPriceOutlookQueryParams,
+ list[FoodPriceOutlookData],
+ ]
+):
+ """Fetch USDA ERS Food Price Outlook Data."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(params: dict[str, Any]) -> FoodPriceOutlookQueryParams:
+ """Transform the query params."""
+ return FoodPriceOutlookQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: FoodPriceOutlookQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected table's tidy rows."""
+ from openbb_government_us.usda.utils import ers_food_price_outlook
+
+ return await ers_food_price_outlook.afetch_table(query.table)
+
+ @staticmethod
+ def transform_data(
+ query: FoodPriceOutlookQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[FoodPriceOutlookData]:
+ """Pivot the tidy rows into the wide layout for the selected table's kind."""
+ kind = FOOD_PRICE_OUTLOOK_FILES[query.table]["kind"]
+ selected = query.item.lower() if query.item else None
+
+ def within_years(year: int | None) -> bool:
+ return year is None or (
+ (query.start_year is None or year >= query.start_year)
+ and (query.end_year is None or year <= query.end_year)
+ )
+
+ def item_matches(item: str) -> bool:
+ return selected is None or item.lower() == selected
+
+ pivoted: dict[Any, dict] = {}
+
+ if kind == "annual":
+ for record in data:
+ if not within_years(record["year"]) or not item_matches(record["item"]):
+ continue
+ row = pivoted.get(record["year"])
+ if row is None:
+ row = {
+ "_year": record["year"],
+ "item": str(record["year"]),
+ "unit": record["unit"],
+ }
+ pivoted[record["year"]] = row
+ row[record["item"]] = record["value"]
+ results = sorted(pivoted.values(), key=lambda row: row["_year"])
+ return [
+ FoodPriceOutlookData.model_validate(
+ {k: v for k, v in row.items() if not k.startswith("_")}
+ )
+ for row in results
+ ]
+
+ if kind == "history":
+ for order, record in enumerate(data):
+ if not within_years(record["year"]) or not item_matches(record["item"]):
+ continue
+ key = (record["item"], record["year"], record["forecast_date"])
+ row = pivoted.get(key)
+ if row is None:
+ row = {
+ "_order": order,
+ "item": record["item"],
+ "year": record["year"],
+ "forecast_date": record["forecast_date"],
+ "unit": record["unit"],
+ }
+ pivoted[key] = row
+ row[record["attribute"]] = record["value"]
+ results = sorted(
+ pivoted.values(),
+ key=lambda row: (
+ row["item"],
+ row["year"],
+ row["forecast_date"],
+ row["_order"],
+ ),
+ )
+ return [
+ FoodPriceOutlookData.model_validate(
+ {k: v for k, v in row.items() if not k.startswith("_")}
+ )
+ for row in results
+ ]
+
+ for order, record in enumerate(data):
+ if not within_years(record["year"]) or not item_matches(record["item"]):
+ continue
+ key = (record["item"], *(record.get(field) for field in HIERARCHY_FIELDS))
+ row = pivoted.get(key)
+ if row is None:
+ row = {
+ "_order": order,
+ "item": record["item"],
+ }
+ for field in HIERARCHY_FIELDS:
+ row[field] = record.get(field)
+ pivoted[key] = row
+ row[record["attribute"]] = record["value"]
+ results = sorted(pivoted.values(), key=lambda row: row["_order"])
+ return [
+ FoodPriceOutlookData.model_validate(
+ {k: v for k, v in row.items() if not k.startswith("_")}
+ )
+ for row in results
+ ]
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/food_security_in_the_united_states.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/food_security_in_the_united_states.py
new file mode 100644
index 00000000000..2b7db65bcc7
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/food_security_in_the_united_states.py
@@ -0,0 +1,251 @@
+"""USDA ERS Food Security in the United States Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.app.service.system_service import SystemService
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_food_security_in_the_united_states import (
+ DIM_FIELDS,
+ FOOD_SECURITY_TABLES,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin, is_null_token
+
+api_prefix = SystemService().system_settings.api_settings.prefix
+
+DEFAULT_TABLE = "national_trend"
+
+
+class FoodSecurityInTheUnitedStatesQueryParams(QueryParams):
+ """USDA ERS Food Security in the United States Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/food-security-in-the-united-states
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "x-widget_config": {
+ "label": "Table",
+ "value": DEFAULT_TABLE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": config["label"], "value": key}
+ for key, config in FOOD_SECURITY_TABLES.items()
+ ],
+ "style": {"popupWidth": 360},
+ },
+ },
+ "category": {
+ "x-widget_config": {
+ "label": "Category",
+ "type": "endpoint",
+ "multiSelect": False,
+ "multiple": False,
+ "optionsEndpoint": f"{api_prefix}/usda/food_security_categories",
+ "optionsParams": {"table": "$table"},
+ "style": {"popupWidth": 320},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ table: str = Field(
+ default=DEFAULT_TABLE,
+ description="Table to retrieve. Each table pivots a set of"
+ + " food-security status measures into wide columns, with the survey"
+ + " year or 3-year period in the rows and the characteristic path or"
+ + " State as pinned dimension columns. Valid tables are:\n "
+ + ", ".join(FOOD_SECURITY_TABLES)
+ + "\n",
+ )
+ category: str | None = Field(
+ default=None,
+ description="Filter by the household characteristic category of a"
+ + " table, e.g. 'Race/ethnicity of households'. If None, all"
+ + " categories of the selected table are included. Ignored by the"
+ + " by_state table, which has no category dimension.",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data, compared against the"
+ + " survey year or the first year of a 3-year period. If None, returns"
+ + " from the beginning of the series.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data, compared against the"
+ + " survey year or the first year of a 3-year period. If None, returns"
+ + " up to the most recent year.",
+ )
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate table."""
+ if not v:
+ return DEFAULT_TABLE
+ table = v.strip() if isinstance(v, str) else v
+ if table not in FOOD_SECURITY_TABLES:
+ raise OpenBBError(
+ f"Invalid table: {table}. Valid tables are: "
+ + ", ".join(FOOD_SECURITY_TABLES)
+ )
+ return table
+
+ @field_validator("category", mode="before", check_fields=False)
+ @classmethod
+ def _validate_category(cls, v):
+ """Normalize category to a stripped string or None."""
+ if not v:
+ return None
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ return str(value).strip() or None
+
+
+class FoodSecurityInTheUnitedStatesData(NullTokenMixin, Data):
+ """USDA ERS Food Security in the United States Data.
+
+ One selected table pivoted to a wide layout: the food-security status
+ measures spread into value columns while the survey year or 3-year period
+ stays in the rows, with any varying household characteristic or State
+ folded into the period label so each row is uniquely labeled.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Food Security in the United States",
+ "$.description": "Prevalence of household and child food"
+ " security, food insecurity, and very low food security by"
+ " year, household characteristic, and State, from the USDA"
+ " Economic Research Service and the Current Population Survey"
+ " Food Security Supplement.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ period: str = Field(
+ description="Row label of the observation: the survey year for annual"
+ + " tables ('2008') or the 3-year period for the by_state table"
+ + " ('2006-2008'), with any varying household characteristic or State"
+ + " prefixed so each row is uniquely labeled"
+ + " ('Households with children — 2008', 'CA — 2006-2008').",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Period",
+ "pinned": "left",
+ "maxWidth": 320,
+ }
+ },
+ )
+
+
+class FoodSecurityInTheUnitedStatesFetcher(
+ Fetcher[
+ FoodSecurityInTheUnitedStatesQueryParams,
+ list[FoodSecurityInTheUnitedStatesData],
+ ]
+):
+ """Fetch USDA ERS Food Security in the United States."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> FoodSecurityInTheUnitedStatesQueryParams:
+ """Transform the query params."""
+ return FoodSecurityInTheUnitedStatesQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: FoodSecurityInTheUnitedStatesQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected table's long-format rows."""
+ from openbb_government_us.usda.utils import (
+ ers_food_security_in_the_united_states,
+ )
+
+ return await ers_food_security_in_the_united_states.afetch_table(query.table)
+
+ @staticmethod
+ def transform_data(
+ query: FoodSecurityInTheUnitedStatesQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[FoodSecurityInTheUnitedStatesData]:
+ """Pivot the long-format rows into period rows with series as columns."""
+ filtered: list[tuple[int, dict]] = []
+ for order, record in enumerate(data):
+ year = record["sort_year"]
+ if query.start_year is not None and year < query.start_year:
+ continue
+ if query.end_year is not None and year > query.end_year:
+ continue
+ if (
+ query.category is not None
+ and record["category"] is not None
+ and record["category"] != query.category
+ ):
+ continue
+ filtered.append((order, record))
+ varying = {
+ field: len(
+ {
+ record[field]
+ for _, record in filtered
+ if record[field] is not None and not is_null_token(record[field])
+ }
+ )
+ > 1
+ for field in DIM_FIELDS
+ }
+ pivoted: dict[tuple, dict] = {}
+ combo_order: dict[tuple, int] = {}
+ col_order: list[str] = []
+ for order, record in filtered:
+ combo = tuple(record[field] for field in DIM_FIELDS)
+ key = (record["year"], *combo)
+ row = pivoted.get(key)
+ if row is None:
+ combo_order.setdefault(combo, len(combo_order))
+ dim_values = [
+ record[field]
+ for field in DIM_FIELDS
+ if varying[field]
+ and record[field] is not None
+ and not is_null_token(record[field])
+ ]
+ row = {
+ "_combo": combo_order[combo],
+ "_sort_year": record["sort_year"],
+ "_order": order,
+ "period": " — ".join([*dim_values, record["year"]]),
+ }
+ pivoted[key] = row
+ column = record["series"]
+ if column not in col_order:
+ col_order.append(column)
+ row[column] = record["value"]
+ results = sorted(
+ pivoted.values(),
+ key=lambda row: (row["_combo"], row["_sort_year"], row["_order"]),
+ )
+ validated: list[FoodSecurityInTheUnitedStatesData] = []
+ for row in results:
+ payload: dict[str, Any] = {"period": row["period"]}
+ for column in col_order:
+ payload[column] = row.get(column)
+ validated.append(FoodSecurityInTheUnitedStatesData.model_validate(payload))
+ return validated
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/frontier_and_remote_area_codes.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/frontier_and_remote_area_codes.py
new file mode 100644
index 00000000000..1862577d637
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/frontier_and_remote_area_codes.py
@@ -0,0 +1,361 @@
+"""USDA ERS Frontier and Remote Area Codes (ZIP) Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.app.service.system_service import SystemService
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_frontier_and_remote_area_codes import (
+ ALL_STATES,
+ DEFAULT_STATE,
+ DEFAULT_YEAR,
+ FAR_ZIP_FILES,
+ year_options,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+api_prefix = SystemService().system_settings.api_settings.prefix
+
+
+class FrontierAndRemoteAreaCodesQueryParams(QueryParams):
+ """USDA ERS Frontier and Remote Area Codes Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/frontier-and-remote-area-codes
+ """
+
+ __json_schema_extra__ = {
+ "year": {
+ "x-widget_config": {
+ "label": "Vintage",
+ "value": DEFAULT_YEAR,
+ "multiSelect": False,
+ "multiple": False,
+ "options": year_options(),
+ },
+ },
+ "state": {
+ "x-widget_config": {
+ "label": "State",
+ "type": "endpoint",
+ "multiSelect": False,
+ "multiple": False,
+ "value": DEFAULT_STATE,
+ "optionsEndpoint": f"{api_prefix}/usda/far_zip_states",
+ "optionsParams": {"year": "$year"},
+ },
+ },
+ }
+
+ year: str = Field(
+ default=DEFAULT_YEAR,
+ description="Decennial classification vintage to retrieve, selecting the"
+ + " source workbook. Valid vintages are:\n "
+ + ", ".join(FAR_ZIP_FILES)
+ + "\n",
+ )
+ state: str = Field(
+ default=DEFAULT_STATE,
+ description="State to retrieve, as a two-letter postal abbreviation. The"
+ + " ZIP-code lookup carries tens of thousands of rows, so the state is"
+ + " the primary scope filter. The 2000 vintage covers the contiguous"
+ + " States and excludes Alaska and Hawaii. If None, defaults to Montana.",
+ )
+
+ @field_validator("year", mode="before", check_fields=False)
+ @classmethod
+ def _validate_year(cls, v):
+ """Validate the classification vintage."""
+ if not v:
+ return DEFAULT_YEAR
+ year = str(v).strip()
+ if year not in FAR_ZIP_FILES:
+ raise OpenBBError(
+ f"Invalid vintage: {year}. Valid vintages are: "
+ + ", ".join(FAR_ZIP_FILES)
+ )
+ return year
+
+ @field_validator("state", mode="before", check_fields=False)
+ @classmethod
+ def _validate_state(cls, v):
+ """Validate and canonicalize the state filter."""
+ if not v:
+ return DEFAULT_STATE
+ state = str(v).strip().upper()
+ if state not in ALL_STATES:
+ raise OpenBBError(
+ f"Invalid state: {state}. Valid states are: "
+ + ", ".join(sorted(ALL_STATES))
+ )
+ return state
+
+
+class FrontierAndRemoteAreaCodesData(NullTokenMixin, Data):
+ """USDA ERS Frontier and Remote Area Codes Data.
+
+ One row per ZIP-code area for the selected vintage, a geographic
+ classification lookup carrying the four Frontier and Remote (FAR) level
+ flags with the area's grid population, land area, and population density,
+ plus the population and population share classified at each FAR level.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Frontier and Remote Area Codes (ZIP)",
+ "$.description": "Frontier and Remote (FAR) area classification"
+ " for U.S. ZIP Code areas at four FAR levels, with grid"
+ " population, land area, and population density, published by"
+ " the USDA Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ zip_code: str = Field(
+ description="ZIP Code of the area.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "ZIP Code",
+ "cellDataType": "text",
+ "pinned": "left",
+ "maxWidth": 110,
+ }
+ },
+ )
+ state: str = Field(
+ description="Two-letter postal abbreviation of the area's State.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "State",
+ "cellDataType": "text",
+ "pinned": "left",
+ "maxWidth": 90,
+ }
+ },
+ )
+ po_name: str | None = Field(
+ default=None,
+ description="Post office name of the ZIP-code area, as published. The"
+ + " 2000 vintage carries no area name.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Area Name",
+ "cellDataType": "text",
+ "pinned": "left",
+ "hide": True,
+ }
+ },
+ )
+ far_level_1: int | None = Field(
+ default=None,
+ description="FAR level 1 flag: 1 when the area is frontier and remote at"
+ + " level 1, else 0.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "FAR Level 1",
+ "cellDataType": "text",
+ "maxWidth": 110,
+ }
+ },
+ )
+ far_level_2: int | None = Field(
+ default=None,
+ description="FAR level 2 flag: 1 when the area is frontier and remote at"
+ + " level 2, else 0.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "FAR Level 2",
+ "cellDataType": "text",
+ "maxWidth": 110,
+ }
+ },
+ )
+ far_level_3: int | None = Field(
+ default=None,
+ description="FAR level 3 flag: 1 when the area is frontier and remote at"
+ + " level 3, else 0.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "FAR Level 3",
+ "cellDataType": "text",
+ "maxWidth": 110,
+ }
+ },
+ )
+ far_level_4: int | None = Field(
+ default=None,
+ description="FAR level 4 flag: 1 when the area is frontier and remote at"
+ + " level 4, else 0.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "FAR Level 4",
+ "cellDataType": "text",
+ "maxWidth": 110,
+ }
+ },
+ )
+ grid_population: float | None = Field(
+ default=None,
+ description="Population of the area's populated grid cells.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Grid Population",
+ "cellDataType": "number",
+ }
+ },
+ )
+ land_area_sq_mi: float | None = Field(
+ default=None,
+ description="Land area of the area, in square miles.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Land Area (sq mi)",
+ "cellDataType": "number",
+ }
+ },
+ )
+ population_density: float | None = Field(
+ default=None,
+ description="Population density of the area, in persons per square mile.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Population Density",
+ "cellDataType": "number",
+ }
+ },
+ )
+ far_level_1_population: float | None = Field(
+ default=None,
+ description="Population of the area classified as FAR level 1.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "FAR Level 1 Population",
+ "cellDataType": "number",
+ }
+ },
+ )
+ far_level_2_population: float | None = Field(
+ default=None,
+ description="Population of the area classified as FAR level 2.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "FAR Level 2 Population",
+ "cellDataType": "number",
+ }
+ },
+ )
+ far_level_3_population: float | None = Field(
+ default=None,
+ description="Population of the area classified as FAR level 3.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "FAR Level 3 Population",
+ "cellDataType": "number",
+ }
+ },
+ )
+ far_level_4_population: float | None = Field(
+ default=None,
+ description="Population of the area classified as FAR level 4.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "FAR Level 4 Population",
+ "cellDataType": "number",
+ }
+ },
+ )
+ far_level_1_population_pct: float | None = Field(
+ default=None,
+ description="Share of the area's grid population classified as FAR"
+ + " level 1, in percent.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "FAR Level 1 Population %",
+ "cellDataType": "number",
+ }
+ },
+ )
+ far_level_2_population_pct: float | None = Field(
+ default=None,
+ description="Share of the area's grid population classified as FAR"
+ + " level 2, in percent.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "FAR Level 2 Population %",
+ "cellDataType": "number",
+ }
+ },
+ )
+ far_level_3_population_pct: float | None = Field(
+ default=None,
+ description="Share of the area's grid population classified as FAR"
+ + " level 3, in percent.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "FAR Level 3 Population %",
+ "cellDataType": "number",
+ }
+ },
+ )
+ far_level_4_population_pct: float | None = Field(
+ default=None,
+ description="Share of the area's grid population classified as FAR"
+ + " level 4, in percent.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "FAR Level 4 Population %",
+ "cellDataType": "number",
+ }
+ },
+ )
+
+
+class FrontierAndRemoteAreaCodesFetcher(
+ Fetcher[
+ FrontierAndRemoteAreaCodesQueryParams,
+ list[FrontierAndRemoteAreaCodesData],
+ ]
+):
+ """Fetch USDA ERS Frontier and Remote Area Codes for ZIP-code areas."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> FrontierAndRemoteAreaCodesQueryParams:
+ """Transform the query params."""
+ return FrontierAndRemoteAreaCodesQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: FrontierAndRemoteAreaCodesQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected vintage's ZIP-code lookup rows."""
+ from openbb_government_us.usda.utils import (
+ ers_frontier_and_remote_area_codes as ers,
+ )
+
+ return await ers.afetch_far_zip(query.year)
+
+ @staticmethod
+ def transform_data(
+ query: FrontierAndRemoteAreaCodesQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[FrontierAndRemoteAreaCodesData]:
+ """Scope the lookup to the selected state and sort by ZIP code."""
+ rows = [record for record in data if record.get("state") == query.state]
+ rows.sort(key=lambda record: record.get("zip_code") or "")
+ return [
+ FrontierAndRemoteAreaCodesData.model_validate(record) for record in rows
+ ]
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/fruit_and_tree_nuts_data.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/fruit_and_tree_nuts_data.py
new file mode 100644
index 00000000000..b3218f2c3b4
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/fruit_and_tree_nuts_data.py
@@ -0,0 +1,316 @@
+"""USDA ERS Fruit and Tree Nuts Data Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.app.service.system_service import SystemService
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_fruit_and_tree_nuts_data import (
+ CATEGORY_LABELS,
+ MONTH_NUMBERS,
+ TABLE_TITLES,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+api_prefix = SystemService().system_settings.api_settings.prefix
+
+DEFAULT_CATEGORY = "A"
+DEFAULT_TABLE = "A-1"
+SERIES_DIMS = ("variable", "market_segment", "geography")
+
+
+def column_label(
+ record: dict, series_dims: list[str], commodity_is_column: bool
+) -> str:
+ """Build the wide-column header for one long-format record.
+
+ Parameters
+ ----------
+ record : dict
+ A long-format record from the parser.
+ series_dims : list[str]
+ The measure dimensions that vary within the table.
+ commodity_is_column : bool
+ Whether the commodity dimension is the sole varying dimension and so
+ becomes the column axis.
+
+ Returns
+ -------
+ str
+ The header text, a comma-joined set of the varying dimension values
+ with the unit in parentheses.
+ """
+ if commodity_is_column:
+ head = record["commodity"]
+ else:
+ parts = [record[dim] for dim in SERIES_DIMS if dim in series_dims]
+ head = ", ".join(parts) if parts else record["variable"]
+ return f"{head} ({record['unit']})"
+
+
+class FruitAndTreeNutsDataQueryParams(QueryParams):
+ """USDA ERS Fruit and Tree Nuts Data Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/fruit-and-tree-nuts-data
+ """
+
+ __json_schema_extra__ = {
+ "category": {
+ "x-widget_config": {
+ "label": "Category",
+ "value": DEFAULT_CATEGORY,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": f"{letter} - {label}", "value": letter}
+ for letter, label in CATEGORY_LABELS.items()
+ ],
+ "style": {"popupWidth": 420},
+ },
+ },
+ "table": {
+ "x-widget_config": {
+ "label": "Table",
+ "value": DEFAULT_TABLE,
+ "type": "endpoint",
+ "multiSelect": False,
+ "multiple": False,
+ "optionsEndpoint": f"{api_prefix}/usda/fruit_yearbook_tables",
+ "optionsParams": {"category": "$category"},
+ "style": {"popupWidth": 640},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ category: str | None = Field(
+ default=DEFAULT_CATEGORY,
+ description="Yearbook table category, used to narrow the table list."
+ + " Valid categories are:\n "
+ + ", ".join(f"{letter} ({label})" for letter, label in CATEGORY_LABELS.items())
+ + "\n",
+ )
+ table: str = Field(
+ default=DEFAULT_TABLE,
+ description="Yearbook table to retrieve, by its short code. Each of the"
+ + " 152 tables shares one long schema and is pivoted to a wide layout"
+ + " where the measure dimensions become value columns, with the unit"
+ + " in each column header. Valid tables are:\n "
+ + ", ".join(TABLE_TITLES)
+ + "\n",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data, compared against the"
+ + " first four digits of the calendar or marketing year label."
+ + " If None, returns from the beginning of the series.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data, compared against the"
+ + " first four digits of the calendar or marketing year label."
+ + " If None, returns up to the most recent year.",
+ )
+
+ @field_validator("category", mode="before", check_fields=False)
+ @classmethod
+ def _validate_category(cls, v):
+ """Validate category."""
+ if not v:
+ return None
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip().upper()
+ if value not in CATEGORY_LABELS:
+ raise OpenBBError(
+ f"Invalid category: {value}. Valid categories are: "
+ + ", ".join(CATEGORY_LABELS)
+ )
+ return value
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate table."""
+ if not v:
+ return DEFAULT_TABLE
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip().upper()
+ if value not in TABLE_TITLES:
+ raise OpenBBError(
+ f"Invalid table: {value}. Valid tables are: " + ", ".join(TABLE_TITLES)
+ )
+ return value
+
+
+class FruitAndTreeNutsData(NullTokenMixin, Data):
+ """USDA ERS Fruit and Tree Nuts Data.
+
+ One selected Fruit and Tree Nuts Yearbook table pivoted to a wide layout.
+ Time stays in the rows while the table's measure dimensions spread into
+ value columns, whose set varies by table.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Fruit and Tree Nuts Data",
+ "$.description": "U.S. fruit, tree nut, and melon area, production,"
+ " price, value, supply, per-capita availability, and import-share"
+ " tables from the Fruit and Tree Nuts Yearbook, published by the"
+ " USDA Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ year: str = Field(
+ description="Period label of the observation, as published: a calendar"
+ + " year such as '1980', a marketing year such as '1980/81', or, on the"
+ + " tables that break a measure out by commodity down the rows, the"
+ + " commodity prefixed to that period.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Period",
+ "pinned": "left",
+ "maxWidth": 260,
+ }
+ },
+ )
+ commodity: str | None = Field(
+ default=None,
+ description="Commodity of the observation, as published. Present only"
+ + " when the table breaks a measure out by commodity down the rows;"
+ + " otherwise the commodities are the value columns.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Commodity",
+ "pinned": "left",
+ "hide": True,
+ }
+ },
+ )
+ month: str | None = Field(
+ default=None,
+ description="Month of the observation, for the monthly price tables."
+ + " Absent on the annual tables.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Month",
+ "pinned": "left",
+ "maxWidth": 120,
+ "hide": True,
+ }
+ },
+ )
+
+
+class FruitAndTreeNutsDataFetcher(
+ Fetcher[
+ FruitAndTreeNutsDataQueryParams,
+ list[FruitAndTreeNutsData],
+ ]
+):
+ """Fetch USDA ERS Fruit and Tree Nuts Data."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(params: dict[str, Any]) -> FruitAndTreeNutsDataQueryParams:
+ """Transform the query params."""
+ return FruitAndTreeNutsDataQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: FruitAndTreeNutsDataQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected table's long-format rows."""
+ from openbb_government_us.usda.utils import ers_fruit_and_tree_nuts_data
+
+ return await ers_fruit_and_tree_nuts_data.afetch_table(query.table)
+
+ @staticmethod
+ def transform_data(
+ query: FruitAndTreeNutsDataQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[FruitAndTreeNutsData]:
+ """Pivot the long-format rows into the table's adaptive wide layout."""
+ distinct: dict[str, set] = {
+ dim: {record[dim] for record in data} for dim in (*SERIES_DIMS, "commodity")
+ }
+ series_dims = [dim for dim in SERIES_DIMS if len(distinct[dim]) > 1]
+ commodity_varies = len(distinct["commodity"]) > 1
+ commodity_is_column = commodity_varies and not series_dims
+ commodity_is_row = commodity_varies and bool(series_dims)
+ monthly = any(record["month"] for record in data)
+
+ column_order: list[str] = []
+ seen_columns: set[str] = set()
+ commodity_index: dict[str, int] = {}
+ for record in data:
+ commodity_index.setdefault(record["commodity"], len(commodity_index))
+ label = column_label(record, series_dims, commodity_is_column)
+ if label not in seen_columns:
+ seen_columns.add(label)
+ column_order.append(label)
+
+ pivoted: dict[tuple, dict] = {}
+ for order, record in enumerate(data):
+ year = record["year"]
+ if query.start_year is not None and year < query.start_year:
+ continue
+ if query.end_year is not None and year > query.end_year:
+ continue
+ key = (
+ record["year_label"],
+ record["month"] if monthly else None,
+ record["commodity"] if commodity_is_row else None,
+ )
+ row = pivoted.get(key)
+ if row is None:
+ row = {
+ "_commodity_index": (
+ commodity_index[record["commodity"]] if commodity_is_row else 0
+ ),
+ "_year": year,
+ "_month": MONTH_NUMBERS.get((record["month"] or "").casefold(), 0),
+ "_order": order,
+ "year": (
+ f"{record['commodity']} — {record['year_label']}"
+ if commodity_is_row
+ else record["year_label"]
+ ),
+ "commodity": (record["commodity"] if commodity_is_row else None),
+ "month": record["month"] if monthly else None,
+ }
+ for label in column_order:
+ row[label] = None
+ pivoted[key] = row
+ row[column_label(record, series_dims, commodity_is_column)] = record[
+ "value"
+ ]
+
+ results = sorted(
+ pivoted.values(),
+ key=lambda row: (
+ row["_commodity_index"],
+ row["_year"],
+ row["_month"],
+ row["_order"],
+ ),
+ )
+ return [
+ FruitAndTreeNutsData.model_validate(
+ {k: v for k, v in row.items() if not k.startswith("_")}
+ )
+ for row in results
+ ]
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/fruit_and_vegetable_prices.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/fruit_and_vegetable_prices.py
new file mode 100644
index 00000000000..77407576baa
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/fruit_and_vegetable_prices.py
@@ -0,0 +1,217 @@
+"""USDA ERS Fruit and Vegetable Prices Model."""
+
+from typing import Any, Literal
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from openbb_core.provider.utils.errors import EmptyDataError
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_fruit_and_vegetable_prices import (
+ DATA_YEAR,
+ FVP_TABLES,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+DEFAULT_TABLE = "fruit"
+
+Table = Literal["fruit", "vegetable"]
+
+
+class FruitAndVegetablePricesQueryParams(QueryParams):
+ """USDA ERS Fruit and Vegetable Prices Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/fruit-and-vegetable-prices
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "x-widget_config": {
+ "label": "Table",
+ "value": DEFAULT_TABLE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": entry["item_label"], "value": key}
+ for key, entry in FVP_TABLES.items()
+ ],
+ },
+ },
+ }
+
+ table: Table = Field(
+ default=DEFAULT_TABLE,
+ description="Commodity group to retrieve. Each group is one tidy table"
+ + " of average retail and cup-equivalent prices, with one row per item"
+ + " and form. Valid tables are:\n "
+ + ", ".join(FVP_TABLES)
+ + "\n",
+ )
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate the selected table key."""
+ if isinstance(v, (list, tuple)):
+ v = next((item for item in v if item), None)
+ if not v:
+ return DEFAULT_TABLE
+ table = str(v).strip().lower()
+ if table not in FVP_TABLES:
+ raise OpenBBError(
+ f"Invalid table: {table}. Valid tables are: " + ", ".join(FVP_TABLES)
+ )
+ return table
+
+
+class FruitAndVegetablePricesData(NullTokenMixin, Data):
+ """USDA ERS Fruit and Vegetable Prices Data.
+
+ One selected commodity group's average retail prices and cup-equivalent
+ prices, with one row per item and form and a fixed set of measure columns.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Fruit & Vegetable Prices",
+ "$.description": "Average retail price, preparation yield factor,"
+ " cup-equivalent size, and average price per edible cup-equivalent"
+ f" for fresh, canned, frozen, dried, and juice forms, for {DATA_YEAR}"
+ " (Circana OmniMarket Core Outlets), published by the USDA Economic"
+ " Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ item: str = Field(
+ description="Fruit or vegetable commodity, as published.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Commodity",
+ "cellDataType": "text",
+ "pinned": "left",
+ }
+ },
+ )
+ form: str = Field(
+ description="Marketed form of the item, e.g. Fresh, Canned, Frozen,"
+ + " Dried, or Juice.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Form",
+ "cellDataType": "text",
+ "pinned": "left",
+ "maxWidth": 120,
+ }
+ },
+ )
+ average_retail_price: float | None = Field(
+ default=None,
+ description="Average retail price of the item in its retail unit.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Average Retail Price",
+ "cellDataType": "number",
+ }
+ },
+ )
+ average_retail_price_unit: str | None = Field(
+ default=None,
+ description="Retail unit the average retail price is measured in, e.g."
+ + " 'per pound' or 'per pint (16 fluid ounces ready to drink)'.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Retail Price Unit",
+ "cellDataType": "text",
+ }
+ },
+ )
+ preparation_yield_factor: float | None = Field(
+ default=None,
+ description="Share of the purchased item that is edible after"
+ + " preparation, as a fraction.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Preparation Yield Factor",
+ "cellDataType": "number",
+ }
+ },
+ )
+ cup_equivalent_size: float | None = Field(
+ default=None,
+ description="Size of one cup-equivalent of the edible item, in the"
+ + " cup-equivalent unit.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Cup-Equivalent Size",
+ "cellDataType": "number",
+ }
+ },
+ )
+ cup_equivalent_unit: str | None = Field(
+ default=None,
+ description="Unit the cup-equivalent size is measured in, e.g. 'pounds'"
+ + " or 'pints'.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Cup-Equivalent Unit",
+ "cellDataType": "text",
+ }
+ },
+ )
+ average_price_per_cup_equivalent: float | None = Field(
+ default=None,
+ description="Average price of one edible cup-equivalent of the item, in"
+ + " U.S. dollars.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Average Price per Cup-Equivalent",
+ "cellDataType": "number",
+ }
+ },
+ )
+
+
+class FruitAndVegetablePricesFetcher(
+ Fetcher[
+ FruitAndVegetablePricesQueryParams,
+ list[FruitAndVegetablePricesData],
+ ]
+):
+ """Fetch USDA ERS Fruit and Vegetable Prices."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(params: dict[str, Any]) -> FruitAndVegetablePricesQueryParams:
+ """Transform the query params."""
+ return FruitAndVegetablePricesQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: FruitAndVegetablePricesQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected table's tidy CSV rows."""
+ from openbb_government_us.usda.utils import ers_fruit_and_vegetable_prices
+
+ return await ers_fruit_and_vegetable_prices.afetch_table(query.table)
+
+ @staticmethod
+ def transform_data(
+ query: FruitAndVegetablePricesQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[FruitAndVegetablePricesData]:
+ """Order the wide-on-measure rows by item and form."""
+ if not data:
+ raise EmptyDataError("No records returned for the given table.")
+ results = sorted(data, key=lambda row: (row["item"], row["form"] or ""))
+ return [FruitAndVegetablePricesData.model_validate(row) for row in results]
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/global_food_assessment.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/global_food_assessment.py
new file mode 100644
index 00000000000..14f5e4eac78
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/global_food_assessment.py
@@ -0,0 +1,240 @@
+"""USDA ERS Global Food Assessment Model."""
+
+from collections import OrderedDict
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_global_food_assessment import (
+ DEFAULT_ELEMENT,
+ ELEMENTS,
+ REGIONS,
+ UNIT,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+ELEMENT_OPTIONS = [{"label": name, "value": name} for name in ELEMENTS]
+REGION_OPTIONS = [{"label": name, "value": name} for name in REGIONS]
+
+
+class GlobalFoodAssessmentQueryParams(QueryParams):
+ """USDA ERS Global Food Assessment Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/global-food-assessment
+ """
+
+ __json_schema_extra__ = {
+ "element": {
+ "x-widget_config": {
+ "label": "Measure",
+ "value": DEFAULT_ELEMENT,
+ "multiSelect": False,
+ "multiple": False,
+ "options": ELEMENT_OPTIONS,
+ "style": {"popupWidth": 320},
+ },
+ },
+ "region": {
+ "x-widget_config": {
+ "label": "Region",
+ "multiSelect": False,
+ "multiple": False,
+ "options": REGION_OPTIONS,
+ "style": {"popupWidth": 320},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ element: str = Field(
+ default=DEFAULT_ELEMENT,
+ description="Measure to retrieve. The measure selects one series whose"
+ + " subregions spread into the wide columns while the base year and the"
+ + " ten-year projection horizon stay in the rows. Valid measures are:\n "
+ + ", ".join(ELEMENTS)
+ + "\n",
+ )
+ region: str | None = Field(
+ default=None,
+ description="Filter to a single region's subregions, e.g."
+ + " 'Sub-Saharan Africa'. If None, every subregion is spread into the"
+ + " columns. Valid regions are:\n "
+ + ", ".join(REGIONS)
+ + "\n",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data, compared against the"
+ + " observation year. If None, returns from the base year.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data, compared against the"
+ + " observation year. If None, returns through the projection horizon.",
+ )
+
+ @field_validator("element", mode="before", check_fields=False)
+ @classmethod
+ def _validate_element(cls, v):
+ """Validate element, defaulting a blank value."""
+ if not v:
+ return DEFAULT_ELEMENT
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ element = str(value).strip()
+ if element not in ELEMENTS:
+ raise OpenBBError(
+ f"Invalid measure: {element}. Valid measures are: "
+ + ", ".join(ELEMENTS)
+ )
+ return element
+
+ @field_validator("region", mode="before", check_fields=False)
+ @classmethod
+ def _validate_region(cls, v):
+ """Normalize region to a stripped string or None, validating membership."""
+ if not v:
+ return None
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ region = str(value).strip()
+ if not region:
+ return None
+ if region not in REGIONS:
+ raise OpenBBError(
+ f"Invalid region: {region}. Valid regions are: " + ", ".join(REGIONS)
+ )
+ return region
+
+
+class GlobalFoodAssessmentData(NullTokenMixin, Data):
+ """USDA ERS Global Food Assessment Data.
+
+ One selected measure pivoted to a wide layout: the base year and the
+ ten-year projection horizon stay in the rows while the subregions spread
+ into value columns, whose set narrows with the optional region filter. The
+ measure and its shared unit are constant for a selection and are carried as
+ hidden context dimensions rather than visible columns.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Global Food Assessment",
+ "$.description": "Grain food demand, other demand, total demand,"
+ " production, and the implied additional supply required to close"
+ " the food gap, for global food assessment regions and subregions"
+ " in the base year and the ten-year projection horizon, published"
+ " by the USDA Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ year: str = Field(
+ description="Year of the observation: the base year or the ten-year"
+ + " projection horizon, e.g. '2025' or '2035'.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Year",
+ "cellDataType": "text",
+ "pinned": "left",
+ "maxWidth": 110,
+ }
+ },
+ )
+ element: str | None = Field(
+ default=None,
+ description="Measure spread into the subregion columns, as published.",
+ json_schema_extra={"x-widget_config": {"headerName": "Measure", "hide": True}},
+ )
+ unit: str | None = Field(
+ default=None,
+ description="Unit shared by every value column for the selection, as"
+ + " published in the source value-column header: 'Millions of metric"
+ + " tons'.",
+ json_schema_extra={"x-widget_config": {"headerName": "Unit", "hide": True}},
+ )
+
+
+class GlobalFoodAssessmentFetcher(
+ Fetcher[
+ GlobalFoodAssessmentQueryParams,
+ list[GlobalFoodAssessmentData],
+ ]
+):
+ """Fetch USDA ERS Global Food Assessment Data."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> GlobalFoodAssessmentQueryParams:
+ """Transform the query params."""
+ return GlobalFoodAssessmentQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: GlobalFoodAssessmentQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the file's long-format rows."""
+ from openbb_government_us.usda.utils import ers_global_food_assessment
+
+ return await ers_global_food_assessment.afetch_records()
+
+ @staticmethod
+ def transform_data(
+ query: GlobalFoodAssessmentQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[GlobalFoodAssessmentData]:
+ """Pivot the long-format rows into the subregion-by-year wide table."""
+ column_order: OrderedDict[str, None] = OrderedDict()
+ by_year: dict[int, dict] = {}
+ for record in data:
+ if record["element"] != query.element:
+ continue
+ if query.region is not None and record["region"] != query.region:
+ continue
+ amount = record["amount"]
+ if amount is None:
+ continue
+ year_label = record["year"]
+ if not year_label.isdigit():
+ continue
+ year_sort = int(year_label)
+ if query.start_year is not None and year_sort < query.start_year:
+ continue
+ if query.end_year is not None and year_sort > query.end_year:
+ continue
+ subregion = record["subregion"]
+ column_order.setdefault(subregion, None)
+ row = by_year.get(year_sort)
+ if row is None:
+ row = {
+ "year": year_label,
+ "element": record["element"],
+ "unit": UNIT,
+ }
+ by_year[year_sort] = row
+ row[subregion] = amount
+ results: list[GlobalFoodAssessmentData] = []
+ for year_sort in sorted(by_year):
+ source = by_year[year_sort]
+ row = {
+ "year": source["year"],
+ "element": source["element"],
+ "unit": source["unit"],
+ }
+ for column in column_order:
+ row[column] = source.get(column)
+ results.append(GlobalFoodAssessmentData.model_validate(row))
+ return results
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/international_agricultural_productivity.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/international_agricultural_productivity.py
new file mode 100644
index 00000000000..80486d90a11
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/international_agricultural_productivity.py
@@ -0,0 +1,262 @@
+"""USDA ERS International Agricultural Productivity Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.app.service.system_service import SystemService
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from openbb_core.provider.utils.errors import EmptyDataError
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_international_agricultural_productivity import (
+ DEFAULT_COUNTRY,
+ DEFAULT_GROUPING,
+ DEFAULT_MEASURE,
+ ENTITY_LABEL,
+ GROUPINGS,
+ MEASURES,
+ grouping_options,
+ measure_options,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+api_prefix = SystemService().system_settings.api_settings.prefix
+
+
+class InternationalAgriculturalProductivityQueryParams(QueryParams):
+ """International Agricultural Productivity Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/international-agricultural-productivity
+ """
+
+ __json_schema_extra__ = {
+ "grouping": {
+ "x-widget_config": {
+ "label": "Grouping",
+ "value": DEFAULT_GROUPING,
+ "multiSelect": False,
+ "multiple": False,
+ "options": grouping_options(),
+ "style": {"popupWidth": 320},
+ },
+ },
+ "country": {
+ "x-widget_config": {
+ "label": "Country / territory",
+ "type": "endpoint",
+ "value": DEFAULT_COUNTRY,
+ "multiSelect": False,
+ "multiple": False,
+ "optionsEndpoint": f"{api_prefix}/usda/ag_productivity_countries",
+ "optionsParams": {"grouping": "$grouping"},
+ "style": {"popupWidth": 360},
+ },
+ },
+ "measure": {
+ "x-widget_config": {
+ "label": "Measure",
+ "value": DEFAULT_MEASURE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": measure_options(),
+ "style": {"popupWidth": 300},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ grouping: str = Field(
+ default=DEFAULT_GROUPING,
+ description="Grouping that scopes the country selector into buckets"
+ + " derived from the entity's region, income level, or aggregate type."
+ + " It scopes the dropdown only; the data is selected by country."
+ + " Valid groupings are:\n "
+ + ", ".join(GROUPINGS)
+ + "\n",
+ )
+ country: int = Field(
+ default=DEFAULT_COUNTRY,
+ description="Country, territory, region, income group, or aggregate to"
+ + " retrieve, as its unique dataset order. The order disambiguates"
+ + " entities sharing a name, e.g. the country 'China' (81) from the"
+ + " income aggregate 'China' (227). Defaults to 220, the world.",
+ )
+ measure: str = Field(
+ default=DEFAULT_MEASURE,
+ description="Measure family spread into the wide value columns."
+ + " 'productivity_indices' emits the seven 2015=100 indices;"
+ + " 'physical_quantities' emits the twelve output and input quantities"
+ + " in their published units. Valid measures are:\n "
+ + ", ".join(MEASURES)
+ + "\n",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data, from 1961."
+ + " If None, returns from the beginning of the series.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data, up to 2023."
+ + " If None, returns up to the most recent year.",
+ )
+
+ @field_validator("grouping", mode="before", check_fields=False)
+ @classmethod
+ def _validate_grouping(cls, v):
+ """Validate grouping."""
+ if not v:
+ return DEFAULT_GROUPING
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip()
+ if value not in GROUPINGS:
+ raise OpenBBError(
+ f"Invalid grouping: {value}. Valid groupings are: "
+ + ", ".join(GROUPINGS)
+ )
+ return value
+
+ @field_validator("measure", mode="before", check_fields=False)
+ @classmethod
+ def _validate_measure(cls, v):
+ """Validate measure."""
+ if not v:
+ return DEFAULT_MEASURE
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip()
+ if value not in MEASURES:
+ raise OpenBBError(
+ f"Invalid measure: {value}. Valid measures are: " + ", ".join(MEASURES)
+ )
+ return value
+
+ @field_validator("country", mode="before", check_fields=False)
+ @classmethod
+ def _validate_country(cls, v):
+ """Validate country."""
+ if v is None or v == "":
+ return DEFAULT_COUNTRY
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ try:
+ order = int(str(value).strip())
+ except (TypeError, ValueError) as error:
+ raise OpenBBError(f"Invalid country: {value}.") from error
+ if order not in ENTITY_LABEL:
+ raise OpenBBError(
+ f"Invalid country order: {order}. It is not a published entity."
+ )
+ return order
+
+
+class InternationalAgriculturalProductivityData(NullTokenMixin, Data):
+ """International Agricultural Productivity Data.
+
+ One selected entity pivoted to the ERS wide layout: each row is a year and
+ the selected measure family spreads into value columns, either the seven
+ 2015=100 productivity indices or the twelve physical output and input
+ quantities, whose set depends on the measure.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS International Agricultural Productivity",
+ "$.description": "Agricultural total factor productivity indices,"
+ " output and input quantity indices, and the underlying physical"
+ " output and input quantities for countries, territories, regions,"
+ " income groups, and the world, published by the USDA Economic"
+ " Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ year: int = Field(
+ description="Year of the observation.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Year",
+ "cellDataType": "text",
+ "pinned": "left",
+ "maxWidth": 90,
+ }
+ },
+ )
+
+
+class InternationalAgriculturalProductivityFetcher(
+ Fetcher[
+ InternationalAgriculturalProductivityQueryParams,
+ list[InternationalAgriculturalProductivityData],
+ ]
+):
+ """Fetch USDA ERS International Agricultural Productivity."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> InternationalAgriculturalProductivityQueryParams:
+ """Transform the query params."""
+ return InternationalAgriculturalProductivityQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: InternationalAgriculturalProductivityQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected entity's long-format observation records."""
+ from openbb_government_us.usda.utils import (
+ ers_international_agricultural_productivity,
+ )
+
+ return await ers_international_agricultural_productivity.afetch_entity(
+ query.country
+ )
+
+ @staticmethod
+ def transform_data(
+ query: InternationalAgriculturalProductivityQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[InternationalAgriculturalProductivityData]:
+ """Pivot the entity's records into the wide year-by-measure layout."""
+ columns = MEASURES[query.measure]["columns"]
+ header_by_variable = {variable: header for variable, header in columns}
+ kept: list[dict] = []
+ for record in data:
+ if record["variable"] not in header_by_variable:
+ continue
+ year = record["year"]
+ if query.start_year is not None and year < query.start_year:
+ continue
+ if query.end_year is not None and year > query.end_year:
+ continue
+ kept.append(record)
+ if not kept:
+ raise EmptyDataError("No records match the given filters.")
+ present = {record["variable"] for record in kept}
+ ordered_headers = [
+ header for variable, header in columns if variable in present
+ ]
+ by_year: dict[int, dict] = {}
+ for record in kept:
+ row = by_year.setdefault(record["year"], {})
+ row[header_by_variable[record["variable"]]] = record["value"]
+ results: list[InternationalAgriculturalProductivityData] = []
+ for year in sorted(by_year):
+ values = by_year[year]
+ row = {"year": year}
+ for header in ordered_headers:
+ row[header] = values.get(header)
+ results.append(
+ InternationalAgriculturalProductivityData.model_validate(row)
+ )
+ return results
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/international_baseline_data.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/international_baseline_data.py
new file mode 100644
index 00000000000..02e9c26cc23
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/international_baseline_data.py
@@ -0,0 +1,288 @@
+"""USDA ERS International Baseline Data Model."""
+
+from collections import OrderedDict
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.app.service.system_service import SystemService
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_international_baseline_data import (
+ COMMODITIES,
+ DEFAULT_ATTRIBUTE,
+ DEFAULT_COMMODITY,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+api_prefix = SystemService().system_settings.api_settings.prefix
+
+COMMODITY_OPTIONS = [{"label": name, "value": name} for name in COMMODITIES]
+
+
+class InternationalBaselineDataQueryParams(QueryParams):
+ """USDA ERS International Baseline Data Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/international-baseline-data
+ """
+
+ __json_schema_extra__ = {
+ "commodity": {
+ "x-widget_config": {
+ "label": "Commodity",
+ "value": DEFAULT_COMMODITY,
+ "multiSelect": False,
+ "multiple": False,
+ "options": COMMODITY_OPTIONS,
+ "style": {"popupWidth": 260},
+ },
+ },
+ "attribute": {
+ "x-widget_config": {
+ "label": "Measure",
+ "type": "endpoint",
+ "value": DEFAULT_ATTRIBUTE,
+ "multiSelect": False,
+ "multiple": False,
+ "optionsEndpoint": f"{api_prefix}/usda/intl_baseline_attributes",
+ "optionsParams": {"commodity": "$commodity"},
+ "style": {"popupWidth": 300},
+ },
+ },
+ "country": {
+ "x-widget_config": {
+ "label": "Country or region",
+ "type": "endpoint",
+ "multiSelect": False,
+ "multiple": False,
+ "optionsEndpoint": f"{api_prefix}/usda/intl_baseline_countries",
+ "optionsParams": {"commodity": "$commodity"},
+ "style": {"popupWidth": 320},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ commodity: str = Field(
+ default=DEFAULT_COMMODITY,
+ description="Commodity to retrieve. The commodity and measure select one"
+ + " projection series whose countries and regions spread into the wide"
+ + " columns while the years stay in the rows. Valid commodities are:\n "
+ + ", ".join(COMMODITIES)
+ + "\n",
+ )
+ attribute: str = Field(
+ default=DEFAULT_ATTRIBUTE,
+ description="Measure to spread into the country columns, e.g."
+ + " 'Production', 'Exports', or 'Ending stocks'. The available measures"
+ + " depend on the commodity.",
+ )
+ country: str | None = Field(
+ default=None,
+ description="Filter to a single country or region column, e.g. 'China'."
+ + " If None, every country and region is spread into the columns.",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data, compared against the"
+ + " first four digits of the crop or calendar year."
+ + " If None, returns from the beginning of the series.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data, compared against the"
+ + " first four digits of the crop or calendar year."
+ + " If None, returns through the last projection year.",
+ )
+
+ @field_validator("commodity", mode="before", check_fields=False)
+ @classmethod
+ def _validate_commodity(cls, v):
+ """Validate commodity."""
+ if not v:
+ return DEFAULT_COMMODITY
+ commodity = v.strip() if isinstance(v, str) else v
+ if commodity not in COMMODITIES:
+ raise OpenBBError(
+ f"Invalid commodity: {commodity}. Valid commodities are: "
+ + ", ".join(COMMODITIES)
+ )
+ return commodity
+
+ @field_validator("attribute", mode="before", check_fields=False)
+ @classmethod
+ def _validate_attribute(cls, v):
+ """Normalize attribute to a stripped string, defaulting to Production."""
+ if not v:
+ return DEFAULT_ATTRIBUTE
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ return str(value).strip() or DEFAULT_ATTRIBUTE
+
+ @field_validator("country", mode="before", check_fields=False)
+ @classmethod
+ def _validate_country(cls, v):
+ """Normalize country to a stripped string or None."""
+ if not v:
+ return None
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ return str(value).strip() or None
+
+
+class InternationalBaselineDataData(NullTokenMixin, Data):
+ """USDA ERS International Baseline Data.
+
+ One selected commodity and measure pivoted to a wide layout: the crop or
+ calendar years stay in the rows while the countries and regions spread into
+ value columns, whose set varies by commodity and measure. The commodity,
+ measure, unit, year type, and topic are constant for a selection and are
+ carried as hidden context dimensions rather than visible columns.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS International Baseline Data",
+ "$.description": "Historical and projected area, yield,"
+ " production, supply, use, and trade for grains, oilseeds,"
+ " cotton, and livestock by country and region through the"
+ " long-term projection horizon, published by the USDA Economic"
+ " Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ year: str = Field(
+ description="Crop or calendar year of the observation, as published,"
+ + " e.g. '2024/25' for crops or '2024' for livestock.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Year",
+ "pinned": "left",
+ "maxWidth": 110,
+ }
+ },
+ )
+ commodity: str | None = Field(
+ default=None,
+ description="Commodity of the selection, as published.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Commodity", "hide": True}
+ },
+ )
+ attribute: str | None = Field(
+ default=None,
+ description="Measure spread into the country columns, as published.",
+ json_schema_extra={"x-widget_config": {"headerName": "Measure", "hide": True}},
+ )
+ unit: str | None = Field(
+ default=None,
+ description="Unit shared by every value column for the selection,"
+ + " as published, e.g. 'Thousand metric tons'.",
+ json_schema_extra={"x-widget_config": {"headerName": "Unit", "hide": True}},
+ )
+ year_type: str | None = Field(
+ default=None,
+ description="Year basis of the selection: 'Crop year' for crops or"
+ + " 'Calendar year' for livestock.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Year type", "hide": True}
+ },
+ )
+ topic: str | None = Field(
+ default=None,
+ description="Topic of the commodity: 'Crops' or 'Livestock'.",
+ json_schema_extra={"x-widget_config": {"headerName": "Topic", "hide": True}},
+ )
+
+
+class InternationalBaselineDataFetcher(
+ Fetcher[
+ InternationalBaselineDataQueryParams,
+ list[InternationalBaselineDataData],
+ ]
+):
+ """Fetch USDA ERS International Baseline Data."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> InternationalBaselineDataQueryParams:
+ """Transform the query params."""
+ return InternationalBaselineDataQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: InternationalBaselineDataQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the projection file's long-format rows."""
+ from openbb_government_us.usda.utils import ers_international_baseline_data
+
+ return await ers_international_baseline_data.afetch_records()
+
+ @staticmethod
+ def transform_data(
+ query: InternationalBaselineDataQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[InternationalBaselineDataData]:
+ """Pivot the long-format rows into the country-by-year wide table."""
+ column_order: OrderedDict[str, None] = OrderedDict()
+ by_year: dict[int, dict] = {}
+ for record in data:
+ if record["commodity"] != query.commodity:
+ continue
+ if record["attribute"] != query.attribute:
+ continue
+ if query.country is not None and record["country"] != query.country:
+ continue
+ amount = record["amount"]
+ if amount is None:
+ continue
+ year_label = record["year"]
+ if not year_label[:4].isdigit():
+ continue
+ year_sort = int(year_label[:4])
+ if query.start_year is not None and year_sort < query.start_year:
+ continue
+ if query.end_year is not None and year_sort > query.end_year:
+ continue
+ country = record["country"]
+ column_order.setdefault(country, None)
+ row = by_year.get(year_sort)
+ if row is None:
+ row = {
+ "_year": year_sort,
+ "year": year_label,
+ "commodity": record["commodity"],
+ "attribute": record["attribute"],
+ "unit": record["unit"],
+ "year_type": record["year_type"],
+ "topic": record["topic"],
+ }
+ by_year[year_sort] = row
+ row[country] = amount
+ results: list[InternationalBaselineDataData] = []
+ for year_sort in sorted(by_year):
+ source = by_year[year_sort]
+ row = {
+ "year": source["year"],
+ "commodity": source["commodity"],
+ "attribute": source["attribute"],
+ "unit": source["unit"],
+ "year_type": source["year_type"],
+ "topic": source["topic"],
+ }
+ for column in column_order:
+ row[column] = source.get(column)
+ results.append(InternationalBaselineDataData.model_validate(row))
+ return results
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/international_macroeconomic_data_set.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/international_macroeconomic_data_set.py
new file mode 100644
index 00000000000..71abe227503
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/international_macroeconomic_data_set.py
@@ -0,0 +1,230 @@
+"""USDA ERS International Macroeconomic Data Set Model."""
+
+from collections import OrderedDict
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_international_macroeconomic_data_set import (
+ DEFAULT_MEASURE,
+ DEFAULT_VARIABLE,
+ INTERNATIONAL_MACRO_FILES,
+ MEASURES,
+ is_growth_unit,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+VARIABLE_OPTIONS = [
+ {"label": config["label"], "value": key}
+ for key, config in INTERNATIONAL_MACRO_FILES.items()
+]
+
+MEASURE_OPTIONS = [{"label": label, "value": key} for key, label in MEASURES.items()]
+
+
+class InternationalMacroeconomicDataSetQueryParams(QueryParams):
+ """USDA ERS International Macroeconomic Data Set Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/international-macroeconomic-data-set
+ """
+
+ __json_schema_extra__ = {
+ "variable": {
+ "x-widget_config": {
+ "label": "Variable",
+ "value": DEFAULT_VARIABLE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": VARIABLE_OPTIONS,
+ "style": {"popupWidth": 360},
+ },
+ },
+ "measure": {
+ "x-widget_config": {
+ "label": "Measure",
+ "value": DEFAULT_MEASURE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": MEASURE_OPTIONS,
+ "style": {"popupWidth": 280},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ variable: str = Field(
+ default=DEFAULT_VARIABLE,
+ description="Macroeconomic variable to retrieve. Each variable is a"
+ + " separate file whose baseline countries and regions spread into the"
+ + " wide columns while the years stay in the rows, running from 1970"
+ + " through the 2035 projection horizon. Valid variables are:\n "
+ + ", ".join(INTERNATIONAL_MACRO_FILES)
+ + "\n",
+ )
+ measure: str = Field(
+ default=DEFAULT_MEASURE,
+ description="Measure to spread. 'level' returns the variable's level"
+ + " unit; 'growth' returns its year-to-year percent change."
+ + " Valid measures are:\n "
+ + ", ".join(MEASURES)
+ + "\n",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data."
+ + " If None, returns from 1970, the first year of the series.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data."
+ + " If None, returns through 2035, the last projection year.",
+ )
+
+ @field_validator("variable", mode="before", check_fields=False)
+ @classmethod
+ def _validate_variable(cls, v):
+ """Validate variable."""
+ if not v:
+ return DEFAULT_VARIABLE
+ variable = v.strip() if isinstance(v, str) else v
+ if variable not in INTERNATIONAL_MACRO_FILES:
+ raise OpenBBError(
+ f"Invalid variable: {variable}. Valid variables are: "
+ + ", ".join(INTERNATIONAL_MACRO_FILES)
+ )
+ return variable
+
+ @field_validator("measure", mode="before", check_fields=False)
+ @classmethod
+ def _validate_measure(cls, v):
+ """Validate measure."""
+ if not v:
+ return DEFAULT_MEASURE
+ measure = v.strip() if isinstance(v, str) else v
+ if measure not in MEASURES:
+ raise OpenBBError(
+ f"Invalid measure: {measure}. Valid measures are: "
+ + ", ".join(MEASURES)
+ )
+ return measure
+
+
+class InternationalMacroeconomicDataSetData(NullTokenMixin, Data):
+ """USDA ERS International Macroeconomic Data Set Data.
+
+ One selected variable and measure pivoted to a wide layout: the years stay
+ in the rows while the baseline countries and regions spread into value
+ columns, whose set varies slightly by variable. A single unit applies to
+ the whole table for a given variable and measure.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS International Macroeconomic Data Set",
+ "$.description": "Historical and projected real GDP, GDP per"
+ " capita, GDP deflator, real GDP shares, real exchange rates,"
+ " consumer price indices, and population for baseline countries"
+ " and regions from 1970 through 2035, published by the USDA"
+ " Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ year: int = Field(
+ description="Calendar year of the observation, from 1970 through the"
+ + " 2035 projection horizon.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Year",
+ "cellDataType": "number",
+ "pinned": "left",
+ "maxWidth": 90,
+ }
+ },
+ )
+ unit: str | None = Field(
+ default=None,
+ description="Unit shared by every value column for the selected"
+ + " variable and measure, as published.",
+ json_schema_extra={"x-widget_config": {"headerName": "Unit", "hide": True}},
+ )
+
+
+class InternationalMacroeconomicDataSetFetcher(
+ Fetcher[
+ InternationalMacroeconomicDataSetQueryParams,
+ list[InternationalMacroeconomicDataSetData],
+ ]
+):
+ """Fetch USDA ERS International Macroeconomic Data Set."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> InternationalMacroeconomicDataSetQueryParams:
+ """Transform the query params."""
+ return InternationalMacroeconomicDataSetQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: InternationalMacroeconomicDataSetQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected variable's long-format rows."""
+ from openbb_government_us.usda.utils import (
+ ers_international_macroeconomic_data_set,
+ )
+
+ return await ers_international_macroeconomic_data_set.afetch_records(
+ query.variable
+ )
+
+ @staticmethod
+ def transform_data(
+ query: InternationalMacroeconomicDataSetQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[InternationalMacroeconomicDataSetData]:
+ """Pivot the long-format rows into the country-by-year wide table."""
+ growth = query.measure == "growth"
+ selected = [
+ record for record in data if is_growth_unit(record["unit"]) == growth
+ ]
+ column_order: OrderedDict[str, None] = OrderedDict()
+ unit: str | None = None
+ for record in selected:
+ column_order.setdefault(record["observation"], None)
+ if unit is None:
+ unit = record["unit"]
+ by_year: dict[int, dict] = {}
+ for record in selected:
+ year = record["year"]
+ if query.start_year is not None and year < query.start_year:
+ continue
+ if query.end_year is not None and year > query.end_year:
+ continue
+ row = by_year.get(year)
+ if row is None:
+ row = {"year": year, "unit": unit}
+ by_year[year] = row
+ row[record["observation"]] = record["value"]
+ results: list[InternationalMacroeconomicDataSetData] = []
+ for year in sorted(by_year):
+ source = by_year[year]
+ row = {"year": source["year"], "unit": source["unit"]}
+ for column in column_order:
+ row[column] = source.get(column)
+ results.append(InternationalMacroeconomicDataSetData.model_validate(row))
+ return results
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/livestock_and_meat_domestic_data.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/livestock_and_meat_domestic_data.py
new file mode 100644
index 00000000000..378040c8373
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/livestock_and_meat_domestic_data.py
@@ -0,0 +1,281 @@
+"""USDA ERS Livestock and Meat Domestic Data Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_livestock_and_meat_domestic_data import (
+ FOLD_DIM_FIELDS,
+ LIVESTOCK_AND_MEAT_FILES,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+DEFAULT_TABLE = "supply_and_disappearance"
+
+
+class LivestockAndMeatDomesticDataQueryParams(QueryParams):
+ """USDA ERS Livestock and Meat Domestic Data Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/livestock-and-meat-domestic-data
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "x-widget_config": {
+ "label": "Table",
+ "value": DEFAULT_TABLE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": config["label"], "value": key}
+ for key, config in LIVESTOCK_AND_MEAT_FILES.items()
+ ],
+ "style": {"popupWidth": 420},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ table: str = Field(
+ default=DEFAULT_TABLE,
+ description="Livestock and meat table to retrieve. Each table is a set"
+ + " of named slaughter, production, weight, cold-storage, supply-and-use,"
+ + " or price series indexed by year and period; the attributes become the"
+ + " wide value columns, prefixed by commodity or sub-table where that"
+ + " dimension splits the series. Valid tables are:\n "
+ + ", ".join(LIVESTOCK_AND_MEAT_FILES)
+ + "\n",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data, compared against the"
+ + " observation year. If None, returns from the beginning of the series.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data, compared against the"
+ + " observation year. If None, returns up to the most recent year.",
+ )
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate table."""
+ if not v:
+ return DEFAULT_TABLE
+ table = v.strip() if isinstance(v, str) else v
+ if table not in LIVESTOCK_AND_MEAT_FILES:
+ raise OpenBBError(
+ f"Invalid table: {table}. Valid tables are: "
+ + ", ".join(LIVESTOCK_AND_MEAT_FILES)
+ )
+ return table
+
+
+class LivestockAndMeatDomesticData(NullTokenMixin, Data):
+ """USDA ERS Livestock and Meat Domestic Data.
+
+ One selected livestock-and-meat table pivoted to a wide layout: the period
+ stays in the rows while the table's attribute dimension spreads into value
+ columns, whose set varies by table. A commodity, sub-table, or geography
+ dimension is prefixed onto the column headers when it splits the series,
+ and folded into the period label otherwise.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Livestock & Meat Domestic Data",
+ "$.description": "U.S. livestock and meat slaughter, production,"
+ " weights, cold storage, supply and disappearance, and livestock"
+ " and wholesale prices, published by the USDA Economic Research"
+ " Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ period: str = Field(
+ description="Row label of the observation: the year for annual tables"
+ + " ('2020'), the year and month, quarter, or feeding window for"
+ + " sub-annual tables ('2020 January', '2020 Q1 Jan-Mar'), with any"
+ + " commodity, sub-table, or geography that is not carried in the"
+ + " column headers prefixed so each row is uniquely labeled.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Period",
+ "pinned": "left",
+ "maxWidth": 260,
+ }
+ },
+ )
+
+
+class LivestockAndMeatDomesticDataFetcher(
+ Fetcher[
+ LivestockAndMeatDomesticDataQueryParams,
+ list[LivestockAndMeatDomesticData],
+ ]
+):
+ """Fetch USDA ERS Livestock and Meat Domestic Data."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> LivestockAndMeatDomesticDataQueryParams:
+ """Transform the query params."""
+ return LivestockAndMeatDomesticDataQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: LivestockAndMeatDomesticDataQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected table's long-format rows."""
+ from openbb_government_us.usda.utils import ers_livestock_and_meat_domestic_data
+
+ return await ers_livestock_and_meat_domestic_data.afetch_table(query.table)
+
+ @staticmethod
+ def _period_label(year: int, period: str | None) -> str:
+ """Fold the year and within-year period into one readable time label."""
+ if not period or period.startswith("Yr"):
+ return str(year)
+ if " to " in period:
+ return period
+ return f"{year} {period}"
+
+ @staticmethod
+ def _classify_dims(
+ rows: list[dict], fold_dims: list[str]
+ ) -> tuple[list[str], list[str]]:
+ """Split folding dims into series-partitioning column dims and row dims.
+
+ Parameters
+ ----------
+ rows : list[dict]
+ Long-format records for one table.
+ fold_dims : list[str]
+ Dimension fields that take more than one value across the records.
+
+ Returns
+ -------
+ tuple[list[str], list[str]]
+ The dims folded into the column header and the dims folded into the
+ row label. A dim goes to the header when it partitions the series,
+ or when the table publishes no more series than the dim has
+ members, which would otherwise leave a near-single value column
+ repeated down the rows.
+ """
+ column_dims: list[str] = []
+ row_dims: list[str] = []
+ for field in fold_dims:
+ distinct = {record[field] for record in rows if record[field]}
+ series_values: dict[str, set] = {}
+ for record in rows:
+ if record[field]:
+ series_values.setdefault(record["column"], set()).add(record[field])
+ if not distinct or not series_values:
+ row_dims.append(field)
+ continue
+ spread = sum(len(members) for members in series_values.values()) / len(
+ series_values
+ )
+ partitions = spread <= 0.6 * len(distinct)
+ if partitions or len(series_values) <= len(distinct):
+ column_dims.append(field)
+ else:
+ row_dims.append(field)
+ return column_dims, row_dims
+
+ @staticmethod
+ def transform_data(
+ query: LivestockAndMeatDomesticDataQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[LivestockAndMeatDomesticData]:
+ """Filter to the requested years and pivot into the wide layout."""
+ rows = [
+ record
+ for record in data
+ if (query.start_year is None or record["year"] >= query.start_year)
+ and (query.end_year is None or record["year"] <= query.end_year)
+ ]
+ if not rows:
+ return []
+ return LivestockAndMeatDomesticDataFetcher._pivot(rows)
+
+ @staticmethod
+ def _pivot(rows: list[dict]) -> list[LivestockAndMeatDomesticData]:
+ """Pivot the long-format rows into newest-first periods with series columns."""
+ fold_dims = [
+ field
+ for field in FOLD_DIM_FIELDS
+ if len({record[field] for record in rows}) > 1
+ ]
+ column_dims, row_dims = LivestockAndMeatDomesticDataFetcher._classify_dims(
+ rows, fold_dims
+ )
+ pivoted: dict[tuple, dict] = {}
+ combo_order: dict[tuple, int] = {}
+ group_order: dict[tuple, int] = {}
+ series_order: dict[str, int] = {}
+ col_rank: dict[str, tuple[int, int]] = {}
+ for order, record in enumerate(rows):
+ dims = tuple(record[field] for field in row_dims)
+ key = (*dims, record["year"], record["period"])
+ row = pivoted.get(key)
+ if row is None:
+ combo_order.setdefault(dims, len(combo_order))
+ time_label = LivestockAndMeatDomesticDataFetcher._period_label(
+ record["year"], record["period"]
+ )
+ dim_values = [value for value in dims if value]
+ row = {
+ "_combo": combo_order[dims],
+ "_order": order,
+ "_year": record["year"],
+ "_period_sort": record["period_sort"],
+ "period": " — ".join([*dim_values, time_label]),
+ }
+ pivoted[key] = row
+ column_values = tuple(
+ str(record[field]) for field in column_dims if record[field]
+ )
+ base = " — ".join([*column_values, record["column"]])
+ column = f"{base} ({record['unit']})" if record["unit"] else base
+ if column not in col_rank:
+ group_order.setdefault(column_values, len(group_order))
+ series_order.setdefault(record["column"], len(series_order))
+ col_rank[column] = (
+ group_order[column_values],
+ series_order[record["column"]],
+ )
+ row[column] = record["amount"]
+ col_order = sorted(col_rank, key=lambda column: col_rank[column])
+ results = sorted(
+ pivoted.values(),
+ key=lambda row: (
+ row["_combo"],
+ -row["_year"],
+ -row["_period_sort"],
+ -row["_order"],
+ ),
+ )
+ validated: list[LivestockAndMeatDomesticData] = []
+ for row in results:
+ payload = {"period": row["period"]}
+ for column in col_order:
+ payload[column] = row.get(column)
+ validated.append(LivestockAndMeatDomesticData.model_validate(payload))
+ return validated
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/livestock_and_meat_international_trade_data.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/livestock_and_meat_international_trade_data.py
new file mode 100644
index 00000000000..d71fc6eeb0f
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/livestock_and_meat_international_trade_data.py
@@ -0,0 +1,332 @@
+"""USDA ERS Livestock and Meat International Trade Data Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.app.service.system_service import SystemService
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from openbb_core.provider.utils.errors import EmptyDataError
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_livestock_and_meat_international_trade_data import (
+ DIRECTION_LABELS,
+ DIRECTIONS,
+ FREQUENCIES,
+ FREQUENCY_LABELS,
+ SPECIES,
+ SPECIES_LABELS,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+api_prefix = SystemService().system_settings.api_settings.prefix
+
+DEFAULT_TABLE = "beef_veal"
+DEFAULT_DIRECTION = "imports"
+DEFAULT_FREQUENCY = "annual"
+
+DIM_KEYS = ("country", "geography_code", "month", "unit")
+
+
+class LivestockAndMeatInternationalTradeDataQueryParams(QueryParams):
+ """USDA ERS Livestock and Meat International Trade Data Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/livestock-and-meat-international-trade-data
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "x-widget_config": {
+ "label": "Species",
+ "value": DEFAULT_TABLE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": label, "value": key}
+ for key, label in SPECIES_LABELS.items()
+ ],
+ "style": {"popupWidth": 280},
+ },
+ },
+ "direction": {
+ "x-widget_config": {
+ "label": "Direction",
+ "value": DEFAULT_DIRECTION,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": label, "value": key}
+ for key, label in DIRECTION_LABELS.items()
+ ],
+ },
+ },
+ "frequency": {
+ "x-widget_config": {
+ "label": "Frequency",
+ "value": DEFAULT_FREQUENCY,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": label, "value": key}
+ for key, label in FREQUENCY_LABELS.items()
+ ],
+ },
+ },
+ "product": {
+ "x-widget_config": {
+ "label": "Product",
+ "type": "endpoint",
+ "multiSelect": False,
+ "multiple": False,
+ "optionsEndpoint": f"{api_prefix}/usda/livestock_trade_products",
+ "optionsParams": {"table": "$table", "direction": "$direction"},
+ "style": {"popupWidth": 360},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ table: str = Field(
+ default=DEFAULT_TABLE,
+ description="Species workbook to retrieve. Each species publishes import"
+ + " and export blocks by trading partner and time; the years become the"
+ + " wide columns while the country stays as a pinned row. Valid species"
+ + " are:\n "
+ + ", ".join(SPECIES)
+ + "\n",
+ )
+ direction: str = Field(
+ default=DEFAULT_DIRECTION,
+ description="Trade direction to retrieve. Valid directions are:\n "
+ + ", ".join(DIRECTIONS)
+ + "\n",
+ )
+ frequency: str = Field(
+ default=DEFAULT_FREQUENCY,
+ description="Reporting frequency. 'annual' spreads calendar years plus"
+ + " the two cumulative year-to-date columns; 'monthly' keeps the year"
+ + " columns and adds a month row dimension. Valid frequencies are:\n "
+ + ", ".join(FREQUENCIES)
+ + "\n",
+ )
+ product: str | None = Field(
+ default=None,
+ description="Product block within the selected species and direction,"
+ + " e.g. 'Cattle, cattle for breeding' or 'Shell-egg'. If None, the"
+ + " direction's first published block is used.",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data."
+ + " If None, returns from 1989, the first year of the series.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data."
+ + " If None, returns up to the most recent year-to-date column.",
+ )
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate table."""
+ if not v:
+ return DEFAULT_TABLE
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip()
+ if value not in SPECIES:
+ raise OpenBBError(
+ f"Invalid table: {value}. Valid tables are: " + ", ".join(SPECIES)
+ )
+ return value
+
+ @field_validator("direction", mode="before", check_fields=False)
+ @classmethod
+ def _validate_direction(cls, v):
+ """Validate direction."""
+ if not v:
+ return DEFAULT_DIRECTION
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip().lower()
+ if value not in DIRECTIONS:
+ raise OpenBBError(
+ f"Invalid direction: {value}. Valid directions are: "
+ + ", ".join(DIRECTIONS)
+ )
+ return value
+
+ @field_validator("frequency", mode="before", check_fields=False)
+ @classmethod
+ def _validate_frequency(cls, v):
+ """Validate frequency."""
+ if not v:
+ return DEFAULT_FREQUENCY
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip().lower()
+ if value not in FREQUENCIES:
+ raise OpenBBError(
+ f"Invalid frequency: {value}. Valid frequencies are: "
+ + ", ".join(FREQUENCIES)
+ )
+ return value
+
+ @field_validator("product", mode="before", check_fields=False)
+ @classmethod
+ def _validate_product(cls, v):
+ """Normalize product to a stripped string or None."""
+ if not v:
+ return None
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ return str(value).strip() or None
+
+
+class LivestockAndMeatInternationalTradeDataData(NullTokenMixin, Data):
+ """USDA ERS Livestock and Meat International Trade Data.
+
+ One species-direction-product block pivoted to a wide layout: each row is a
+ trading partner while the years spread into value columns, oldest first with
+ the two cumulative year-to-date columns trailing. Monthly frequency adds a
+ month row dimension so each row is one partner-month with a value per year.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Livestock and Meat International Trade Data",
+ "$.description": "U.S. import and export trade in live cattle,"
+ " hogs, sheep, and goats and in beef and veal, pork, lamb and"
+ " mutton, poultry meat, and eggs, by trading partner and year,"
+ " published by the USDA Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ country: str = Field(
+ description="Trading-partner geography, as published, including the"
+ + " 'Other geographies' residual. On the monthly frequency the month is"
+ + " appended so each partner-month row is labeled, e.g. 'Canada — Jan',"
+ + " and the year columns hold that month's value across years.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Country", "pinned": "left"}
+ },
+ )
+ month: str | None = Field(
+ default=None,
+ description="Month of the observation for the monthly frequency, as a"
+ + " three-letter abbreviation. None for the annual frequency.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Month",
+ "pinned": "left",
+ "hide": True,
+ }
+ },
+ )
+ geography_code: int | None = Field(
+ default=None,
+ description="U.S. Census Bureau geography code of the trading partner."
+ + " None for the 'Other geographies' residual.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Geography code",
+ "cellDataType": "number",
+ "hide": True,
+ "maxWidth": 130,
+ }
+ },
+ )
+ unit: str | None = Field(
+ default=None,
+ description="Unit shared by every value column of the selected block,"
+ + " e.g. 'Carcass weight, 1,000 pounds', 'Head', or '1,000 dozen'.",
+ json_schema_extra={"x-widget_config": {"headerName": "Unit", "hide": True}},
+ )
+
+
+class LivestockAndMeatInternationalTradeDataFetcher(
+ Fetcher[
+ LivestockAndMeatInternationalTradeDataQueryParams,
+ list[LivestockAndMeatInternationalTradeDataData],
+ ]
+):
+ """Fetch USDA ERS Livestock and Meat International Trade Data."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> LivestockAndMeatInternationalTradeDataQueryParams:
+ """Transform the query params."""
+ return LivestockAndMeatInternationalTradeDataQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: LivestockAndMeatInternationalTradeDataQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected species-direction-product block's long records."""
+ from openbb_government_us.usda.utils import (
+ ers_livestock_and_meat_international_trade_data,
+ )
+
+ return await ers_livestock_and_meat_international_trade_data.afetch_records(
+ query.table, query.direction, query.frequency, query.product
+ )
+
+ @staticmethod
+ def transform_data(
+ query: LivestockAndMeatInternationalTradeDataQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[LivestockAndMeatInternationalTradeDataData]:
+ """Pivot the long records into wide rows with years as columns."""
+ monthly = any(record["month"] for record in data)
+ pivoted: dict[tuple, dict] = {}
+ col_meta: dict[str, tuple] = {}
+ for record in data:
+ year = record["year"]
+ if query.start_year is not None and year < query.start_year:
+ continue
+ if query.end_year is not None and year > query.end_year:
+ continue
+ col_meta[record["col_key"]] = (record["is_ytd"], year)
+ key = (record["country"], record["month"])
+ row = pivoted.get(key)
+ if row is None:
+ row = {
+ "_rank": record["rank"],
+ "_month_ord": record["month_ord"],
+ "country": (
+ f"{record['country']} — {record['month']}"
+ if monthly and record["month"]
+ else record["country"]
+ ),
+ "geography_code": record["geography_code"],
+ "month": record["month"],
+ "unit": record["unit"],
+ }
+ pivoted[key] = row
+ row[record["col_key"]] = record["value"]
+ if not pivoted:
+ raise EmptyDataError("No records match the given filters.")
+ column_order = sorted(col_meta, key=lambda col: col_meta[col])
+ results = sorted(
+ pivoted.values(),
+ key=lambda row: (row["_rank"], row["_month_ord"], row["country"]),
+ )
+ validated: list[LivestockAndMeatInternationalTradeDataData] = []
+ for row in results:
+ payload = {key: row[key] for key in DIM_KEYS}
+ for col in column_order:
+ payload[col] = row.get(col)
+ validated.append(
+ LivestockAndMeatInternationalTradeDataData.model_validate(payload)
+ )
+ return validated
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/major_land_uses.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/major_land_uses.py
new file mode 100644
index 00000000000..32c3079d650
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/major_land_uses.py
@@ -0,0 +1,264 @@
+"""Major Land Uses Model."""
+
+from typing import Any, Literal
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_major_land_uses import (
+ CROPLAND_VARIABLES,
+ GEOGRAPHIES,
+ LAND_USE_CATEGORIES,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+Table = Literal["land_use", "cropland_used_for_crops"]
+
+GEOGRAPHY_LOOKUP = {name.casefold(): name for name in GEOGRAPHIES}
+
+
+class MajorLandUsesQueryParams(QueryParams):
+ """Major Land Uses Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/major-land-uses
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "x-widget_config": {
+ "label": "Table",
+ "options": [
+ {"label": "Land use by region & State", "value": "land_use"},
+ {
+ "label": "Cropland used for crops (annual)",
+ "value": "cropland_used_for_crops",
+ },
+ ],
+ },
+ },
+ "land_use": {
+ "multiple_items_allowed": True,
+ "x-widget_config": {
+ "label": "Land use",
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": slug.replace("_", " ").capitalize(), "value": slug}
+ for slug in sorted(LAND_USE_CATEGORIES)
+ ],
+ "style": {"popupWidth": 340},
+ },
+ },
+ "geography": {
+ "multiple_items_allowed": True,
+ "x-widget_config": {
+ "label": "Geography",
+ "multiSelect": False,
+ "multiple": False,
+ "value": "48 States",
+ "options": [{"label": name, "value": name} for name in GEOGRAPHIES],
+ "style": {"popupWidth": 300},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ table: Table = Field(
+ default="land_use",
+ description="Dataset to retrieve. 'land_use' is land by major use,"
+ + " region, and State for Census of Agriculture benchmark years"
+ + " 1945-2017. 'cropland_used_for_crops' is the national annual"
+ + " cropland-used-for-crops series, 1910 to the current year.",
+ )
+ land_use: str | None = Field(
+ default=None,
+ description="Land use category(ies) to retrieve, as a comma-separated"
+ + " list. Only applies when table='land_use'; if None, all categories"
+ + " are returned. Valid categories are:\n "
+ + ", ".join(sorted(LAND_USE_CATEGORIES))
+ + "\n",
+ )
+ geography: str | None = Field(
+ default=None,
+ description="Geography(ies) to retrieve, as a comma-separated list of"
+ + " State names, farm production region names, '48 States', or"
+ + " 'U.S. total'. Only applies when table='land_use'; if None, all"
+ + " geographies are returned. Valid geographies are:\n "
+ + ", ".join(GEOGRAPHIES)
+ + "\n",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data."
+ + " None returns from the beginning of the series.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data."
+ + " If None, returns up to the most recent year.",
+ )
+
+ @field_validator("land_use", mode="before", check_fields=False)
+ @classmethod
+ def _validate_land_use(cls, v):
+ """Validate land_use."""
+ if not v:
+ return None
+ items = v.split(",") if isinstance(v, str) else list(v)
+ items = [item.strip() for item in items if item and item.strip()]
+ if not items:
+ return None
+ unknown = [item for item in items if item not in LAND_USE_CATEGORIES]
+ if unknown:
+ raise OpenBBError(
+ f"Invalid land_use value(s): {', '.join(unknown)}."
+ + " Valid categories are: "
+ + ", ".join(sorted(LAND_USE_CATEGORIES))
+ )
+ return ",".join(items)
+
+ @field_validator("geography", mode="before", check_fields=False)
+ @classmethod
+ def _validate_geography(cls, v):
+ """Validate geography."""
+ if not v:
+ return None
+ items = v.split(",") if isinstance(v, str) else list(v)
+ items = [item.strip() for item in items if item and item.strip()]
+ if not items:
+ return None
+ unknown = [item for item in items if item.casefold() not in GEOGRAPHY_LOOKUP]
+ if unknown:
+ raise OpenBBError(
+ f"Invalid geography value(s): {', '.join(unknown)}."
+ + " Valid geographies are: "
+ + ", ".join(GEOGRAPHIES)
+ )
+ return ",".join(GEOGRAPHY_LOOKUP[item.casefold()] for item in items)
+
+
+class MajorLandUsesData(NullTokenMixin, Data):
+ """Major Land Uses Data.
+
+ Land area by major use in the United States, from the USDA ERS Major
+ Land Uses series.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Major Land Uses",
+ "$.description": "Land area by major use for the United States, its regions, and States, published by the USDA Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ geography: str = Field(
+ description="Geography of the observation: a State name, a farm"
+ + " production region name, '48 States', or 'U.S. total' for the"
+ + " land_use table; 'United States' for the annual table.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Geography", "pinned": "left"}
+ },
+ )
+ land_use: str = Field(
+ description="Land use category of the observation. For the land_use"
+ + " table, one of the 16 category slugs; for the annual table, one"
+ + " of: "
+ + ", ".join(CROPLAND_VARIABLES)
+ + ".",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Land use", "pinned": "left"}
+ },
+ )
+ region: str | None = Field(
+ default=None,
+ description="Farm production region the State belongs to."
+ + " None for regional and national aggregate rows.",
+ json_schema_extra={"x-widget_config": {"headerName": "Region", "hide": True}},
+ )
+
+
+class MajorLandUsesFetcher(
+ Fetcher[
+ MajorLandUsesQueryParams,
+ list[MajorLandUsesData],
+ ]
+):
+ """Fetch USDA ERS Major Land Uses."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(params: dict[str, Any]) -> MajorLandUsesQueryParams:
+ """Transform the query params."""
+ return MajorLandUsesQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: MajorLandUsesQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected table's tidy records."""
+ from openbb_government_us.usda.utils import ers_major_land_uses
+
+ if query.table == "cropland_used_for_crops":
+ return await ers_major_land_uses.afetch_cropland_used_for_crops()
+ return await ers_major_land_uses.afetch_land_use()
+
+ @staticmethod
+ def transform_data(
+ query: MajorLandUsesQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[MajorLandUsesData]:
+ """Transform the data."""
+ selected_uses = (
+ set(query.land_use.split(","))
+ if query.land_use is not None and query.table == "land_use"
+ else None
+ )
+ selected_geographies = (
+ set(query.geography.split(","))
+ if query.geography is not None and query.table == "land_use"
+ else None
+ )
+ pivoted: dict[tuple, dict] = {}
+ for order, record in enumerate(data):
+ if query.start_year is not None and record["year"] < query.start_year:
+ continue
+ if query.end_year is not None and record["year"] > query.end_year:
+ continue
+ if selected_uses is not None and record["land_use"] not in selected_uses:
+ continue
+ if (
+ selected_geographies is not None
+ and record["geography"] not in selected_geographies
+ ):
+ continue
+ key = (record["geography"], record["land_use"])
+ row = pivoted.get(key)
+ if row is None:
+ row = {
+ "_order": order,
+ "geography": record["geography"],
+ "land_use": record["land_use"],
+ "region": record.get("region"),
+ }
+ pivoted[key] = row
+ row[str(record["year"])] = record["value"]
+ results = sorted(pivoted.values(), key=lambda row: row["_order"])
+ return [
+ MajorLandUsesData.model_validate(
+ {k: v for k, v in row.items() if k != "_order"}
+ )
+ for row in results
+ ]
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/meat_price_spreads.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/meat_price_spreads.py
new file mode 100644
index 00000000000..6e30b64d5d2
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/meat_price_spreads.py
@@ -0,0 +1,212 @@
+"""USDA ERS Meat Price Spreads Model."""
+
+from typing import Any, Literal
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_meat_price_spreads import (
+ MEAT_PRICE_SPREADS_FILES,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+DEFAULT_TABLE = "choice_beef"
+
+ANNUAL_DESCRIPTORS = frozenset({"Annual", "Calendar year"})
+
+Frequency = Literal["annual", "quarterly", "monthly"]
+
+
+class MeatPriceSpreadsQueryParams(QueryParams):
+ """USDA ERS Meat Price Spreads Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/meat-price-spreads
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "x-widget_config": {
+ "label": "Table",
+ "multiSelect": False,
+ "multiple": False,
+ "value": DEFAULT_TABLE,
+ "options": [
+ {"label": entry["title"], "value": key}
+ for key, entry in MEAT_PRICE_SPREADS_FILES.items()
+ ],
+ "style": {"popupWidth": 480},
+ },
+ },
+ "frequency": {
+ "x-widget_config": {
+ "label": "Frequency",
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": "Annual", "value": "annual"},
+ {"label": "Quarterly", "value": "quarterly"},
+ {"label": "Monthly", "value": "monthly"},
+ ],
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ table: str = Field(
+ default=DEFAULT_TABLE,
+ description="Table to retrieve. Each table is a self-contained set of"
+ + " data items pivoted into columns; the column set differs by table."
+ + " Valid tables are:\n "
+ + ", ".join(MEAT_PRICE_SPREADS_FILES)
+ + "\n",
+ )
+ frequency: Frequency | None = Field(
+ default=None,
+ description="Filter rows by observation frequency. Only the choice_beef"
+ + " and pork tables mix annual, quarterly, and monthly rows; the other"
+ + " tables carry only monthly rows. If None, all frequencies are"
+ + " included.",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data."
+ + " None returns from the beginning of the series"
+ + " (historical_monthly reaches 1970).",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data."
+ + " If None, returns up to the most recent year.",
+ )
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate the selected table key."""
+ if isinstance(v, (list, tuple)):
+ v = next((item for item in v if item), None)
+ if not v:
+ return DEFAULT_TABLE
+ table = str(v).strip()
+ if table not in MEAT_PRICE_SPREADS_FILES:
+ raise OpenBBError(
+ f"Invalid table: {table}. Valid tables are: "
+ + ", ".join(MEAT_PRICE_SPREADS_FILES)
+ )
+ return table
+
+
+class MeatPriceSpreadsData(NullTokenMixin, Data):
+ """USDA ERS Meat Price Spreads Data."""
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Meat Price Spreads",
+ "$.description": "Retail values, wholesale values, farm values,"
+ " price spreads, and farmers' shares for beef, pork, poultry,"
+ " and eggs, published by the USDA Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ period: str = Field(
+ description="Row label of the observation: the year for annual rows"
+ + " ('2020'), the year and the within-year period for sub-annual rows"
+ + " ('2020 January', '2020 Quarter 1, January-March'). The table's"
+ + " published data items spread into the wide value columns.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Period",
+ "pinned": "left",
+ "maxWidth": 240,
+ }
+ },
+ )
+
+
+class MeatPriceSpreadsFetcher(
+ Fetcher[
+ MeatPriceSpreadsQueryParams,
+ list[MeatPriceSpreadsData],
+ ]
+):
+ """Fetch USDA ERS Meat Price Spreads."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(params: dict[str, Any]) -> MeatPriceSpreadsQueryParams:
+ """Transform the query params."""
+ return MeatPriceSpreadsQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: MeatPriceSpreadsQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected table's long CSV rows."""
+ from openbb_government_us.usda.utils import ers_meat_price_spreads
+
+ return await ers_meat_price_spreads.afetch_table(query.table)
+
+ @staticmethod
+ def _period_label(year: int, period: str | None) -> str:
+ """Fold the year and within-year period into one readable time label."""
+ if not period or period.startswith("Yr") or period in ANNUAL_DESCRIPTORS:
+ return str(year)
+ if " to " in period:
+ return period
+ return f"{year} {period}"
+
+ @staticmethod
+ def transform_data(
+ query: MeatPriceSpreadsQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[MeatPriceSpreadsData]:
+ """Pivot the long rows into one wide row per period label."""
+ pivoted: dict[tuple, dict] = {}
+ col_order: list[str] = []
+ for record in data:
+ year = record["year"]
+ if query.start_year is not None and year < query.start_year:
+ continue
+ if query.end_year is not None and year > query.end_year:
+ continue
+ if query.frequency is not None and record["frequency"] != query.frequency:
+ continue
+ key = (year, record["period"])
+ row = pivoted.get(key)
+ if row is None:
+ row = {
+ "_year": year,
+ "_period_sort": record["period_number"],
+ "period": MeatPriceSpreadsFetcher._period_label(
+ year, record["period"]
+ ),
+ }
+ pivoted[key] = row
+ column = record["data_item"]
+ if column not in col_order:
+ col_order.append(column)
+ row[column] = record["value"]
+ results = sorted(
+ pivoted.values(),
+ key=lambda row: (row["_year"], row["_period_sort"]),
+ )
+ validated: list[MeatPriceSpreadsData] = []
+ for row in results:
+ payload = {"period": row["period"]}
+ for column in col_order:
+ payload[column] = row.get(column)
+ validated.append(MeatPriceSpreadsData.model_validate(payload))
+ return validated
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/milk_cost_of_production.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/milk_cost_of_production.py
new file mode 100644
index 00000000000..7d9a3d7a1eb
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/milk_cost_of_production.py
@@ -0,0 +1,400 @@
+"""Milk Cost Of Production Model."""
+
+from typing import Any, Literal
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.app.service.system_service import SystemService
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from openbb_core.provider.utils.errors import EmptyDataError
+from pydantic import ConfigDict, Field, field_validator, model_validator
+
+from openbb_government_us.usda.utils.ers_milk_cost_of_production import (
+ CATEGORY_CHOICES,
+ ITEM_CHOICES,
+ SIZE_CHOICES,
+ STATE_CHOICES,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+api_prefix = SystemService().system_settings.api_settings.prefix
+
+Report = Literal["by_state", "by_size_of_operation"]
+
+
+def _validate_selection(v, choices: dict[str, str], name: str) -> str | None:
+ """Normalize a list or comma-string selection against valid choices.
+
+ Parameters
+ ----------
+ v : Any
+ Raw selection value, a list or comma-separated string.
+ choices : dict[str, str]
+ Valid choice keys mapped to published labels.
+ name : str
+ Field name used in the error message.
+
+ Returns
+ -------
+ str | None
+ Comma-joined normalized selection, or None when empty.
+
+ Raises
+ ------
+ OpenBBError
+ If any selected value is not a valid choice.
+ """
+ if not v:
+ return None
+ values = v.split(",") if isinstance(v, str) else list(v)
+ values = [value.strip() for value in values if value and value.strip()]
+ if not values:
+ return None
+ unknown = [value for value in values if value not in choices]
+ if unknown:
+ raise OpenBBError(
+ f"Invalid {name}(s): {', '.join(unknown)}. Valid choices are: "
+ + ", ".join(sorted(choices))
+ )
+ return ",".join(values)
+
+
+class MilkCostOfProductionQueryParams(QueryParams):
+ """Milk Cost Of Production Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/milk-cost-of-production-estimates
+ """
+
+ __json_schema_extra__ = {
+ "report": {
+ "x-widget_config": {
+ "label": "Report",
+ "options": [
+ {"label": "By state", "value": "by_state"},
+ {
+ "label": "By herd-size class",
+ "value": "by_size_of_operation",
+ },
+ ],
+ },
+ },
+ "state": {
+ "multiple_items_allowed": True,
+ "x-widget_config": {
+ "label": "State",
+ "multiSelect": False,
+ "multiple": False,
+ "value": "us_total",
+ "options": [
+ {"label": label, "value": key}
+ for key, label in STATE_CHOICES.items()
+ ],
+ },
+ },
+ "size": {
+ "multiple_items_allowed": True,
+ "x-widget_config": {
+ "label": "Herd-size class",
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": label, "value": key}
+ for key, label in SIZE_CHOICES.items()
+ ],
+ },
+ },
+ "category": {
+ "multiple_items_allowed": True,
+ "x-widget_config": {
+ "label": "Cost category",
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": label, "value": key}
+ for key, label in CATEGORY_CHOICES.items()
+ ],
+ "style": {"popupWidth": 300},
+ },
+ },
+ "item": {
+ "multiple_items_allowed": True,
+ "x-widget_config": {
+ "label": "Cost item",
+ "type": "endpoint",
+ "multiSelect": False,
+ "multiple": False,
+ "optionsEndpoint": f"{api_prefix}/usda/milk_cost_items",
+ "optionsParams": {"report": "$report", "category": "$category"},
+ "style": {"popupWidth": 360},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ report: Report = Field(
+ default="by_state",
+ description="Report to retrieve: estimates by state or by size of"
+ + " operation (herd-size class).",
+ )
+ state: str | None = Field(
+ default=None,
+ description="State(s) to filter, as a comma-separated list."
+ + " Only valid with report='by_state'."
+ + " If None, all states and the U.S. total are returned."
+ + " Valid states are:\n "
+ + ", ".join(sorted(STATE_CHOICES))
+ + "\n",
+ )
+ size: str | None = Field(
+ default=None,
+ description="Herd-size class(es) to filter, as a comma-separated list."
+ + " Only valid with report='by_size_of_operation'."
+ + " If None, all size classes are returned. Valid sizes are:\n "
+ + ", ".join(sorted(SIZE_CHOICES))
+ + "\n",
+ )
+ category: str | None = Field(
+ default=None,
+ description="Estimate category(s) to filter, as a comma-separated list."
+ + " If None, all categories are returned. Valid categories are:\n "
+ + ", ".join(sorted(CATEGORY_CHOICES))
+ + "\n",
+ )
+ item: str | None = Field(
+ default=None,
+ description="Estimate item(s) to filter, as a comma-separated list."
+ + " If None, all items are returned. Valid items are:\n "
+ + ", ".join(sorted(ITEM_CHOICES))
+ + "\n",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data."
+ + " If None, returns from the beginning of the series.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data."
+ + " If None, returns up to the most recent year.",
+ )
+
+ @field_validator("state", mode="before", check_fields=False)
+ @classmethod
+ def _validate_state(cls, v):
+ """Validate state."""
+ return _validate_selection(v, STATE_CHOICES, "state")
+
+ @field_validator("size", mode="before", check_fields=False)
+ @classmethod
+ def _validate_size(cls, v):
+ """Validate size."""
+ return _validate_selection(v, SIZE_CHOICES, "size")
+
+ @field_validator("category", mode="before", check_fields=False)
+ @classmethod
+ def _validate_category(cls, v):
+ """Validate category."""
+ return _validate_selection(v, CATEGORY_CHOICES, "category")
+
+ @field_validator("item", mode="before", check_fields=False)
+ @classmethod
+ def _validate_item(cls, v):
+ """Validate item."""
+ return _validate_selection(v, ITEM_CHOICES, "item")
+
+ @model_validator(mode="after")
+ def _validate_report_filters(self):
+ """Validate that state and size match the selected report."""
+ if self.report == "by_state" and self.size is not None:
+ raise OpenBBError(
+ "The 'size' filter applies only to report='by_size_of_operation'."
+ )
+ if self.report == "by_size_of_operation" and self.state is not None:
+ raise OpenBBError("The 'state' filter applies only to report='by_state'.")
+ return self
+
+
+class MilkCostOfProductionData(NullTokenMixin, Data):
+ """Milk Cost Of Production Data.
+
+ Annual milk cost-of-production estimates from USDA ERS, built from the
+ 2021 Agricultural Resource Management Survey base. Each row is one
+ cost-and-returns line for one State or one herd-size class, with one
+ value column per year.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Milk Cost of Production",
+ "$.description": "Annual milk cost-of-production estimates by state and by size of operation, published by the USDA Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ group: str = Field(
+ description="Population the estimate covers: the State, or 'U.S. total'"
+ + " for the national aggregate, in the by_state report; the herd-size"
+ + " class in the by_size_of_operation report.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "State / herd size", "pinned": "left"}
+ },
+ )
+ category: str = Field(
+ description="Estimate category, as published. One of: Gross value of"
+ + " production, Operating costs, Allocated overhead, Costs listed,"
+ + " Net value, Supporting information.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Cost category", "pinned": "left"}
+ },
+ )
+ item: str = Field(
+ description="Estimate item, as published, with footnote superscripts"
+ + " stripped. Includes published subtotal and net-value rows.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Cost item", "pinned": "left"}
+ },
+ )
+ unit: str = Field(
+ description="Unit of the value, as published. Cost and return items"
+ + " are 'dollars per hundredweight sold'; 'Milk cows' is"
+ + " 'head per farm' and 'Output per cow' is 'pounds'.",
+ json_schema_extra={"x-widget_config": {"headerName": "Unit"}},
+ )
+
+
+class MilkCostOfProductionFetcher(
+ Fetcher[
+ MilkCostOfProductionQueryParams,
+ list[MilkCostOfProductionData],
+ ]
+):
+ """Fetch USDA ERS Milk Cost of Production Estimates."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> MilkCostOfProductionQueryParams:
+ """Transform the query params."""
+ return MilkCostOfProductionQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: MilkCostOfProductionQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected report's tidy CSV rows."""
+ from openbb_government_us.usda.utils import ers_milk_cost_of_production
+
+ return await ers_milk_cost_of_production.afetch_report(query.report)
+
+ @staticmethod
+ def transform_data(
+ query: MilkCostOfProductionQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[MilkCostOfProductionData]:
+ """Filter the report's rows and spread the years into ascending columns.
+
+ Returns
+ -------
+ list[MilkCostOfProductionData]
+ One record per State or herd-size class, cost category and item,
+ ordered by the published dimension, category and item order, with
+ subtotal lines last within a category.
+
+ Raises
+ ------
+ EmptyDataError
+ If no record survives the filters.
+ """
+ states = (
+ {STATE_CHOICES[value] for value in query.state.split(",")}
+ if query.state
+ else None
+ )
+ sizes = (
+ {SIZE_CHOICES[value] for value in query.size.split(",")}
+ if query.size
+ else None
+ )
+ categories = (
+ {CATEGORY_CHOICES[value] for value in query.category.split(",")}
+ if query.category
+ else None
+ )
+ items = (
+ {ITEM_CHOICES[value] for value in query.item.split(",")}
+ if query.item
+ else None
+ )
+ group_order = list(
+ (STATE_CHOICES if query.report == "by_state" else SIZE_CHOICES).values()
+ )
+ category_order = list(CATEGORY_CHOICES.values())
+ pivoted: dict[tuple, dict] = {}
+ years: set[int] = set()
+ for record in data:
+ if query.start_year is not None and record["year"] < query.start_year:
+ continue
+ if query.end_year is not None and record["year"] > query.end_year:
+ continue
+ if states is not None and record["state"] not in states:
+ continue
+ if sizes is not None and record["size_of_operation"] not in sizes:
+ continue
+ if categories is not None and record["category"] not in categories:
+ continue
+ if items is not None and record["item"] not in items:
+ continue
+ group = record.get("state") or record.get("size_of_operation") or ""
+ key = (group, record["category"], record["item"], record["unit"])
+ row = pivoted.get(key)
+ if row is None:
+ row = {
+ "_group_index": (
+ group_order.index(group)
+ if group in group_order
+ else len(group_order)
+ ),
+ "_category_index": (
+ category_order.index(record["category"])
+ if record["category"] in category_order
+ else len(category_order)
+ ),
+ "_total_last": 1 if record["item"].startswith("Total,") else 0,
+ "group": group,
+ "category": record["category"],
+ "item": record["item"],
+ "unit": record["unit"],
+ }
+ pivoted[key] = row
+ years.add(record["year"])
+ row[str(record["year"])] = record["value"]
+ if not pivoted:
+ raise EmptyDataError("No records match the given filters.")
+ columns = [str(year) for year in sorted(years)]
+ results = sorted(
+ pivoted.values(),
+ key=lambda row: (
+ row["_group_index"],
+ row["_category_index"],
+ row["_total_last"],
+ row["item"],
+ ),
+ )
+ validated: list[MilkCostOfProductionData] = []
+ for row in results:
+ payload = {key: row[key] for key in ("group", "category", "item", "unit")}
+ for column in columns:
+ payload[column] = row.get(column)
+ validated.append(MilkCostOfProductionData.model_validate(payload))
+ return validated
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/natural_amenities_scale.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/natural_amenities_scale.py
new file mode 100644
index 00000000000..3ba6f751951
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/natural_amenities_scale.py
@@ -0,0 +1,362 @@
+"""USDA ERS Natural Amenities Scale Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from openbb_core.provider.utils.errors import EmptyDataError
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_natural_amenities_scale import (
+ ALL_STATES,
+ state_options,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+
+class NaturalAmenitiesScaleQueryParams(QueryParams):
+ """USDA ERS Natural Amenities Scale Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/natural-amenities-scale
+ """
+
+ __json_schema_extra__ = {
+ "state": {
+ "x-widget_config": {
+ "label": "State",
+ "multiSelect": False,
+ "multiple": False,
+ "options": state_options(),
+ "style": {"popupWidth": 260},
+ },
+ },
+ }
+
+ state: str | None = Field(
+ default=None,
+ description="Two-letter state postal abbreviation to scope the county"
+ + " rows to a single state. If None, returns every county. The scale"
+ + " covers the 48 contiguous states and the District of Columbia;"
+ + " Alaska, Hawaii, and the territories are not scored.",
+ )
+
+ @field_validator("state", mode="before", check_fields=False)
+ @classmethod
+ def _validate_state(cls, v):
+ """Validate the optional state filter."""
+ if not v:
+ return None
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ state = str(value).strip().upper()
+ if not state:
+ return None
+ if state not in ALL_STATES:
+ raise OpenBBError(
+ f"Invalid state: {value}. Use a two-letter postal abbreviation,"
+ + " e.g. 'TX'."
+ )
+ return state
+
+
+class NaturalAmenitiesScaleData(NullTokenMixin, Data):
+ """USDA ERS Natural Amenities Scale Data.
+
+ A county lookup: one row per county FIPS carrying the six underlying
+ physical measures, their standardized Z-scores, the composite natural
+ amenity scale value, the 1-to-7 amenity rank, and the census division,
+ Rural-Urban Continuum, Urban Influence, and topography classification
+ codes.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Natural Amenities Scale",
+ "$.description": "County-level natural amenities scale and its"
+ " six climate, topography, and water-area components for the"
+ " contiguous United States, published by the USDA Economic"
+ " Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ fips: str = Field(
+ description="County FIPS code, five digits with a leading zero.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "FIPS",
+ "cellDataType": "text",
+ "pinned": "left",
+ "maxWidth": 100,
+ }
+ },
+ )
+ fips_used_for_measures: str | None = Field(
+ default=None,
+ description="County FIPS whose measures this row carries, equal to the"
+ + " FIPS code except for the 41 combined areas whose measures are"
+ + " reported under a neighboring county.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "FIPS Used for Measures",
+ "cellDataType": "text",
+ "hide": True,
+ }
+ },
+ )
+ state: str | None = Field(
+ default=None,
+ description="Two-letter state postal abbreviation.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "State",
+ "cellDataType": "text",
+ "pinned": "left",
+ "maxWidth": 90,
+ }
+ },
+ )
+ county_name: str | None = Field(
+ default=None,
+ description="County or county-equivalent area name, as published.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "County",
+ "pinned": "left",
+ "minWidth": 180,
+ }
+ },
+ )
+ census_division: str | None = Field(
+ default=None,
+ description="Census division code, 1 to 9.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Census Division",
+ "cellDataType": "text",
+ "maxWidth": 150,
+ }
+ },
+ )
+ rural_urban_continuum_code: str | None = Field(
+ default=None,
+ description="Rural-Urban Continuum (Beale) code, 1993 vintage.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Rural-Urban Continuum Code (1993)",
+ "cellDataType": "text",
+ }
+ },
+ )
+ urban_influence_code: str | None = Field(
+ default=None,
+ description="Urban Influence Code, 1993 vintage.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Urban Influence Code (1993)",
+ "cellDataType": "text",
+ }
+ },
+ )
+ mean_january_temperature: float | None = Field(
+ default=None,
+ description="Mean January temperature in degrees Fahrenheit, 1941-70.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Mean January Temp (F)",
+ "cellDataType": "number",
+ }
+ },
+ )
+ mean_january_sunlight: float | None = Field(
+ default=None,
+ description="Mean hours of January sunlight, 1941-70.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Mean January Sunlight (hrs)",
+ "cellDataType": "number",
+ }
+ },
+ )
+ mean_july_temperature: float | None = Field(
+ default=None,
+ description="Mean July temperature in degrees Fahrenheit, 1941-70.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Mean July Temp (F)",
+ "cellDataType": "number",
+ }
+ },
+ )
+ mean_july_humidity: float | None = Field(
+ default=None,
+ description="Mean relative July humidity in percent, 1941-70.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Mean July Humidity (%)",
+ "cellDataType": "number",
+ }
+ },
+ )
+ topography_code: str | None = Field(
+ default=None,
+ description="Land surface form topography code, 1 to 21.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Topography Code",
+ "cellDataType": "text",
+ }
+ },
+ )
+ percent_water_area: float | None = Field(
+ default=None,
+ description="Water area as a percent of the county's total area.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Percent Water Area",
+ "cellDataType": "number",
+ }
+ },
+ )
+ log_water_area: float | None = Field(
+ default=None,
+ description="Natural log of the county's water area times 100.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Log Water Area x100",
+ "cellDataType": "number",
+ }
+ },
+ )
+ january_temperature_z: float | None = Field(
+ default=None,
+ description="Standardized Z-score of the mean January temperature.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Jan Temp (Z)",
+ "cellDataType": "number",
+ }
+ },
+ )
+ january_sunlight_z: float | None = Field(
+ default=None,
+ description="Standardized Z-score of the mean January sunlight.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Jan Sun (Z)",
+ "cellDataType": "number",
+ }
+ },
+ )
+ july_temperature_z: float | None = Field(
+ default=None,
+ description="Standardized Z-score of the mean July temperature.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Jul Temp (Z)",
+ "cellDataType": "number",
+ }
+ },
+ )
+ july_humidity_z: float | None = Field(
+ default=None,
+ description="Standardized Z-score of the mean July humidity, inverted"
+ + " so that lower humidity scores higher.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Jul Humidity (Z)",
+ "cellDataType": "number",
+ }
+ },
+ )
+ topography_z: float | None = Field(
+ default=None,
+ description="Standardized Z-score of the topography code.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Topography (Z)",
+ "cellDataType": "number",
+ }
+ },
+ )
+ log_water_area_z: float | None = Field(
+ default=None,
+ description="Standardized Z-score of the log water area.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Log Water Area (Z)",
+ "cellDataType": "number",
+ }
+ },
+ )
+ natural_amenity_scale: float | None = Field(
+ default=None,
+ description="Composite natural amenity scale, the sum of the six"
+ + " standardized Z-scores.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Natural Amenity Scale",
+ "cellDataType": "number",
+ }
+ },
+ )
+ natural_amenity_rank: str | None = Field(
+ default=None,
+ description="Natural amenity rank derived from the scale, 1 for the"
+ + " lowest amenity through 7 for the highest.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Amenity Rank (1=Low, 7=High)",
+ "cellDataType": "text",
+ }
+ },
+ )
+
+
+class NaturalAmenitiesScaleFetcher(
+ Fetcher[
+ NaturalAmenitiesScaleQueryParams,
+ list[NaturalAmenitiesScaleData],
+ ]
+):
+ """Fetch USDA ERS Natural Amenities Scale."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(params: dict[str, Any]) -> NaturalAmenitiesScaleQueryParams:
+ """Transform the query params."""
+ return NaturalAmenitiesScaleQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: NaturalAmenitiesScaleQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the county-lookup records."""
+ from openbb_government_us.usda.utils import ers_natural_amenities_scale
+
+ return await ers_natural_amenities_scale.afetch_records()
+
+ @staticmethod
+ def transform_data(
+ query: NaturalAmenitiesScaleQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[NaturalAmenitiesScaleData]:
+ """Scope the county rows to the selected state and sort by FIPS."""
+ selected = [
+ record
+ for record in data
+ if query.state is None or record.get("state") == query.state
+ ]
+ if not selected:
+ raise EmptyDataError("No records match the given filters.")
+ selected.sort(key=lambda record: record["fips"])
+ return [NaturalAmenitiesScaleData.model_validate(record) for record in selected]
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/normalized_prices.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/normalized_prices.py
new file mode 100644
index 00000000000..88d8fe6142d
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/normalized_prices.py
@@ -0,0 +1,201 @@
+"""USDA ERS Normalized Prices Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from openbb_core.provider.utils.errors import EmptyDataError
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_normalized_prices import (
+ DEFAULT_TABLE,
+ NORMALIZED_PRICES_TABLES,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+TABLE_OPTIONS = [
+ {"label": config["label"], "value": key}
+ for key, config in NORMALIZED_PRICES_TABLES.items()
+]
+
+
+class NormalizedPricesQueryParams(QueryParams):
+ """USDA ERS Normalized Prices Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/normalized-prices
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "x-widget_config": {
+ "label": "Table",
+ "value": DEFAULT_TABLE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": TABLE_OPTIONS,
+ "style": {"popupWidth": 520},
+ },
+ },
+ }
+
+ table: str = Field(
+ default=DEFAULT_TABLE,
+ description="Table to retrieve. 'table1_national_prices' spreads the"
+ + " report years into columns with one row per commodity;"
+ + " 'table2_national_indices' spreads the index years into columns with"
+ + " one row per national price index series;"
+ + " 'table3_state_prices' spreads the States into columns with one row"
+ + " per commodity for the current report year. Valid tables are:\n "
+ + ", ".join(NORMALIZED_PRICES_TABLES)
+ + "\n",
+ )
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate table."""
+ if not v:
+ return DEFAULT_TABLE
+ table = v[0] if isinstance(v, (list, tuple)) else v
+ table = str(table).strip()
+ if table not in NORMALIZED_PRICES_TABLES:
+ raise OpenBBError(
+ f"Invalid table: {table}. Valid tables are: "
+ + ", ".join(NORMALIZED_PRICES_TABLES)
+ )
+ return table
+
+
+class NormalizedPricesData(NullTokenMixin, Data):
+ """USDA ERS Normalized Prices Data.
+
+ One selected table pivoted to a wide layout: each row is a commodity or
+ price-index series with its unit, and the report years, index years, or
+ States spread into the value columns, whose set varies by table. Values
+ are the raw published normalized prices; suppressed State cells are None.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Normalized Prices",
+ "$.description": "National and State-level normalized prices for"
+ " crops, livestock, milk, poultry, eggs, and wool, plus national"
+ " prices-received and prices-paid indices, published by the USDA"
+ " Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ commodity: str = Field(
+ description="Commodity (national and State price tables) or national"
+ + " price index series (the index table), as published.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Commodity", "pinned": "left"}
+ },
+ )
+ units: str | None = Field(
+ default=None,
+ description="Unit of the row's prices, as published, e.g. '$ / bu',"
+ + " '$ / cwt', or 'Index, 2011=100' for the price index table.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Units",
+ "pinned": "left",
+ "maxWidth": 150,
+ }
+ },
+ )
+ description: str | None = Field(
+ default=None,
+ description="Commodity qualifier, as published, e.g. 'for grain' or"
+ + " 'lint, upland'. Absent for the price index table.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Description", "hide": True}
+ },
+ )
+
+
+class NormalizedPricesFetcher(
+ Fetcher[
+ NormalizedPricesQueryParams,
+ list[NormalizedPricesData],
+ ]
+):
+ """Fetch USDA ERS Normalized Prices."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(params: dict[str, Any]) -> NormalizedPricesQueryParams:
+ """Transform the query params."""
+ return NormalizedPricesQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: NormalizedPricesQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected table's long-format rows."""
+ from openbb_government_us.usda.utils import ers_normalized_prices
+
+ return await ers_normalized_prices.afetch_table(query.table)
+
+ @staticmethod
+ def transform_data(
+ query: NormalizedPricesQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[NormalizedPricesData]:
+ """Pivot the long-format rows into the wide layout for the table.
+
+ The report or index years spread into ascending-ordered columns, or
+ the States into alphabetically-ordered columns, one row per commodity
+ or price-index series in published order.
+ """
+ dimension = NORMALIZED_PRICES_TABLES[query.table]["dimension"]
+ pivoted: dict[tuple, dict] = {}
+ column_keys: set[str] = set()
+ for record in data:
+ key = (record["commodity"], record["description"], record["units"])
+ row = pivoted.get(key)
+ if row is None:
+ row = {
+ "_order": record["order"],
+ "commodity": record["commodity"],
+ "units": record["units"],
+ "description": record["description"],
+ }
+ pivoted[key] = row
+ column = str(record["column"])
+ column_keys.add(column)
+ row[column] = record["value"]
+ if not pivoted:
+ raise EmptyDataError("No records match the given filters.")
+ populated = {
+ column
+ for column in column_keys
+ if any(row.get(column) is not None for row in pivoted.values())
+ }
+ ordered_columns = (
+ sorted(populated, key=int) if dimension == "year" else sorted(populated)
+ )
+ results = sorted(pivoted.values(), key=lambda row: row["_order"])
+ validated: list[NormalizedPricesData] = []
+ for row in results:
+ dimensions = {
+ "commodity": row["commodity"],
+ "units": row["units"],
+ "description": row["description"],
+ }
+ columns = {column: row.get(column) for column in ordered_columns}
+ validated.append(
+ NormalizedPricesData.model_validate({**dimensions, **columns})
+ )
+ return validated
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/oil_crops_yearbook.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/oil_crops_yearbook.py
new file mode 100644
index 00000000000..e5b83b11716
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/oil_crops_yearbook.py
@@ -0,0 +1,331 @@
+"""USDA ERS Oil Crops Yearbook Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.app.service.system_service import SystemService
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from openbb_core.provider.utils.errors import EmptyDataError
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_oil_crops_yearbook import (
+ DEFAULT_FREQUENCY,
+ DEFAULT_TABLE,
+ FREQUENCIES_BY_TABLE,
+ FREQUENCY_LABELS,
+ OIL_CROPS_TABLES,
+ format_period,
+ period_ordinal,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin, is_null_token
+
+api_prefix = SystemService().system_settings.api_settings.prefix
+
+FOLD_DIM_FIELDS = ("commodity", "geography")
+
+
+class OilCropsYearbookQueryParams(QueryParams):
+ """USDA ERS Oil Crops Yearbook Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/oil-crops-yearbook
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "x-widget_config": {
+ "label": "Table",
+ "value": DEFAULT_TABLE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": config["label"], "value": key}
+ for key, config in OIL_CROPS_TABLES.items()
+ ],
+ "style": {"popupWidth": 520},
+ },
+ },
+ "frequency": {
+ "x-widget_config": {
+ "label": "Frequency",
+ "type": "endpoint",
+ "multiSelect": False,
+ "multiple": False,
+ "optionsEndpoint": f"{api_prefix}/usda/oil_crops_frequencies",
+ "optionsParams": {"table": "$table"},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ table: str = Field(
+ default=DEFAULT_TABLE,
+ description="Oil Crops Yearbook table to retrieve. Each table is a set"
+ + " of named supply, use, price, or trade series indexed by year, and"
+ + " sometimes by month or quarter; the series spread into value columns"
+ + " while time stays in the rows. Valid tables are:\n "
+ + ", ".join(OIL_CROPS_TABLES)
+ + "\n",
+ )
+ frequency: str | None = Field(
+ default=None,
+ description="Observation frequency to keep, one of the frequencies the"
+ + " selected table publishes: 'point_in_time', 'annual', 'quarterly',"
+ + " or 'monthly'. Filtering to one frequency yields a single aligned"
+ + " time series. If None, the table's default frequency is used.",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data, compared against the"
+ + " first four digits of the marketing or calendar year."
+ + " If None, returns from the beginning of the series.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data, compared against the"
+ + " first four digits of the marketing or calendar year."
+ + " If None, returns up to the most recent year.",
+ )
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate table."""
+ if not v:
+ return DEFAULT_TABLE
+ table = v.strip() if isinstance(v, str) else v
+ if table not in OIL_CROPS_TABLES:
+ raise OpenBBError(
+ f"Invalid table: {table}. Valid tables are: "
+ + ", ".join(OIL_CROPS_TABLES)
+ )
+ return table
+
+ @field_validator("frequency", mode="before", check_fields=False)
+ @classmethod
+ def _validate_frequency(cls, v):
+ """Validate frequency."""
+ if not v:
+ return None
+ frequency = v.strip().lower() if isinstance(v, str) else v
+ if frequency not in FREQUENCY_LABELS:
+ raise OpenBBError(
+ f"Invalid frequency: {v}. Valid frequencies are: "
+ + ", ".join(FREQUENCY_LABELS)
+ )
+ return frequency
+
+
+class OilCropsYearbookData(NullTokenMixin, Data):
+ """USDA ERS Oil Crops Yearbook Data.
+
+ One selected Oil Crops Yearbook table for one frequency, pivoted to a wide
+ layout: the table's series spread into value columns while the period stays
+ in the rows, with any varying commodity or geography folded into the period
+ label. Series columns carry their unit in the header when the table reports
+ more than one unit.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Oil Crops Yearbook",
+ "$.description": "U.S. and world oilseed, oilmeal, and vegetable"
+ " oil supply, use, prices, and trade for soybeans, peanuts,"
+ " cottonseed, sunflowerseed, canola, and flaxseed, published by"
+ " the USDA Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ period: str = Field(
+ description="Row label of the observation: the marketing or calendar"
+ + " year for annual tables ('2024/25'), the year and month, quarter, or"
+ + " stocks date for sub-annual tables ('2024/25 October', '2024/25"
+ + " March 1'), with any varying commodity or geography prefixed so each"
+ + " row is uniquely labeled.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Period",
+ "pinned": "left",
+ "maxWidth": 320,
+ }
+ },
+ )
+
+
+class OilCropsYearbookFetcher(
+ Fetcher[
+ OilCropsYearbookQueryParams,
+ list[OilCropsYearbookData],
+ ]
+):
+ """Fetch USDA ERS Oil Crops Yearbook Data."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(params: dict[str, Any]) -> OilCropsYearbookQueryParams:
+ """Transform the query params."""
+ return OilCropsYearbookQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: OilCropsYearbookQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected table's long-format rows."""
+ from openbb_government_us.usda.utils import ers_oil_crops_yearbook
+
+ return await ers_oil_crops_yearbook.afetch_table(query.table)
+
+ @staticmethod
+ def _resolve_frequency(query: OilCropsYearbookQueryParams) -> str:
+ """Resolve the requested frequency against the table's frequencies."""
+ available = FREQUENCIES_BY_TABLE[query.table]
+ requested = query.frequency
+ if requested is not None and requested in available:
+ return requested
+ return DEFAULT_FREQUENCY[query.table]
+
+ @staticmethod
+ def transform_data(
+ query: OilCropsYearbookQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[OilCropsYearbookData]:
+ """Filter to one frequency and pivot the series into columns."""
+ if not data:
+ return []
+ frequency = OilCropsYearbookFetcher._resolve_frequency(query)
+ rows = [
+ record
+ for record in data
+ if record["frequency"] == frequency
+ and (query.start_year is None or record["year"] >= query.start_year)
+ and (query.end_year is None or record["year"] <= query.end_year)
+ ]
+ if not rows:
+ raise EmptyDataError("No records match the given filters.")
+ return OilCropsYearbookFetcher._pivot(rows, frequency)
+
+ @staticmethod
+ def _dim_value(record: dict, field: str):
+ """Return a dimension value, treating placeholder tokens as absent."""
+ value = record[field]
+ return None if is_null_token(value) else value
+
+ @staticmethod
+ def _classify_dims(
+ rows: list[dict], fold_dims: list[str]
+ ) -> tuple[list[str], list[str]]:
+ """Split folding dims into series-partitioning column dims and row dims."""
+ value = OilCropsYearbookFetcher._dim_value
+ column_dims: list[str] = []
+ row_dims: list[str] = []
+ for field in fold_dims:
+ distinct = {
+ value(record, field)
+ for record in rows
+ if value(record, field) is not None
+ }
+ series_values: dict[str, set] = {}
+ for record in rows:
+ member = value(record, field)
+ if member is not None:
+ series_values.setdefault(record["series"], set()).add(member)
+ spread = (
+ sum(len(members) for members in series_values.values())
+ / len(series_values)
+ if series_values
+ else 0.0
+ )
+ if distinct and series_values and spread <= 0.6 * len(distinct):
+ column_dims.append(field)
+ else:
+ row_dims.append(field)
+ return column_dims, row_dims
+
+ @staticmethod
+ def _pivot(rows: list[dict], frequency: str) -> list[OilCropsYearbookData]:
+ """Pivot to period rows with the series as unit-tagged columns."""
+ value = OilCropsYearbookFetcher._dim_value
+ fold_dims = [
+ field
+ for field in FOLD_DIM_FIELDS
+ if len({value(record, field) for record in rows}) > 1
+ ]
+ column_dims, row_dims = OilCropsYearbookFetcher._classify_dims(rows, fold_dims)
+ multi_unit = len({record["unit_desc"] for record in rows}) > 1
+ pivoted: dict[tuple, dict[str, Any]] = {}
+ column_order: list[str] = []
+ seen_columns: set[str] = set()
+ for order, record in enumerate(rows):
+ marketing_year = record["marketing_year"]
+ if record["period"]:
+ display = format_period(
+ record["period"], frequency, record["my_definition"]
+ )
+ time_label = f"{marketing_year} {display}"
+ else:
+ time_label = marketing_year
+ dim_values = [
+ str(value(record, field))
+ for field in row_dims
+ if value(record, field) is not None
+ ]
+ label = " — ".join([*dim_values, time_label])
+ key = (
+ tuple(value(record, field) for field in row_dims),
+ marketing_year,
+ record["period"],
+ )
+ row = pivoted.get(key)
+ if row is None:
+ row = {
+ "_dims": tuple(dim_values),
+ "_year": record["year"],
+ "_ord": period_ordinal(
+ record["period"], frequency, record["my_definition"]
+ ),
+ "_order": order,
+ "period": label,
+ }
+ pivoted[key] = row
+ column_values = [
+ str(value(record, field))
+ for field in column_dims
+ if value(record, field) is not None
+ ]
+ base = " — ".join([*column_values, record["series"]])
+ column = (
+ f"{base} ({record['unit_desc']})"
+ if multi_unit and record["unit_desc"]
+ else base
+ )
+ if column not in seen_columns:
+ seen_columns.add(column)
+ column_order.append(column)
+ row[column] = record["amount"]
+ ordered = sorted(
+ pivoted.values(),
+ key=lambda row: (
+ row["_dims"],
+ -row["_year"],
+ -row["_ord"],
+ row["_order"],
+ ),
+ )
+ validated: list[OilCropsYearbookData] = []
+ for row in ordered:
+ payload = {"period": row["period"]}
+ for column in column_order:
+ payload[column] = row.get(column)
+ validated.append(OilCropsYearbookData.model_validate(payload))
+ return validated
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/poverty_area_measures.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/poverty_area_measures.py
new file mode 100644
index 00000000000..686df5cd1e2
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/poverty_area_measures.py
@@ -0,0 +1,553 @@
+"""USDA ERS Poverty Area Measures Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.app.service.system_service import SystemService
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from openbb_core.provider.utils.errors import EmptyDataError
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_poverty_area_measures import (
+ DEFAULT_EDITION,
+ EDITION_OPTIONS,
+ EDITIONS,
+ LEVEL_OPTIONS,
+ LEVELS,
+ STATE_NAMES,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+api_prefix = SystemService().system_settings.api_settings.prefix
+
+
+def _flag_config(header: str, hide: bool = False) -> dict:
+ """Build a per-field widget config for a poverty-area classification flag."""
+ config: dict[str, Any] = {"headerName": header, "cellDataType": "text"}
+ if hide:
+ config["hide"] = True
+ return config
+
+
+class PovertyAreaMeasuresQueryParams(QueryParams):
+ """USDA ERS Poverty Area Measures Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/poverty-area-measures
+ """
+
+ __json_schema_extra__ = {
+ "edition": {
+ "x-widget_config": {
+ "label": "Edition",
+ "value": DEFAULT_EDITION,
+ "multiSelect": False,
+ "multiple": False,
+ "options": EDITION_OPTIONS,
+ "style": {"popupWidth": 300},
+ },
+ },
+ "level": {
+ "x-widget_config": {
+ "label": "Geography Level",
+ "value": "county",
+ "multiSelect": False,
+ "multiple": False,
+ "options": LEVEL_OPTIONS,
+ "style": {"popupWidth": 240},
+ },
+ },
+ "state": {
+ "x-widget_config": {
+ "label": "State",
+ "type": "endpoint",
+ "multiSelect": False,
+ "multiple": False,
+ "optionsEndpoint": f"{api_prefix}/usda/poverty_area_measures_states",
+ "optionsParams": {"edition": "$edition"},
+ "style": {"popupWidth": 260},
+ },
+ },
+ }
+
+ edition: str = Field(
+ default=DEFAULT_EDITION,
+ description="Published edition to retrieve. Each edition is a separate"
+ + " release of the poverty-area classification, differing in census-tract"
+ + " geography, metro/Beale/RUCA vintage, and the most recent ACS"
+ + " measurement periods carried. Valid editions are:\n "
+ + ", ".join(EDITIONS)
+ + "\n",
+ )
+ level: str = Field(
+ default="county",
+ description="Geographic level of the lookup. 'county' returns one row"
+ + " per county FIPS reading the county classification flags; 'tract'"
+ + " returns one row per census tract reading the tract flags. Valid"
+ + " levels are: "
+ + ", ".join(LEVELS)
+ + ".",
+ )
+ state: str | None = Field(
+ default=None,
+ description="Two-letter state postal abbreviation to scope the rows to a"
+ + " single state. If None, returns every area in the edition. At tract"
+ + " level the state filter is the primary usability lever.",
+ )
+
+ @field_validator("edition", mode="before", check_fields=False)
+ @classmethod
+ def _validate_edition(cls, v):
+ """Validate the edition."""
+ if not v:
+ return DEFAULT_EDITION
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip()
+ if value not in EDITIONS:
+ raise OpenBBError(
+ f"Invalid edition: {value}. Valid editions are: " + ", ".join(EDITIONS)
+ )
+ return value
+
+ @field_validator("level", mode="before", check_fields=False)
+ @classmethod
+ def _validate_level(cls, v):
+ """Validate the geography level."""
+ if not v:
+ return "county"
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip().lower()
+ if value not in LEVELS:
+ raise OpenBBError(
+ f"Invalid level: {value}. Valid levels are: " + ", ".join(LEVELS)
+ )
+ return value
+
+ @field_validator("state", mode="before", check_fields=False)
+ @classmethod
+ def _validate_state(cls, v):
+ """Validate and normalize the optional state filter."""
+ if not v:
+ return None
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip().upper()
+ if not value:
+ return None
+ if value not in STATE_NAMES:
+ raise OpenBBError(
+ f"Invalid state: {value}. Valid states are: " + ", ".join(STATE_NAMES)
+ )
+ return value
+
+
+class PovertyAreaMeasuresData(NullTokenMixin, Data):
+ """USDA ERS Poverty Area Measures.
+
+ One selected edition as a geographic lookup: each row is a county or census
+ tract carrying the poverty-area classification flags (high-poverty,
+ extreme-poverty, persistent-poverty, and enduring-poverty) across the
+ measurement periods, plus the identity and geography columns. Flags are kept
+ raw: -1 marks not available, 0 not classified, 1 classified; enduring
+ poverty is a 0-3 category.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Poverty Area Measures",
+ "$.description": "County- and census-tract-level poverty-area"
+ " classifications (high-poverty, extreme-poverty,"
+ " persistent-poverty, and enduring-poverty flags) for U.S. areas,"
+ " published in successive editions by the USDA Economic Research"
+ " Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ fips: str = Field(
+ description="Area FIPS code of the row, with leading zeros preserved: the"
+ + " five-digit county FIPS at the county level, or the eleven-digit"
+ + " census-tract FIPS at the tract level.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "FIPS",
+ "cellDataType": "text",
+ "pinned": "left",
+ "maxWidth": 130,
+ }
+ },
+ )
+ state: str | None = Field(
+ default=None,
+ description="Two-letter state postal abbreviation.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "State",
+ "cellDataType": "text",
+ "pinned": "left",
+ "maxWidth": 80,
+ }
+ },
+ )
+ county_name: str | None = Field(
+ default=None,
+ description="County name with its state, as published.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "County",
+ "cellDataType": "text",
+ "pinned": "left",
+ "minWidth": 200,
+ }
+ },
+ )
+ metro_nonmetro: int | None = Field(
+ default=None,
+ description="Metro-nonmetro status of the county: 1 metropolitan, 0"
+ + " nonmetropolitan, under the edition's metro definition vintage.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Metro", "cellDataType": "text"}
+ },
+ )
+ rucc_code: int | None = Field(
+ default=None,
+ description="Rural-Urban Continuum (Beale) code of the county, 1 to 9,"
+ + " for the edition's Beale vintage.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Beale (RUCC)", "cellDataType": "text"}
+ },
+ )
+ region: int | None = Field(
+ default=None,
+ description="Census region code: 1 Northeast, 2 Midwest, 3 South, 4 West.",
+ json_schema_extra={"x-widget_config": _flag_config("Region", hide=True)},
+ )
+ subregion: int | None = Field(
+ default=None,
+ description="ERS subregion code, 1 to 9.",
+ json_schema_extra={"x-widget_config": _flag_config("ERS Subregion", hide=True)},
+ )
+ tract_fips: str | None = Field(
+ default=None,
+ description="Eleven-digit census-tract FIPS code; populated at the tract"
+ + " level only.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Tract FIPS",
+ "cellDataType": "text",
+ "hide": True,
+ }
+ },
+ )
+ tract: str | None = Field(
+ default=None,
+ description="Census-tract number as published; populated at the tract"
+ + " level only.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Tract",
+ "cellDataType": "text",
+ "hide": True,
+ }
+ },
+ )
+ tract_name: str | None = Field(
+ default=None,
+ description="Census-tract name; populated at the tract level only.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Tract Name",
+ "cellDataType": "text",
+ "hide": True,
+ "minWidth": 200,
+ }
+ },
+ )
+ ruca_code: int | None = Field(
+ default=None,
+ description="Rural-Urban Commuting Area code of the tract for the"
+ + " edition's RUCA vintage; populated at the tract level only.",
+ json_schema_extra={"x-widget_config": _flag_config("RUCA", hide=True)},
+ )
+ bna: int | None = Field(
+ default=None,
+ description="Block-numbering-area flag: 1 if the tract is a"
+ + " block-numbering area; populated at the tract level only.",
+ json_schema_extra={"x-widget_config": _flag_config("BNA Tract", hide=True)},
+ )
+ high_poverty_1960: int | None = Field(
+ default=None,
+ description="High-poverty flag for 1960: 1 if the poverty rate was 20"
+ + " percent or higher, 0 if below, -1 if not available.",
+ json_schema_extra={
+ "x-widget_config": _flag_config("High Poverty 1960", hide=True)
+ },
+ )
+ high_poverty_1970: int | None = Field(
+ default=None,
+ description="High-poverty flag for 1970: 1 if the poverty rate was 20"
+ + " percent or higher, 0 if below, -1 if not available.",
+ json_schema_extra={"x-widget_config": _flag_config("High Poverty 1970")},
+ )
+ high_poverty_1980: int | None = Field(
+ default=None,
+ description="High-poverty flag for 1980: 1 if the poverty rate was 20"
+ + " percent or higher, 0 if below, -1 if not available.",
+ json_schema_extra={"x-widget_config": _flag_config("High Poverty 1980")},
+ )
+ high_poverty_1990: int | None = Field(
+ default=None,
+ description="High-poverty flag for 1990: 1 if the poverty rate was 20"
+ + " percent or higher, 0 if below, -1 if not available.",
+ json_schema_extra={"x-widget_config": _flag_config("High Poverty 1990")},
+ )
+ high_poverty_2000: int | None = Field(
+ default=None,
+ description="High-poverty flag for 2000: 1 if the poverty rate was 20"
+ + " percent or higher, 0 if below, -1 if not available.",
+ json_schema_extra={"x-widget_config": _flag_config("High Poverty 2000")},
+ )
+ high_poverty_2007_11: int | None = Field(
+ default=None,
+ description="High-poverty flag for the 2007-11 ACS period: 1 if the"
+ + " poverty rate was 20 percent or higher, 0 if below, -1 if not"
+ + " available.",
+ json_schema_extra={"x-widget_config": _flag_config("High Poverty 2007-11")},
+ )
+ high_poverty_2015_19: int | None = Field(
+ default=None,
+ description="High-poverty flag for the 2015-19 ACS period: 1 if the"
+ + " poverty rate was 20 percent or higher, 0 if below, -1 if not"
+ + " available. Present in the 2022 and 2023 editions.",
+ json_schema_extra={
+ "x-widget_config": _flag_config("High Poverty 2015-19", hide=True)
+ },
+ )
+ high_poverty_2017_21: int | None = Field(
+ default=None,
+ description="High-poverty flag for the 2017-21 ACS period: 1 if the"
+ + " poverty rate was 20 percent or higher, 0 if below, -1 if not"
+ + " available. Present in the 2023 and 2025 editions.",
+ json_schema_extra={
+ "x-widget_config": _flag_config("High Poverty 2017-21", hide=True)
+ },
+ )
+ high_poverty_2018_22: int | None = Field(
+ default=None,
+ description="High-poverty flag for the 2018-22 ACS period: 1 if the"
+ + " poverty rate was 20 percent or higher, 0 if below, -1 if not"
+ + " available. Present in the 2025 edition.",
+ json_schema_extra={
+ "x-widget_config": _flag_config("High Poverty 2018-22", hide=True)
+ },
+ )
+ high_poverty_2019_23: int | None = Field(
+ default=None,
+ description="High-poverty flag for the 2019-23 ACS period: 1 if the"
+ + " poverty rate was 20 percent or higher, 0 if below, -1 if not"
+ + " available. Present in the 2025 edition.",
+ json_schema_extra={
+ "x-widget_config": _flag_config("High Poverty 2019-23", hide=True)
+ },
+ )
+ extreme_poverty_1960: int | None = Field(
+ default=None,
+ description="Extreme-poverty flag for 1960: 1 if the poverty rate was 40"
+ + " percent or higher, 0 if below, -1 if not available.",
+ json_schema_extra={
+ "x-widget_config": _flag_config("Extreme Poverty 1960", hide=True)
+ },
+ )
+ extreme_poverty_1970: int | None = Field(
+ default=None,
+ description="Extreme-poverty flag for 1970: 1 if the poverty rate was 40"
+ + " percent or higher, 0 if below, -1 if not available.",
+ json_schema_extra={"x-widget_config": _flag_config("Extreme Poverty 1970")},
+ )
+ extreme_poverty_1980: int | None = Field(
+ default=None,
+ description="Extreme-poverty flag for 1980: 1 if the poverty rate was 40"
+ + " percent or higher, 0 if below, -1 if not available.",
+ json_schema_extra={"x-widget_config": _flag_config("Extreme Poverty 1980")},
+ )
+ extreme_poverty_1990: int | None = Field(
+ default=None,
+ description="Extreme-poverty flag for 1990: 1 if the poverty rate was 40"
+ + " percent or higher, 0 if below, -1 if not available.",
+ json_schema_extra={"x-widget_config": _flag_config("Extreme Poverty 1990")},
+ )
+ extreme_poverty_2000: int | None = Field(
+ default=None,
+ description="Extreme-poverty flag for 2000: 1 if the poverty rate was 40"
+ + " percent or higher, 0 if below, -1 if not available.",
+ json_schema_extra={"x-widget_config": _flag_config("Extreme Poverty 2000")},
+ )
+ extreme_poverty_2007_11: int | None = Field(
+ default=None,
+ description="Extreme-poverty flag for the 2007-11 ACS period: 1 if the"
+ + " poverty rate was 40 percent or higher, 0 if below, -1 if not"
+ + " available.",
+ json_schema_extra={"x-widget_config": _flag_config("Extreme Poverty 2007-11")},
+ )
+ extreme_poverty_2015_19: int | None = Field(
+ default=None,
+ description="Extreme-poverty flag for the 2015-19 ACS period: 1 if the"
+ + " poverty rate was 40 percent or higher, 0 if below, -1 if not"
+ + " available. Present in the 2022 and 2023 editions.",
+ json_schema_extra={
+ "x-widget_config": _flag_config("Extreme Poverty 2015-19", hide=True)
+ },
+ )
+ extreme_poverty_2017_21: int | None = Field(
+ default=None,
+ description="Extreme-poverty flag for the 2017-21 ACS period: 1 if the"
+ + " poverty rate was 40 percent or higher, 0 if below, -1 if not"
+ + " available. Present in the 2023 and 2025 editions.",
+ json_schema_extra={
+ "x-widget_config": _flag_config("Extreme Poverty 2017-21", hide=True)
+ },
+ )
+ extreme_poverty_2018_22: int | None = Field(
+ default=None,
+ description="Extreme-poverty flag for the 2018-22 ACS period: 1 if the"
+ + " poverty rate was 40 percent or higher, 0 if below, -1 if not"
+ + " available. Present in the 2025 edition.",
+ json_schema_extra={
+ "x-widget_config": _flag_config("Extreme Poverty 2018-22", hide=True)
+ },
+ )
+ extreme_poverty_2019_23: int | None = Field(
+ default=None,
+ description="Extreme-poverty flag for the 2019-23 ACS period: 1 if the"
+ + " poverty rate was 40 percent or higher, 0 if below, -1 if not"
+ + " available. Present in the 2025 edition.",
+ json_schema_extra={
+ "x-widget_config": _flag_config("Extreme Poverty 2019-23", hide=True)
+ },
+ )
+ persistent_poverty_1990: int | None = Field(
+ default=None,
+ description="Persistent-poverty flag anchored at 1990: 1 if the poverty"
+ + " rate was 20 percent or higher across the four consecutive periods"
+ + " spanning roughly thirty years, 0 otherwise, -1 if not available.",
+ json_schema_extra={
+ "x-widget_config": _flag_config("Persistent Poverty 1990", hide=True)
+ },
+ )
+ persistent_poverty_2000: int | None = Field(
+ default=None,
+ description="Persistent-poverty flag anchored at 2000: 1 if the poverty"
+ + " rate was 20 percent or higher across the four consecutive periods"
+ + " spanning roughly thirty years, 0 otherwise, -1 if not available.",
+ json_schema_extra={"x-widget_config": _flag_config("Persistent Poverty 2000")},
+ )
+ persistent_poverty_2007_11: int | None = Field(
+ default=None,
+ description="Persistent-poverty flag anchored at the 2007-11 ACS period:"
+ + " 1 if the poverty rate was 20 percent or higher across the four"
+ + " consecutive periods spanning roughly thirty years, 0 otherwise, -1"
+ + " if not available.",
+ json_schema_extra={
+ "x-widget_config": _flag_config("Persistent Poverty 2007-11")
+ },
+ )
+ persistent_poverty_2015_19: int | None = Field(
+ default=None,
+ description="Persistent-poverty flag anchored at the 2015-19 ACS period:"
+ + " 1 if the poverty rate was 20 percent or higher across the four"
+ + " consecutive periods spanning roughly thirty years, 0 otherwise, -1"
+ + " if not available. Present in the 2022 and 2023 editions.",
+ json_schema_extra={
+ "x-widget_config": _flag_config("Persistent Poverty 2015-19", hide=True)
+ },
+ )
+ persistent_poverty_2017_21: int | None = Field(
+ default=None,
+ description="Persistent-poverty flag anchored at the 2017-21 ACS period:"
+ + " 1 if the poverty rate was 20 percent or higher across the four"
+ + " consecutive periods spanning roughly thirty years, 0 otherwise, -1"
+ + " if not available. Present in the 2023 and 2025 editions.",
+ json_schema_extra={
+ "x-widget_config": _flag_config("Persistent Poverty 2017-21", hide=True)
+ },
+ )
+ enduring_poverty_2007_11: int | None = Field(
+ default=None,
+ description="Enduring-poverty classification anchored at the 2007-11 ACS"
+ + " period: 0 not enduring, 1/2/3 enduring by the earliest qualifying"
+ + " decade (poverty 20 percent or higher across five or more consecutive"
+ + " periods over forty or more years), -1 if not available.",
+ json_schema_extra={"x-widget_config": _flag_config("Enduring Poverty 2007-11")},
+ )
+ enduring_poverty_2015_19: int | None = Field(
+ default=None,
+ description="Enduring-poverty classification anchored at the 2015-19 ACS"
+ + " period: 0 not enduring, 1/2/3 enduring by the earliest qualifying"
+ + " decade, -1 if not available. Present in the 2022 and 2023 editions.",
+ json_schema_extra={
+ "x-widget_config": _flag_config("Enduring Poverty 2015-19", hide=True)
+ },
+ )
+ enduring_poverty_2017_21: int | None = Field(
+ default=None,
+ description="Enduring-poverty classification anchored at the 2017-21 ACS"
+ + " period: 0 not enduring, 1/2/3 enduring by the earliest qualifying"
+ + " decade, -1 if not available. Present in the 2023 and 2025 editions.",
+ json_schema_extra={
+ "x-widget_config": _flag_config("Enduring Poverty 2017-21", hide=True)
+ },
+ )
+
+
+class PovertyAreaMeasuresFetcher(
+ Fetcher[
+ PovertyAreaMeasuresQueryParams,
+ list[PovertyAreaMeasuresData],
+ ]
+):
+ """Fetch USDA ERS Poverty Area Measures."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(params: dict[str, Any]) -> PovertyAreaMeasuresQueryParams:
+ """Transform the query params."""
+ return PovertyAreaMeasuresQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: PovertyAreaMeasuresQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected edition's records at the requested level."""
+ from openbb_government_us.usda.utils import ers_poverty_area_measures
+
+ return await ers_poverty_area_measures.afetch_edition(
+ query.edition, query.level
+ )
+
+ @staticmethod
+ def transform_data(
+ query: PovertyAreaMeasuresQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[PovertyAreaMeasuresData]:
+ """Scope the rows to the chosen state and sort by area FIPS."""
+ rows = [
+ record
+ for record in data
+ if query.state is None or record["state"] == query.state
+ ]
+ if not rows:
+ raise EmptyDataError("No records match the given filters.")
+ rows.sort(key=lambda record: (record["fips"], record.get("tract_fips") or ""))
+ if query.level == "tract":
+ for record in rows:
+ if record.get("tract_fips"):
+ record["fips"] = record["tract_fips"]
+ return [PovertyAreaMeasuresData.model_validate(record) for record in rows]
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/price_spreads.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/price_spreads.py
new file mode 100644
index 00000000000..fcd273a4a32
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/price_spreads.py
@@ -0,0 +1,296 @@
+"""Farm To Consumer Price Spreads Model."""
+
+from typing import Any, Literal
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.app.service.system_service import SystemService
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_price_spreads import PRICE_SPREADS_FILES
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+api_prefix = SystemService().system_settings.api_settings.prefix
+
+Category = Literal[
+ "dairy", "fresh_fruit", "fresh_vegetables", "processed", "field_crops"
+]
+
+
+class FarmToConsumerPriceSpreadsQueryParams(QueryParams):
+ """Farm To Consumer Price Spreads Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/price-spreads-from-farm-to-consumer
+ """
+
+ __json_schema_extra__ = {
+ "category": {
+ "x-widget_config": {
+ "label": "Category",
+ "value": "dairy",
+ "options": [
+ {"label": "Dairy", "value": "dairy"},
+ {"label": "Fresh fruit", "value": "fresh_fruit"},
+ {"label": "Fresh vegetables", "value": "fresh_vegetables"},
+ {"label": "Processed", "value": "processed"},
+ {"label": "Field crops", "value": "field_crops"},
+ ],
+ },
+ },
+ "item": {
+ "multiple_items_allowed": True,
+ "x-widget_config": {
+ "label": "Item",
+ "type": "endpoint",
+ "multiSelect": False,
+ "multiple": False,
+ "value": "whole_milk",
+ "optionsEndpoint": f"{api_prefix}/usda/price_spread_items",
+ "optionsParams": {"category": "$category"},
+ "style": {"popupWidth": 320},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ category: Category | None = Field(
+ default=None,
+ description="Filter items by data product category."
+ + " If None, all categories are included.",
+ )
+ item: str | None = Field(
+ default=None,
+ description="Item(s) to retrieve, as a comma-separated list of slugs."
+ + " If None, all items are returned. Valid items are:\n "
+ + ", ".join(sorted(PRICE_SPREADS_FILES))
+ + "\n",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data."
+ + " None returns from the beginning of each series."
+ + " Marketing-year series filter on the first year of the marketing year.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data."
+ + " If None, returns up to the most recent year."
+ + " Marketing-year series filter on the first year of the marketing year.",
+ )
+
+ @field_validator("item", mode="before", check_fields=False)
+ @classmethod
+ def _validate_item(cls, v):
+ """Validate item."""
+ if not v:
+ return None
+ items = v.split(",") if isinstance(v, str) else list(v)
+ items = [item.strip() for item in items if item and item.strip()]
+ if not items:
+ return None
+ unknown = [item for item in items if item not in PRICE_SPREADS_FILES]
+ if unknown:
+ raise OpenBBError(
+ f"Invalid item(s): {', '.join(unknown)}. Valid items are: "
+ + ", ".join(sorted(PRICE_SPREADS_FILES))
+ )
+ return ",".join(items)
+
+
+class FarmToConsumerPriceSpreadsData(NullTokenMixin, Data):
+ """Farm To Consumer Price Spreads Data.
+
+ Annual national-average comparisons of retail food prices with the value
+ of the farm commodities used to produce them, published by USDA ERS.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Price Spreads",
+ "$.description": "Retail prices, farm values, and the farm-to-retail price spread for food items and market baskets, published by the USDA Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ commodity: str = Field(
+ description="Commodity name, as published.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Commodity", "pinned": "left"}
+ },
+ )
+ category: str = Field(
+ description="Data product category of the item.",
+ json_schema_extra={"x-widget_config": {"headerName": "Category", "hide": True}},
+ )
+ year: int = Field(
+ description="Calendar year of the observation."
+ + " For marketing-year series, the first year of the marketing year.",
+ json_schema_extra={"x-widget_config": {"headerName": "Year", "maxWidth": 90}},
+ )
+ marketing_year: str | None = Field(
+ default=None,
+ description="Marketing year of the observation, as published,"
+ + " e.g. '2024/25'. Only present for marketing-year series.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Marketing year", "hide": True}
+ },
+ )
+ retail_price: float | None = Field(
+ default=None,
+ description="Retail price, in dollars per published unit.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Retail price", "cellDataType": "number"}
+ },
+ )
+ farm_value: float | None = Field(
+ default=None,
+ description="Farm value, in dollars per published unit.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Farm value", "cellDataType": "number"}
+ },
+ )
+ farm_price: float | None = Field(
+ default=None,
+ description="Farm price, in dollars per published unit.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Farm price", "cellDataType": "number"}
+ },
+ )
+ farm_share: float | None = Field(
+ default=None,
+ description="Farm share of the retail price, in percent.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Farm share %", "cellDataType": "number"}
+ },
+ )
+ retail_cost_index: float | None = Field(
+ default=None,
+ description="Retail cost index.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Retail cost index",
+ "cellDataType": "number",
+ "hide": True,
+ }
+ },
+ )
+ farm_value_index: float | None = Field(
+ default=None,
+ description="Farm value index.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Farm value index",
+ "cellDataType": "number",
+ "hide": True,
+ }
+ },
+ )
+ farm_to_retail_spread_index: float | None = Field(
+ default=None,
+ description="Farm-to-retail spread index.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Spread index",
+ "cellDataType": "number",
+ "hide": True,
+ }
+ },
+ )
+
+
+class FarmToConsumerPriceSpreadsFetcher(
+ Fetcher[
+ FarmToConsumerPriceSpreadsQueryParams,
+ list[FarmToConsumerPriceSpreadsData],
+ ]
+):
+ """Fetch USDA ERS Price Spreads from Farm to Consumer."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> FarmToConsumerPriceSpreadsQueryParams:
+ """Transform the query params."""
+ return FarmToConsumerPriceSpreadsQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: FarmToConsumerPriceSpreadsQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected items' tidy CSV rows."""
+ import asyncio
+
+ from openbb_government_us.usda.utils import ers_price_spreads
+
+ selected = (
+ sorted(PRICE_SPREADS_FILES) if query.item is None else query.item.split(",")
+ )
+ if query.category is not None:
+ selected = [
+ item
+ for item in selected
+ if PRICE_SPREADS_FILES[item]["category"] == query.category
+ ]
+ if not selected:
+ raise OpenBBError(
+ "No items match the combination of 'item' and 'category' filters."
+ )
+ results = await asyncio.gather(
+ *(ers_price_spreads.afetch_item(item) for item in selected)
+ )
+ records: list[dict] = []
+ for item, rows in zip(selected, results):
+ category = PRICE_SPREADS_FILES[item]["category"]
+ for row in rows:
+ records.append({**row, "item": item, "category": category})
+ return records
+
+ @staticmethod
+ def transform_data(
+ query: FarmToConsumerPriceSpreadsQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[FarmToConsumerPriceSpreadsData]:
+ """Transform the data."""
+ from openbb_government_us.usda.utils.ers_price_spreads import parse_attribute
+
+ pivoted: dict[tuple, dict] = {}
+ for record in data:
+ raw_year = str(record["year"])
+ marketing_year = raw_year if "/" in raw_year else None
+ year = int(raw_year[:4]) if marketing_year else int(raw_year)
+ if query.start_year is not None and year < query.start_year:
+ continue
+ if query.end_year is not None and year > query.end_year:
+ continue
+ measure, _ = parse_attribute(record["attribute"])
+ key = (record["item"], year, marketing_year)
+ row = pivoted.get(key)
+ if row is None:
+ row = {
+ "_item": record["item"],
+ "commodity": record["commodity"],
+ "category": record["category"],
+ "year": year,
+ "marketing_year": marketing_year,
+ }
+ pivoted[key] = row
+ row[measure] = record["value"]
+ results = sorted(pivoted.values(), key=lambda row: (row["_item"], row["year"]))
+ return [
+ FarmToConsumerPriceSpreadsData.model_validate(
+ {k: v for k, v in row.items() if not k.startswith("_")}
+ )
+ for row in results
+ ]
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/purchase_to_plate.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/purchase_to_plate.py
new file mode 100644
index 00000000000..31270a36c8e
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/purchase_to_plate.py
@@ -0,0 +1,236 @@
+"""Purchase to Plate National Average Prices Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from openbb_core.provider.utils.errors import EmptyDataError
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_purchase_to_plate import (
+ CYCLE_HEADERS,
+ CYCLE_ORDER,
+ DEFAULT_FOOD_GROUP,
+ FOOD_GROUPS,
+ group_options,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+
+def _cycle(field: str) -> dict:
+ """Build the numeric column config for one survey-cycle price field."""
+ return {
+ "x-widget_config": {
+ "headerName": CYCLE_HEADERS[field],
+ "cellDataType": "number",
+ }
+ }
+
+
+class PurchaseToPlateQueryParams(QueryParams):
+ """Purchase to Plate National Average Prices Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/purchase-to-plate
+ """
+
+ __json_schema_extra__ = {
+ "food_group": {
+ "x-widget_config": {
+ "label": "Food group",
+ "value": DEFAULT_FOOD_GROUP,
+ "multiSelect": False,
+ "multiple": False,
+ "options": group_options(),
+ "style": {"popupWidth": 320},
+ },
+ },
+ }
+
+ food_group: str = Field(
+ default=DEFAULT_FOOD_GROUP,
+ description="Food group to retrieve, derived from the FNDDS food-code"
+ + " leading digit. Valid food groups are:\n "
+ + ", ".join(FOOD_GROUPS)
+ + "\n",
+ )
+
+ @field_validator("food_group", mode="before", check_fields=False)
+ @classmethod
+ def _validate_food_group(cls, v):
+ """Validate food group."""
+ if not v:
+ return DEFAULT_FOOD_GROUP
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip()
+ canonical = {slug.casefold(): slug for slug in FOOD_GROUPS}.get(
+ value.casefold()
+ )
+ if canonical is None:
+ raise OpenBBError(
+ f"Invalid food_group: {value}. Valid food groups are: "
+ + ", ".join(FOOD_GROUPS)
+ )
+ return canonical
+
+
+class PurchaseToPlateData(NullTokenMixin, Data):
+ """Purchase to Plate National Average Prices Data.
+
+ One row per food item, with the USDA ERS Purchase to Plate national
+ average price, in nominal dollars per 100 edible grams, spread across the
+ four biennial survey cycles as columns.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Purchase to Plate National Average Prices",
+ "$.description": "Estimated national average prices, in nominal"
+ " dollars per 100 edible grams, for foods reported in NHANES,"
+ " across the 2011/2012 through 2017/2018 survey cycles,"
+ " published by the USDA Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ food_code: str = Field(
+ description="Eight-digit FNDDS food code identifying the food item.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Food code",
+ "cellDataType": "text",
+ "pinned": "left",
+ "maxWidth": 130,
+ }
+ },
+ )
+ mod_code: str | None = Field(
+ default=None,
+ description="Recipe modification code, '0' for an unmodified item."
+ + " Non-zero only on the 2011/2012 recipe-modification rows.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Modification code", "hide": True}
+ },
+ )
+ food_description: str = Field(
+ description="Food description, as published in the most recent survey"
+ + " cycle the item appears in.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Food description", "pinned": "left"}
+ },
+ )
+ method_description: str | None = Field(
+ default=None,
+ description="Price-estimation method, as published in the most recent"
+ + " survey cycle the item appears in.",
+ json_schema_extra={"x-widget_config": {"headerName": "Method", "hide": True}},
+ )
+ nhanes: str | None = Field(
+ default=None,
+ description="NHANES reporting flag, as published in the most recent"
+ + " survey cycle the item appears in.",
+ json_schema_extra={"x-widget_config": {"headerName": "NHANES", "hide": True}},
+ )
+ cycle_2011_2012: float | None = Field(
+ default=None,
+ description="National average price for the 2011/2012 survey cycle,"
+ + " in nominal U.S. dollars per 100 edible grams.",
+ json_schema_extra=_cycle("cycle_2011_2012"),
+ )
+ cycle_2013_2014: float | None = Field(
+ default=None,
+ description="National average price for the 2013/2014 survey cycle,"
+ + " in nominal U.S. dollars per 100 edible grams.",
+ json_schema_extra=_cycle("cycle_2013_2014"),
+ )
+ cycle_2015_2016: float | None = Field(
+ default=None,
+ description="National average price for the 2015/2016 survey cycle,"
+ + " in nominal U.S. dollars per 100 edible grams.",
+ json_schema_extra=_cycle("cycle_2015_2016"),
+ )
+ cycle_2017_2018: float | None = Field(
+ default=None,
+ description="National average price for the 2017/2018 survey cycle,"
+ + " in nominal U.S. dollars per 100 edible grams.",
+ json_schema_extra=_cycle("cycle_2017_2018"),
+ )
+
+
+class PurchaseToPlateFetcher(
+ Fetcher[
+ PurchaseToPlateQueryParams,
+ list[PurchaseToPlateData],
+ ]
+):
+ """Fetch USDA ERS Purchase to Plate National Average Prices."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(params: dict[str, Any]) -> PurchaseToPlateQueryParams:
+ """Transform the query params."""
+ return PurchaseToPlateQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: PurchaseToPlateQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the long national-average-prices records."""
+ from openbb_government_us.usda.utils import ers_purchase_to_plate
+
+ return await ers_purchase_to_plate.afetch_table()
+
+ @staticmethod
+ def transform_data(
+ query: PurchaseToPlateQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[PurchaseToPlateData]:
+ """Pivot the selected food group's rows to one wide row per food item."""
+ groups: dict[tuple, dict] = {}
+ order: list[tuple] = []
+ for record in data:
+ if record["food_group"] != query.food_group:
+ continue
+ key = (record["food_code"], record["mod_code"])
+ row = groups.get(key)
+ if row is None:
+ row = {
+ "_order": len(order),
+ "_recent": -1,
+ "food_code": record["food_code"],
+ "mod_code": record["mod_code"],
+ "food_description": None,
+ "method_description": None,
+ "nhanes": None,
+ }
+ groups[key] = row
+ order.append(key)
+ row[record["cycle"]] = record["price"]
+ if record["cycle_index"] >= row["_recent"]:
+ row["_recent"] = record["cycle_index"]
+ row["food_description"] = record["food_description"]
+ row["method_description"] = record["method_description"]
+ row["nhanes"] = record["nhanes"]
+ kept = [
+ groups[key]
+ for key in order
+ if any(groups[key].get(cycle) is not None for cycle in CYCLE_ORDER)
+ ]
+ if not kept:
+ raise EmptyDataError("No records match the given filters.")
+ kept.sort(key=lambda row: (row["food_code"], row["mod_code"]))
+ return [
+ PurchaseToPlateData.model_validate(
+ {k: v for k, v in row.items() if not k.startswith("_")}
+ )
+ for row in kept
+ ]
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/report_calendar.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/report_calendar.py
new file mode 100644
index 00000000000..5df56198d04
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/report_calendar.py
@@ -0,0 +1,201 @@
+"""FAS Report Calendar Model."""
+
+from datetime import (
+ date as dateType,
+ datetime,
+)
+from typing import Any, Literal
+
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from openbb_core.provider.utils.descriptions import QUERY_DESCRIPTIONS
+from pydantic import ConfigDict, Field
+
+from openbb_government_us.usda.utils.fas_report_calendar import (
+ COMMODITIES,
+ REPORT_TYPES,
+)
+
+ReportType = Literal["export_sales", "trade_forecast", "world_markets_trade"]
+
+
+class FasReportCalendarQueryParams(QueryParams):
+ """FAS Report Calendar Query Parameters.
+
+ Source: https://www.fas.usda.gov/data/scheduled-reports
+ """
+
+ __json_schema_extra__ = {
+ "commodity": {"choices": sorted(COMMODITIES)},
+ "report_type": {"choices": sorted(REPORT_TYPES)},
+ }
+
+ report_type: ReportType | None = Field(
+ default=None,
+ description="Filter by the type of report.",
+ )
+ commodity: str | None = Field(
+ default=None,
+ description="Filter by commodity.",
+ )
+ keyword: str | None = Field(
+ default=None,
+ description="Free-text filter. Matches a release's associated"
+ + " commodities as well as its title, so it is broader than a title"
+ + " search.",
+ )
+ start_date: dateType | None = Field(
+ default=None,
+ description=QUERY_DESCRIPTIONS.get("start_date", "")
+ + " Applied to the release date after fetching.",
+ )
+ end_date: dateType | None = Field(
+ default=None,
+ description=QUERY_DESCRIPTIONS.get("end_date", "")
+ + " Applied to the release date after fetching.",
+ )
+
+
+class FasReportCalendarData(Data):
+ """FAS Report Calendar Data.
+
+ The published schedule of upcoming USDA Foreign Agricultural Service
+ report releases. The calendar is forward-looking only and does not
+ enumerate past releases.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA FAS Report Calendar",
+ "$.description": "Upcoming release schedule for USDA Foreign"
+ " Agricultural Service reports.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "FAS"],
+ },
+ }
+ )
+
+ release_date: dateType = Field(
+ description="Date of the release, in Eastern Time.",
+ )
+ release_time: str | None = Field(
+ default=None,
+ description="Time of day of the release, in Eastern Time.",
+ )
+ day_of_week: str | None = Field(
+ default=None,
+ description="Day of the week of the release.",
+ )
+ release_datetime: datetime | None = Field(
+ default=None,
+ description="Timestamp of the release, in UTC. None for releases whose"
+ + " scheduled time has already passed, which the source stops"
+ + " publishing a calendar entry for.",
+ )
+ report_type: str | None = Field(
+ default=None,
+ description="Type of the report.",
+ )
+ title: str = Field(
+ description="Title of the report.",
+ )
+ url: str | None = Field(
+ default=None,
+ description="URL of the release announcement.",
+ )
+ released: bool = Field(
+ description="Whether the release time has already passed.",
+ )
+
+
+class FasReportCalendarFetcher(
+ Fetcher[
+ FasReportCalendarQueryParams,
+ list[FasReportCalendarData],
+ ]
+):
+ """Fetch the USDA FAS report release calendar."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(params: dict[str, Any]) -> FasReportCalendarQueryParams:
+ """Transform the query params."""
+ return FasReportCalendarQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: FasReportCalendarQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the calendar from the FAS website."""
+ from openbb_government_us.usda.utils.fas_report_calendar import (
+ afetch_calendar,
+ build_url,
+ parse_calendar,
+ )
+
+ html = await afetch_calendar(
+ build_url(
+ report_type=query.report_type,
+ commodity=query.commodity,
+ keyword=query.keyword,
+ )
+ )
+ return parse_calendar(html)
+
+ @staticmethod
+ def transform_data(
+ query: FasReportCalendarQueryParams, data: list[dict], **kwargs: Any
+ ) -> list[FasReportCalendarData]:
+ """Transform the data."""
+ from zoneinfo import ZoneInfo
+
+ from openbb_core.provider.utils.errors import EmptyDataError
+
+ eastern = ZoneInfo("America/New_York")
+ results: list[FasReportCalendarData] = []
+ for row in data:
+ release_datetime = row["release_datetime"]
+ if release_datetime is not None:
+ release_date = release_datetime.astimezone(eastern).date()
+ else:
+ release_date = _fallback_date(row["month_day"], eastern)
+ if query.start_date and release_date < query.start_date:
+ continue
+ if query.end_date and release_date > query.end_date:
+ continue
+ results.append(
+ FasReportCalendarData.model_validate(
+ {
+ "release_date": release_date,
+ "release_time": row["release_time"],
+ "day_of_week": row["day_of_week"],
+ "release_datetime": release_datetime,
+ "report_type": row["report_type"],
+ "title": row["title"],
+ "url": row["url"],
+ "released": row["released"],
+ }
+ )
+ )
+ if not results:
+ raise EmptyDataError("The request was returned empty.")
+ return sorted(results, key=lambda item: (item.release_date, item.title))
+
+
+def _fallback_date(month_day: str | None, eastern: Any) -> dateType:
+ """Derive a release date for a card that carries no calendar entry.
+
+ Cards omit their iCalendar block only once the scheduled time has passed,
+ so the release is the current Eastern-Time day.
+ """
+ from openbb_core.app.model.abstract.error import OpenBBError
+
+ if not month_day:
+ raise OpenBBError("The release carries neither a timestamp nor a date.")
+ return datetime.now(tz=eastern).date()
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/resource_requirements_of_food_demand.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/resource_requirements_of_food_demand.py
new file mode 100644
index 00000000000..c10144912d9
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/resource_requirements_of_food_demand.py
@@ -0,0 +1,308 @@
+"""USDA ERS Resource Requirements of Food Demand Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_resource_requirements_of_food_demand import (
+ TABLES,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+DEFAULT_TABLE = "Xf0000"
+
+
+def _number(header: str, hide: bool = False) -> dict:
+ """Build a numeric column config for one measure field."""
+ config: dict = {"headerName": header, "cellDataType": "number"}
+ if hide:
+ config["hide"] = True
+ return {"x-widget_config": config}
+
+
+class ResourceRequirementsOfFoodDemandQueryParams(QueryParams):
+ """USDA ERS Resource Requirements of Food Demand Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/resource-requirements-of-food-demand
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "x-widget_config": {
+ "label": "Food group",
+ "value": DEFAULT_TABLE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": f"{code} - {label}", "value": code}
+ for code, label in TABLES.items()
+ ],
+ "style": {"popupWidth": 520},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ table: str = Field(
+ default=DEFAULT_TABLE,
+ description="Food group, commodity, or aggregate table to retrieve,"
+ + " identified by its Xf table number. Each table holds the"
+ + " employment, freshwater withdrawal, and energy resources embodied"
+ + " across the food supply chain to meet final demand for that"
+ + " category; the resource-and-source measures become the wide value"
+ + " columns while year and supply-chain stage stay as pinned rows."
+ + " Valid tables are:\n "
+ + ", ".join(f"{code} ({label})" for code, label in TABLES.items())
+ + "\n",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data, compared against the"
+ + " observation year. If None, returns from the beginning of the"
+ + " series.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data, compared against the"
+ + " observation year. If None, returns up to the most recent year.",
+ )
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate table."""
+ if not v:
+ return DEFAULT_TABLE
+ table = v.strip() if isinstance(v, str) else v
+ if table not in TABLES:
+ raise OpenBBError(
+ f"Invalid table: {table}. Valid tables are: " + ", ".join(TABLES)
+ )
+ return table
+
+
+class ResourceRequirementsOfFoodDemandData(NullTokenMixin, Data):
+ """USDA ERS Resource Requirements of Food Demand Data.
+
+ One selected food group pivoted to a wide layout: year and supply-chain
+ stage stay in the rows while the resource-and-source measures spread into
+ value columns, with the energy total and its by-source breakdown.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Resource Requirements of Food Demand",
+ "$.description": "Employment, freshwater withdrawals, and energy"
+ " use, total and by source, embodied across the U.S. food supply"
+ " chain to meet final consumer demand for each food, beverage, and"
+ " food-related category, published by the USDA Economic Research"
+ " Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ year: int = Field(
+ description="Year of the observation.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Year",
+ "cellDataType": "text",
+ "pinned": "left",
+ "maxWidth": 90,
+ }
+ },
+ )
+ stage: str | None = Field(
+ default=None,
+ description="Supply-chain stage the resources are attributed to, as"
+ + " published, e.g. 'Total', 'Crops', 'Food retail', or a household"
+ + " home-kitchen stage.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Supply chain stage", "pinned": "left"}
+ },
+ )
+ employment: float | None = Field(
+ default=None,
+ description="Full- or part-time employees per year embodied at the" + " stage.",
+ json_schema_extra=_number("Employment (employees/yr)"),
+ )
+ water: float | None = Field(
+ default=None,
+ description="Freshwater withdrawals embodied at the stage, in million"
+ + " gallons per year.",
+ json_schema_extra=_number("Water withdrawals (Mgal/yr)"),
+ )
+ energy_total: float | None = Field(
+ default=None,
+ description="Total energy from all sources embodied at the stage, in"
+ + " billion British thermal units per year.",
+ json_schema_extra=_number("Energy: Total (bBtu/yr)"),
+ )
+ energy_pa: float | None = Field(
+ default=None,
+ description="Energy from all petroleum products embodied at the stage,"
+ + " in billion British thermal units per year.",
+ json_schema_extra=_number("Energy: Petroleum products (bBtu/yr)", hide=True),
+ )
+ energy_ng: float | None = Field(
+ default=None,
+ description="Energy from natural gas, including supplemental gaseous"
+ + " fuels, embodied at the stage, in billion British thermal units per"
+ + " year.",
+ json_schema_extra=_number("Energy: Natural gas (bBtu/yr)", hide=True),
+ )
+ energy_es: float | None = Field(
+ default=None,
+ description="Energy from electricity sales embodied at the stage, in"
+ + " billion British thermal units per year.",
+ json_schema_extra=_number("Energy: Electricity sales (bBtu/yr)", hide=True),
+ )
+ energy_cl: float | None = Field(
+ default=None,
+ description="Energy from coal embodied at the stage, in billion British"
+ + " thermal units per year.",
+ json_schema_extra=_number("Energy: Coal (bBtu/yr)", hide=True),
+ )
+ energy_lo: float | None = Field(
+ default=None,
+ description="Electrical system energy losses embodied at the stage, in"
+ + " billion British thermal units per year.",
+ json_schema_extra=_number(
+ "Energy: Electrical system losses (bBtu/yr)", hide=True
+ ),
+ )
+ energy_bf: float | None = Field(
+ default=None,
+ description="Energy from biofuel embodied at the stage, in billion"
+ + " British thermal units per year.",
+ json_schema_extra=_number("Energy: Biofuel (bBtu/yr)", hide=True),
+ )
+ energy_ww: float | None = Field(
+ default=None,
+ description="Energy from wood and biomass waste embodied at the stage,"
+ + " in billion British thermal units per year.",
+ json_schema_extra=_number(
+ "Energy: Wood and biomass waste (bBtu/yr)", hide=True
+ ),
+ )
+ energy_wd: float | None = Field(
+ default=None,
+ description="Energy from wood embodied at the stage, in billion British"
+ + " thermal units per year.",
+ json_schema_extra=_number("Energy: Wood (bBtu/yr)", hide=True),
+ )
+ energy_hy: float | None = Field(
+ default=None,
+ description="Energy from hydroelectric power embodied at the stage, in"
+ + " billion British thermal units per year.",
+ json_schema_extra=_number("Energy: Hydroelectric (bBtu/yr)", hide=True),
+ )
+ energy_ge: float | None = Field(
+ default=None,
+ description="Energy from geothermal power embodied at the stage, in"
+ + " billion British thermal units per year.",
+ json_schema_extra=_number("Energy: Geothermal (bBtu/yr)", hide=True),
+ )
+ energy_so: float | None = Field(
+ default=None,
+ description="Energy from solar power embodied at the stage, in billion"
+ + " British thermal units per year.",
+ json_schema_extra=_number("Energy: Solar (bBtu/yr)", hide=True),
+ )
+ energy_wy: float | None = Field(
+ default=None,
+ description="Energy from wind power embodied at the stage, in billion"
+ + " British thermal units per year.",
+ json_schema_extra=_number("Energy: Wind (bBtu/yr)", hide=True),
+ )
+ energy_cc: float | None = Field(
+ default=None,
+ description="Energy from coal coke embodied at the stage, in billion"
+ + " British thermal units per year.",
+ json_schema_extra=_number("Energy: Coal coke (bBtu/yr)", hide=True),
+ )
+ energy_sf: float | None = Field(
+ default=None,
+ description="Energy from supplemental gaseous fuels embodied at the"
+ + " stage, in billion British thermal units per year.",
+ json_schema_extra=_number(
+ "Energy: Supplemental gaseous fuels (bBtu/yr)", hide=True
+ ),
+ )
+
+
+class ResourceRequirementsOfFoodDemandFetcher(
+ Fetcher[
+ ResourceRequirementsOfFoodDemandQueryParams,
+ list[ResourceRequirementsOfFoodDemandData],
+ ]
+):
+ """Fetch USDA ERS Resource Requirements of Food Demand."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> ResourceRequirementsOfFoodDemandQueryParams:
+ """Transform the query params."""
+ return ResourceRequirementsOfFoodDemandQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: ResourceRequirementsOfFoodDemandQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected table's long-format rows."""
+ from openbb_government_us.usda.utils import (
+ ers_resource_requirements_of_food_demand,
+ )
+
+ return await ers_resource_requirements_of_food_demand.afetch_table(query.table)
+
+ @staticmethod
+ def transform_data(
+ query: ResourceRequirementsOfFoodDemandQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[ResourceRequirementsOfFoodDemandData]:
+ """Pivot the resource-and-source measures into the wide layout."""
+ pivoted: dict[tuple, dict] = {}
+ for order, record in enumerate(data):
+ year = record["year"]
+ if query.start_year is not None and year < query.start_year:
+ continue
+ if query.end_year is not None and year > query.end_year:
+ continue
+ key = (year, record["stage_ord"], record["stage"])
+ row = pivoted.get(key)
+ if row is None:
+ row = {
+ "_order": order,
+ "_stage_ord": record["stage_ord"],
+ "_table": record["table"],
+ "year": year,
+ "stage": record["stage"],
+ }
+ pivoted[key] = row
+ row[record["column"]] = record["value"]
+ results = sorted(
+ pivoted.values(),
+ key=lambda row: (row["year"], row["_stage_ord"], row["_order"]),
+ )
+ return [
+ ResourceRequirementsOfFoodDemandData.model_validate(
+ {k: v for k, v in row.items() if not k.startswith("_")}
+ )
+ for row in results
+ ]
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/rice_yearbook.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/rice_yearbook.py
new file mode 100644
index 00000000000..3056056040f
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/rice_yearbook.py
@@ -0,0 +1,408 @@
+"""USDA ERS Rice Yearbook Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.app.service.system_service import SystemService
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from openbb_core.provider.utils.errors import EmptyDataError
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_rice_yearbook import (
+ ANNUAL,
+ FREQUENCY_ORDER,
+ RICE_TABLES,
+ _period_sort_key,
+ classify_frequency,
+ display_period,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin, is_null_token
+
+api_prefix = SystemService().system_settings.api_settings.prefix
+
+DEFAULT_TABLE = "us_supply_disappearance_price"
+MAX_WIDE_COLUMNS = 60
+
+FOLD_DIM_FIELDS = ("rice_class", "location", "rank", "aggregate_level")
+
+
+class RiceYearbookQueryParams(QueryParams):
+ """USDA ERS Rice Yearbook Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/rice-yearbook
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "x-widget_config": {
+ "label": "Table",
+ "value": DEFAULT_TABLE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": config["label"], "value": key}
+ for key, config in RICE_TABLES.items()
+ ],
+ "style": {"popupWidth": 460},
+ },
+ },
+ "frequency": {
+ "x-widget_config": {
+ "label": "Frequency",
+ "type": "endpoint",
+ "multiSelect": False,
+ "multiple": False,
+ "optionsEndpoint": f"{api_prefix}/usda/rice_frequencies",
+ "optionsParams": {"table": "$table"},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ table: str = Field(
+ default=DEFAULT_TABLE,
+ description="Rice-yearbook table to retrieve. Each table is a set of"
+ + " named acreage, supply-use, price, or trade series indexed by year"
+ + " and period; the series become the wide value columns while time"
+ + " stays in the rows. Valid tables are:\n "
+ + ", ".join(RICE_TABLES)
+ + "\n",
+ )
+ frequency: str | None = Field(
+ default=None,
+ description="Observation frequency to keep, one of the frequencies the"
+ + " selected table publishes: 'Annual', 'Point-in-time', 'Weekly', or"
+ + " 'Monthly'. Filtering to one frequency yields a single aligned time"
+ + " series. If None, the finest frequency the table offers is used.",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data, compared against the"
+ + " observation year. If None, returns from the beginning of the series.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data, compared against the"
+ + " observation year. If None, returns up to the most recent year.",
+ )
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate table."""
+ if not v:
+ return DEFAULT_TABLE
+ table = v.strip() if isinstance(v, str) else v
+ if table not in RICE_TABLES:
+ raise OpenBBError(
+ f"Invalid table: {table}. Valid tables are: " + ", ".join(RICE_TABLES)
+ )
+ return table
+
+ @field_validator("frequency", mode="before", check_fields=False)
+ @classmethod
+ def _validate_frequency(cls, v):
+ """Normalize frequency to a stripped string or None."""
+ if not v:
+ return None
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ return str(value).strip() or None
+
+
+class RiceYearbookData(NullTokenMixin, Data):
+ """USDA ERS Rice Yearbook Data.
+
+ One selected rice-yearbook table for one frequency, pivoted to a wide
+ layout: the table's series spread into value columns while the period stays
+ in the rows, with any varying class, location, or rank dimension folded into
+ the period label. Series columns carry their unit in the header when the
+ table reports more than one unit.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Rice Yearbook",
+ "$.description": "U.S. and world rice acreage, supply, use,"
+ " stocks, prices, and trade, published by the USDA Economic"
+ " Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ period: str = Field(
+ description="Row label of the observation: the year for annual tables"
+ + " ('2020'), the year and month or stocks date for sub-annual tables"
+ + " ('2020 August', '2020 March 1'), with any varying class, location,"
+ + " or rank dimension prefixed so each row is uniquely labeled.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Period",
+ "pinned": "left",
+ "maxWidth": 320,
+ }
+ },
+ )
+
+
+class RiceYearbookFetcher(
+ Fetcher[
+ RiceYearbookQueryParams,
+ list[RiceYearbookData],
+ ]
+):
+ """Fetch USDA ERS Rice Yearbook Data."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(params: dict[str, Any]) -> RiceYearbookQueryParams:
+ """Transform the query params."""
+ return RiceYearbookQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: RiceYearbookQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected table's long-format rows."""
+ from openbb_government_us.usda.utils import ers_rice_yearbook
+
+ return await ers_rice_yearbook.afetch_table(query.table)
+
+ @staticmethod
+ def _resolve_frequency(
+ query: RiceYearbookQueryParams, data: list[dict]
+ ) -> tuple[str, list[str]]:
+ """Resolve the requested frequency against the table's real frequencies."""
+ present = {classify_frequency(record["period"]) for record in data}
+ available = [freq for freq in FREQUENCY_ORDER if freq in present]
+ requested = query.frequency
+ if requested is not None and requested in available:
+ return requested, available
+ return (available[0] if available else FREQUENCY_ORDER[-1]), available
+
+ @staticmethod
+ def transform_data(
+ query: RiceYearbookQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[RiceYearbookData]:
+ """Filter to one frequency and pivot the series into columns."""
+ if not data:
+ return []
+ frequency, _ = RiceYearbookFetcher._resolve_frequency(query, data)
+ rows = [
+ record
+ for record in data
+ if classify_frequency(record["period"]) == frequency
+ and (query.start_year is None or record["year"] >= query.start_year)
+ and (query.end_year is None or record["year"] <= query.end_year)
+ ]
+ if not rows:
+ raise EmptyDataError("No records match the given filters.")
+ return RiceYearbookFetcher._pivot(rows, frequency)
+
+ @staticmethod
+ def _dim_value(record: dict, field: str):
+ """Return a dimension value, treating placeholder tokens as absent."""
+ value = record[field]
+ return None if is_null_token(value) else value
+
+ @staticmethod
+ def _projected_columns(rows: list[dict], column_dims: list[str]) -> int:
+ """Count the wide columns a candidate set of column dims would produce."""
+ value = RiceYearbookFetcher._dim_value
+ return len(
+ {
+ (*(value(record, field) for field in column_dims), record["series"])
+ for record in rows
+ }
+ )
+
+ @staticmethod
+ def _classify_dims(
+ rows: list[dict], fold_dims: list[str]
+ ) -> tuple[list[str], list[str]]:
+ """Split folding dims into series-partitioning column dims and row dims.
+
+ Parameters
+ ----------
+ rows : list[dict]
+ Long-format records for one table and frequency.
+ fold_dims : list[str]
+ Dimension fields taking more than one value across the records.
+
+ Returns
+ -------
+ tuple[list[str], list[str]]
+ The dims folded into the column header and the dims folded into the
+ row label. A dim becomes a column dim when it partitions the series,
+ or when the table publishes no more series than the dim has members
+ and spreading it stays within MAX_WIDE_COLUMNS, which would
+ otherwise leave one value column repeated down the rows.
+ """
+ value = RiceYearbookFetcher._dim_value
+ column_dims: list[str] = []
+ row_dims: list[str] = []
+ for field in fold_dims:
+ distinct = {
+ value(record, field)
+ for record in rows
+ if value(record, field) is not None
+ }
+ series_values: dict[str, set] = {}
+ for record in rows:
+ member = value(record, field)
+ if member is not None:
+ series_values.setdefault(record["series"], set()).add(member)
+ if not distinct or not series_values:
+ row_dims.append(field)
+ continue
+ spread = sum(len(members) for members in series_values.values()) / len(
+ series_values
+ )
+ partitions = spread <= 0.6 * len(distinct)
+ compact = len(series_values) <= len(distinct) and (
+ RiceYearbookFetcher._projected_columns(rows, [*column_dims, field])
+ <= MAX_WIDE_COLUMNS
+ )
+ if partitions or compact:
+ column_dims.append(field)
+ else:
+ row_dims.append(field)
+ return RiceYearbookFetcher._drop_redundant_dims(rows, column_dims, row_dims)
+
+ @staticmethod
+ def _is_determined(rows: list[dict], field: str, others: list[str]) -> bool:
+ """Return whether the other dims and the period already fix a dim's value."""
+ value = RiceYearbookFetcher._dim_value
+ seen: dict[tuple, Any] = {}
+ for record in rows:
+ key = (
+ *(value(record, name) for name in others),
+ record["year"],
+ record["period"],
+ )
+ member = value(record, field)
+ if seen.setdefault(key, member) != member:
+ return False
+ return True
+
+ @staticmethod
+ def _drop_redundant_dims(
+ rows: list[dict], column_dims: list[str], row_dims: list[str]
+ ) -> tuple[list[str], list[str]]:
+ """Drop dims whose value the remaining dims and the period already fix."""
+ columns, labels = list(column_dims), list(row_dims)
+ for field in [*column_dims, *row_dims]:
+ others = [name for name in [*columns, *labels] if name != field]
+ if RiceYearbookFetcher._is_determined(rows, field, others):
+ columns = [name for name in columns if name != field]
+ labels = [name for name in labels if name != field]
+ return columns, labels
+
+ @staticmethod
+ def _basis_partitions_series(rows: list[dict]) -> bool:
+ """Return whether the annual bases each publish their own series."""
+ bases = {record["period"] for record in rows}
+ if len(bases) < 2:
+ return False
+ series_bases: dict[str, set] = {}
+ for record in rows:
+ series_bases.setdefault(record["series"], set()).add(record["period"])
+ spread = sum(len(members) for members in series_bases.values()) / len(
+ series_bases
+ )
+ return spread <= 0.6 * len(bases)
+
+ @staticmethod
+ def _pivot(rows: list[dict], frequency: str) -> list[RiceYearbookData]:
+ """Pivot to period rows with the series as unit-tagged columns."""
+ value = RiceYearbookFetcher._dim_value
+ fold_dims = [
+ field
+ for field in FOLD_DIM_FIELDS
+ if len({value(record, field) for record in rows}) > 1
+ ]
+ column_dims, row_dims = RiceYearbookFetcher._classify_dims(rows, fold_dims)
+ period_varies = len({record["period"] for record in rows}) > 1
+ basis_in_columns = (
+ frequency == ANNUAL
+ and period_varies
+ and RiceYearbookFetcher._basis_partitions_series(rows)
+ )
+ multi_unit = len({record["unit"] for record in rows}) > 1
+ pivoted: dict[tuple, dict[str, Any]] = {}
+ column_order: list[str] = []
+ seen_columns: set[str] = set()
+ for order, record in enumerate(rows):
+ year = record["year"]
+ display = display_period(record["period"])
+ if frequency == ANNUAL:
+ labeled_basis = period_varies and display and not basis_in_columns
+ time_label = f"{year} — {display}" if labeled_basis else str(year)
+ else:
+ time_label = f"{year} {display}" if display else str(year)
+ dim_values = [
+ str(value(record, field))
+ for field in row_dims
+ if value(record, field) is not None
+ ]
+ label = " — ".join([*dim_values, time_label])
+ key = (
+ tuple(value(record, field) for field in row_dims),
+ year,
+ None if basis_in_columns else record["period"],
+ )
+ row = pivoted.get(key)
+ if row is None:
+ row = {
+ "_dims": tuple(dim_values),
+ "_year": year,
+ "_period": _period_sort_key(display, frequency),
+ "_order": order,
+ "period": label,
+ }
+ pivoted[key] = row
+ column_values = [
+ str(value(record, field))
+ for field in column_dims
+ if value(record, field) is not None
+ ]
+ series = (
+ f"{record['series']} — {display}"
+ if basis_in_columns and display
+ else record["series"]
+ )
+ base = " — ".join([*column_values, series])
+ column = (
+ f"{base} ({record['unit']})" if multi_unit and record["unit"] else base
+ )
+ if column not in seen_columns:
+ seen_columns.add(column)
+ column_order.append(column)
+ row[column] = record["value"]
+ ordered = sorted(
+ pivoted.values(),
+ key=lambda row: (
+ row["_dims"],
+ -row["_year"],
+ -row["_period"],
+ row["_order"],
+ ),
+ )
+ validated: list[RiceYearbookData] = []
+ for row in ordered:
+ payload = {"period": row["period"]}
+ for column in column_order:
+ payload[column] = row.get(column)
+ validated.append(RiceYearbookData.model_validate(payload))
+ return validated
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/rural_urban_commuting_area_codes.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/rural_urban_commuting_area_codes.py
new file mode 100644
index 00000000000..232e124c0cd
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/rural_urban_commuting_area_codes.py
@@ -0,0 +1,342 @@
+"""USDA ERS Rural-Urban Commuting Area Codes Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.app.service.system_service import SystemService
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from openbb_core.provider.utils.errors import EmptyDataError
+from pydantic import ConfigDict, Field, field_validator, model_validator
+
+from openbb_government_us.usda.utils.ers_rural_urban_commuting_area_codes import (
+ DEFAULT_STATE,
+ DEFAULT_TABLE,
+ TABLE_LABELS,
+ allowed_states,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+api_prefix = SystemService().system_settings.api_settings.prefix
+
+TABLE_OPTIONS = [{"label": label, "value": key} for key, label in TABLE_LABELS.items()]
+
+SERVED_FIELDS = (
+ "area_fips",
+ "state",
+ "county",
+ "area_name",
+ "area_type",
+ "primary_ruca",
+ "primary_ruca_description",
+ "secondary_ruca",
+ "secondary_ruca_description",
+ "population",
+ "land_area",
+ "population_density",
+)
+
+
+class RuralUrbanCommutingAreaCodesQueryParams(QueryParams):
+ """USDA ERS Rural-Urban Commuting Area Codes Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "x-widget_config": {
+ "label": "Table",
+ "value": DEFAULT_TABLE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": TABLE_OPTIONS,
+ "style": {"popupWidth": 260},
+ },
+ },
+ "state": {
+ "x-widget_config": {
+ "label": "State",
+ "type": "endpoint",
+ "value": DEFAULT_STATE,
+ "multiSelect": False,
+ "multiple": False,
+ "optionsEndpoint": f"{api_prefix}/usda/ruca_states",
+ "optionsParams": {"table": "$table"},
+ "style": {"popupWidth": 280},
+ },
+ },
+ }
+
+ table: str = Field(
+ default=DEFAULT_TABLE,
+ description="Vintage and geography of the code set to retrieve, as one"
+ + " lookup table per census-tract or ZIP-code vintage. Valid tables"
+ + " are:\n "
+ + ", ".join(TABLE_LABELS)
+ + "\n",
+ )
+ state: str | None = Field(
+ default=DEFAULT_STATE,
+ description="Two-letter state or territory code to scope the lookup to,"
+ + " e.g. 'DE'. If None, every area in the table is returned, which is"
+ + " tens of thousands of rows. The set of states differs by table.",
+ )
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate table."""
+ if not v:
+ return DEFAULT_TABLE
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip()
+ if value not in TABLE_LABELS:
+ raise OpenBBError(
+ f"Invalid table: {value}. Valid tables are: " + ", ".join(TABLE_LABELS)
+ )
+ return value
+
+ @field_validator("state", mode="before", check_fields=False)
+ @classmethod
+ def _validate_state(cls, v):
+ """Normalize state to an upper-case two-letter code or None for all."""
+ if not v:
+ return None
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip().upper()
+ return value or None
+
+ @model_validator(mode="after")
+ def _validate_state_for_table(self):
+ """Validate that the state is published for the selected table."""
+ if self.state is None:
+ return self
+ codes = allowed_states(self.table)
+ if self.state not in codes:
+ raise OpenBBError(
+ f"Invalid state '{self.state}' for table '{self.table}'."
+ + " Valid states are: "
+ + ", ".join(codes)
+ )
+ return self
+
+
+class RuralUrbanCommutingAreaCodesData(NullTokenMixin, Data):
+ """USDA ERS Rural-Urban Commuting Area Codes Data.
+
+ One row per geographic unit (census tract or ZIP code) of the selected
+ vintage, carrying its primary and secondary RUCA classification codes,
+ the code descriptions, and any published area attributes. This is a
+ lookup table, not a time series, so there is no pivot.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Rural-Urban Commuting Area (RUCA) Codes",
+ "$.description": "Whole-number and decimal Rural-Urban"
+ " Commuting Area classification codes for U.S. census tracts"
+ " and ZIP codes across the 1990, 2000, 2010, and 2020"
+ " vintages, published by the USDA Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ area_fips: str = Field(
+ description="Geographic identifier of the row: the 11-digit census"
+ + " tract FIPS code for a tract table, or the 5-digit ZIP code for a"
+ + " ZIP table.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "FIPS / ZIP Code",
+ "cellDataType": "text",
+ "pinned": "left",
+ }
+ },
+ )
+ state: str | None = Field(
+ default=None,
+ description="Two-letter state or territory code of the area.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "State",
+ "cellDataType": "text",
+ "pinned": "left",
+ "maxWidth": 90,
+ }
+ },
+ )
+ county: str | None = Field(
+ default=None,
+ description="County of the census tract, for the tract tables that"
+ + " publish it. Not published for the ZIP or 1990 tables.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "County",
+ "cellDataType": "text",
+ "pinned": "left",
+ "minWidth": 160,
+ }
+ },
+ )
+ area_name: str | None = Field(
+ default=None,
+ description="Name of the area: the census-tract name for the 2020"
+ + " tract table or the post-office name for the 2020 ZIP table."
+ + " Not published for the other tables.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Area name",
+ "cellDataType": "text",
+ "minWidth": 160,
+ }
+ },
+ )
+ area_type: str | None = Field(
+ default=None,
+ description="Type of the ZIP record, 'ZIP Code Area' or a post office"
+ + " or large-volume customer, for the ZIP tables. Not published for"
+ + " the tract tables.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "ZIP type",
+ "cellDataType": "text",
+ "hide": True,
+ }
+ },
+ )
+ primary_ruca: str | None = Field(
+ default=None,
+ description="Primary RUCA code, a whole number 1 to 10 or 99,"
+ + " classifying the area by its primary commuting flow.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Primary RUCA",
+ "cellDataType": "text",
+ "maxWidth": 130,
+ }
+ },
+ )
+ primary_ruca_description: str | None = Field(
+ default=None,
+ description="Description of the primary RUCA code.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Primary RUCA description",
+ "cellDataType": "text",
+ }
+ },
+ )
+ secondary_ruca: str | None = Field(
+ default=None,
+ description="Secondary RUCA code, the primary code optionally extended"
+ + " with a decimal sub-code for a secondary commuting flow.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Secondary RUCA",
+ "cellDataType": "text",
+ "maxWidth": 140,
+ }
+ },
+ )
+ secondary_ruca_description: str | None = Field(
+ default=None,
+ description="Description of the secondary RUCA code.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Secondary RUCA description",
+ "cellDataType": "text",
+ }
+ },
+ )
+ population: int | None = Field(
+ default=None,
+ description="Population of the area in the vintage year, for the tract"
+ + " tables that publish it. Not published for the ZIP tables.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Population",
+ "cellDataType": "number",
+ "maxWidth": 140,
+ }
+ },
+ )
+ land_area: float | None = Field(
+ default=None,
+ description="Land area of the tract in square miles, for the tract"
+ + " tables that publish it. Not published for the 2000 or ZIP tables.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Land area (sq mi)",
+ "cellDataType": "number",
+ "maxWidth": 150,
+ }
+ },
+ )
+ population_density: float | None = Field(
+ default=None,
+ description="Population per square mile of the tract, for the 2020 and"
+ + " 2010 tract tables that publish it.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Population density (per sq mi)",
+ "cellDataType": "number",
+ "maxWidth": 200,
+ }
+ },
+ )
+
+
+class RuralUrbanCommutingAreaCodesFetcher(
+ Fetcher[
+ RuralUrbanCommutingAreaCodesQueryParams,
+ list[RuralUrbanCommutingAreaCodesData],
+ ]
+):
+ """Fetch USDA ERS Rural-Urban Commuting Area Codes."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> RuralUrbanCommutingAreaCodesQueryParams:
+ """Transform the query params."""
+ return RuralUrbanCommutingAreaCodesQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: RuralUrbanCommutingAreaCodesQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Download and parse the selected vintage's file for the state filter."""
+ from openbb_government_us.usda.utils import (
+ ers_rural_urban_commuting_area_codes,
+ )
+
+ return await ers_rural_urban_commuting_area_codes.afetch_table(
+ query.table, query.state
+ )
+
+ @staticmethod
+ def transform_data(
+ query: RuralUrbanCommutingAreaCodesQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[RuralUrbanCommutingAreaCodesData]:
+ """Order the lookup records by identifier and validate the row schema."""
+ if not data:
+ raise EmptyDataError("No records match the given filters.")
+ ordered = sorted(data, key=lambda record: record.get("area_fips") or "")
+ return [
+ RuralUrbanCommutingAreaCodesData.model_validate(
+ {key: record.get(key) for key in SERVED_FIELDS}
+ )
+ for record in ordered
+ ]
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/rural_urban_continuum_codes.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/rural_urban_continuum_codes.py
new file mode 100644
index 00000000000..2d6bbea5854
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/rural_urban_continuum_codes.py
@@ -0,0 +1,246 @@
+"""USDA ERS Rural-Urban Continuum Codes Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.app.service.system_service import SystemService
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from openbb_core.provider.utils.errors import EmptyDataError
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_rural_urban_continuum_codes import (
+ DEFAULT_VINTAGE,
+ STATE_NAMES,
+ VINTAGE_OPTIONS,
+ VINTAGES,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+api_prefix = SystemService().system_settings.api_settings.prefix
+
+
+class RuralUrbanContinuumCodesQueryParams(QueryParams):
+ """USDA ERS Rural-Urban Continuum Codes Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/rural-urban-continuum-codes
+ """
+
+ __json_schema_extra__ = {
+ "vintage": {
+ "x-widget_config": {
+ "label": "Vintage",
+ "value": DEFAULT_VINTAGE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": VINTAGE_OPTIONS,
+ },
+ },
+ "state": {
+ "x-widget_config": {
+ "label": "State",
+ "type": "endpoint",
+ "multiSelect": False,
+ "multiple": False,
+ "optionsEndpoint": f"{api_prefix}/usda/rural_urban_continuum_states",
+ "optionsParams": {"vintage": "$vintage"},
+ "style": {"popupWidth": 280},
+ },
+ },
+ }
+
+ vintage: str = Field(
+ default=DEFAULT_VINTAGE,
+ description="Classification vintage to retrieve. Each vintage is a"
+ + " separate county lookup published in that year, carrying the RUCC"
+ + " code, its vintage-specific description, and the county population;"
+ + " codes and descriptions are vintage-specific because the"
+ + " urbanization thresholds changed over time. Valid vintages are:\n "
+ + ", ".join(VINTAGES)
+ + "\n",
+ )
+ state: str | None = Field(
+ default=None,
+ description="Two-letter state or territory code to scope the county"
+ + " rows to a single state. If None, returns every county of the"
+ + " vintage. The available states vary by vintage: the 2023 and 2013"
+ + " vintages include the AS, GU, MP, VI, PR, and DC territories, and the"
+ + " 2003 vintage adds PR to the fifty states and DC.",
+ )
+
+ @field_validator("vintage", mode="before", check_fields=False)
+ @classmethod
+ def _validate_vintage(cls, v):
+ """Validate vintage."""
+ if not v:
+ return DEFAULT_VINTAGE
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip()
+ if value not in VINTAGES:
+ raise OpenBBError(
+ f"Invalid vintage: {value}. Valid vintages are: " + ", ".join(VINTAGES)
+ )
+ return value
+
+ @field_validator("state", mode="before", check_fields=False)
+ @classmethod
+ def _validate_state(cls, v):
+ """Validate and normalize the optional state filter."""
+ if not v:
+ return None
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip().upper()
+ if value not in STATE_NAMES:
+ raise OpenBBError(
+ f"Invalid state: {value}. Valid states are: " + ", ".join(STATE_NAMES)
+ )
+ return value
+
+
+class RuralUrbanContinuumCodesData(NullTokenMixin, Data):
+ """USDA ERS Rural-Urban Continuum Codes Data.
+
+ One selected vintage as a county lookup: each row is a county or
+ county-equivalent area, carrying its FIPS code, state, name, RUCC
+ classification code, the code's vintage-specific description, and the
+ published population.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Rural-Urban Continuum Codes",
+ "$.description": "County-level Rural-Urban Continuum Codes"
+ " (RUCC) classifying U.S. counties by metropolitan status and"
+ " degree of urbanization, published in vintages by the USDA"
+ " Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ fips: str = Field(
+ description="Five-digit county FIPS code, with leading zeros preserved.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "FIPS",
+ "cellDataType": "text",
+ "pinned": "left",
+ "maxWidth": 100,
+ }
+ },
+ )
+ state: str | None = Field(
+ default=None,
+ description="Two-letter state or territory postal code.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "State",
+ "cellDataType": "text",
+ "pinned": "left",
+ "maxWidth": 90,
+ }
+ },
+ )
+ county_name: str | None = Field(
+ default=None,
+ description="County or county-equivalent area name, as published.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "County / area",
+ "pinned": "left",
+ "minWidth": 200,
+ }
+ },
+ )
+ rucc_code: str | None = Field(
+ default=None,
+ description="Rural-Urban Continuum Code for the vintage: 1 to 3 for"
+ + " metro counties and 4 to 9 for nonmetro counties.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "RUCC code",
+ "cellDataType": "text",
+ "maxWidth": 120,
+ }
+ },
+ )
+ description: str | None = Field(
+ default=None,
+ description="Vintage-specific description of the RUCC code, carried per"
+ + " row from the source.",
+ json_schema_extra={"x-widget_config": {"headerName": "Description"}},
+ )
+ population: int | None = Field(
+ default=None,
+ description="Published county population for the vintage's reference"
+ + " census, as a full count.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Population",
+ "cellDataType": "number",
+ "maxWidth": 140,
+ }
+ },
+ )
+
+
+class RuralUrbanContinuumCodesFetcher(
+ Fetcher[
+ RuralUrbanContinuumCodesQueryParams,
+ list[RuralUrbanContinuumCodesData],
+ ]
+):
+ """Fetch USDA ERS Rural-Urban Continuum Codes."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> RuralUrbanContinuumCodesQueryParams:
+ """Transform the query params."""
+ return RuralUrbanContinuumCodesQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: RuralUrbanContinuumCodesQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected vintage's county-lookup records."""
+ from openbb_government_us.usda.utils import ers_rural_urban_continuum_codes
+
+ return await ers_rural_urban_continuum_codes.afetch_vintage(query.vintage)
+
+ @staticmethod
+ def transform_data(
+ query: RuralUrbanContinuumCodesQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[RuralUrbanContinuumCodesData]:
+ """Filter the records to the chosen state and sort them by FIPS code."""
+ rows = [
+ record
+ for record in data
+ if query.state is None or record["state"] == query.state
+ ]
+ if not rows:
+ raise EmptyDataError("No records match the given filters.")
+ rows.sort(key=lambda record: record["fips"])
+ return [
+ RuralUrbanContinuumCodesData.model_validate(
+ {
+ "fips": record["fips"],
+ "state": record["state"],
+ "county_name": record["county_name"],
+ "rucc_code": record["rucc_code"],
+ "description": record["description"],
+ "population": record["population"],
+ }
+ )
+ for record in rows
+ ]
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/season_average_price_forecasts.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/season_average_price_forecasts.py
new file mode 100644
index 00000000000..4864e18825f
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/season_average_price_forecasts.py
@@ -0,0 +1,430 @@
+"""Season-Average Price Forecasts Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_season_average_price_forecasts import (
+ COMMODITIES,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+DEFAULT_COMMODITY = "Corn"
+
+
+def _number(header: str, hide: bool = False) -> dict:
+ """Build a numeric column config for one measure field."""
+ config: dict = {"headerName": header, "cellDataType": "number"}
+ if hide:
+ config["hide"] = True
+ return {"x-widget_config": config}
+
+
+class SeasonAveragePriceForecastsQueryParams(QueryParams):
+ """Season-Average Price Forecasts Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/season-average-price-forecasts
+ """
+
+ __json_schema_extra__ = {
+ "commodity": {
+ "x-widget_config": {
+ "label": "Commodity",
+ "value": DEFAULT_COMMODITY,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [{"label": name, "value": name} for name in COMMODITIES],
+ "style": {"popupWidth": 280},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ "latest": {"x-widget_config": {"label": "Latest vintage only"}},
+ }
+
+ commodity: str = Field(
+ default=DEFAULT_COMMODITY,
+ description="Commodity to retrieve. Valid commodities are:\n "
+ + ", ".join(COMMODITIES)
+ + "\n",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data, compared against the"
+ + " marketing year. If None, returns from the beginning of the series.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data, compared against the"
+ + " marketing year. If None, returns up to the most recent year.",
+ )
+ latest: bool = Field(
+ default=False,
+ description="Return only the most recent weekly model forecast vintage."
+ + " If False, every historical vintage is returned.",
+ )
+
+ @field_validator("commodity", mode="before", check_fields=False)
+ @classmethod
+ def _validate_commodity(cls, v):
+ """Validate commodity."""
+ if not v:
+ return DEFAULT_COMMODITY
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip()
+ canonical = {name.casefold(): name for name in COMMODITIES}.get(
+ value.casefold()
+ )
+ if canonical is None:
+ raise OpenBBError(
+ f"Invalid commodity: {value}. Valid commodities are: "
+ + ", ".join(COMMODITIES)
+ )
+ return canonical
+
+
+class SeasonAveragePriceForecastsData(NullTokenMixin, Data):
+ """Season-Average Price Forecasts Data.
+
+ One row per weekly model forecast vintage and marketing year, with the
+ USDA ERS season-average price forecast measures spread across columns.
+ """
+
+ model_config = ConfigDict(
+ protected_namespaces=(),
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Season-Average Price Forecasts",
+ "$.description": "Weekly ERS model and monthly WASDE season-average"
+ " price forecasts for corn, soybeans, wheat, and cotton, with the"
+ " farm-bill Price Loss Coverage and Agriculture Risk Coverage"
+ " benchmarks, published by the USDA Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ },
+ )
+
+ model_forecast_date: str = Field(
+ description="Weekly forecast vintage date the model was run, as"
+ + " published (YYYY-MM-DD).",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Forecast date",
+ "cellDataType": "date",
+ "pinned": "left",
+ "maxWidth": 130,
+ }
+ },
+ )
+ marketing_year: str = Field(
+ description="Marketing year of the forecast, as published.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Marketing year",
+ "cellDataType": "text",
+ "pinned": "left",
+ "maxWidth": 130,
+ }
+ },
+ )
+ commodity: str = Field(
+ description="Commodity name, as published.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Commodity", "hide": True}
+ },
+ )
+ futures_model_type: str = Field(
+ description="Futures model type: 'Single contract' or 'Three contract"
+ + " aggregate'. Wheat publishes both on the same date.",
+ json_schema_extra={"x-widget_config": {"headerName": "Model type"}},
+ )
+ futures_exchange: str = Field(
+ description="Futures exchange the model prices reference, as published.",
+ json_schema_extra={"x-widget_config": {"headerName": "Exchange"}},
+ )
+ mya_price_model_forecast: float | None = Field(
+ default=None,
+ description="ERS model forecast of the marketing-year average price.",
+ json_schema_extra=_number("MYA price (model)"),
+ )
+ mya_price_wasde_forecast: float | None = Field(
+ default=None,
+ description="WASDE forecast of the marketing-year average price.",
+ json_schema_extra=_number("MYA price (WASDE)"),
+ )
+ wasde_forecast_date: str | None = Field(
+ default=None,
+ description="Date of the WASDE forecast, as published.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "WASDE forecast date",
+ "cellDataType": "text",
+ }
+ },
+ )
+ target_price: float | None = Field(
+ default=None,
+ description="Farm-bill target price for the marketing year.",
+ json_schema_extra=_number("Target price", hide=True),
+ )
+ national_loan_rate: float | None = Field(
+ default=None,
+ description="Farm-bill national loan rate for the marketing year.",
+ json_schema_extra=_number("National loan rate", hide=True),
+ )
+ direct_payment_rate: float | None = Field(
+ default=None,
+ description="Farm-bill direct payment rate for the marketing year.",
+ json_schema_extra=_number("Direct payment rate", hide=True),
+ )
+ reference_price: float | None = Field(
+ default=None,
+ description="Farm-bill statutory reference price for the marketing year.",
+ json_schema_extra=_number("Reference price", hide=True),
+ )
+ ccp_effective_price_model_forecast: float | None = Field(
+ default=None,
+ description="Counter-cyclical payment effective price, model forecast.",
+ json_schema_extra=_number("CCP effective price (model)", hide=True),
+ )
+ ccp_rate_model_forecast: float | None = Field(
+ default=None,
+ description="Counter-cyclical payment rate, model forecast.",
+ json_schema_extra=_number("CCP rate (model)", hide=True),
+ )
+ ccp_effective_price_wasde_forecast: float | None = Field(
+ default=None,
+ description="Counter-cyclical payment effective price, WASDE forecast.",
+ json_schema_extra=_number("CCP effective price (WASDE)", hide=True),
+ )
+ ccp_rate_wasde_forecast: float | None = Field(
+ default=None,
+ description="Counter-cyclical payment rate, WASDE forecast.",
+ json_schema_extra=_number("CCP rate (WASDE)", hide=True),
+ )
+ historical_mya_price_year1: float | None = Field(
+ default=None,
+ description="Marketing-year average price of the first historical year"
+ + " in the five-year benchmark window.",
+ json_schema_extra=_number("Historical MYA price yr 1", hide=True),
+ )
+ historical_mya_price_year2: float | None = Field(
+ default=None,
+ description="Marketing-year average price of the second historical year"
+ + " in the five-year benchmark window.",
+ json_schema_extra=_number("Historical MYA price yr 2", hide=True),
+ )
+ historical_mya_price_year3: float | None = Field(
+ default=None,
+ description="Marketing-year average price of the third historical year"
+ + " in the five-year benchmark window.",
+ json_schema_extra=_number("Historical MYA price yr 3", hide=True),
+ )
+ historical_mya_price_year4: float | None = Field(
+ default=None,
+ description="Marketing-year average price of the fourth historical year"
+ + " in the five-year benchmark window.",
+ json_schema_extra=_number("Historical MYA price yr 4", hide=True),
+ )
+ historical_mya_price_year5: float | None = Field(
+ default=None,
+ description="Marketing-year average price of the fifth historical year"
+ + " in the five-year benchmark window.",
+ json_schema_extra=_number("Historical MYA price yr 5", hide=True),
+ )
+ historical_mya_5year_range: str | None = Field(
+ default=None,
+ description="Range of years spanned by the five-year benchmark window,"
+ + " as published, e.g. '2019-2023'.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Historical MYA 5-year range",
+ "hide": True,
+ }
+ },
+ )
+ historical_mya_price_5year_olympic_average: float | None = Field(
+ default=None,
+ description="Olympic average of the five historical marketing-year"
+ + " average prices.",
+ json_schema_extra=_number("Historical MYA 5-year Olympic avg", hide=True),
+ )
+ effective_reference_price: float | None = Field(
+ default=None,
+ description="Farm-bill effective reference price for the marketing year.",
+ json_schema_extra=_number("Effective reference price"),
+ )
+ effective_price_model_forecast: float | None = Field(
+ default=None,
+ description="Price Loss Coverage effective price, model forecast.",
+ json_schema_extra=_number("Effective price (model)", hide=True),
+ )
+ plc_payment_rate_model_forecast: float | None = Field(
+ default=None,
+ description="Price Loss Coverage payment rate, model forecast.",
+ json_schema_extra=_number("PLC payment rate (model)", hide=True),
+ )
+ effective_price_wasde_forecast: float | None = Field(
+ default=None,
+ description="Price Loss Coverage effective price, WASDE forecast.",
+ json_schema_extra=_number("Effective price (WASDE)", hide=True),
+ )
+ plc_payment_rate_wasde_forecast: float | None = Field(
+ default=None,
+ description="Price Loss Coverage payment rate, WASDE forecast.",
+ json_schema_extra=_number("PLC payment rate (WASDE)", hide=True),
+ )
+ maximum_plc_payment_rate: float | None = Field(
+ default=None,
+ description="Maximum Price Loss Coverage payment rate for the year.",
+ json_schema_extra=_number("Maximum PLC payment rate", hide=True),
+ )
+ arc_annual_benchmark_price_year1: float | None = Field(
+ default=None,
+ description="Agriculture Risk Coverage annual benchmark price of the"
+ + " first historical year in the window.",
+ json_schema_extra=_number("ARC benchmark price yr 1", hide=True),
+ )
+ arc_annual_benchmark_price_year2: float | None = Field(
+ default=None,
+ description="Agriculture Risk Coverage annual benchmark price of the"
+ + " second historical year in the window.",
+ json_schema_extra=_number("ARC benchmark price yr 2", hide=True),
+ )
+ arc_annual_benchmark_price_year3: float | None = Field(
+ default=None,
+ description="Agriculture Risk Coverage annual benchmark price of the"
+ + " third historical year in the window.",
+ json_schema_extra=_number("ARC benchmark price yr 3", hide=True),
+ )
+ arc_annual_benchmark_price_year4: float | None = Field(
+ default=None,
+ description="Agriculture Risk Coverage annual benchmark price of the"
+ + " fourth historical year in the window.",
+ json_schema_extra=_number("ARC benchmark price yr 4", hide=True),
+ )
+ arc_annual_benchmark_price_year5: float | None = Field(
+ default=None,
+ description="Agriculture Risk Coverage annual benchmark price of the"
+ + " fifth historical year in the window.",
+ json_schema_extra=_number("ARC benchmark price yr 5", hide=True),
+ )
+ arc_annual_benchmark_price_5year_range: str | None = Field(
+ default=None,
+ description="Range of years spanned by the ARC five-year benchmark"
+ + " window, as published, e.g. '2008-2012'.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "ARC benchmark 5-year range",
+ "hide": True,
+ }
+ },
+ )
+ arc_co_benchmark_price: float | None = Field(
+ default=None,
+ description="Agriculture Risk Coverage county-option benchmark price.",
+ json_schema_extra=_number("ARC-CO benchmark price", hide=True),
+ )
+ arc_co_price_model_forecast: float | None = Field(
+ default=None,
+ description="Agriculture Risk Coverage county-option price, model"
+ + " forecast.",
+ json_schema_extra=_number("ARC-CO price (model)", hide=True),
+ )
+ arc_co_price_wasde_forecast: float | None = Field(
+ default=None,
+ description="Agriculture Risk Coverage county-option price, WASDE"
+ + " forecast.",
+ json_schema_extra=_number("ARC-CO price (WASDE)", hide=True),
+ )
+ arc_ic_price_model_forecast: float | None = Field(
+ default=None,
+ description="Agriculture Risk Coverage individual-coverage price, model"
+ + " forecast.",
+ json_schema_extra=_number("ARC-IC price (model)", hide=True),
+ )
+ arc_ic_price_wasde_forecast: float | None = Field(
+ default=None,
+ description="Agriculture Risk Coverage individual-coverage price, WASDE"
+ + " forecast.",
+ json_schema_extra=_number("ARC-IC price (WASDE)", hide=True),
+ )
+ actual_mya_price: float | None = Field(
+ default=None,
+ description="Actual realized marketing-year average price, once known.",
+ json_schema_extra=_number("Actual MYA price"),
+ )
+ price_unit: str = Field(
+ description="Unit of the price columns, as published, e.g. 'U.S. dollars"
+ + " per bushel' or 'U.S. dollars per pound'.",
+ json_schema_extra={"x-widget_config": {"headerName": "Price unit"}},
+ )
+
+
+class SeasonAveragePriceForecastsFetcher(
+ Fetcher[
+ SeasonAveragePriceForecastsQueryParams,
+ list[SeasonAveragePriceForecastsData],
+ ]
+):
+ """Fetch USDA ERS Season-Average Price Forecasts."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> SeasonAveragePriceForecastsQueryParams:
+ """Transform the query params."""
+ return SeasonAveragePriceForecastsQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: SeasonAveragePriceForecastsQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the output-forecast rows for the selected commodity."""
+ from openbb_government_us.usda.utils import ers_season_average_price_forecasts
+
+ records = await ers_season_average_price_forecasts.afetch_output_forecast()
+ return [record for record in records if record["commodity"] == query.commodity]
+
+ @staticmethod
+ def transform_data(
+ query: SeasonAveragePriceForecastsQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[SeasonAveragePriceForecastsData]:
+ """Filter by year and vintage, then emit newest-first wide rows."""
+ latest_date = (
+ max(record["model_forecast_date"] for record in data) if data else None
+ )
+ selected: list[dict] = []
+ for record in data:
+ if query.latest and record["model_forecast_date"] != latest_date:
+ continue
+ year = int(record["marketing_year"])
+ if query.start_year is not None and year < query.start_year:
+ continue
+ if query.end_year is not None and year > query.end_year:
+ continue
+ selected.append(record)
+ selected.sort(
+ key=lambda record: (
+ record["commodity"],
+ record["model_forecast_date"],
+ int(record["marketing_year"]),
+ ),
+ reverse=True,
+ )
+ return [
+ SeasonAveragePriceForecastsData.model_validate(record)
+ for record in selected
+ ]
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/snap_policy_data_sets.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/snap_policy_data_sets.py
new file mode 100644
index 00000000000..763afeda5d5
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/snap_policy_data_sets.py
@@ -0,0 +1,483 @@
+"""SNAP Policy Data Sets Model."""
+
+from datetime import date as dateType
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.app.service.system_service import SystemService
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from openbb_core.provider.utils.errors import EmptyDataError
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_snap_policy_data_sets import (
+ POLICY_FIELDS,
+ STATE_LABELS,
+ state_options,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+api_prefix = SystemService().system_settings.api_settings.prefix
+
+DEFAULT_STATE = "CA"
+
+
+def _policy(header: str, hide: bool = False) -> dict:
+ """Build a numeric policy field's widget config.
+
+ Parameters
+ ----------
+ header : str
+ Column header shown for the field.
+ hide : bool
+ Whether to hide the column by default, for sparse fields that apply to
+ only a few states.
+
+ Returns
+ -------
+ dict
+ The json_schema_extra carrying the field's x-widget_config.
+ """
+ config: dict[str, Any] = {"headerName": header, "cellDataType": "number"}
+ if hide:
+ config["hide"] = True
+ return {"x-widget_config": config}
+
+
+class SnapPolicyDataSetsQueryParams(QueryParams):
+ """SNAP Policy Data Sets Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/snap-policy-data-sets
+ """
+
+ __json_schema_extra__ = {
+ "state": {
+ "x-widget_config": {
+ "label": "State",
+ "value": DEFAULT_STATE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": state_options(),
+ "style": {"popupWidth": 280},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ state: str = Field(
+ default=DEFAULT_STATE,
+ description="State to retrieve, as a two-letter postal code. The SNAP"
+ + " Policy Database is published for the fifty states and the District"
+ + " of Columbia; each state is one monthly time series of its forty-five"
+ + " policy variables.",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data."
+ + " If None, returns from January 1996.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data."
+ + " If None, returns through December 2020.",
+ )
+
+ @field_validator("state", mode="before", check_fields=False)
+ @classmethod
+ def _validate_state(cls, v):
+ """Validate state."""
+ if not v:
+ return DEFAULT_STATE
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip().upper()
+ if value not in STATE_LABELS:
+ raise OpenBBError(
+ f"Invalid state: {value}. Valid states are: " + ", ".join(STATE_LABELS)
+ )
+ return value
+
+
+class SnapPolicyDataSetsData(NullTokenMixin, Data):
+ """SNAP Policy Data Sets Data.
+
+ One selected state's monthly SNAP policy record: each row is a month, with
+ the forty-five policy variables as columns. The not-applicable ``-9``
+ sentinel on the BBCE sub-fields (meaning the state does not use broad-based
+ categorical eligibility, which the ``bbce`` flag already records) and blank
+ missing cells are coerced to null; the ``-8`` and ``-7`` policy codes on the
+ elderly/disabled income-limit field are meaningful and kept.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS SNAP Policy Data Sets",
+ "$.description": "State-level monthly SNAP policy options -"
+ " broad-based categorical eligibility, recertification periods,"
+ " EBT issuance, interview waivers, fingerprinting, noncitizen"
+ " eligibility, online application, outreach spending, and"
+ " vehicle-asset rules - from January 1996 to December 2020,"
+ " published by the USDA Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ date: dateType = Field(
+ description="First day of the observation month.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Date",
+ "cellDataType": "date",
+ "pinned": "left",
+ "maxWidth": 120,
+ }
+ },
+ )
+ state: str = Field(
+ description="Name of the state of the observation.",
+ json_schema_extra={"x-widget_config": {"headerName": "State", "hide": True}},
+ )
+ bbce: float | None = Field(
+ default=None,
+ description="Whether the state uses broad-based categorical eligibility"
+ + " (BBCE): 0 = no, 1 = yes.",
+ json_schema_extra=_policy("Uses BBCE"),
+ )
+ bbce_asset: float | None = Field(
+ default=None,
+ description="BBCE asset test: 0 = asset limit raised but not eliminated,"
+ + " 1 = asset limit eliminated. Blank when the state does not use BBCE.",
+ json_schema_extra=_policy("BBCE asset test (0=raised, 1=eliminated)"),
+ )
+ bbce_a_amt: float | None = Field(
+ default=None,
+ description="BBCE asset limit in thousands of dollars. Blank when the"
+ + " state does not use BBCE or BBCE eliminates the asset test; applies"
+ + " to only the few states that keep an asset limit, so hidden by"
+ + " default.",
+ json_schema_extra=_policy("BBCE asset limit ($000)", hide=True),
+ )
+ bbce_a_veh: float | None = Field(
+ default=None,
+ description="BBCE vehicle rule: 1 = exempt an amount, 2 = exclude at"
+ + " least one, 3 = exclude all. Blank when the state does not use BBCE"
+ + " or the asset test is eliminated; applies to only the few states that"
+ + " keep an asset limit, so hidden by default.",
+ json_schema_extra=_policy(
+ "BBCE vehicle rule (1=amount, 2=one, 3=all)", hide=True
+ ),
+ )
+ bbce_inclmt: float | None = Field(
+ default=None,
+ description="BBCE gross income limit as a percentage of the Federal"
+ + " poverty level. Blank when the state does not use BBCE.",
+ json_schema_extra=_policy("BBCE gross income limit (% FPL)"),
+ )
+ bbce_child: float | None = Field(
+ default=None,
+ description="Whether BBCE is limited to households with dependent"
+ + " children: 0 = no, 1 = yes. Blank when the state does not use BBCE.",
+ json_schema_extra=_policy("BBCE limited to households with children"),
+ )
+ bbce_elddisinclmt: float | None = Field(
+ default=None,
+ description="BBCE gross income limit (% FPL) for households with a"
+ + " senior or disabled member; -8 = gross income test eliminated,"
+ + " -7 = 200% FPL only for all-senior/disabled households. Blank when"
+ + " the state does not use BBCE.",
+ json_schema_extra=_policy("BBCE elderly/disabled income limit (% FPL)"),
+ )
+ bbce_multiple: float | None = Field(
+ default=None,
+ description="Whether multiple BBCE guidelines apply to different"
+ + " household types: 0 = no, 1 = yes. Blank when the state does not use"
+ + " BBCE.",
+ json_schema_extra=_policy("Multiple BBCE guidelines"),
+ )
+ call_any: float | None = Field(
+ default=None,
+ description="Whether the state operates call centers in any part of the"
+ + " state: 0 = no, 1 = yes.",
+ json_schema_extra=_policy("Call centers"),
+ )
+ cap: float | None = Field(
+ default=None,
+ description="Whether the state operates a Combined Application Project"
+ + " for SSI recipients: 0 = no, 1 = yes.",
+ json_schema_extra=_policy("SSI Combined Application Project"),
+ )
+ certearn_0103: float | None = Field(
+ default=None,
+ description="Proportion of SNAP units with earnings with 1-3 month"
+ + " recertification periods.",
+ json_schema_extra=_policy("Recert share, earners 1-3 mo"),
+ )
+ certearn_0406: float | None = Field(
+ default=None,
+ description="Proportion of SNAP units with earnings with 4-6 month"
+ + " recertification periods.",
+ json_schema_extra=_policy("Recert share, earners 4-6 mo"),
+ )
+ certearn_0712: float | None = Field(
+ default=None,
+ description="Proportion of SNAP units with earnings with 7-12 month"
+ + " recertification periods.",
+ json_schema_extra=_policy("Recert share, earners 7-12 mo"),
+ )
+ certearn_1399: float | None = Field(
+ default=None,
+ description="Proportion of SNAP units with earnings with 13+ month"
+ + " recertification periods.",
+ json_schema_extra=_policy("Recert share, earners 13+ mo"),
+ )
+ certearnavg: float | None = Field(
+ default=None,
+ description="Average recertification period in months for SNAP units"
+ + " with earnings.",
+ json_schema_extra=_policy("Avg recert months, earners"),
+ )
+ certearnmed: float | None = Field(
+ default=None,
+ description="Median recertification period in months for SNAP units"
+ + " with earnings.",
+ json_schema_extra=_policy("Median recert months, earners"),
+ )
+ certeld_0103: float | None = Field(
+ default=None,
+ description="Proportion of SNAP units with no earnings and a senior"
+ + " member with 1-3 month recertification periods.",
+ json_schema_extra=_policy("Recert share, no earnings w/ senior 1-3 mo"),
+ )
+ certeld_0406: float | None = Field(
+ default=None,
+ description="Proportion of SNAP units with no earnings and a senior"
+ + " member with 4-6 month recertification periods.",
+ json_schema_extra=_policy("Recert share, no earnings w/ senior 4-6 mo"),
+ )
+ certeld_0712: float | None = Field(
+ default=None,
+ description="Proportion of SNAP units with no earnings and a senior"
+ + " member with 7-12 month recertification periods.",
+ json_schema_extra=_policy("Recert share, no earnings w/ senior 7-12 mo"),
+ )
+ certeld_1399: float | None = Field(
+ default=None,
+ description="Proportion of SNAP units with no earnings and a senior"
+ + " member with 13+ month recertification periods.",
+ json_schema_extra=_policy("Recert share, no earnings w/ senior 13+ mo"),
+ )
+ certeldavg: float | None = Field(
+ default=None,
+ description="Average recertification period in months for SNAP units"
+ + " with no earnings and a senior member.",
+ json_schema_extra=_policy("Avg recert months, no earnings w/ senior"),
+ )
+ certeldmed: float | None = Field(
+ default=None,
+ description="Median recertification period in months for SNAP units"
+ + " with no earnings and a senior member.",
+ json_schema_extra=_policy("Median recert months, no earnings w/ senior"),
+ )
+ certnonearn_0103: float | None = Field(
+ default=None,
+ description="Proportion of SNAP units with no earnings and no senior"
+ + " member with 1-3 month recertification periods.",
+ json_schema_extra=_policy("Recert share, no earnings no senior 1-3 mo"),
+ )
+ certnonearn_0406: float | None = Field(
+ default=None,
+ description="Proportion of SNAP units with no earnings and no senior"
+ + " member with 4-6 month recertification periods.",
+ json_schema_extra=_policy("Recert share, no earnings no senior 4-6 mo"),
+ )
+ certnonearn_0712: float | None = Field(
+ default=None,
+ description="Proportion of SNAP units with no earnings and no senior"
+ + " member with 7-12 month recertification periods.",
+ json_schema_extra=_policy("Recert share, no earnings no senior 7-12 mo"),
+ )
+ certnonearn_1399: float | None = Field(
+ default=None,
+ description="Proportion of SNAP units with no earnings and no senior"
+ + " member with 13+ month recertification periods.",
+ json_schema_extra=_policy("Recert share, no earnings no senior 13+ mo"),
+ )
+ certnonearnavg: float | None = Field(
+ default=None,
+ description="Average recertification period in months for SNAP units"
+ + " with no earnings and no senior member.",
+ json_schema_extra=_policy("Avg recert months, no earnings no senior"),
+ )
+ certnonearnmed: float | None = Field(
+ default=None,
+ description="Median recertification period in months for SNAP units"
+ + " with no earnings and no senior member.",
+ json_schema_extra=_policy("Median recert months, no earnings no senior"),
+ )
+ ebtissuance: float | None = Field(
+ default=None,
+ description="Proportion of the dollar value of all SNAP benefits issued"
+ + " via electronic benefit transfer (EBT).",
+ json_schema_extra=_policy("Share of benefits via EBT"),
+ )
+ faceini: float | None = Field(
+ default=None,
+ description="Whether the state has a waiver to use a telephone"
+ + " interview at initial certification: 0 = no, 1 = yes.",
+ json_schema_extra=_policy("Telephone-interview waiver at initial cert"),
+ )
+ facerec: float | None = Field(
+ default=None,
+ description="Whether the state has a waiver to use a telephone"
+ + " interview at recertification: 0 = no, 1 = yes.",
+ json_schema_extra=_policy("Telephone-interview waiver at recert"),
+ )
+ fingerprint: float | None = Field(
+ default=None,
+ description="Whether the state requires fingerprinting of applicants:"
+ + " 0 = no, 1 = statewide, 2 = select parts of the state.",
+ json_schema_extra=_policy("Fingerprinting (0=no, 1=statewide, 2=partial)"),
+ )
+ noncitadultfull: float | None = Field(
+ default=None,
+ description="Whether all legal noncitizen adults meeting other"
+ + " requirements are eligible: 0 = no, 1 = yes.",
+ json_schema_extra=_policy("Noncitizen adults eligible (full)"),
+ )
+ noncitadultpart: float | None = Field(
+ default=None,
+ description="Whether some, but not all, legal noncitizen adults meeting"
+ + " other requirements are eligible: 0 = no, 1 = yes.",
+ json_schema_extra=_policy("Noncitizen adults eligible (partial)"),
+ )
+ noncitchildfull: float | None = Field(
+ default=None,
+ description="Whether all legal noncitizen children meeting other"
+ + " requirements are eligible: 0 = no, 1 = yes.",
+ json_schema_extra=_policy("Noncitizen children eligible (full)"),
+ )
+ noncitchildpart: float | None = Field(
+ default=None,
+ description="Whether some, but not all, legal noncitizen children"
+ + " meeting other requirements are eligible: 0 = no, 1 = yes.",
+ json_schema_extra=_policy("Noncitizen children eligible (partial)"),
+ )
+ nonciteldfull: float | None = Field(
+ default=None,
+ description="Whether all legal noncitizen seniors meeting other"
+ + " requirements are eligible: 0 = no, 1 = yes.",
+ json_schema_extra=_policy("Noncitizen seniors eligible (full)"),
+ )
+ nonciteldpart: float | None = Field(
+ default=None,
+ description="Whether some, but not all, legal noncitizen seniors"
+ + " meeting other requirements are eligible: 0 = no, 1 = yes.",
+ json_schema_extra=_policy("Noncitizen seniors eligible (partial)"),
+ )
+ oapp: float | None = Field(
+ default=None,
+ description="Whether the state allows online SNAP applications: 0 = no,"
+ + " 1 = statewide, 2 = select parts of the state.",
+ json_schema_extra=_policy("Online application (0=no, 1=statewide, 2=partial)"),
+ )
+ outreach: float | None = Field(
+ default=None,
+ description="Sum of Federal, State, and grant outreach spending in"
+ + " nominal thousands of dollars, spread across the fiscal year.",
+ json_schema_extra=_policy("Outreach spending ($000, nominal)"),
+ )
+ reportsimple: float | None = Field(
+ default=None,
+ description="Whether the state uses simplified reporting for households"
+ + " with earnings: 0 = no, 1 = yes.",
+ json_schema_extra=_policy("Simplified reporting"),
+ )
+ transben: float | None = Field(
+ default=None,
+ description="Whether the state offers transitional SNAP benefits to"
+ + " families leaving cash assistance: 0 = no, 1 = yes.",
+ json_schema_extra=_policy("Transitional benefits"),
+ )
+ vehexclall: float | None = Field(
+ default=None,
+ description="Whether the state excludes all vehicles from the asset"
+ + " test: 0 = no, 1 = yes.",
+ json_schema_extra=_policy("Vehicle: exclude all"),
+ )
+ vehexclamt: float | None = Field(
+ default=None,
+ description="Whether the state exempts an amount above the standard"
+ + " auto exemption from a vehicle's value: 0 = no, 1 = yes.",
+ json_schema_extra=_policy("Vehicle: exempt amount"),
+ )
+ vehexclone: float | None = Field(
+ default=None,
+ description="Whether the state excludes at least one, but not all,"
+ + " vehicles from the asset test: 0 = no, 1 = yes.",
+ json_schema_extra=_policy("Vehicle: exclude one"),
+ )
+
+
+class SnapPolicyDataSetsFetcher(
+ Fetcher[
+ SnapPolicyDataSetsQueryParams,
+ list[SnapPolicyDataSetsData],
+ ]
+):
+ """Fetch USDA ERS SNAP Policy Data Sets."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> SnapPolicyDataSetsQueryParams:
+ """Transform the query params."""
+ return SnapPolicyDataSetsQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: SnapPolicyDataSetsQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected state's monthly policy records."""
+ from openbb_government_us.usda.utils import ers_snap_policy_data_sets
+
+ return await ers_snap_policy_data_sets.afetch_state(query.state)
+
+ @staticmethod
+ def transform_data(
+ query: SnapPolicyDataSetsQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[SnapPolicyDataSetsData]:
+ """Filter to the year range and emit one row per month, newest first."""
+ rows = []
+ for record in data:
+ yearmonth = record["yearmonth"]
+ year = yearmonth // 100
+ if query.start_year is not None and year < query.start_year:
+ continue
+ if query.end_year is not None and year > query.end_year:
+ continue
+ rows.append(record)
+ if not rows:
+ raise EmptyDataError("No records match the given filters.")
+ rows.sort(key=lambda record: record["yearmonth"], reverse=True)
+ validated: list[SnapPolicyDataSetsData] = []
+ for record in rows:
+ yearmonth = record["yearmonth"]
+ values = {
+ "date": dateType(yearmonth // 100, yearmonth % 100, 1),
+ "state": record["state"],
+ }
+ for field in POLICY_FIELDS:
+ values[field] = record[field]
+ validated.append(SnapPolicyDataSetsData.model_validate(values))
+ return validated
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/state_agricultural_trade.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/state_agricultural_trade.py
new file mode 100644
index 00000000000..c1890a16534
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/state_agricultural_trade.py
@@ -0,0 +1,318 @@
+"""USDA ERS State Agricultural Trade Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.app.service.system_service import SystemService
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from openbb_core.provider.utils.errors import EmptyDataError
+from pydantic import ConfigDict, Field, field_validator, model_validator
+
+from openbb_government_us.usda.utils.ers_state_agricultural_trade import (
+ COMMODITIES,
+ DATASET_A_STATE_NAMES,
+ DEFAULT_COMMODITY,
+ DEFAULT_STATE,
+ DEFAULT_TABLE,
+ STATE_CODE_TO_NAME,
+ STATE_NAME_TO_CODE,
+ STATE_SCOPED_TABLES,
+ TABLE_LABELS,
+ TABLE_UNITS,
+ allowed_state_names,
+ commodity_options,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+api_prefix = SystemService().system_settings.api_settings.prefix
+
+
+class StateAgriculturalTradeQueryParams(QueryParams):
+ """USDA ERS State Agricultural Trade Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/state-agricultural-trade-data
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "x-widget_config": {
+ "label": "Table",
+ "value": DEFAULT_TABLE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": label, "value": key}
+ for key, label in TABLE_LABELS.items()
+ ],
+ "style": {"popupWidth": 420},
+ },
+ },
+ "state": {
+ "x-widget_config": {
+ "label": "State",
+ "type": "endpoint",
+ "value": DEFAULT_STATE,
+ "multiSelect": False,
+ "multiple": False,
+ "optionsEndpoint": f"{api_prefix}/usda/state_ag_trade_states",
+ "optionsParams": {"table": "$table"},
+ "style": {"popupWidth": 280},
+ },
+ },
+ "commodity": {
+ "x-widget_config": {
+ "label": "Commodity (exports by state)",
+ "value": DEFAULT_COMMODITY,
+ "multiSelect": False,
+ "multiple": False,
+ "options": commodity_options(),
+ "style": {"popupWidth": 320},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ table: str = Field(
+ default=DEFAULT_TABLE,
+ description="Table to retrieve. 'exports_by_commodity' spreads a state's"
+ + " export commodities across the calendar years; 'exports_by_state'"
+ + " spreads the states across the calendar years for one commodity;"
+ + " 'top_exports' and 'top_imports' spread a state's top fiscal-year"
+ + " commodities across the fiscal years. Valid tables are:\n "
+ + ", ".join(TABLE_LABELS)
+ + "\n",
+ )
+ state: str = Field(
+ default=DEFAULT_STATE,
+ description="State whose commodities are spread into columns for the"
+ + " 'exports_by_commodity', 'top_exports', and 'top_imports' tables, as"
+ + " a full state name or two-letter code, or 'United States' for the"
+ + " national total. Ignored by the 'exports_by_state' table. The"
+ + " calendar-year tables cover the 50 states and the United States; the"
+ + " fiscal-year tables also cover the District of Columbia, Puerto Rico,"
+ + " and the Virgin Islands.",
+ )
+ commodity: str = Field(
+ default=DEFAULT_COMMODITY,
+ description="Commodity whose states are spread into columns for the"
+ + " 'exports_by_state' table. Ignored by the other tables. Valid"
+ + " commodities are:\n "
+ + ", ".join(COMMODITIES)
+ + "\n",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data, compared against the"
+ + " calendar or fiscal year. If None, returns from the first published"
+ + " year.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data, compared against the"
+ + " calendar or fiscal year. If None, returns up to the most recent"
+ + " year.",
+ )
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate table."""
+ if not v:
+ return DEFAULT_TABLE
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip()
+ if value not in TABLE_LABELS:
+ raise OpenBBError(
+ f"Invalid table: {value}. Valid tables are: " + ", ".join(TABLE_LABELS)
+ )
+ return value
+
+ @field_validator("state", mode="before", check_fields=False)
+ @classmethod
+ def _validate_state(cls, v):
+ """Validate state, accepting a full name or a two-letter code."""
+ if not v:
+ return DEFAULT_STATE
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip()
+ if value.upper() in STATE_CODE_TO_NAME:
+ return STATE_CODE_TO_NAME[value.upper()]
+ if value in STATE_NAME_TO_CODE:
+ return value
+ raise OpenBBError(
+ f"Invalid state: {value}. Valid states are: "
+ + ", ".join(STATE_NAME_TO_CODE)
+ )
+
+ @field_validator("commodity", mode="before", check_fields=False)
+ @classmethod
+ def _validate_commodity(cls, v):
+ """Validate commodity."""
+ if not v:
+ return DEFAULT_COMMODITY
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip()
+ if value not in COMMODITIES:
+ raise OpenBBError(
+ f"Invalid commodity: {value}. Valid commodities are: "
+ + ", ".join(COMMODITIES)
+ )
+ return value
+
+ @model_validator(mode="after")
+ def _validate_state_for_table(self):
+ """Validate that the state is published for a state-scoped table."""
+ if self.table in STATE_SCOPED_TABLES:
+ names = allowed_state_names(self.table)
+ if self.state not in names:
+ raise OpenBBError(
+ f"Invalid state '{self.state}' for table '{self.table}'."
+ " This table is published for: " + ", ".join(names)
+ )
+ return self
+
+
+class StateAgriculturalTradeData(NullTokenMixin, Data):
+ """USDA ERS State Agricultural Trade Data.
+
+ One selected table pivoted to a wide layout: the years stay in the rows
+ while the commodity or state dimension spreads into value columns, whose
+ set is fixed for the all-data tables and varies by state for the
+ top-commodity tables.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS State Agricultural Trade",
+ "$.description": "State-level U.S. agricultural exports by"
+ " commodity and by state on a calendar-year basis, and the top"
+ " export and import commodities by state on a fiscal-year basis,"
+ " published by the USDA Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ year: int = Field(
+ description="Calendar year for the all-data tables, or fiscal year for"
+ + " the top-commodity tables.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Year",
+ "cellDataType": "number",
+ "pinned": "left",
+ "maxWidth": 90,
+ }
+ },
+ )
+ state: str | None = Field(
+ default=None,
+ description="State whose commodities the columns spread, for the"
+ + " commodity and top-commodity tables. None for the exports-by-state"
+ + " table.",
+ json_schema_extra={"x-widget_config": {"headerName": "State", "hide": True}},
+ )
+ commodity: str | None = Field(
+ default=None,
+ description="Commodity whose states the columns spread, for the"
+ + " exports-by-state table. None for the other tables.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Commodity", "hide": True}
+ },
+ )
+ unit: str | None = Field(
+ default=None,
+ description="Unit shared by every value column: 'Million dollars' for"
+ + " the calendar-year tables, 'Dollars' for the fiscal-year tables.",
+ json_schema_extra={"x-widget_config": {"headerName": "Unit", "hide": True}},
+ )
+
+
+class StateAgriculturalTradeFetcher(
+ Fetcher[
+ StateAgriculturalTradeQueryParams,
+ list[StateAgriculturalTradeData],
+ ]
+):
+ """Fetch USDA ERS State Agricultural Trade."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(params: dict[str, Any]) -> StateAgriculturalTradeQueryParams:
+ """Transform the query params."""
+ return StateAgriculturalTradeQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: StateAgriculturalTradeQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected table's long-format rows."""
+ from openbb_government_us.usda.utils import ers_state_agricultural_trade
+
+ return await ers_state_agricultural_trade.afetch_table(query.table)
+
+ @staticmethod
+ def transform_data(
+ query: StateAgriculturalTradeQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[StateAgriculturalTradeData]:
+ """Pivot the long-format rows into the selected wide table."""
+ table = query.table
+ unit = TABLE_UNITS[table]
+ if table == "exports_by_commodity":
+ records = [row for row in data if row["state"] == query.state]
+ column_of = "commodity"
+ order_ref: tuple[str, ...] | None = COMMODITIES
+ dims = {"state": query.state, "commodity": None, "unit": unit}
+ elif table == "exports_by_state":
+ records = [row for row in data if row["commodity"] == query.commodity]
+ column_of = "state"
+ order_ref = DATASET_A_STATE_NAMES
+ dims = {"state": None, "commodity": query.commodity, "unit": unit}
+ else:
+ code = STATE_NAME_TO_CODE.get(query.state, query.state)
+ records = [row for row in data if row["state"] == code]
+ column_of = "commodity"
+ order_ref = None
+ dims = {"state": query.state, "commodity": None, "unit": unit}
+
+ by_year: dict[int, dict] = {}
+ present: set[str] = set()
+ for record in records:
+ year = record["year"]
+ if query.start_year is not None and year < query.start_year:
+ continue
+ if query.end_year is not None and year > query.end_year:
+ continue
+ column = record[column_of]
+ present.add(column)
+ row = by_year.get(year)
+ if row is None:
+ row = {"year": year, **dims}
+ by_year[year] = row
+ row[column] = record["value"]
+ if not by_year:
+ raise EmptyDataError("No records match the given filters.")
+ if order_ref is None:
+ column_order = sorted(present)
+ else:
+ column_order = [column for column in order_ref if column in present]
+ column_order += sorted(present.difference(order_ref))
+ results: list[StateAgriculturalTradeData] = []
+ for year in sorted(by_year):
+ source = by_year[year]
+ row = {"year": year, **dims}
+ for column in column_order:
+ row[column] = source.get(column)
+ results.append(StateAgriculturalTradeData.model_validate(row))
+ return results
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/sugar_sweeteners_yearbook.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/sugar_sweeteners_yearbook.py
new file mode 100644
index 00000000000..de9ff678458
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/sugar_sweeteners_yearbook.py
@@ -0,0 +1,394 @@
+"""USDA ERS Sugar and Sweeteners Yearbook Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.app.service.system_service import SystemService
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from openbb_core.provider.utils.errors import EmptyDataError
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_sugar_sweeteners_yearbook import (
+ FOLD_DIM_FIELDS,
+ SUGAR_SWEETENERS_FILES,
+ frequency_rank,
+ period_sort_num,
+ within_year_label,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin, is_null_token
+
+api_prefix = SystemService().system_settings.api_settings.prefix
+
+DEFAULT_TABLE = "table_24a"
+MAX_WIDE_COLUMNS = 60
+
+
+class SugarSweetenersYearbookQueryParams(QueryParams):
+ """USDA ERS Sugar and Sweeteners Yearbook Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/sugar-and-sweeteners-yearbook-tables
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "x-widget_config": {
+ "label": "Table",
+ "value": DEFAULT_TABLE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": config["label"], "value": key}
+ for key, config in SUGAR_SWEETENERS_FILES.items()
+ ],
+ "style": {"popupWidth": 740},
+ },
+ },
+ "frequency": {
+ "x-widget_config": {
+ "label": "Frequency",
+ "type": "endpoint",
+ "multiSelect": False,
+ "multiple": False,
+ "optionsEndpoint": f"{api_prefix}/usda/sugar_frequencies",
+ "optionsParams": {"table": "$table"},
+ "style": {"popupWidth": 320},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ table: str = Field(
+ default=DEFAULT_TABLE,
+ description="Sugar and Sweeteners Yearbook table to retrieve. Each table"
+ + " is a set of named price, supply-and-use, production, or trade series"
+ + " indexed by year and period; the attributes become the wide value"
+ + " columns while time stays in the rows. Valid tables are:\n "
+ + ", ".join(SUGAR_SWEETENERS_FILES)
+ + "\n",
+ )
+ frequency: str | None = Field(
+ default=None,
+ description="Period basis to keep, one of the bases the selected table"
+ + " publishes, e.g. 'Calendar year', 'Fiscal year', 'Calendar year"
+ + " quarter', or 'Month'. Filtering to one basis yields a single"
+ + " aligned time series. If None, the table's first annual basis is"
+ + " used.",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data, compared against the"
+ + " observation year. If None, returns from the beginning of the"
+ + " series.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data, compared against the"
+ + " observation year. If None, returns up to the most recent year.",
+ )
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate table."""
+ if not v:
+ return DEFAULT_TABLE
+ table = v.strip() if isinstance(v, str) else v
+ if table not in SUGAR_SWEETENERS_FILES:
+ raise OpenBBError(
+ f"Invalid table: {table}. Valid tables are: "
+ + ", ".join(SUGAR_SWEETENERS_FILES)
+ )
+ return table
+
+ @field_validator("frequency", mode="before", check_fields=False)
+ @classmethod
+ def _validate_frequency(cls, v):
+ """Normalize frequency to a stripped string or None."""
+ if not v:
+ return None
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ return str(value).strip() or None
+
+
+class SugarSweetenersYearbookData(NullTokenMixin, Data):
+ """USDA ERS Sugar and Sweeteners Yearbook Data.
+
+ One selected yearbook table for one period basis, pivoted to a wide layout:
+ the table's attributes spread into value columns while the period stays in
+ the rows, with any varying commodity, geography, or source dimension folded
+ into the period label. Attribute columns carry their unit in the header when
+ the table reports more than one unit.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Sugar & Sweeteners Yearbook",
+ "$.description": "U.S., Mexican, and world sugar and sweetener"
+ " prices, supply and use, production, consumption, and trade"
+ " from the Sugar and Sweeteners Yearbook tables, published by"
+ " the USDA Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ period: str = Field(
+ description="Row label of the observation: the year for annual bases"
+ + " ('2020'), the year and month or quarter for sub-annual bases"
+ + " ('2020 Jan', '2020 Q1'), with any varying commodity, geography, or"
+ + " source dimension prefixed so each row is uniquely labeled.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Period",
+ "pinned": "left",
+ "maxWidth": 320,
+ }
+ },
+ )
+
+
+class SugarSweetenersYearbookFetcher(
+ Fetcher[
+ SugarSweetenersYearbookQueryParams,
+ list[SugarSweetenersYearbookData],
+ ]
+):
+ """Fetch USDA ERS Sugar and Sweeteners Yearbook data."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(
+ params: dict[str, Any],
+ ) -> SugarSweetenersYearbookQueryParams:
+ """Transform the query params."""
+ return SugarSweetenersYearbookQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: SugarSweetenersYearbookQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected table's long-format rows."""
+ from openbb_government_us.usda.utils import ers_sugar_sweeteners_yearbook
+
+ return await ers_sugar_sweeteners_yearbook.afetch_table(query.table)
+
+ @staticmethod
+ def _resolve_frequency(
+ query: SugarSweetenersYearbookQueryParams, data: list[dict]
+ ) -> tuple[str | None, list[str]]:
+ """Resolve the requested period basis against the table's real bases."""
+ available = sorted(
+ {record["period_cat"] for record in data if record["period_cat"]},
+ key=lambda cat: (frequency_rank(cat), cat),
+ )
+ if query.frequency in available:
+ return query.frequency, available
+ return (available[0] if available else None), available
+
+ @staticmethod
+ def transform_data(
+ query: SugarSweetenersYearbookQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[SugarSweetenersYearbookData]:
+ """Filter to one period basis and pivot the attributes into columns."""
+ if not data:
+ return []
+ frequency, _ = SugarSweetenersYearbookFetcher._resolve_frequency(query, data)
+ rows = [
+ record
+ for record in data
+ if record["period_cat"] == frequency
+ and (query.start_year is None or record["year"] >= query.start_year)
+ and (query.end_year is None or record["year"] <= query.end_year)
+ ]
+ vintages = {r["forecast_yearmonth"] for r in rows if r["forecast_yearmonth"]}
+ if len(vintages) > 1:
+ latest = max(vintages)
+ rows = [r for r in rows if r["forecast_yearmonth"] == latest]
+ if not rows:
+ raise EmptyDataError("No records match the given filters.")
+ return SugarSweetenersYearbookFetcher._pivot(rows)
+
+ @staticmethod
+ def _dim_value(record: dict, field: str) -> str | None:
+ """Return a dimension value, treating source placeholders as absent."""
+ value = record[field]
+ return None if is_null_token(value) else value
+
+ @staticmethod
+ def _projected_columns(rows: list[dict], column_dims: list[str]) -> int:
+ """Count the wide columns a candidate set of column dims would produce."""
+ value = SugarSweetenersYearbookFetcher._dim_value
+ return len(
+ {
+ (*(value(record, field) for field in column_dims), record["attribute"])
+ for record in rows
+ }
+ )
+
+ @staticmethod
+ def _classify_dims(
+ rows: list[dict], fold_dims: list[str]
+ ) -> tuple[list[str], list[str]]:
+ """Split folding dims into attribute-partitioning column dims and row dims.
+
+ Parameters
+ ----------
+ rows : list[dict]
+ Long-format records for one table and period basis.
+ fold_dims : list[str]
+ Dimension fields taking more than one value across the records.
+
+ Returns
+ -------
+ tuple[list[str], list[str]]
+ The dims folded into the column header and the dims folded into the
+ row label. A dim becomes a column dim when it partitions the
+ attributes, or when the table publishes no more attributes than the
+ dim has members and spreading it stays within MAX_WIDE_COLUMNS,
+ which would otherwise leave one value column repeated down the rows.
+ """
+ value = SugarSweetenersYearbookFetcher._dim_value
+ column_dims: list[str] = []
+ row_dims: list[str] = []
+ for field in fold_dims:
+ distinct = {
+ value(record, field)
+ for record in rows
+ if value(record, field) is not None
+ }
+ attr_values: dict[str, set] = {}
+ for record in rows:
+ member = value(record, field)
+ if member is not None:
+ attr_values.setdefault(record["attribute"], set()).add(member)
+ if not distinct or not attr_values:
+ row_dims.append(field)
+ continue
+ spread = sum(len(members) for members in attr_values.values()) / len(
+ attr_values
+ )
+ partitions = spread <= 0.6 * len(distinct)
+ compact = len(attr_values) <= len(distinct) and (
+ SugarSweetenersYearbookFetcher._projected_columns(
+ rows, [*column_dims, field]
+ )
+ <= MAX_WIDE_COLUMNS
+ )
+ if partitions or compact:
+ column_dims.append(field)
+ else:
+ row_dims.append(field)
+ return SugarSweetenersYearbookFetcher._drop_redundant_dims(
+ rows, column_dims, row_dims
+ )
+
+ @staticmethod
+ def _is_determined(rows: list[dict], field: str, others: list[str]) -> bool:
+ """Return whether the other dims and the period already fix a dim's value."""
+ value = SugarSweetenersYearbookFetcher._dim_value
+ seen: dict[tuple, Any] = {}
+ for record in rows:
+ key = (
+ *(value(record, name) for name in others),
+ record["year"],
+ record["period"],
+ )
+ member = value(record, field)
+ if seen.setdefault(key, member) != member:
+ return False
+ return True
+
+ @staticmethod
+ def _drop_redundant_dims(
+ rows: list[dict], column_dims: list[str], row_dims: list[str]
+ ) -> tuple[list[str], list[str]]:
+ """Drop dims whose value the remaining dims and the period already fix."""
+ columns, labels = list(column_dims), list(row_dims)
+ for field in [*column_dims, *row_dims]:
+ others = [name for name in [*columns, *labels] if name != field]
+ if SugarSweetenersYearbookFetcher._is_determined(rows, field, others):
+ columns = [name for name in columns if name != field]
+ labels = [name for name in labels if name != field]
+ return columns, labels
+
+ @staticmethod
+ def _pivot(rows: list[dict]) -> list[SugarSweetenersYearbookData]:
+ """Pivot to period rows with the attributes as unit-tagged columns."""
+ value = SugarSweetenersYearbookFetcher._dim_value
+ fold_dims = [
+ field
+ for field in FOLD_DIM_FIELDS
+ if len({value(record, field) for record in rows}) > 1
+ ]
+ column_dims, row_dims = SugarSweetenersYearbookFetcher._classify_dims(
+ rows, fold_dims
+ )
+ multi_unit = len({record["unit"] for record in rows}) > 1
+ pivoted: dict[tuple, dict[str, Any]] = {}
+ column_order: list[str] = []
+ seen_columns: set[str] = set()
+ for order, record in enumerate(rows):
+ year = record["year"]
+ within = within_year_label(record["period_cat"], record["period_desc"])
+ time_label = f"{year} {within}" if within else str(year)
+ row_values = [
+ str(value(record, field))
+ for field in row_dims
+ if value(record, field) is not None
+ ]
+ label = " — ".join([*row_values, time_label])
+ key = (
+ tuple(value(record, field) for field in row_dims),
+ year,
+ record["period"],
+ )
+ row = pivoted.get(key)
+ if row is None:
+ row = {
+ "_dims": tuple(row_values),
+ "_year": year,
+ "_period": period_sort_num(record["period"]),
+ "_order": order,
+ "period": label,
+ }
+ pivoted[key] = row
+ column_values = [
+ str(value(record, field))
+ for field in column_dims
+ if value(record, field) is not None
+ ]
+ base = " — ".join([*column_values, record["attribute"]])
+ column = (
+ f"{base} ({record['unit']})" if multi_unit and record["unit"] else base
+ )
+ if column not in seen_columns:
+ seen_columns.add(column)
+ column_order.append(column)
+ row[column] = record["value"]
+ ordered = sorted(
+ pivoted.values(),
+ key=lambda row: (
+ row["_dims"],
+ -row["_year"],
+ -row["_period"],
+ row["_order"],
+ ),
+ )
+ validated: list[SugarSweetenersYearbookData] = []
+ for row in ordered:
+ payload = {"period": row["period"]}
+ for column in column_order:
+ payload[column] = row.get(column)
+ validated.append(SugarSweetenersYearbookData.model_validate(payload))
+ return validated
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/urban_influence_codes.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/urban_influence_codes.py
new file mode 100644
index 00000000000..fd4bcc20f26
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/urban_influence_codes.py
@@ -0,0 +1,256 @@
+"""Urban Influence Codes Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.app.service.system_service import SystemService
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from openbb_core.provider.utils.errors import EmptyDataError
+from pydantic import ConfigDict, Field, field_validator, model_validator
+
+from openbb_government_us.usda.utils.ers_urban_influence_codes import (
+ STATE_LABELS,
+ VINTAGE_LABELS,
+ VINTAGES,
+ allowed_states,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+api_prefix = SystemService().system_settings.api_settings.prefix
+
+DEFAULT_VINTAGE = "2024"
+
+
+class UrbanInfluenceCodesQueryParams(QueryParams):
+ """Urban Influence Codes Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/urban-influence-codes
+ """
+
+ __json_schema_extra__ = {
+ "vintage": {
+ "x-widget_config": {
+ "label": "Vintage",
+ "value": DEFAULT_VINTAGE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": label, "value": key}
+ for key, label in VINTAGE_LABELS.items()
+ ],
+ "style": {"popupWidth": 320},
+ },
+ },
+ "state": {
+ "x-widget_config": {
+ "label": "State",
+ "type": "endpoint",
+ "multiSelect": False,
+ "multiple": False,
+ "optionsEndpoint": f"{api_prefix}/usda/urban_influence_code_states",
+ "optionsParams": {"vintage": "$vintage"},
+ "style": {"popupWidth": 280},
+ },
+ },
+ }
+
+ vintage: str = Field(
+ default=DEFAULT_VINTAGE,
+ description="Classification vintage to retrieve; each vintage is a"
+ + " separate published file. The 2024 revision uses a nine-code scheme,"
+ + " 2013 and 2003 use a twelve-code scheme, and 1993 uses a different"
+ + " nine-code scheme. Valid vintages are:\n "
+ + ", ".join(VINTAGES)
+ + "\n",
+ )
+ state: str | None = Field(
+ default=None,
+ description="State to retrieve, as a two-letter code, scoping the rows"
+ + " to one state's counties. If None, every county in the vintage is"
+ + " returned. The published state set varies by vintage: the 2024"
+ + " revision adds the American Samoa, Guam, Northern Mariana Islands,"
+ + " Puerto Rico, and U.S. Virgin Islands territories, 2013 and 2003 add"
+ + " Puerto Rico, and 1993 covers the states and the District of Columbia"
+ + " only.",
+ )
+
+ @field_validator("vintage", mode="before", check_fields=False)
+ @classmethod
+ def _validate_vintage(cls, v):
+ """Validate vintage."""
+ if not v:
+ return DEFAULT_VINTAGE
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip()
+ if value not in VINTAGES:
+ raise OpenBBError(
+ f"Invalid vintage: {value}. Valid vintages are: " + ", ".join(VINTAGES)
+ )
+ return value
+
+ @field_validator("state", mode="before", check_fields=False)
+ @classmethod
+ def _validate_state(cls, v):
+ """Validate state."""
+ if not v:
+ return None
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ value = str(value).strip().upper()
+ if not value:
+ return None
+ if value not in STATE_LABELS:
+ raise OpenBBError(
+ f"Invalid state: {value}. Valid states are: " + ", ".join(STATE_LABELS)
+ )
+ return value
+
+ @model_validator(mode="after")
+ def _validate_state_for_vintage(self):
+ """Validate that the state is published for the selected vintage."""
+ if self.state is not None and self.state not in allowed_states(self.vintage):
+ raise OpenBBError(
+ f"Invalid state '{self.state}' for vintage '{self.vintage}'."
+ f" This vintage is published for: "
+ + ", ".join(allowed_states(self.vintage))
+ )
+ return self
+
+
+class UrbanInfluenceCodesData(NullTokenMixin, Data):
+ """Urban Influence Codes Data.
+
+ A county-level lookup classifying every U.S. county by metropolitan status,
+ size, and adjacency, published by USDA ERS. Each row is one county FIPS,
+ carrying its classification code, the code's published description, and the
+ published population and population density.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Urban Influence Codes",
+ "$.description": "A county-level classification of U.S. counties"
+ " by metropolitan status, size, and adjacency to metro and micro"
+ " areas, published in the 1993, 2003, 2013, and 2024 vintages by"
+ " the USDA Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ fips: str = Field(
+ description="Five-digit county FIPS code, zero-padded.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "FIPS",
+ "cellDataType": "text",
+ "pinned": "left",
+ "maxWidth": 110,
+ }
+ },
+ )
+ state: str = Field(
+ description="Two-letter state or territory postal code of the county.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "State",
+ "cellDataType": "text",
+ "pinned": "left",
+ "maxWidth": 90,
+ }
+ },
+ )
+ county_name: str = Field(
+ description="County or municipio name, as published.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "County", "pinned": "left"}
+ },
+ )
+ urban_influence_code: str | None = Field(
+ default=None,
+ description="Urban Influence Code of the county, as an ordinal string:"
+ + " one to nine for the 2024 and 1993 vintages, one to twelve for the"
+ + " 2013 and 2003 vintages.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "UIC",
+ "cellDataType": "text",
+ "maxWidth": 90,
+ }
+ },
+ )
+ description: str | None = Field(
+ default=None,
+ description="Published description of the county's Urban Influence Code.",
+ json_schema_extra={"x-widget_config": {"headerName": "Description"}},
+ )
+ population: int | float | None = Field(
+ default=None,
+ description="Published population count of the county; the 2020 census"
+ + " for the 2024 vintage, the 2010 census for 2013, and the 2000 census"
+ + " for the 2003 and 1993 vintages.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Population", "cellDataType": "number"}
+ },
+ )
+ population_density: float | None = Field(
+ default=None,
+ description="Published population density in persons per square mile,"
+ + " from the 2000 census. Populated only for the 2003 and 1993 vintages;"
+ + " the 2024 and 2013 files carry no density.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Persons per sq. mile",
+ "cellDataType": "number",
+ "hide": True,
+ }
+ },
+ )
+
+
+class UrbanInfluenceCodesFetcher(
+ Fetcher[
+ UrbanInfluenceCodesQueryParams,
+ list[UrbanInfluenceCodesData],
+ ]
+):
+ """Fetch USDA ERS Urban Influence Codes."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(params: dict[str, Any]) -> UrbanInfluenceCodesQueryParams:
+ """Transform the query params."""
+ return UrbanInfluenceCodesQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: UrbanInfluenceCodesQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected vintage's county records."""
+ from openbb_government_us.usda.utils import ers_urban_influence_codes
+
+ return await ers_urban_influence_codes.afetch_vintage(query.vintage)
+
+ @staticmethod
+ def transform_data(
+ query: UrbanInfluenceCodesQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[UrbanInfluenceCodesData]:
+ """Filter the lookup to the selected state and sort by county FIPS."""
+ rows = (
+ data
+ if query.state is None
+ else [row for row in data if row["state"] == query.state]
+ )
+ if not rows:
+ raise EmptyDataError("No records match the given filters.")
+ rows = sorted(rows, key=lambda row: row["fips"])
+ return [UrbanInfluenceCodesData.model_validate(row) for row in rows]
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/us_agricultural_trade.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/us_agricultural_trade.py
new file mode 100644
index 00000000000..676888f7bfa
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/us_agricultural_trade.py
@@ -0,0 +1,354 @@
+"""US Agricultural Trade Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.app.service.system_service import SystemService
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_fatus_trade import (
+ DIRECTION_LABELS,
+ PERIOD_BASIS_ORDER,
+ SPREAD_DIMENSION,
+ TABLE_MEASURES,
+ TRADE_TABLES,
+ classify_period,
+ vintage_order,
+ within_year_rank,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+api_prefix = SystemService().system_settings.api_settings.prefix
+
+DEFAULT_TABLE = "exports_ytd"
+
+DEFAULT_BASIS = {
+ "summary": "Fiscal year",
+ "monthly": "Monthly",
+ "exports_ytd": "Monthly",
+ "imports_ytd": "Monthly",
+ "top_export_markets": "Monthly",
+ "top_import_sources": "Monthly",
+}
+
+TABLE_LABELS = {
+ "summary": "Summary",
+ "monthly": "Monthly total",
+ "exports_ytd": "Exports, year-to-date",
+ "imports_ytd": "Imports, year-to-date",
+ "top_export_markets": "Top export markets",
+ "top_import_sources": "Top import sources",
+}
+
+TABLE_OPTIONS = [{"label": TABLE_LABELS[slug], "value": slug} for slug in TRADE_TABLES]
+
+
+def _column_label(spread: str, record: dict) -> str:
+ """Build a wide column key with the source unit appended."""
+ if spread == "direction":
+ base = DIRECTION_LABELS[record["direction"]]
+ elif spread == "commodity":
+ base = record["commodity"]
+ else:
+ base = record["country"]
+ unit = record["units"]
+ return f"{base} ({unit})" if unit else base
+
+
+def _resolve_commodity(requested: str | None, records: list[dict]) -> str | None:
+ """Pick the partner table's row-selected commodity, defaulting to the first."""
+ available = list(dict.fromkeys(record["commodity"] for record in records))
+ if requested and requested in available:
+ return requested
+ return available[0] if available else None
+
+
+class UsAgriculturalTradeQueryParams(QueryParams):
+ """US Agricultural Trade Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/foreign-agricultural-trade-of-the-united-states-fatus/us-agricultural-trade-data-update
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "x-widget_config": {
+ "label": "Table",
+ "value": DEFAULT_TABLE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": TABLE_OPTIONS,
+ "style": {"popupWidth": 320},
+ },
+ },
+ "period_basis": {
+ "x-widget_config": {
+ "label": "Period basis",
+ "type": "endpoint",
+ "multiSelect": False,
+ "multiple": False,
+ "optionsEndpoint": f"{api_prefix}/usda/agricultural_trade_periods",
+ "optionsParams": {"table": "$table"},
+ "style": {"popupWidth": 300},
+ },
+ },
+ "value_type": {
+ "x-widget_config": {
+ "label": "Measure",
+ "type": "endpoint",
+ "value": "value",
+ "multiSelect": False,
+ "multiple": False,
+ "optionsEndpoint": f"{api_prefix}/usda/agricultural_trade_measures",
+ "optionsParams": {"table": "$table"},
+ },
+ },
+ "commodity": {
+ "x-widget_config": {
+ "label": "Commodity (partner tables)",
+ "type": "endpoint",
+ "multiSelect": False,
+ "multiple": False,
+ "optionsEndpoint": f"{api_prefix}/usda/agricultural_trade_commodities",
+ "optionsParams": {"table": "$table"},
+ "style": {"popupWidth": 360},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ "latest": {"x-widget_config": {"label": "Latest vintage only"}},
+ }
+
+ table: str = Field(
+ default=DEFAULT_TABLE,
+ description="Table to retrieve, pivoted to a wide layout. The natural"
+ + " 'many' dimension spreads into columns (trade direction for the"
+ + " summary and monthly tables, commodity for the year-to-date export"
+ + " and import tables, trade partner for the top-market tables) while"
+ + " a single pinned period column labels the rows. Valid tables are:\n "
+ + ", ".join(TRADE_TABLES)
+ + "\n",
+ )
+ period_basis: str | None = Field(
+ default=None,
+ description="Reporting basis to keep, one of those the selected table"
+ + " publishes: 'Fiscal year', 'Calendar year', 'Fiscal year-to-date',"
+ + " 'Calendar year-to-date', or 'Monthly'. Filtering to one basis yields"
+ + " a single aligned time series instead of mixing annual totals,"
+ + " year-to-date spans, and single months. If None, the table's default"
+ + " basis is used.",
+ )
+ value_type: str | None = Field(
+ default=None,
+ description="Measure to spread into the wide cells, scoped to the"
+ + " table: 'value' (customs value), 'volume', or 'cif' (cost,"
+ + " insurance, and freight). If None or invalid for the table, the"
+ + " table's first measure is used.",
+ )
+ commodity: str | None = Field(
+ default=None,
+ description="Commodity whose trade partners spread into columns, for the"
+ + " top-export-market and top-import-source tables only, as a single"
+ + " exact name as published, e.g. 'Soybeans'. If None, the table's first"
+ + " commodity is used. Ignored by the summary, monthly, and"
+ + " year-to-date tables.",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data."
+ + " None returns from the beginning of the series.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data."
+ + " If None, returns up to the most recent year.",
+ )
+ latest: bool = Field(
+ default=False,
+ description="Return only the most recent monthly update vintage for"
+ + " tables that retain the marketing year's prior vintages."
+ + " No effect on the summary and monthly tables.",
+ )
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate table."""
+ if not v:
+ return DEFAULT_TABLE
+ table = v[0] if isinstance(v, (list, tuple)) else v
+ table = table.strip() if isinstance(table, str) else table
+ if not table:
+ return DEFAULT_TABLE
+ if table not in TRADE_TABLES:
+ raise OpenBBError(
+ f"Invalid table: {table}. Valid tables are: " + ", ".join(TRADE_TABLES)
+ )
+ return table
+
+ @field_validator(
+ "value_type", "commodity", "period_basis", mode="before", check_fields=False
+ )
+ @classmethod
+ def _validate_labels(cls, v):
+ """Normalize an optional label to a single stripped string or None."""
+ if not v:
+ return None
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ return str(value).strip() or None
+
+
+class UsAgriculturalTradeData(NullTokenMixin, Data):
+ """US Agricultural Trade Data."""
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS U.S. Agricultural Trade",
+ "$.description": "Monthly U.S. agricultural exports, imports, and the trade balance by commodity and partner, published by the USDA Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ period: str = Field(
+ description="Row label of the observation: the year with the published"
+ + " time span appended, e.g. '2026 May', '2026 October-May', or"
+ + " '2024 Fiscal year, October-September'.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Period",
+ "pinned": "left",
+ "maxWidth": 240,
+ }
+ },
+ )
+
+
+class UsAgriculturalTradeFetcher(
+ Fetcher[
+ UsAgriculturalTradeQueryParams,
+ list[UsAgriculturalTradeData],
+ ]
+):
+ """Fetch USDA ERS U.S. Agricultural Trade Data Update."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(params: dict[str, Any]) -> UsAgriculturalTradeQueryParams:
+ """Transform the query params."""
+ return UsAgriculturalTradeQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: UsAgriculturalTradeQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected table's rows from the all-data CSV."""
+ from openbb_government_us.usda.utils import ers_fatus_trade
+
+ records = await ers_fatus_trade.afetch_trade_data()
+ return [record for record in records if record["table"] == query.table]
+
+ @staticmethod
+ def _period_label(basis: str, year: int, period: str) -> str:
+ """Label a row: the bare year for annual bases, year plus span otherwise."""
+ if basis in ("Fiscal year", "Calendar year"):
+ return str(year)
+ return f"{year} {period}"
+
+ @staticmethod
+ def _resolve_basis(table: str, requested: str | None, records: list[dict]) -> str:
+ """Resolve the requested period basis against the table's real bases."""
+ present = {classify_period(record["time_period"]) for record in records}
+ available = [basis for basis in PERIOD_BASIS_ORDER if basis in present]
+ if requested is not None and requested in available:
+ return requested
+ default = DEFAULT_BASIS.get(table)
+ if default is not None and default in available:
+ return default
+ return available[0] if available else DEFAULT_BASIS[table]
+
+ @staticmethod
+ def transform_data(
+ query: UsAgriculturalTradeQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[UsAgriculturalTradeData]:
+ """Filter to one period basis and pivot into the single-period layout."""
+ from openbb_government_us.usda.utils.ers_fatus_trade import filter_latest
+
+ table = query.table
+ measures = TABLE_MEASURES[table]
+ measure = query.value_type if query.value_type in measures else measures[0]
+ spread = SPREAD_DIMENSION[table]
+ records = [record for record in data if record["table"] == table]
+ if query.latest:
+ records = filter_latest(records)
+ records = [record for record in records if record["value_type"] == measure]
+ basis = UsAgriculturalTradeFetcher._resolve_basis(
+ table, query.period_basis, records
+ )
+ records = [
+ record
+ for record in records
+ if classify_period(record["time_period"]) == basis
+ ]
+ if spread == "country":
+ commodity = _resolve_commodity(query.commodity, records)
+ records = [record for record in records if record["commodity"] == commodity]
+ kept: list[tuple[int, dict]] = []
+ for record in records:
+ year = int(record["year"][:4])
+ if query.start_year is not None and year < query.start_year:
+ continue
+ if query.end_year is not None and year > query.end_year:
+ continue
+ kept.append((year, record))
+ if spread == "direction":
+ labels_by_direction: dict[str, str] = {}
+ for _, record in kept:
+ labels_by_direction.setdefault(
+ record["direction"], _column_label(spread, record)
+ )
+ column_order = [
+ labels_by_direction[direction]
+ for direction in DIRECTION_LABELS
+ if direction in labels_by_direction
+ ]
+ else:
+ column_order = list(
+ dict.fromkeys(_column_label(spread, record) for _, record in kept)
+ )
+ by_key: dict[tuple, dict] = {}
+ for year, record in sorted(
+ kept, key=lambda pair: vintage_order(pair[1]["update_version"])
+ ):
+ key = (year, record["time_period"])
+ row = by_key.get(key)
+ if row is None:
+ row = {
+ "_year": year,
+ "_rank": within_year_rank(basis, record["time_period"]),
+ "period": UsAgriculturalTradeFetcher._period_label(
+ basis, year, record["time_period"]
+ ),
+ }
+ by_key[key] = row
+ row[_column_label(spread, record)] = record["value"]
+ ordered = sorted(
+ by_key.values(), key=lambda row: (-row["_year"], -row["_rank"])
+ )
+ results: list[UsAgriculturalTradeData] = []
+ for row in ordered:
+ payload: dict[str, Any] = {"period": row["period"]}
+ for column in column_order:
+ payload[column] = row.get(column)
+ results.append(UsAgriculturalTradeData.model_validate(payload))
+ return results
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/us_bioenergy_statistics.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/us_bioenergy_statistics.py
new file mode 100644
index 00000000000..97b77f03a32
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/us_bioenergy_statistics.py
@@ -0,0 +1,218 @@
+"""USDA ERS U.S. Bioenergy Statistics Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_us_bioenergy_statistics import BIOENERGY_TABLES
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+DEFAULT_TABLE = "ethanol_supply_marketing_year"
+
+SUBANNUAL_CADENCES = frozenset({"Marketing year and quarter", "Monthly"})
+
+
+class UsBioenergyStatisticsQueryParams(QueryParams):
+ """USDA ERS U.S. Bioenergy Statistics Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/us-bioenergy-statistics
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "x-widget_config": {
+ "label": "Table",
+ "value": DEFAULT_TABLE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": config["label"], "value": key}
+ for key, config in BIOENERGY_TABLES.items()
+ ],
+ "style": {"popupWidth": 520},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ table: str = Field(
+ default=DEFAULT_TABLE,
+ description="Bioenergy-statistics table to retrieve. Each table is a"
+ + " set of named supply, use, capacity, or price series indexed by"
+ + " year and period; the series become the wide value columns while a"
+ + " single period column, with any varying location folded in, stays"
+ + " as the pinned row column. Valid tables are:\n "
+ + ", ".join(BIOENERGY_TABLES)
+ + "\n",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data, compared against the"
+ + " observation year. If None, returns from the beginning of the"
+ + " series.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data, compared against the"
+ + " observation year. If None, returns up to the most recent year.",
+ )
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate table."""
+ if not v:
+ return DEFAULT_TABLE
+ table = v.strip() if isinstance(v, str) else v
+ if table not in BIOENERGY_TABLES:
+ raise OpenBBError(
+ f"Invalid table: {table}. Valid tables are: "
+ + ", ".join(BIOENERGY_TABLES)
+ )
+ return table
+
+
+class UsBioenergyStatisticsData(NullTokenMixin, Data):
+ """USDA ERS U.S. Bioenergy Statistics, one table pivoted to a wide layout."""
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS U.S. Bioenergy Statistics",
+ "$.description": "U.S. ethanol, biodiesel, and renewable diesel"
+ " supply, use, capacity, consumption, and prices, with related"
+ " feedstock and co-product balances, published by the USDA"
+ " Economic Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ period: str = Field(
+ description="Row label of the observation: the year for annual tables"
+ + " ('2020'), the year and month or quarter for sub-annual tables"
+ + " ('2020 Jan', '2020 Q1 Sep-Nov'), with any varying State or market"
+ + " city folded in so each row is uniquely labeled ('Texas — 2025').",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Period",
+ "pinned": "left",
+ "maxWidth": 240,
+ }
+ },
+ )
+
+
+class UsBioenergyStatisticsFetcher(
+ Fetcher[
+ UsBioenergyStatisticsQueryParams,
+ list[UsBioenergyStatisticsData],
+ ]
+):
+ """Fetch USDA ERS U.S. Bioenergy Statistics."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(params: dict[str, Any]) -> UsBioenergyStatisticsQueryParams:
+ """Transform the query params."""
+ return UsBioenergyStatisticsQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: UsBioenergyStatisticsQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected table's long-format rows."""
+ from openbb_government_us.usda.utils import ers_us_bioenergy_statistics
+
+ return await ers_us_bioenergy_statistics.afetch_table(query.table)
+
+ @staticmethod
+ def _is_annual_token(period: str | None) -> bool:
+ """Return whether a period token describes a whole year."""
+ if not period:
+ return True
+ lowered = period.casefold()
+ return period.startswith("Yr") or lowered == "annual" or "year" in lowered
+
+ @staticmethod
+ def _time_label(cadence: str, year: int, period: str | None) -> str:
+ """Fold the year and any within-year period into one time label."""
+ if period is None:
+ return str(year)
+ if cadence not in SUBANNUAL_CADENCES or (
+ UsBioenergyStatisticsFetcher._is_annual_token(period)
+ ):
+ return str(year)
+ if " to " in period:
+ return period
+ return f"{year} {period}"
+
+ @staticmethod
+ def transform_data(
+ query: UsBioenergyStatisticsQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[UsBioenergyStatisticsData]:
+ """Fold time and location into one period label and pivot series to columns."""
+ cadence = BIOENERGY_TABLES[query.table]["cadence"]
+ rows = [
+ record
+ for record in data
+ if (query.start_year is None or record["year"] >= query.start_year)
+ and (query.end_year is None or record["year"] <= query.end_year)
+ ]
+ fold_location = (
+ len({record["location"] for record in rows if record["location"]}) > 1
+ )
+ pivoted: dict[str, dict] = {}
+ combo_order: dict[tuple, int] = {}
+ col_order: list[str] = []
+ for order, record in enumerate(rows):
+ year = record["year"]
+ location = record["location"] if fold_location else None
+ time_label = UsBioenergyStatisticsFetcher._time_label(
+ cadence, year, record["period"]
+ )
+ label = " — ".join(part for part in (location, time_label) if part)
+ row = pivoted.get(label)
+ if row is None:
+ combo = (location,)
+ combo_order.setdefault(combo, len(combo_order))
+ row = {
+ "_combo": combo_order[combo],
+ "_year": year,
+ "_period_ord": record["period_ord"],
+ "_order": order,
+ "period": label,
+ }
+ pivoted[label] = row
+ column = record["series"]
+ if column not in col_order:
+ col_order.append(column)
+ row[column] = record["value"]
+ results = sorted(
+ pivoted.values(),
+ key=lambda row: (
+ row["_combo"],
+ row["_year"],
+ row["_period_ord"],
+ row["_order"],
+ ),
+ )
+ validated: list[UsBioenergyStatisticsData] = []
+ for row in results:
+ payload = {"period": row["period"]}
+ for column in col_order:
+ payload[column] = row.get(column)
+ validated.append(UsBioenergyStatisticsData.model_validate(payload))
+ return validated
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/us_food_imports.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/us_food_imports.py
new file mode 100644
index 00000000000..941e7394721
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/us_food_imports.py
@@ -0,0 +1,403 @@
+"""USDA ERS U.S. Food Imports Model."""
+
+from collections import OrderedDict
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.app.service.system_service import SystemService
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_us_food_imports import (
+ DEFAULT_COMMODITY,
+ DEFAULT_FOOD_GROUP,
+ FOOD_GROUPS,
+ MEASURES,
+ SOURCE_MEASURES,
+ clean_country,
+ food_group_column_label,
+ product_lines,
+ unit_label,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+api_prefix = SystemService().system_settings.api_settings.prefix
+
+TABLES = ("by_food_group", "by_source")
+
+TABLE_OPTIONS = [
+ {"label": "By food group", "value": "by_food_group"},
+ {"label": "By source country", "value": "by_source"},
+]
+
+MEASURE_OPTIONS = [
+ {"label": "Value (million $)", "value": "value"},
+ {"label": "Volume", "value": "volume"},
+ {"label": "Value, year-over-year change (%)", "value": "value_change"},
+ {"label": "Volume, year-over-year change (%)", "value": "volume_change"},
+ {"label": "Unit price", "value": "unit_price"},
+ {"label": "Price inflation (%)", "value": "inflation"},
+]
+
+FOOD_GROUP_OPTIONS = [
+ {"label": label, "value": code} for code, label in FOOD_GROUPS.items()
+]
+
+
+def _column_key(base_label: str, uniform_unit: str | None, uom: str) -> str:
+ """Build a wide column key, appending the source unit when it varies by column."""
+ if uniform_unit is None:
+ return f"{base_label} ({unit_label(uom)})"
+ return base_label
+
+
+def _finalize(
+ by_year: dict[int, dict], column_order: "OrderedDict[str, None]"
+) -> list["UsFoodImportsData"]:
+ """Emit year rows in ascending order with a consistent wide column set."""
+ results: list[UsFoodImportsData] = []
+ for year in sorted(by_year):
+ source = by_year[year]
+ row = {
+ "year": source["year"],
+ "food_group": source["food_group"],
+ "commodity": source["commodity"],
+ "unit": source["unit"],
+ }
+ for key in column_order:
+ row[key] = source.get(key)
+ results.append(UsFoodImportsData.model_validate(row))
+ return results
+
+
+def _pivot_food_group(
+ query: "UsFoodImportsQueryParams", records: list[dict], spec: dict
+) -> list["UsFoodImportsData"]:
+ """Pivot aggregate rows into a year-by-food-group wide table."""
+ selected = [
+ record
+ for record in records
+ if record["category"] == spec["category"]
+ and record["subcategory"] in spec["subcategories"]
+ and (record["uom"] == "percent") == spec["percent"]
+ ]
+ column_order: OrderedDict[str, None] = OrderedDict()
+ for record in sorted(selected, key=lambda record: record["row_number"]):
+ base = food_group_column_label(record["commodity"])
+ column_order.setdefault(
+ _column_key(base, spec["uniform_unit"], record["uom"]), None
+ )
+ by_year: dict[int, dict] = {}
+ for record in selected:
+ year = record["year"]
+ if query.start_year is not None and year < query.start_year:
+ continue
+ if query.end_year is not None and year > query.end_year:
+ continue
+ base = food_group_column_label(record["commodity"])
+ key = _column_key(base, spec["uniform_unit"], record["uom"])
+ row = by_year.get(year)
+ if row is None:
+ row = {
+ "year": year,
+ "food_group": None,
+ "commodity": None,
+ "unit": spec["uniform_unit"],
+ }
+ by_year[year] = row
+ row[key] = record["value"]
+ return _finalize(by_year, column_order)
+
+
+def _pivot_source(
+ query: "UsFoodImportsQueryParams", records: list[dict], spec: dict
+) -> list["UsFoodImportsData"]:
+ """Pivot one food group's detail rows into a year-by-source-country wide table."""
+ if query.measure not in SOURCE_MEASURES:
+ return []
+ group = query.food_group
+ commodity = query.commodity
+ if not commodity:
+ lines = product_lines(records, group)
+ commodity = lines[0] if lines else DEFAULT_COMMODITY
+ selected = [
+ record
+ for record in records
+ if record["category"] == group
+ and record["subcategory"] == "Foods"
+ and record["commodity"] == commodity
+ and ((record["uom"] == "Million $") == (query.measure == "value"))
+ ]
+ column_order: OrderedDict[str, None] = OrderedDict()
+ for record in sorted(selected, key=lambda record: record["row_number"]):
+ base = clean_country(record["country"])
+ column_order.setdefault(
+ _column_key(base, spec["uniform_unit"], record["uom"]), None
+ )
+ group_label = FOOD_GROUPS.get(group, group)
+ by_year: dict[int, dict] = {}
+ for record in selected:
+ year = record["year"]
+ if query.start_year is not None and year < query.start_year:
+ continue
+ if query.end_year is not None and year > query.end_year:
+ continue
+ base = clean_country(record["country"])
+ key = _column_key(base, spec["uniform_unit"], record["uom"])
+ row = by_year.get(year)
+ if row is None:
+ row = {
+ "year": year,
+ "food_group": group_label,
+ "commodity": commodity,
+ "unit": spec["uniform_unit"],
+ }
+ by_year[year] = row
+ row[key] = record["value"]
+ return _finalize(by_year, column_order)
+
+
+class UsFoodImportsQueryParams(QueryParams):
+ """USDA ERS U.S. Food Imports Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/us-food-imports
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "x-widget_config": {
+ "label": "Table",
+ "value": "by_food_group",
+ "multiSelect": False,
+ "multiple": False,
+ "options": TABLE_OPTIONS,
+ },
+ },
+ "measure": {
+ "x-widget_config": {
+ "label": "Measure",
+ "value": "value",
+ "multiSelect": False,
+ "multiple": False,
+ "options": MEASURE_OPTIONS,
+ "style": {"popupWidth": 300},
+ },
+ },
+ "food_group": {
+ "x-widget_config": {
+ "label": "Food group (source table)",
+ "value": DEFAULT_FOOD_GROUP,
+ "multiSelect": False,
+ "multiple": False,
+ "options": FOOD_GROUP_OPTIONS,
+ "style": {"popupWidth": 280},
+ },
+ },
+ "commodity": {
+ "x-widget_config": {
+ "label": "Commodity (source table)",
+ "type": "endpoint",
+ "value": DEFAULT_COMMODITY,
+ "multiSelect": False,
+ "multiple": False,
+ "optionsEndpoint": f"{api_prefix}/usda/food_import_commodities",
+ "optionsParams": {"food_group": "$food_group"},
+ "style": {"popupWidth": 380},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ table: str = Field(
+ default="by_food_group",
+ description="Table to retrieve. 'by_food_group' spreads the 14 food"
+ + " groups and a total-foods column across the years; 'by_source'"
+ + " spreads the source countries of the selected food group and"
+ + " product line across the years. Valid tables are:\n "
+ + ", ".join(TABLES)
+ + "\n",
+ )
+ measure: str = Field(
+ default="value",
+ description="Measure to spread into the wide columns. 'by_source'"
+ + " supports only 'value' and 'volume'. Valid measures are:\n "
+ + ", ".join(MEASURES)
+ + "\n",
+ )
+ food_group: str = Field(
+ default=DEFAULT_FOOD_GROUP,
+ description="Food group whose source countries are spread into columns"
+ + " for the 'by_source' table. Ignored by the 'by_food_group' table."
+ + " Valid food groups are:\n "
+ + ", ".join(FOOD_GROUPS)
+ + "\n",
+ )
+ commodity: str | None = Field(
+ default=None,
+ description="Product line within the selected food group for the"
+ + " 'by_source' table, e.g. 'Fresh or chilled fruit'. If None, the"
+ + " food group's total line is used. Ignored by the 'by_food_group'"
+ + " table.",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data."
+ + " If None, returns from 1999, the first year of the series.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data."
+ + " If None, returns up to the most recent year.",
+ )
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate table."""
+ if not v:
+ return "by_food_group"
+ table = v.strip() if isinstance(v, str) else v
+ if table not in TABLES:
+ raise OpenBBError(
+ f"Invalid table: {table}. Valid tables are: " + ", ".join(TABLES)
+ )
+ return table
+
+ @field_validator("measure", mode="before", check_fields=False)
+ @classmethod
+ def _validate_measure(cls, v):
+ """Validate measure."""
+ if not v:
+ return "value"
+ measure = v.strip() if isinstance(v, str) else v
+ if measure not in MEASURES:
+ raise OpenBBError(
+ f"Invalid measure: {measure}. Valid measures are: "
+ + ", ".join(MEASURES)
+ )
+ return measure
+
+ @field_validator("food_group", mode="before", check_fields=False)
+ @classmethod
+ def _validate_food_group(cls, v):
+ """Validate food_group."""
+ if not v:
+ return DEFAULT_FOOD_GROUP
+ group = v.strip() if isinstance(v, str) else v
+ if group not in FOOD_GROUPS:
+ raise OpenBBError(
+ f"Invalid food group: {group}. Valid food groups are: "
+ + ", ".join(FOOD_GROUPS)
+ )
+ return group
+
+ @field_validator("commodity", mode="before", check_fields=False)
+ @classmethod
+ def _validate_commodity(cls, v):
+ """Normalize commodity to a stripped string or None."""
+ if not v:
+ return None
+ value = v[0] if isinstance(v, (list, tuple)) else v
+ return str(value).strip() or None
+
+
+class UsFoodImportsData(NullTokenMixin, Data):
+ """USDA ERS U.S. Food Imports Data.
+
+ One selected table pivoted to a wide layout: the years stay in the rows
+ while the food-group or source-country dimension spreads into value
+ columns, whose set is fixed for the food-group table and varies by food
+ group for the source-country table.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS U.S. Food Imports",
+ "$.description": "Annual U.S. food import value, volume, unit"
+ " prices, and price inflation by food group, and by source"
+ " country within a food group, published by the USDA Economic"
+ " Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ year: int = Field(
+ description="Calendar year of the observation.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Year",
+ "cellDataType": "number",
+ "pinned": "left",
+ "maxWidth": 90,
+ }
+ },
+ )
+ food_group: str | None = Field(
+ default=None,
+ description="Food group of the source-country table."
+ + " None for the by-food-group table.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Food group", "hide": True}
+ },
+ )
+ commodity: str | None = Field(
+ default=None,
+ description="Product line of the source-country table, as published."
+ + " None for the by-food-group table.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Commodity", "hide": True}
+ },
+ )
+ unit: str | None = Field(
+ default=None,
+ description="Unit shared by every value column, when uniform;"
+ + " None when the unit varies by column and is carried in each"
+ + " column header instead.",
+ json_schema_extra={"x-widget_config": {"headerName": "Unit", "hide": True}},
+ )
+
+
+class UsFoodImportsFetcher(
+ Fetcher[
+ UsFoodImportsQueryParams,
+ list[UsFoodImportsData],
+ ]
+):
+ """Fetch USDA ERS U.S. Food Imports."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(params: dict[str, Any]) -> UsFoodImportsQueryParams:
+ """Transform the query params."""
+ return UsFoodImportsQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: UsFoodImportsQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the summary file's long-format rows."""
+ from openbb_government_us.usda.utils import ers_us_food_imports
+
+ return await ers_us_food_imports.afetch_records()
+
+ @staticmethod
+ def transform_data(
+ query: UsFoodImportsQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[UsFoodImportsData]:
+ """Pivot the long-format rows into the selected wide table."""
+ spec = MEASURES[query.measure]
+ if query.table == "by_source":
+ return _pivot_source(query, data, spec)
+ return _pivot_food_group(query, data, spec)
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/vegetables_and_pulses.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/vegetables_and_pulses.py
new file mode 100644
index 00000000000..9c899899a26
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/vegetables_and_pulses.py
@@ -0,0 +1,238 @@
+"""USDA ERS Vegetables and Pulses Yearbook Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from openbb_core.provider.utils.errors import EmptyDataError
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_vegetables_and_pulses import (
+ DEFAULT_TABLE,
+ TABLE_LABELS,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin
+
+
+class VegetablesAndPulsesQueryParams(QueryParams):
+ """USDA ERS Vegetables and Pulses Yearbook Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/vegetables-and-pulses-data/vegetables-and-pulses-yearbook-tables
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "x-widget_config": {
+ "label": "Table",
+ "value": DEFAULT_TABLE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": label, "value": key}
+ for key, label in TABLE_LABELS.items()
+ ],
+ "style": {"popupWidth": 520},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ table: str = Field(
+ default=DEFAULT_TABLE,
+ description="Yearbook table to retrieve. Each table is a supply,"
+ + " availability, price, cash-receipts, or world-production series"
+ + " for one commodity or aggregate; the data elements (production,"
+ + " supply, imports, exports, availability, price) become the wide"
+ + " columns while year, commodity, and location stay as rows."
+ + " Valid tables are:\n "
+ + ", ".join(TABLE_LABELS)
+ + "\n",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data, compared against the"
+ + " observation year. If None, returns from the beginning of the series.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data, compared against the"
+ + " observation year. If None, returns up to the most recent year.",
+ )
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate table."""
+ if not v:
+ return DEFAULT_TABLE
+ table = v[0] if isinstance(v, (list, tuple)) else v
+ table = str(table).strip()
+ if table not in TABLE_LABELS:
+ raise OpenBBError(
+ f"Invalid table: {table}. Valid tables are: " + ", ".join(TABLE_LABELS)
+ )
+ return table
+
+
+class VegetablesAndPulsesData(NullTokenMixin, Data):
+ """USDA ERS Vegetables and Pulses Yearbook Data.
+
+ One selected yearbook table pivoted to a wide layout: year, commodity, and
+ location stay in the rows while the table's data elements spread into value
+ columns, whose set and units vary by table.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Vegetables & Pulses Yearbook",
+ "$.description": "U.S. and world vegetable and pulse-crop"
+ " production, supply, use, trade, per capita availability,"
+ " prices, and cash receipts from the annual Vegetables and"
+ " Pulses Yearbook, published by the USDA Economic Research"
+ " Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ year: int = Field(
+ description="Year of the observation.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Year",
+ "cellDataType": "text",
+ "pinned": "left",
+ "maxWidth": 90,
+ }
+ },
+ )
+ commodity: str | None = Field(
+ default=None,
+ description="Vegetable or pulse commodity, as published.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Commodity", "pinned": "left"}
+ },
+ )
+ location: str | None = Field(
+ default=None,
+ description="Location of the observation: a country for the"
+ + " world-production table, a state or the United States for the"
+ + " cash-receipts table, and the United States for the commodity"
+ + " supply tables.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Location", "pinned": "left"}
+ },
+ )
+ end_use: str | None = Field(
+ default=None,
+ description="Market of the observation, as published, e.g. 'Fresh',"
+ + " 'Processing', 'Dry', 'Canning', 'Freezing', or 'All uses'.",
+ json_schema_extra={"x-widget_config": {"headerName": "End use", "hide": True}},
+ )
+ geographical_level: str | None = Field(
+ default=None,
+ description="Geographical level of the location, 'Country' or 'State'.",
+ json_schema_extra={
+ "x-widget_config": {"headerName": "Geographic level", "hide": True}
+ },
+ )
+
+
+class VegetablesAndPulsesFetcher(
+ Fetcher[
+ VegetablesAndPulsesQueryParams,
+ list[VegetablesAndPulsesData],
+ ]
+):
+ """Fetch USDA ERS Vegetables and Pulses Yearbook Data."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(params: dict[str, Any]) -> VegetablesAndPulsesQueryParams:
+ """Transform the query params."""
+ return VegetablesAndPulsesQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: VegetablesAndPulsesQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected table's long-format rows."""
+ from openbb_government_us.usda.utils import ers_vegetables_and_pulses
+
+ return await ers_vegetables_and_pulses.afetch_table(query.table)
+
+ @staticmethod
+ def transform_data(
+ query: VegetablesAndPulsesQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[VegetablesAndPulsesData]:
+ """Pivot the long-format rows into the wide layout, item into columns."""
+ pivoted: dict[tuple, dict] = {}
+ item_order: list[str] = []
+ seen_items: set[str] = set()
+ for record in data:
+ year = record["year"]
+ if query.start_year is not None and year < query.start_year:
+ continue
+ if query.end_year is not None and year > query.end_year:
+ continue
+ item = record["item"]
+ if item not in seen_items:
+ seen_items.add(item)
+ item_order.append(item)
+ key = (
+ record["commodity"],
+ record["end_use"],
+ record["location"],
+ year,
+ )
+ row = pivoted.get(key)
+ if row is None:
+ row = {
+ "_commodity": record["commodity"] or "",
+ "_location": record["location"] or "",
+ "_end_use": record["end_use"] or "",
+ "_year": year,
+ "year": year,
+ "commodity": record["commodity"],
+ "location": record["location"],
+ "end_use": record["end_use"],
+ "geographical_level": record["geographical_level"],
+ }
+ pivoted[key] = row
+ row[item] = record["value"]
+ if not pivoted:
+ raise EmptyDataError("No records match the given filters.")
+ results = sorted(
+ pivoted.values(),
+ key=lambda row: (
+ row["_commodity"],
+ row["_location"],
+ row["_end_use"],
+ row["_year"],
+ ),
+ )
+ validated: list[VegetablesAndPulsesData] = []
+ for row in results:
+ ordered = {
+ "year": row["year"],
+ "commodity": row["commodity"],
+ "location": row["location"],
+ "end_use": row["end_use"],
+ "geographical_level": row["geographical_level"],
+ }
+ for item in item_order:
+ if item in row:
+ ordered[item] = row[item]
+ validated.append(VegetablesAndPulsesData.model_validate(ordered))
+ return validated
diff --git a/openbb_platform/providers/government_us/openbb_government_us/models/weather_bulletin.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/weather_bulletin.py
similarity index 74%
rename from openbb_platform/providers/government_us/openbb_government_us/models/weather_bulletin.py
rename to openbb_platform/providers/government_us/openbb_government_us/usda/models/weather_bulletin.py
index 585f9b719ba..8fdddbfa447 100644
--- a/openbb_platform/providers/government_us/openbb_government_us/models/weather_bulletin.py
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/weather_bulletin.py
@@ -1,6 +1,5 @@
"""Government US Weather Bulletin Model"""
-# pylint: disable=unused-argument
from datetime import datetime
from typing import Any
@@ -13,7 +12,7 @@ from openbb_core.provider.standard_models.weather_bulletin import (
from pydantic import ConfigDict, field_validator
-class GovernmentUsWeatherBulletinQueryParams(WeatherBulletinQueryParams):
+class UsdaWeatherBulletinQueryParams(WeatherBulletinQueryParams):
"""US Government Weather Bulletin Query Params.
Source: https://esmis.nal.usda.gov/publication/weekly-weather-and-crop-bulletin
@@ -30,26 +29,18 @@ class GovernmentUsWeatherBulletinQueryParams(WeatherBulletinQueryParams):
return value
-class GovernmentUsWeatherBulletinData(WeatherBulletinData):
- """US Government Weather Bulletin Data.
-
- The Weekly Weather and Crop Bulletin is released by 4:00 p.m. on the second workday of each week.
-
- The Bulletin includes the National Summary, State Stories, current data for weather, temperature and precipitation
- and international agricultural weather. The full report is jointly published by the National Oceanic
- and Atmospheric Administration of the U.S. Department of Commerce, the National Agricultural Statistics Service,
- and the World Agricultural Outlook Board.
- """
+class UsdaWeatherBulletinData(WeatherBulletinData):
+ """US Government Weather Bulletin Data."""
model_config = ConfigDict(
extra="ignore",
)
-class GovernmentUsWeatherBulletinFetcher(
+class UsdaWeatherBulletinFetcher(
Fetcher[
- GovernmentUsWeatherBulletinQueryParams,
- list[GovernmentUsWeatherBulletinData],
+ UsdaWeatherBulletinQueryParams,
+ list[UsdaWeatherBulletinData],
]
):
"""US Government Weather Bulletin Fetcher."""
@@ -57,18 +48,17 @@ class GovernmentUsWeatherBulletinFetcher(
@staticmethod
def transform_query(
params: dict[str, Any],
- ) -> GovernmentUsWeatherBulletinQueryParams:
+ ) -> UsdaWeatherBulletinQueryParams:
"""Transform params into the query params model."""
- return GovernmentUsWeatherBulletinQueryParams(**params)
+ return UsdaWeatherBulletinQueryParams(**params)
@staticmethod
async def aextract_data(
- query: GovernmentUsWeatherBulletinQueryParams,
+ query: UsdaWeatherBulletinQueryParams,
credentials: dict[str, str] | None = None,
**kwargs: Any,
) -> list:
"""Asynchronously extract data from the US Government Weather Bulletin API."""
- # pylint: disable=import-outside-toplevel
import asyncio # noqa
import re
from openbb_core.provider.utils.helpers import get_async_requests_session
@@ -84,7 +74,7 @@ class GovernmentUsWeatherBulletinFetcher(
try:
async with await get_async_requests_session() as session:
- session._max_field_size = 32768 # pylint: disable=protected-access
+ session._max_field_size = 32768
async def fetch_month(m: int) -> list[dict]:
"""Fetch bulletins for a single month."""
@@ -106,33 +96,26 @@ class GovernmentUsWeatherBulletinFetcher(
matches = re.findall(pattern, html, re.DOTALL)
for pdf_path, date_str in matches:
- # Skip the "latest release" which appears on every page
if pdf_path == "/sites/default/release-files/795708/wwcb.pdf":
continue
- # Parse the date
try:
dt = datetime.strptime(date_str, "%Y-%m-%d")
except ValueError:
continue
- # Only include if it matches the requested year/month
if dt.year != year:
continue
if month is not None and dt.month != m:
continue
- # Calculate week of month (1-5)
week_of_month = (dt.day - 1) // 7 + 1
- # Filter by week if specified
if week is not None and week_of_month != week:
continue
- # Extract filename from path
filename = pdf_path.split("/")[-1]
- # Create human-readable label
label = f"Weekly Weather Bulletin - {dt.strftime('%Y-%m-%d')}"
bulletins.append(
@@ -159,10 +142,8 @@ class GovernmentUsWeatherBulletinFetcher(
if isinstance(result, list):
all_bulletins.extend(result)
- # Sort by date descending (most recent first)
all_bulletins.sort(key=lambda x: x["date"], reverse=True)
- # Remove duplicates (same date can appear in adjacent month pages)
seen_dates = set()
unique_bulletins = []
for b in all_bulletins:
@@ -177,13 +158,13 @@ class GovernmentUsWeatherBulletinFetcher(
@staticmethod
def transform_data(
- query: GovernmentUsWeatherBulletinQueryParams,
+ query: UsdaWeatherBulletinQueryParams,
data: list,
**kwargs: Any,
- ) -> list[GovernmentUsWeatherBulletinData]:
+ ) -> list[UsdaWeatherBulletinData]:
"""Transform data into the standard model."""
return [
- GovernmentUsWeatherBulletinData(
+ UsdaWeatherBulletinData(
label=item["label"],
value=item["pdf_url"],
)
diff --git a/openbb_platform/providers/government_us/openbb_government_us/models/weather_bulletin_download.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/weather_bulletin_download.py
similarity index 74%
rename from openbb_platform/providers/government_us/openbb_government_us/models/weather_bulletin_download.py
rename to openbb_platform/providers/government_us/openbb_government_us/usda/models/weather_bulletin_download.py
index 7c68d671687..6cebdb8857b 100644
--- a/openbb_platform/providers/government_us/openbb_government_us/models/weather_bulletin_download.py
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/weather_bulletin_download.py
@@ -1,7 +1,5 @@
"""Government US Weather Bulletin Download Model."""
-# pylint: disable=unused-argument
-
from typing import Any
from openbb_core.app.model.abstract.error import OpenBBError
@@ -13,9 +11,7 @@ from openbb_core.provider.standard_models.weather_bulletin_download import (
from pydantic import Field
-class GovernmentUsWeatherBulletinDownloadQueryParams(
- WeatherBulletinDownloadQueryParams
-):
+class UsdaWeatherBulletinDownloadQueryParams(WeatherBulletinDownloadQueryParams):
"""US Government Weather Bulletin Download Query Params."""
__json_schema_extra__ = {
@@ -25,16 +21,16 @@ class GovernmentUsWeatherBulletinDownloadQueryParams(
}
-class GovernmentUsWeatherBulletinDownloadData(WeatherBulletinDownloadData):
+class UsdaWeatherBulletinDownloadData(WeatherBulletinDownloadData):
"""US Government Weather Bulletin Download Data."""
data_format: dict[str, str] = Field(description="Data format information.")
-class GovernmentUsWeatherBulletinDownloadFetcher(
+class UsdaWeatherBulletinDownloadFetcher(
Fetcher[
- GovernmentUsWeatherBulletinDownloadQueryParams,
- list[GovernmentUsWeatherBulletinDownloadData],
+ UsdaWeatherBulletinDownloadQueryParams,
+ list[UsdaWeatherBulletinDownloadData],
]
):
"""US Government Weather Bulletin Download Fetcher."""
@@ -42,24 +38,22 @@ class GovernmentUsWeatherBulletinDownloadFetcher(
@staticmethod
def transform_query(
params: dict[str, Any],
- ) -> GovernmentUsWeatherBulletinDownloadQueryParams:
+ ) -> UsdaWeatherBulletinDownloadQueryParams:
"""Transform params into the query params model."""
- return GovernmentUsWeatherBulletinDownloadQueryParams(**params)
+ return UsdaWeatherBulletinDownloadQueryParams(**params)
@staticmethod
async def aextract_data(
- query: GovernmentUsWeatherBulletinDownloadQueryParams,
+ query: UsdaWeatherBulletinDownloadQueryParams,
credentials: dict[str, Any] | None,
**kwargs: Any,
) -> dict:
"""Extract the raw PDF content."""
- # pylint: disable=import-outside-toplevel
from openbb_core.provider.utils.helpers import get_async_requests_session
results: dict = {}
urls = query.urls
- # Verify that all URLs are going to be valid USDA URLs
for url in urls:
if not url.lower().startswith("https://esmis.nal.usda.gov/"):
raise OpenBBError(
@@ -76,7 +70,7 @@ class GovernmentUsWeatherBulletinDownloadFetcher(
try:
async with await get_async_requests_session() as session:
- session._max_field_size = 32768 # pylint: disable=protected-access
+ session._max_field_size = 32768
for url in urls:
async with await session.get(url) as response:
@@ -95,15 +89,14 @@ class GovernmentUsWeatherBulletinDownloadFetcher(
@staticmethod
def transform_data(
- query: GovernmentUsWeatherBulletinDownloadQueryParams,
+ query: UsdaWeatherBulletinDownloadQueryParams,
data: dict,
**kwargs: Any,
- ) -> list[GovernmentUsWeatherBulletinDownloadData]:
+ ) -> list[UsdaWeatherBulletinDownloadData]:
"""Transform the raw PDF content into the data model."""
- # pylint: disable=import-outside-toplevel
import base64
- output: list[GovernmentUsWeatherBulletinDownloadData] = []
+ output: list[UsdaWeatherBulletinDownloadData] = []
for url, content in data.items():
content_b64 = base64.b64encode(content).decode("utf-8")
@@ -111,7 +104,7 @@ class GovernmentUsWeatherBulletinDownloadFetcher(
"data_type": "pdf",
"filename": url.split("/")[-1],
}
- bulletin_data = GovernmentUsWeatherBulletinDownloadData(
+ bulletin_data = UsdaWeatherBulletinDownloadData(
content=content_b64,
data_format=data_format,
)
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/models/wheat_data.py b/openbb_platform/providers/government_us/openbb_government_us/usda/models/wheat_data.py
new file mode 100644
index 00000000000..66c58811717
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/models/wheat_data.py
@@ -0,0 +1,233 @@
+"""USDA ERS Wheat Data Model."""
+
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+from openbb_core.provider.abstract.data import Data
+from openbb_core.provider.abstract.fetcher import Fetcher
+from openbb_core.provider.abstract.query_params import QueryParams
+from pydantic import ConfigDict, Field, field_validator
+
+from openbb_government_us.usda.utils.ers_wheat_data import (
+ ROW_DIM_FIELDS,
+ WHEAT_DATA_FILES,
+)
+from openbb_government_us.utils.serializers import NullTokenMixin, is_null_token
+
+DEFAULT_TABLE = "us_supply_and_disappearance"
+
+LABEL_DIM_ORDER = ("commodity_group", "wheat_class", "product", "geography", "unit")
+
+
+class WheatDataQueryParams(QueryParams):
+ """USDA ERS Wheat Data Query Parameters.
+
+ Source: https://www.ers.usda.gov/data-products/wheat-data
+ """
+
+ __json_schema_extra__ = {
+ "table": {
+ "x-widget_config": {
+ "label": "Table",
+ "value": DEFAULT_TABLE,
+ "multiSelect": False,
+ "multiple": False,
+ "options": [
+ {"label": config["label"], "value": key}
+ for key, config in WHEAT_DATA_FILES.items()
+ ],
+ "style": {"popupWidth": 420},
+ },
+ },
+ "start_year": {"x-widget_config": {"label": "Start year"}},
+ "end_year": {"x-widget_config": {"label": "End year"}},
+ }
+
+ table: str = Field(
+ default=DEFAULT_TABLE,
+ description="Wheat-data table to retrieve. Each table is a set of"
+ + " named balance-sheet, price, or trade series indexed by year and"
+ + " period; the series become the wide value columns while any varying"
+ + " wheat class, destination, market, product, or unit is folded into"
+ + " the pinned period label. Valid tables are:\n "
+ + ", ".join(WHEAT_DATA_FILES)
+ + "\n",
+ )
+ start_year: int | None = Field(
+ default=None,
+ description="Start year for filtering the data, compared against the"
+ + " first four digits of the marketing, calendar, or fiscal year."
+ + " If None, returns from the beginning of the series.",
+ )
+ end_year: int | None = Field(
+ default=None,
+ description="End year for filtering the data, compared against the"
+ + " first four digits of the marketing, calendar, or fiscal year."
+ + " If None, returns up to the most recent year.",
+ )
+
+ @field_validator("table", mode="before", check_fields=False)
+ @classmethod
+ def _validate_table(cls, v):
+ """Validate table."""
+ if not v:
+ return DEFAULT_TABLE
+ table = v.strip() if isinstance(v, str) else v
+ if table not in WHEAT_DATA_FILES:
+ raise OpenBBError(
+ f"Invalid table: {table}. Valid tables are: "
+ + ", ".join(WHEAT_DATA_FILES)
+ )
+ return table
+
+
+class WheatDataData(NullTokenMixin, Data):
+ """USDA ERS Wheat Data.
+
+ One selected wheat-data table pivoted to a wide layout: the period stays in
+ the rows while the table's series dimension spreads into value columns, whose
+ set varies by table. Any varying wheat class, destination, market, product,
+ or unit is folded into the period label so every row is uniquely labeled.
+ """
+
+ model_config = ConfigDict(
+ json_schema_extra={
+ "x-widget_config": {
+ "$.name": "USDA ERS Wheat Data",
+ "$.description": "U.S. and world wheat supply, use, trade, prices,"
+ " and by-class balance sheets, published by the USDA Economic"
+ " Research Service.",
+ "$.category": "Economy",
+ "$.subCategory": "Agriculture",
+ "$.source": ["USDA", "ERS"],
+ },
+ }
+ )
+
+ period: str = Field(
+ description="Row label of the observation: the marketing, calendar, or"
+ + " fiscal year and its within-year period ('2024/25 MY Jun-May',"
+ + " '1989/90 Apr'), with any varying wheat class, destination, market,"
+ + " product, or unit prefixed so each row is uniquely labeled.",
+ json_schema_extra={
+ "x-widget_config": {
+ "headerName": "Period",
+ "pinned": "left",
+ "maxWidth": 300,
+ }
+ },
+ )
+
+
+class WheatDataFetcher(
+ Fetcher[
+ WheatDataQueryParams,
+ list[WheatDataData],
+ ]
+):
+ """Fetch USDA ERS Wheat Data."""
+
+ require_credentials = False
+
+ @staticmethod
+ def transform_query(params: dict[str, Any]) -> WheatDataQueryParams:
+ """Transform the query params."""
+ return WheatDataQueryParams(**params)
+
+ @staticmethod
+ async def aextract_data(
+ query: WheatDataQueryParams,
+ credentials: dict[str, str] | None,
+ **kwargs: Any,
+ ) -> list[dict]:
+ """Extract the selected table's long-format rows."""
+ from openbb_government_us.usda.utils import ers_wheat_data
+
+ return await ers_wheat_data.afetch_table(query.table)
+
+ @staticmethod
+ def _time_label(marketing_year: str, period: str | None) -> str:
+ """Fold the year label and within-year period into one time label."""
+ if period:
+ return f"{marketing_year} {period}"
+ return marketing_year
+
+ @staticmethod
+ def transform_data(
+ query: WheatDataQueryParams,
+ data: list[dict],
+ **kwargs: Any,
+ ) -> list[WheatDataData]:
+ """Pivot the long-format rows into uniquely labeled period rows."""
+ filtered: list[dict] = []
+ for order, record in enumerate(data):
+ year = record["year"]
+ if query.start_year is not None and year < query.start_year:
+ continue
+ if query.end_year is not None and year > query.end_year:
+ continue
+ clean = dict(record)
+ for field in ROW_DIM_FIELDS:
+ if is_null_token(clean.get(field)):
+ clean[field] = None
+ clean["_order"] = order
+ filtered.append(clean)
+ if not filtered:
+ return []
+ distinct: dict[str, set] = {
+ field: {r[field] for r in filtered if r[field] is not None}
+ for field in ROW_DIM_FIELDS
+ }
+ varying = {field for field in ROW_DIM_FIELDS if len(distinct[field]) > 1}
+ table_unit = (
+ next(iter(distinct["unit"]))
+ if "unit" not in varying and len(distinct["unit"]) == 1
+ else None
+ )
+ pivoted: dict[tuple, dict] = {}
+ col_order: list[str] = []
+ seen_cols: set[str] = set()
+ for record in filtered:
+ key = (
+ record["marketing_year"],
+ record["period"],
+ *(record[field] for field in ROW_DIM_FIELDS),
+ )
+ row = pivoted.get(key)
+ if row is None:
+ dim_parts = [
+ record[field]
+ for field in LABEL_DIM_ORDER
+ if field in varying and record[field] is not None
+ ]
+ time_label = WheatDataFetcher._time_label(
+ record["marketing_year"], record["period"]
+ )
+ row = {
+ "_sort": tuple(record[field] or "" for field in LABEL_DIM_ORDER),
+ "_year": record["year"],
+ "_order": record["_order"],
+ "period": " — ".join([*dim_parts, time_label]),
+ }
+ pivoted[key] = row
+ series = record["series"]
+ column = (
+ f"{series} ({table_unit})"
+ if table_unit and f"({table_unit})" not in series
+ else series
+ )
+ if column not in seen_cols:
+ seen_cols.add(column)
+ col_order.append(column)
+ row[column] = row.get(column, 0.0) + record["amount"]
+ results = sorted(
+ pivoted.values(),
+ key=lambda row: (row["_sort"], row["_year"], row["_order"]),
+ )
+ validated: list[WheatDataData] = []
+ for row in results:
+ payload: dict[str, Any] = {"period": row["period"]}
+ for column in col_order:
+ payload[column] = row.get(column)
+ validated.append(WheatDataData.model_validate(payload))
+ return validated
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/usda_router.py b/openbb_platform/providers/government_us/openbb_government_us/usda/usda_router.py
new file mode 100644
index 00000000000..e63d5be9e0b
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/usda_router.py
@@ -0,0 +1,3570 @@
+"""USDA Router."""
+
+from datetime import datetime
+from pathlib import Path
+
+from fastapi.exceptions import HTTPException
+from fastapi.responses import HTMLResponse
+from openbb_core.app.model.command_context import CommandContext
+from openbb_core.app.model.example import APIEx, PythonEx
+from openbb_core.app.model.obbject import OBBject
+from openbb_core.app.provider_interface import (
+ ExtraParams,
+ ProviderChoices,
+ StandardParams,
+)
+from openbb_core.app.query import Query as OpenBBQuery
+from openbb_core.app.router import Router
+from openbb_core.app.service.system_service import SystemService
+
+from openbb_government_us.usda import COMMODITY_INSTALLED
+from openbb_government_us.usda.utils.ers_catalog import load_catalog, viz_paths
+
+router = Router(
+ prefix="",
+ description="Data connector to the U.S. Department of Agriculture.",
+)
+api_prefix = SystemService().system_settings.api_settings.prefix
+
+DEFAULT_ERS_VIZ = "Marketbaskets/Marketbaskets"
+
+
+async def get_usda_apps_json() -> list[dict]:
+ """Serve the USDA app definition for OpenBB Workspace."""
+ import json
+
+ apps_file = Path(__file__).parent / "assets" / "apps.json"
+ try:
+ with apps_file.open("r", encoding="utf-8") as file:
+ return json.load(file)
+ except Exception: # noqa: BLE001
+ return []
+
+
+router._api_router.add_api_route(
+ path="/apps.json",
+ endpoint=get_usda_apps_json,
+ methods=["GET"],
+ include_in_schema=False,
+)
+
+
+async def ers_viz_catalog() -> list[dict]:
+ """List the USDA ERS Tableau Public visualizations.
+
+ Returns
+ -------
+ list[dict]
+ One record per visualization, with its embed path and public URL.
+ """
+ return [
+ {
+ "viz": f"{entry['workbook']}/{entry['default_view']}",
+ "title": entry["title"],
+ "description": entry["description"],
+ "last_updated": entry["last_updated"],
+ "url": f"https://public.tableau.com/views/{entry['workbook']}"
+ f"/{entry['default_view']}",
+ }
+ for entry in load_catalog()
+ ]
+
+
+router._api_router.add_api_route(
+ path="/ers_viz_catalog",
+ endpoint=ers_viz_catalog,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+async def ers_chart(
+ viz: str = DEFAULT_ERS_VIZ,
+ theme: str | None = "dark",
+) -> HTMLResponse:
+ """Render an embedded USDA ERS Tableau Public visualization (OpenBB Workspace iframe widget)."""
+ import json
+
+ entry = viz_paths().get(viz)
+ if entry is None:
+ raise HTTPException(
+ status_code=404,
+ detail=f"Unknown ERS visualization '{viz}'."
+ " Valid values are 'workbook/view' paths from the catalog.",
+ )
+ template = (Path(__file__).parent / "assets" / "ers_chart.html").read_text(
+ encoding="utf-8"
+ )
+ payload = {
+ "viz": viz,
+ "title": entry["title"],
+ "description": entry["description"],
+ "last_updated": entry["last_updated"],
+ "theme": "light" if (theme or "").lower() == "light" else "dark",
+ }
+ blob = (
+ json.dumps(payload)
+ .replace("<", "\\u003c")
+ .replace(">", "\\u003e")
+ .replace("&", "\\u0026")
+ )
+ return HTMLResponse(
+ content=template.replace("__ERS_CHART_DATA__", blob),
+ headers={"Cache-Control": "no-cache"},
+ )
+
+
+router._api_router.add_api_route(
+ path="/ers_chart",
+ endpoint=ers_chart,
+ methods=["GET"],
+ response_class=HTMLResponse,
+ openapi_extra={
+ "widget_config": {
+ "name": "USDA ERS Interactive Charts",
+ "description": "Interactive data visualizations published by the"
+ " USDA Economic Research Service on Tableau Public.",
+ "category": "Economy",
+ "subCategory": "Agriculture",
+ "source": ["USDA", "ERS"],
+ "type": "iframe",
+ "widgetId": "usda_ers_chart_usda_obb",
+ "gridData": {
+ "w": 40,
+ "h": 30,
+ },
+ "params": [
+ {
+ "label": "Visualization",
+ "show": True,
+ "paramName": "viz",
+ "value": DEFAULT_ERS_VIZ,
+ "type": "text",
+ "options": [
+ {
+ "label": entry["title"],
+ "value": f"{entry['workbook']}/{entry['default_view']}",
+ }
+ for entry in load_catalog()
+ ],
+ "style": {"popupWidth": 600},
+ },
+ {"paramName": "theme", "show": False},
+ ],
+ "refetchInterval": False,
+ }
+ },
+)
+
+
+@router.command(
+ model="FarmToConsumerPriceSpreads",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get the milk and dairy market basket alongside whole milk, from 2015.",
+ parameters={
+ "item": "milk_and_dairy_basket,whole_milk",
+ "start_year": 2015,
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get every fresh vegetable series.",
+ parameters={"category": "fresh_vegetables", "provider": "usda"},
+ ),
+ ],
+)
+async def price_spreads(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS price spreads from farm to consumer - retail price versus farm value for food items and market baskets."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+async def price_spread_items(category: str | None = None) -> list:
+ """List price-spread items, narrowed to a category when provided.
+
+ Parameters
+ ----------
+ category : str | None
+ Data product category. When None, every item is returned.
+ """
+ from openbb_government_us.usda.utils.ers_price_spreads import PRICE_SPREADS_FILES
+
+ return [
+ {"label": slug.replace("_", " ").capitalize(), "value": slug}
+ for slug, meta in PRICE_SPREADS_FILES.items()
+ if not category or meta["category"] == category
+ ]
+
+
+router._api_router.add_api_route(
+ path="/price_spread_items",
+ endpoint=price_spread_items,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+async def milk_cost_items(
+ report: str = "by_state", category: str | None = None
+) -> list:
+ """List milk cost items, narrowed to a cost category when provided.
+
+ Parameters
+ ----------
+ report : str
+ Report key, by_state or by_size_of_operation.
+ category : str | None
+ Cost category key. When None, every item is returned.
+ """
+ from openbb_government_us.usda.utils import ers_milk_cost_of_production as milk
+
+ rows = await milk.afetch_report(
+ report if report in milk.MILK_COST_FILES else "by_state"
+ )
+ label_to_key = {label: key for key, label in milk.ITEM_CHOICES.items()}
+ category_label = milk.CATEGORY_CHOICES.get(category or "", category)
+ entries: list = []
+ seen: set = set()
+ for row in rows:
+ if category and row["category"] != category_label:
+ continue
+ key = label_to_key.get(row["item"])
+ if key and key not in seen:
+ seen.add(key)
+ entries.append({"label": row["item"], "value": key})
+ return entries
+
+
+router._api_router.add_api_route(
+ path="/milk_cost_items",
+ endpoint=milk_cost_items,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+async def bell_report_commodities(is_workspace: bool = False) -> list:
+ """List the commodities available for the Bell Report."""
+ from openbb_government_us.usda.utils.fas_bell_report import get_commodities
+
+ commodities = await get_commodities()
+ if is_workspace:
+ return [
+ {
+ "label": f"{row['commodity_name']} ({row['commodity_code']})",
+ "value": str(row["commodity_code"]),
+ }
+ for row in commodities
+ ]
+ return commodities
+
+
+router._api_router.add_api_route(
+ path="/bell_report_commodities",
+ endpoint=bell_report_commodities,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+async def bell_report_weeks(is_workspace: bool = False, limit: int = 260) -> list:
+ """List the published period ending dates for the Bell Report."""
+ from openbb_government_us.usda.utils.fas_bell_report import get_published_weeks
+
+ weeks = (await get_published_weeks())[:limit]
+ if is_workspace:
+ return [
+ {
+ "label": row["week_ending"].strftime("%Y-%m-%d"),
+ "value": row["week_ending"].strftime("%Y-%m-%d"),
+ }
+ for row in weeks
+ ]
+ return [
+ {
+ "week_ending": row["week_ending"],
+ "released_at": row["released_at"],
+ }
+ for row in weeks
+ ]
+
+
+router._api_router.add_api_route(
+ path="/bell_report_weeks",
+ endpoint=bell_report_weeks,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+async def bell_report_urls(
+ commodity_code: str = "101",
+ week_ending: str | None = None,
+) -> list:
+ """List the downloadable Bell Reports for a commodity and period.
+
+ One entry per marketing year report. Only published periods are
+ offered, so an embargoed report is never addressable.
+ """
+ from openbb_core.app.model.abstract.error import OpenBBError
+
+ from openbb_government_us.usda.utils.fas_bell_report import (
+ REPORT_NAMES,
+ build_report_url,
+ get_commodities,
+ get_published_weeks,
+ )
+
+ codes = [int(c.strip()) for c in str(commodity_code).split(",") if c.strip()]
+ commodities = await get_commodities()
+ by_code = {row["commodity_code"]: row for row in commodities}
+ unknown = [c for c in codes if c not in by_code]
+
+ if unknown:
+ raise OpenBBError(
+ f"Unknown commodity code(s) -> {unknown}. Valid codes are: "
+ + ", ".join(str(c) for c in sorted(by_code))
+ )
+
+ weeks = await get_published_weeks()
+ if week_ending:
+ matches = [
+ w for w in weeks if w["week_ending"].strftime("%Y-%m-%d") == week_ending
+ ]
+ if not matches:
+ raise OpenBBError(
+ f"No published Bell Report for the week ending {week_ending}."
+ f" The most recent published week is {weeks[0]['week_ending']}."
+ )
+ week = matches[0]
+ else:
+ week = weeks[0]
+
+ names = ", ".join(by_code[c]["commodity_name"] for c in codes)
+ stamp = week["week_ending"].strftime("%Y-%m-%d")
+ entries: list = []
+
+ for report in REPORT_NAMES:
+ url = build_report_url(
+ report,
+ week["week_ending"],
+ week["released_at"].date(),
+ [by_code[c]["id"] for c in codes],
+ )
+ entries.append(
+ {
+ "label": f"Bell Report {report.upper()} - {names} - {stamp}",
+ "value": url,
+ }
+ )
+
+ return entries
+
+
+router._api_router.add_api_route(
+ path="/bell_report_urls",
+ endpoint=bell_report_urls,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+@router.command(
+ methods=["POST"],
+ model="FasBellReport",
+ no_validate=True,
+ widget_config={
+ "name": "USDA FAS Bell Report",
+ "description": "Weekly export sales commitments by commodity and"
+ " marketing year, published by the Foreign Agricultural Service.",
+ "type": "multi_file_viewer",
+ "refetchInterval": False,
+ "gridData": {"w": 40, "h": 30},
+ "category": "Commodity",
+ "subCategory": "Agriculture",
+ "source": ["USDA", "FAS"],
+ "params": [
+ {
+ "paramName": "urls",
+ "type": "endpoint",
+ "optionsEndpoint": f"{api_prefix}/usda/bell_report_urls",
+ "optionsParams": {
+ "commodity_code": "$commodity_code",
+ "week_ending": "$week_ending",
+ },
+ "show": False,
+ "multiSelect": True,
+ "roles": ["fileSelector"],
+ },
+ {
+ "paramName": "commodity_code",
+ "label": "Commodity",
+ "type": "endpoint",
+ "optionsEndpoint": f"{api_prefix}/usda/bell_report_commodities",
+ "optionsParams": {"is_workspace": True},
+ "value": "101",
+ "multiSelect": True,
+ "style": {"popupWidth": 500},
+ },
+ {
+ "paramName": "week_ending",
+ "label": "Period Ending Date",
+ "type": "endpoint",
+ "optionsEndpoint": f"{api_prefix}/usda/bell_report_weeks",
+ "optionsParams": {"is_workspace": True},
+ "description": "Only published weeks are listed.",
+ },
+ ],
+ },
+ examples=[
+ APIEx(
+ parameters={
+ "provider": "usda",
+ "urls": [
+ "https://apps.fas.usda.gov/esrqs/ReportsHome.aspx?RN=BR&wed=07%2F03%2F2025&YGO=07%2F04%2F2024&RD=07%2F10%2F2025&MY=N&CID=1%2C&AppUserId=0"
+ ],
+ },
+ ),
+ PythonEx(
+ description="List the reports published for wheat and corn, then download them.",
+ code=[
+ "urls = [d['value'] for d in obb.usda.bell_report_urls(commodity_code='101,401')]",
+ "pdfs = obb.usda.bell_report(urls=urls)",
+ ],
+ ),
+ ],
+)
+async def bell_report(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Download one, or more, USDA FAS Bell Reports.
+
+ This command returns only the results portion of the OBBject response.
+ It contains a list of dictionaries where the base64 encoded content of the
+ document is under the 'content' key.
+ """
+ response = await OBBject.from_query(OpenBBQuery(**locals()))
+ return response.model_dump().get("results", {})
+
+
+@router.command(
+ model="ErsPublications",
+ widget_config={"exclude": True},
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get the Livestock, Dairy, and Poultry Outlook releases.",
+ parameters={"series": "LDPM", "provider": "usda"},
+ ),
+ APIEx(
+ description="Get every research report released since 2025.",
+ parameters={
+ "series": "research-reports",
+ "start_date": "2025-01-01",
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def publications(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get the USDA ERS publications catalog - outlook, research, and discontinued report series."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+async def ers_publication_urls(
+ series: str = "outlook-reports",
+ limit: int = 25,
+) -> list:
+ """List the most recent ERS publications as downloadable choices."""
+ from openbb_government_us.usda.utils.ers_publications import fetch_publications
+
+ rows = await fetch_publications(series)
+ rows.sort(key=lambda row: row["release_date"] or "", reverse=True)
+ return [
+ {
+ "label": f"{row['release_date']} - {row['title']}",
+ "value": row["url"],
+ }
+ for row in rows[:limit]
+ ]
+
+
+router._api_router.add_api_route(
+ path="/ers_publication_urls",
+ endpoint=ers_publication_urls,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+async def ers_publication_series() -> list:
+ """List the ERS publication series groups and codes."""
+ from openbb_government_us.usda.utils.ers_publications import (
+ SERIES_CODES,
+ SERIES_GROUPS,
+ )
+
+ return [
+ {"label": group.replace("-", " ").title(), "value": group}
+ for group in SERIES_GROUPS
+ ] + [
+ {"label": f"{code}: {name}", "value": code}
+ for code, name in SERIES_CODES.items()
+ ]
+
+
+router._api_router.add_api_route(
+ path="/ers_publication_series",
+ endpoint=ers_publication_series,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+@router.command(
+ methods=["POST"],
+ model="ErsPublicationDownload",
+ no_validate=True,
+ widget_config={
+ "name": "USDA ERS Publications Viewer",
+ "description": "Full Report PDFs published by the USDA Economic Research"
+ " Service, including the Outlook report series.",
+ "type": "multi_file_viewer",
+ "refetchInterval": False,
+ "gridData": {"w": 20, "h": 30},
+ "category": "Economy",
+ "subCategory": "Agriculture",
+ "source": ["USDA", "ERS"],
+ "params": [
+ {
+ "paramName": "urls",
+ "type": "endpoint",
+ "optionsEndpoint": f"{api_prefix}/usda/ers_publication_urls",
+ "optionsParams": {"series": "$series"},
+ "show": False,
+ "multiSelect": True,
+ "roles": ["fileSelector"],
+ },
+ {
+ "paramName": "series",
+ "label": "Series",
+ "value": "outlook-reports",
+ "type": "endpoint",
+ "optionsEndpoint": f"{api_prefix}/usda/ers_publication_series",
+ "style": {"popupWidth": 500},
+ },
+ ],
+ },
+ examples=[
+ APIEx(
+ parameters={
+ "provider": "usda",
+ "urls": ["https://www.ers.usda.gov/publications/115092"],
+ }
+ ),
+ PythonEx(
+ description="Download the latest Livestock, Dairy, and Poultry Outlook.",
+ code=[
+ "rows = obb.usda.publications(series='LDPM').to_df()",
+ "pdfs = obb.usda.publication_documents(urls=[rows.iloc[0]['url']])",
+ ],
+ ),
+ ],
+)
+async def publication_documents(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Download one, or more, USDA ERS publication Full Report PDFs.
+
+ This command returns only the results portion of the OBBject response.
+ It contains a list of dictionaries where the base64 encoded content of the
+ document is under the 'content' key.
+ """
+ response = await OBBject.from_query(OpenBBQuery(**locals()))
+ return response.model_dump().get("results", {})
+
+
+async def psd_report_commodities() -> list:
+ """List the commodities that publish a PSD circular."""
+ from openbb_government_us.usda.utils.psd_circulars import COMMODITIES
+
+ return [
+ {"label": commodity.replace("_", " ").title(), "value": commodity}
+ for commodity in COMMODITIES
+ ]
+
+
+router._api_router.add_api_route(
+ path="/psd_report_commodities",
+ endpoint=psd_report_commodities,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+async def psd_report_urls(commodity: str | None = None) -> list:
+ """List PSD circulars for the file selector.
+
+ Parameters
+ ----------
+ commodity : str | None
+ Commodity(ies) to list, as a comma-separated list. When a commodity is
+ given, every historical circular for it is returned, newest first. When
+ None, only the latest circular for every commodity is returned.
+ """
+ from openbb_government_us.usda.utils.psd_circulars import (
+ historical_circulars,
+ latest_circulars,
+ )
+
+ codes = (
+ [c.strip() for c in str(commodity).split(",") if c.strip()]
+ if commodity
+ else [None]
+ )
+ entries: list = []
+
+ for code in codes:
+ rows = (
+ await historical_circulars(code) if code else await latest_circulars(None)
+ )
+ for row in rows:
+ stamp = f"{row['year']}-{row['month']:02d}"
+ label = f"{row['commodity'].replace('_', ' ').title()} - {stamp}"
+ entries.append({"label": label, "value": row["url"]})
+
+ return entries
+
+
+router._api_router.add_api_route(
+ path="/psd_report_urls",
+ endpoint=psd_report_urls,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+def _psd_attribute_label(key: str) -> str:
+ """Format a PSD attribute key for display."""
+ return (
+ key.replace("_", " ")
+ .title()
+ .replace("Us", "US")
+ .replace("Ty", "TY")
+ .replace("Fsi", "FSI")
+ )
+
+
+async def psd_data_attributes(commodity: str | None = None) -> list:
+ """List the PSD attributes available for a commodity.
+
+ Parameters
+ ----------
+ commodity : str | None
+ Commodity name. When None (report mode), every attribute is listed.
+ """
+ from openbb_government_us.usda.utils.psd_codes import ATTRIBUTES, COMMODITIES
+ from openbb_government_us.usda.utils.psd_data_downloader import (
+ _get_commodity_attributes,
+ )
+
+ code = COMMODITIES.get(commodity or "")
+ keys = _get_commodity_attributes(code) if code else sorted(ATTRIBUTES)
+ return [{"label": "All Attributes", "value": None}] + [
+ {"label": _psd_attribute_label(key), "value": key} for key in keys
+ ]
+
+
+router._api_router.add_api_route(
+ path="/psd_data_attributes",
+ endpoint=psd_data_attributes,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+async def psd_data_countries(commodity: str | None = None) -> list:
+ """List the PSD countries available for a commodity.
+
+ Parameters
+ ----------
+ commodity : str | None
+ Commodity name. When None (report mode), every country and region is
+ listed.
+ """
+ from openbb_government_us.usda.utils.psd_codes import (
+ COMMODITIES,
+ COUNTRIES,
+ REGIONS,
+ )
+ from openbb_government_us.usda.utils.psd_data_downloader import (
+ _get_commodity_countries,
+ )
+
+ code = COMMODITIES.get(commodity or "")
+ options: list = [{"label": "All Countries", "value": None}]
+ if code:
+ for name, ccode in _get_commodity_countries(code).items():
+ options.append({"label": name, "value": ccode})
+ else:
+ choices = {
+ **{name: c for name, c in COUNTRIES.items() if name != "world"},
+ **REGIONS,
+ }
+ for name, ccode in choices.items():
+ options.append({"label": name.replace("_", " ").title(), "value": ccode})
+ return options
+
+
+router._api_router.add_api_route(
+ path="/psd_data_countries",
+ endpoint=psd_data_countries,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+@router.command(
+ model="FasReportCalendar",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get the upcoming Weekly Export Sales releases.",
+ parameters={"report_type": "export_sales", "provider": "usda"},
+ ),
+ APIEx(
+ description="Get upcoming releases covering cotton.",
+ parameters={"commodity": "cotton", "provider": "usda"},
+ ),
+ ],
+)
+async def report_calendar(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get the USDA Foreign Agricultural Service calendar of upcoming report releases."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+@router.command(
+ model="DairyData",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get the dairy situation at a glance since 2020.",
+ parameters={
+ "table": "situation_at_a_glance",
+ "start_year": 2020,
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def dairy_data(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS dairy data - supply, use, prices, and trade for milk and dairy products."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+@router.command(
+ model="MilkCostOfProduction",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get milk production costs by size of operation.",
+ parameters={"report": "by_size_of_operation", "provider": "usda"},
+ ),
+ APIEx(
+ description="Get Wisconsin operating costs since 2022.",
+ parameters={
+ "state": "wisconsin",
+ "category": "operating costs",
+ "start_year": 2022,
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def milk_cost_of_production(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS milk cost of production estimates, by state or by size of operation."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+@router.command(
+ model="MajorLandUses",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get cropland used for crops.",
+ parameters={"table": "cropland_used_for_crops", "provider": "usda"},
+ ),
+ ],
+)
+async def major_land_uses(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS major land uses - acreage by land use class for the United States and its regions."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+@router.command(
+ model="UsAgriculturalTrade",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get the top export markets for corn, by volume.",
+ parameters={
+ "table": "top_export_markets",
+ "commodity": "Corn",
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get the monthly agricultural trade totals since 2020.",
+ parameters={
+ "table": "monthly",
+ "start_year": 2020,
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def agricultural_trade(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get the ERS U.S. agricultural trade data update - each table pivoted to a wide layout with the year and period in the rows and the trade direction, commodity, or trade partner spread into columns."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+async def agricultural_trade_measures(table: str | None = None) -> list:
+ """List the measures available in an agricultural-trade table.
+
+ Parameters
+ ----------
+ table : str | None
+ Table slug to scope the measures to. When None or unknown, the
+ year-to-date exports table's measures are returned.
+ """
+ from openbb_government_us.usda.utils.ers_fatus_trade import (
+ MEASURE_LABELS,
+ TABLE_MEASURES,
+ )
+
+ measures = TABLE_MEASURES.get(table or "", TABLE_MEASURES["exports_ytd"])
+ return [
+ {"label": MEASURE_LABELS[measure], "value": measure} for measure in measures
+ ]
+
+
+router._api_router.add_api_route(
+ path="/agricultural_trade_measures",
+ endpoint=agricultural_trade_measures,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+async def agricultural_trade_commodities(table: str | None = None) -> list:
+ """List the commodities available in an agricultural-trade table.
+
+ Parameters
+ ----------
+ table : str | None
+ Table slug to scope the commodities to. When None, every commodity is
+ returned.
+ """
+ from openbb_government_us.usda.utils.ers_fatus_trade import distinct_field
+
+ return [
+ {"label": name, "value": name}
+ for name in await distinct_field("commodity", table)
+ ]
+
+
+router._api_router.add_api_route(
+ path="/agricultural_trade_commodities",
+ endpoint=agricultural_trade_commodities,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+async def agricultural_trade_periods(table: str = "exports_ytd") -> list:
+ """List the reporting bases an agricultural-trade table publishes.
+
+ Parameters
+ ----------
+ table : str
+ Table slug to scope the bases to. When unknown, the year-to-date
+ exports table is used.
+ """
+ from openbb_government_us.usda.utils.ers_fatus_trade import (
+ TRADE_TABLES,
+ table_period_bases,
+ )
+
+ if table not in TRADE_TABLES:
+ table = "exports_ytd"
+ return [
+ {"label": basis, "value": basis} for basis in await table_period_bases(table)
+ ]
+
+
+router._api_router.add_api_route(
+ path="/agricultural_trade_periods",
+ endpoint=agricultural_trade_periods,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+@router.command(
+ model="MeatPriceSpreads",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get the pork values and spreads table, annual rows only.",
+ parameters={
+ "table": "pork",
+ "frequency": "annual",
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get the historical monthly beef, pork, and broiler series since 2000.",
+ parameters={
+ "table": "historical_monthly",
+ "start_year": 2000,
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def meat_price_spreads(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS meat price spreads - retail values, farm values, price spreads, and farmers' shares for beef, pork, poultry, and eggs."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+@router.command(
+ model="WheatData",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get world and U.S. wheat supply and disappearance since 2010.",
+ parameters={
+ "table": "world_supply_and_disappearance",
+ "start_year": 2010,
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get the historical by-class quarterly balance sheet.",
+ parameters={"table": "by_class_quarterly", "provider": "usda"},
+ ),
+ ],
+)
+async def wheat_data(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS wheat data - the Wheat Yearbook supply, use, price, and trade tables, each pivoted to a wide layout by year."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+@router.command(
+ model="CommodityCostsAndReturns",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get the U.S. total corn budget since 2020.",
+ parameters={
+ "commodity": "corn",
+ "region": "U.S. total",
+ "start_year": 2020,
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get farrow-to-finish hog operating costs.",
+ parameters={
+ "commodity": "hogs_farrow_finish",
+ "category": "operating_costs",
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def commodity_costs_and_returns(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS commodity costs and returns - annual per-unit cost-and-return budgets by commodity and ERS Farm Resource Region."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+async def commodity_cost_regions(commodity: str = "corn") -> list:
+ """List the ERS Farm Resource Regions for a commodity's cost budget.
+
+ Parameters
+ ----------
+ commodity : str
+ Commodity key. Unknown commodities fall back to corn's regions.
+ """
+ from openbb_government_us.usda.utils.ers_commodity_costs_and_returns import (
+ COMMODITY_REGIONS,
+ )
+
+ regions = COMMODITY_REGIONS.get(commodity) or COMMODITY_REGIONS["corn"]
+ return [{"label": region, "value": region} for region in regions]
+
+
+router._api_router.add_api_route(
+ path="/commodity_cost_regions",
+ endpoint=commodity_cost_regions,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+async def rice_frequencies(table: str = "us_supply_disappearance_price") -> list:
+ """List the observation frequencies a Rice Yearbook table publishes.
+
+ Parameters
+ ----------
+ table : str
+ Table key from RICE_TABLES. When absent, the default table is used.
+ """
+ from openbb_government_us.usda.utils.ers_rice_yearbook import (
+ RICE_TABLES,
+ table_frequencies,
+ )
+
+ if table not in RICE_TABLES:
+ table = "us_supply_disappearance_price"
+ return [{"label": freq, "value": freq} for freq in await table_frequencies(table)]
+
+
+router._api_router.add_api_route(
+ path="/rice_frequencies",
+ endpoint=rice_frequencies,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+@router.command(
+ model="RiceYearbook",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get world rice trade on a milled basis since 2020.",
+ parameters={
+ "table": "world_trade_milled_basis",
+ "start_year": 2020,
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get the top 10 U.S. rice export markets.",
+ parameters={"table": "us_top_export_markets", "provider": "usda"},
+ ),
+ ],
+)
+async def rice_yearbook(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS rice data - the Rice Yearbook acreage, supply, use, stocks, price, and trade tables, each pivoted to a wide layout by year."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+async def sugar_frequencies(table: str = "table_24a") -> list:
+ """List the period bases a Sugar and Sweeteners Yearbook table publishes.
+
+ Parameters
+ ----------
+ table : str
+ Table key from SUGAR_SWEETENERS_FILES. When absent, the default table
+ is used.
+ """
+ from openbb_government_us.usda.utils.ers_sugar_sweeteners_yearbook import (
+ SUGAR_SWEETENERS_FILES,
+ table_frequencies,
+ )
+
+ if table not in SUGAR_SWEETENERS_FILES:
+ table = "table_24a"
+ return [{"label": freq, "value": freq} for freq in await table_frequencies(table)]
+
+
+router._api_router.add_api_route(
+ path="/sugar_frequencies",
+ endpoint=sugar_frequencies,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+@router.command(
+ model="SugarSweetenersYearbook",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get U.S. sugar supply and use since 2020.",
+ parameters={
+ "table": "table_24a",
+ "start_year": 2020,
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get the world white refined sugar futures price table.",
+ parameters={"table": "table_2", "provider": "usda"},
+ ),
+ APIEx(
+ description="Get U.S. honey imports by country of source since 2020.",
+ parameters={
+ "table": "table_48a",
+ "start_year": 2020,
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def sugar_sweeteners_yearbook(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS Sugar and Sweeteners Yearbook tables - U.S., Mexican, and world sugar and sweetener prices, supply and use, production, consumption, and trade, each pivoted to a wide layout by attribute."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+async def oil_crops_frequencies(
+ table: str = "soybeans_supply_disappearance_price",
+) -> list:
+ """List the observation frequencies an Oil Crops Yearbook table publishes.
+
+ Parameters
+ ----------
+ table : str
+ Table key from OIL_CROPS_TABLES. When absent, the default table is used.
+ """
+ from openbb_government_us.usda.utils.ers_oil_crops_yearbook import (
+ FREQUENCIES_BY_TABLE,
+ FREQUENCY_LABELS,
+ OIL_CROPS_TABLES,
+ )
+
+ if table not in OIL_CROPS_TABLES:
+ table = "soybeans_supply_disappearance_price"
+ return [
+ {"label": FREQUENCY_LABELS[freq], "value": freq}
+ for freq in FREQUENCIES_BY_TABLE[table]
+ ]
+
+
+router._api_router.add_api_route(
+ path="/oil_crops_frequencies",
+ endpoint=oil_crops_frequencies,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+@router.command(
+ model="OilCropsYearbook",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get U.S. soybean exports by destination since 2015.",
+ parameters={
+ "table": "soybean_exports_by_destination",
+ "start_year": 2015,
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get world oilseed supply and distribution, by oilseed.",
+ parameters={
+ "table": "world_oilseed_supply_and_distribution",
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def oil_crops_yearbook(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS Oil Crops Yearbook data - the U.S. and world oilseed, oilmeal, and vegetable-oil supply, use, price, and trade tables, each pivoted to a wide layout by year."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+@router.command(
+ model="FeedGrainsDatabase",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get corn and sorghum farm prices since 1990.",
+ parameters={
+ "table": "corn_and_sorghum_farm_prices",
+ "start_year": 1990,
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get U.S. corn and sorghum exports by selected destinations.",
+ parameters={
+ "table": "corn_and_sorghum_exports_by_destination",
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def feed_grains_database(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS feed grains data - the Feed Grains Yearbook acreage, supply, use, price, and trade tables, each pivoted to a wide layout by year."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+async def fruit_yearbook_tables(category: str | None = None) -> list:
+ """List the Fruit and Tree Nuts Yearbook tables, narrowed to a category.
+
+ Parameters
+ ----------
+ category : str | None
+ Letter category A through H. When None, every table is returned.
+ """
+ from openbb_government_us.usda.utils.ers_fruit_and_tree_nuts_data import (
+ TABLE_TITLES,
+ category_tables,
+ )
+
+ return [
+ {"label": f"{code} - {TABLE_TITLES[code]}", "value": code}
+ for code in category_tables(category)
+ ]
+
+
+router._api_router.add_api_route(
+ path="/fruit_yearbook_tables",
+ endpoint=fruit_yearbook_tables,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+@router.command(
+ model="FruitAndTreeNutsData",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get U.S. apple production, utilization, and grower price since 2010.",
+ parameters={"table": "B-3", "start_year": 2010, "provider": "usda"},
+ ),
+ APIEx(
+ description="Get the tree-nut area-bearing table, one column per nut.",
+ parameters={"category": "F", "table": "F-1", "provider": "usda"},
+ ),
+ ],
+)
+async def fruit_and_tree_nuts_data(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS fruit and tree nuts data - the Fruit and Tree Nuts Yearbook area, production, price, supply, per-capita availability, and import-share tables, each pivoted to a wide layout by year."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+@router.command(
+ model="VegetablesAndPulses",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get the U.S. fresh asparagus balance sheet since 2015.",
+ parameters={
+ "table": "Table13_Asparagus",
+ "start_year": 2015,
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get selected world vegetable production since 2020.",
+ parameters={
+ "table": "Table09_WorldVegProduction",
+ "start_year": 2020,
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def vegetables_and_pulses(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS vegetables and pulses yearbook data - U.S. and world vegetable and pulse-crop supply, availability, per capita use, price, trade, and cash receipts, each table pivoted to a wide layout with the data elements as columns and years as rows."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+async def cotton_frequencies(table: str = "cotton_supply_and_use") -> list:
+ """List the reporting frequencies a cotton, wool, or textile table publishes.
+
+ Parameters
+ ----------
+ table : str
+ Table key from CWT_TABLES. When absent, the default table is used.
+ """
+ from openbb_government_us.usda.utils.ers_cotton_wool_and_textile_data import (
+ CWT_TABLES,
+ table_frequencies,
+ )
+
+ if table not in CWT_TABLES:
+ table = "cotton_supply_and_use"
+ return [{"label": freq, "value": freq} for freq in await table_frequencies(table)]
+
+
+router._api_router.add_api_route(
+ path="/cotton_frequencies",
+ endpoint=cotton_frequencies,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+@router.command(
+ model="CottonWoolAndTextileData",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get world cotton supply and use since 2010.",
+ parameters={
+ "table": "world_cotton_supply_and_use",
+ "start_year": 2010,
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get U.S. cotton textile and apparel imports by country of origin.",
+ parameters={
+ "table": "rfe_cotton_textile_imports_by_origin",
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def cotton_wool_and_textile_data(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS cotton, wool, and textile data - the Cotton and Wool Yearbook supply, use, price, and trade tables plus the raw-fiber equivalents of U.S. textile trade, each pivoted to a wide layout by year."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+@router.command(
+ model="FertilizerUseAndPrice",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get average U.S. farm prices of selected fertilizers since 2010.",
+ parameters={
+ "table": "farm_prices",
+ "start_year": 2010,
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get the percent of corn acreage receiving nitrogen, by State.",
+ parameters={"table": "corn_nitrogen_share", "provider": "usda"},
+ ),
+ ],
+)
+async def fertilizer_use_and_price(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS fertilizer use and price - U.S. nutrient and material consumption, farm prices, price indexes, and crop-by-State application rates and shares, each pivoted to a wide layout by year."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+@router.command(
+ model="FoodPriceOutlook",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get the annual consumer food price index changes since 2000.",
+ parameters={
+ "table": "cpi_annual",
+ "start_year": 2000,
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get the consumer forecast prediction intervals for beef and eggs.",
+ parameters={
+ "table": "cpi_forecast",
+ "item": "beef,eggs",
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get the historical consumer forecast vintages for all food.",
+ parameters={
+ "table": "cpi_forecast_history",
+ "item": "all food",
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def food_price_outlook(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS Food Price Outlook - consumer and producer food price index forecasts, prediction intervals, and the annual percent-change record, each pivoted to a wide layout."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+async def food_price_outlook_items(table: str = "cpi_forecast") -> list:
+ """List the price-index items available in a Food Price Outlook table.
+
+ Parameters
+ ----------
+ table : str
+ Table key. Unknown tables fall back to the CPI forecast table.
+ """
+ from openbb_government_us.usda.utils import ers_food_price_outlook as fpo
+
+ key = table if table in fpo.FOOD_PRICE_OUTLOOK_FILES else "cpi_forecast"
+ seen: dict[str, None] = {}
+ for record in await fpo.afetch_table(key):
+ item = record.get("item")
+ if item:
+ seen.setdefault(item, None)
+ return [{"label": item, "value": item} for item in seen]
+
+
+router._api_router.add_api_route(
+ path="/food_price_outlook_items",
+ endpoint=food_price_outlook_items,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+@router.command(
+ model="FruitAndVegetablePrices",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get 2023 average retail and cup-equivalent prices for vegetables.",
+ parameters={"table": "vegetable", "provider": "usda"},
+ ),
+ ],
+)
+async def fruit_and_vegetable_prices(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS fruit and vegetable prices - 2023 average retail price, preparation yield factor, cup-equivalent size, and average price per edible cup-equivalent, one row per commodity and marketed form."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+@router.command(
+ model="LivestockAndMeatDomesticData",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get red meat and poultry production since 2020.",
+ parameters={
+ "table": "production",
+ "start_year": 2020,
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get monthly livestock prices since 2024.",
+ parameters={
+ "table": "livestock_prices",
+ "start_year": 2024,
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def livestock_and_meat_domestic_data(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS livestock and meat domestic data - slaughter, production, weights, cold storage, supply and disappearance, and livestock and wholesale prices, each pivoted to a wide layout by attribute."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+@router.command(
+ model="SeasonAveragePriceForecasts",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get only the latest weekly forecast vintage for soybeans.",
+ parameters={
+ "commodity": "Soybeans",
+ "latest": True,
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get wheat season-average price forecasts since 2015.",
+ parameters={
+ "commodity": "Wheat",
+ "start_year": 2015,
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def season_average_price_forecasts(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS season-average price forecasts - weekly ERS model and monthly WASDE marketing-year average price forecasts, with farm-bill PLC and ARC benchmarks, for corn, soybeans, wheat, and cotton."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+async def farm_income_states(table: str = "income_statement") -> list:
+ """List the states published for a farm income and wealth table.
+
+ Parameters
+ ----------
+ table : str
+ Table key. Unknown tables fall back to the income statement's states.
+ """
+ from openbb_government_us.usda.utils.ers_farm_income_and_wealth_statistics import (
+ TABLE_PREFIXES,
+ state_options,
+ )
+
+ return state_options(table if table in TABLE_PREFIXES else "income_statement")
+
+
+router._api_router.add_api_route(
+ path="/farm_income_states",
+ endpoint=farm_income_states,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+@router.command(
+ model="FarmHouseholdIncomeAndCharacteristics",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get mean and median U.S. farm operator household income"
+ " since 2000.",
+ parameters={
+ "table": "mean_median_income",
+ "start_year": 2000,
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get 2024 household characteristics by age of the principal"
+ " operator.",
+ parameters={
+ "table": "by_age_2024",
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def farm_household_income_and_characteristics(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS Farm Household Income and Characteristics - income, finances, and characteristics of U.S. farm operator households, each pivoted to a wide layout by year or category."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+@router.command(
+ model="FarmIncomeAndWealthStatistics",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get the Iowa farm income statement since 2015.",
+ parameters={
+ "table": "income_statement",
+ "state": "IA",
+ "start_year": 2015,
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get the U.S. farm sector balance sheet.",
+ parameters={"table": "balance_sheet", "provider": "usda"},
+ ),
+ APIEx(
+ description="Get U.S. farm financial ratios since 2000.",
+ parameters={
+ "table": "financial_ratios",
+ "start_year": 2000,
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def farm_income_and_wealth_statistics(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS farm income and wealth statistics - U.S. and State-level farm sector income statements, production expenses, cash receipts, government payments, farm-related income, home consumption, inventory change, balance sheets, financial ratios, and farm business income, each pivoted to a wide layout by year."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+@router.command(
+ model="FoodDollarSeries",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get the real (2017-dollar) food dollar in millions of dollars.",
+ parameters={
+ "series": "real",
+ "units": "level",
+ "table": 1,
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get the beef, pork, and other meats commodity-group breakdown.",
+ parameters={"table": 9, "provider": "usda"},
+ ),
+ ],
+)
+async def food_dollar_series(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS food dollar series - the marketing-bill breakdown of the U.S. food dollar into industry groups and primary factors, in nominal and real terms, each table pivoted to a wide layout by year."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+async def food_dollar_components(series: str = "nominal") -> list:
+ """List the primary-factor components available for a food-dollar series.
+
+ Parameters
+ ----------
+ series : str
+ Series key, nominal or real. Unknown series fall back to nominal.
+ """
+ from openbb_government_us.usda.utils.ers_food_dollar_series import (
+ COMPONENT_LABELS,
+ SERIES_COMPONENTS,
+ )
+
+ components = SERIES_COMPONENTS.get(series) or SERIES_COMPONENTS["nominal"]
+ return [{"label": COMPONENT_LABELS[key], "value": key} for key in components]
+
+
+router._api_router.add_api_route(
+ path="/food_dollar_components",
+ endpoint=food_dollar_components,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+async def food_dollar_tables(series: str = "nominal") -> list:
+ """List the food-dollar tables available for a series.
+
+ Parameters
+ ----------
+ series : str
+ Series key, nominal or real. Unknown series fall back to nominal.
+ """
+ from openbb_government_us.usda.utils.ers_food_dollar_series import (
+ FOOD_DOLLAR_FILES,
+ TABLE_NAMES,
+ )
+
+ tables = (FOOD_DOLLAR_FILES.get(series) or FOOD_DOLLAR_FILES["nominal"])["tables"]
+ return [{"label": f"{num} - {TABLE_NAMES[num]}", "value": num} for num in tables]
+
+
+router._api_router.add_api_route(
+ path="/food_dollar_tables",
+ endpoint=food_dollar_tables,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+@router.command(
+ model="FoodExpenditureSeries",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get constant-dollar food and alcohol expenditures since 2015.",
+ parameters={
+ "measure": "constant",
+ "start_year": 2015,
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get monthly food sales.",
+ parameters={"table": "monthly", "provider": "usda"},
+ ),
+ APIEx(
+ description="Get food expenditures by final purchaser since 2010.",
+ parameters={
+ "table": "by_final_purchaser",
+ "start_year": 2010,
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get California per-capita food sales without taxes and tips.",
+ parameters={
+ "table": "state_per_capita",
+ "state": "california",
+ "taxes": "without",
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def food_expenditure_series(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS Food Expenditure Series - U.S. food and alcohol expenditures by outlet, final purchaser, month, and state, in nominal and constant dollars, each pivoted to a wide layout by year."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+@router.command(
+ model="UsFoodImports",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get U.S. food import volume by food group since 2015.",
+ parameters={
+ "table": "by_food_group",
+ "measure": "volume",
+ "start_year": 2015,
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get fresh fruit imports by source country.",
+ parameters={
+ "table": "by_source",
+ "food_group": "Fruits",
+ "commodity": "Fresh or chilled fruit",
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def us_food_imports(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS U.S. food imports - annual value, volume, unit prices, and price inflation by food group and by source country."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+async def food_import_commodities(food_group: str = "Fruits") -> list:
+ """List the product lines available for a food group's source-country table.
+
+ Parameters
+ ----------
+ food_group : str
+ Detail food-group category code, e.g. 'Fruits'.
+ """
+ from openbb_government_us.usda.utils.ers_us_food_imports import (
+ FOOD_GROUPS,
+ afetch_records,
+ product_lines,
+ )
+
+ group = food_group if food_group in FOOD_GROUPS else "Fruits"
+ records = await afetch_records()
+ return [{"label": name, "value": name} for name in product_lines(records, group)]
+
+
+router._api_router.add_api_route(
+ path="/food_import_commodities",
+ endpoint=food_import_commodities,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+@router.command(
+ model="UsBioenergyStatistics",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get fuel ethanol supply and disappearance by calendar"
+ " year since 2015.",
+ parameters={
+ "table": "ethanol_supply_calendar_year",
+ "start_year": 2015,
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get fuel ethanol production capacity by State.",
+ parameters={
+ "table": "ethanol_capacity_by_state",
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def us_bioenergy_statistics(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS U.S. Bioenergy Statistics - ethanol, biodiesel, and renewable diesel supply, use, capacity, consumption, and prices, each pivoted to a wide layout by period."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+@router.command(
+ model="AgriculturalExchangeRates",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get real bilateral exchange rates for North American"
+ " partner countries.",
+ parameters={
+ "provider": "usda",
+ "table": "real_bilateral_annual",
+ "region": "North America",
+ },
+ ),
+ APIEx(
+ description="Get the monthly U.S.-import-weighted real trade-weighted"
+ " index since 2020.",
+ parameters={
+ "provider": "usda",
+ "table": "real_index_monthly",
+ "weights": "U.S. suppliers (U.S. import weights)",
+ "start_year": 2020,
+ },
+ ),
+ ],
+)
+async def agricultural_exchange_rates(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS agricultural exchange rates - real and nominal commodity trade-weighted exchange rate indexes and bilateral local-currency-per-USD rates, each pivoted to a wide layout with time in the rows and the commodity (index tables) or partner country (bilateral tables) in the columns."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+async def agricultural_productivity_states(table: str = "national_indices") -> list:
+ """List the states published for an agricultural productivity table.
+
+ Parameters
+ ----------
+ table : str
+ Table key. Unknown tables fall back to the national table's states.
+ """
+ from openbb_government_us.usda.utils.ers_agricultural_productivity import (
+ AGRICULTURAL_PRODUCTIVITY_FILES,
+ state_options,
+ )
+
+ return state_options(
+ table if table in AGRICULTURAL_PRODUCTIVITY_FILES else "national_indices"
+ )
+
+
+router._api_router.add_api_route(
+ path="/agricultural_productivity_states",
+ endpoint=agricultural_productivity_states,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+@router.command(
+ model="AgriculturalProductivity",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get the U.S. farm output, input, and TFP indices since 2000.",
+ parameters={
+ "table": "national_indices",
+ "start_year": 2000,
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get California's relative TFP, output, and input levels.",
+ parameters={
+ "table": "state_relative_levels",
+ "state": "CA",
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def agricultural_productivity(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS agricultural productivity - the farm output, input, and total factor productivity indices for the United States and the relative productivity levels of the 48 contiguous States, each pivoted to a wide layout with the series as columns and years as rows."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+async def ag_productivity_countries(grouping: str = "country_grouping") -> list:
+ """List the countries, territories, and aggregates in a grouping.
+
+ Parameters
+ ----------
+ grouping : str
+ Grouping key. Unknown groupings fall back to the country grouping.
+ """
+ from openbb_government_us.usda.utils.ers_international_agricultural_productivity import ( # noqa: E501
+ GROUPINGS,
+ entity_options,
+ )
+
+ return entity_options(grouping if grouping in GROUPINGS else "country_grouping")
+
+
+router._api_router.add_api_route(
+ path="/ag_productivity_countries",
+ endpoint=ag_productivity_countries,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+@router.command(
+ model="InternationalAgriculturalProductivity",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get U.S. agricultural productivity indices since 2000.",
+ parameters={
+ "grouping": "north_america",
+ "country": 179,
+ "start_year": 2000,
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get India's physical output and input quantities.",
+ parameters={
+ "grouping": "asia_pacific",
+ "country": 103,
+ "measure": "physical_quantities",
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def international_agricultural_productivity(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS international agricultural productivity - agricultural total factor productivity indices, output and input quantity indices, and the underlying physical output and input quantities for countries, territories, regions, income groups, and the world, pivoted to a wide layout by year."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+@router.command(
+ model="InternationalMacroeconomicDataSet",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get real GDP year-to-year growth rates by country and"
+ " region since 2000.",
+ parameters={
+ "variable": "real_gdp",
+ "measure": "growth",
+ "start_year": 2000,
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get historical and projected population by country and"
+ " region.",
+ parameters={
+ "variable": "population",
+ "measure": "level",
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get consumer price indices, 2017 = 100, since 2010.",
+ parameters={
+ "variable": "cpi",
+ "measure": "level",
+ "start_year": 2010,
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def international_macroeconomic_data_set(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS International Macroeconomic Data Set - historical and projected real GDP, GDP per capita, GDP deflator, real GDP shares, real exchange rates, consumer price indices, and population for baseline countries and regions from 1970 through the 2035 projection horizon, pivoted to a wide layout with the countries and regions as columns and the years as rows."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+async def livestock_trade_products(
+ table: str = "beef_veal", direction: str = "imports"
+) -> list:
+ """List the product blocks published for a species and trade direction.
+
+ Parameters
+ ----------
+ table : str
+ Species key. Unknown species fall back to beef and veal.
+ direction : str
+ 'imports' or 'exports'. Unknown directions fall back to imports.
+ """
+ from openbb_government_us.usda.utils.ers_livestock_and_meat_international_trade_data import ( # noqa: E501
+ DIRECTIONS,
+ SPECIES,
+ afetch_products,
+ )
+
+ species = table if table in SPECIES else "beef_veal"
+ trade = direction if direction in DIRECTIONS else "imports"
+ return await afetch_products(species, trade)
+
+
+router._api_router.add_api_route(
+ path="/livestock_trade_products",
+ endpoint=livestock_trade_products,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+@router.command(
+ model="LivestockAndMeatInternationalTradeData",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get U.S. beef and veal exports by partner since 2015.",
+ parameters={
+ "table": "beef_veal",
+ "direction": "exports",
+ "start_year": 2015,
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get monthly U.S. pork imports by partner.",
+ parameters={
+ "table": "pork",
+ "direction": "imports",
+ "frequency": "monthly",
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get U.S. shell-egg exports by partner.",
+ parameters={
+ "table": "poultry_eggs",
+ "direction": "exports",
+ "product": "Shell-egg",
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def livestock_and_meat_international_trade_data(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS livestock and meat international trade data - U.S. import and export trade in live cattle, hogs, sheep, and goats and in beef and veal, pork, lamb and mutton, poultry meat, and eggs, by trading partner, each pivoted to a wide layout with years as columns and the two cumulative year-to-date columns trailing."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+async def state_ag_trade_states(table: str = "exports_by_commodity") -> list:
+ """List the states published for a state agricultural trade table.
+
+ Parameters
+ ----------
+ table : str
+ Table key. Unknown tables fall back to the calendar-year states.
+ """
+ from openbb_government_us.usda.utils.ers_state_agricultural_trade import (
+ TABLE_LABELS,
+ state_options,
+ )
+
+ return state_options(table if table in TABLE_LABELS else "exports_by_commodity")
+
+
+router._api_router.add_api_route(
+ path="/state_ag_trade_states",
+ endpoint=state_ag_trade_states,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+@router.command(
+ model="StateAgriculturalTrade",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get California's agricultural exports by commodity.",
+ parameters={
+ "table": "exports_by_commodity",
+ "state": "California",
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get total agricultural exports spread across the states.",
+ parameters={
+ "table": "exports_by_state",
+ "commodity": "Total agricultural exports",
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get Texas's top export commodities by fiscal year.",
+ parameters={
+ "table": "top_exports",
+ "state": "Texas",
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get California's top import commodities by fiscal year.",
+ parameters={
+ "table": "top_imports",
+ "state": "California",
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def state_agricultural_trade(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS state agricultural trade - state-level U.S. agricultural exports by commodity and by state on a calendar-year basis, and the top export and import commodities by state on a fiscal-year basis, each pivoted to a wide layout with the years in the rows."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+@router.command(
+ model="NormalizedPrices",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get the national prices-received and prices-paid"
+ " indices (2011=100).",
+ parameters={"table": "table2_national_indices", "provider": "usda"},
+ ),
+ APIEx(
+ description="Get State-level normalized prices for the current"
+ " report year.",
+ parameters={"table": "table3_state_prices", "provider": "usda"},
+ ),
+ ],
+)
+async def normalized_prices(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS normalized prices - national and State-level normalized prices for crops, livestock, milk, poultry, eggs, and wool, plus national prices-received and prices-paid indices, each pivoted to a wide layout with report years, index years, or States in the columns."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+@router.command(
+ model="AgriculturalTradeMultipliers",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get the trade multipliers for soybeans and corn.",
+ parameters={"commodity": "soybeans,corn", "provider": "usda"},
+ ),
+ ],
+)
+async def agricultural_trade_multipliers(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS agricultural trade multipliers - producer- and port-level output and employment multipliers per commodity, with export value, in a wide layout."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+@router.command(
+ model="AgriculturalAndFoodRdExpenditures",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get nominal (current-dollar) R&D expenditures since 2000.",
+ parameters={
+ "measure": "nominal",
+ "start_year": 2000,
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def agricultural_and_food_rd_expenditures(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS agricultural and food research and development (R&D) expenditures - public and private U.S. R&D spending in current and constant dollars with the R&D price index, pivoted to a wide layout with the R&D series as columns and years as rows."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+async def ge_crop_states(crop: str = "corn") -> list:
+ """List the states published for a crop's genetically engineered adoption data.
+
+ Parameters
+ ----------
+ crop : str
+ Crop key. Unknown crops fall back to corn.
+ """
+ from openbb_government_us.usda.utils.ers_adoption_of_genetically_engineered_crops_in_the_united_states import ( # noqa: E501
+ CROPS,
+ state_options,
+ )
+
+ return state_options(crop if crop in CROPS else "corn")
+
+
+router._api_router.add_api_route(
+ path="/ge_crop_states",
+ endpoint=ge_crop_states,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+@router.command(
+ model="AdoptionOfGeneticallyEngineeredCropsInTheUnitedStates",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get U.S. corn GE adoption by trait since 2010.",
+ parameters={
+ "crop": "corn",
+ "state": "United States",
+ "start_year": 2010,
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get Texas upland cotton GE adoption.",
+ parameters={"crop": "cotton", "state": "Texas", "provider": "usda"},
+ ),
+ APIEx(
+ description="Get U.S. herbicide-tolerant soybean adoption.",
+ parameters={
+ "crop": "soybeans",
+ "state": "United States",
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def adoption_of_genetically_engineered_crops(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS adoption of genetically engineered crops - the annual share of planted acres in genetically engineered corn, upland cotton, and soybean varieties, by trait and by State, pivoted to a wide layout with the GE traits as columns and years as rows."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+@router.command(
+ model="FoodSecurityInTheUnitedStates",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get food security prevalence by race and ethnicity"
+ " since 2015.",
+ parameters={
+ "table": "by_characteristic",
+ "category": "Race/ethnicity of households",
+ "start_year": 2015,
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get the by-State 3-year-average food insecurity prevalence.",
+ parameters={"table": "by_state", "provider": "usda"},
+ ),
+ ],
+)
+async def food_security_in_the_united_states(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS food security in the United States - household and child food security, food insecurity, and very low food security prevalence by year, characteristic, and State, each pivoted to a wide layout."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+async def food_security_categories(table: str = "by_characteristic") -> list:
+ """List the category options for a food-security table.
+
+ Parameters
+ ----------
+ table : str
+ Table key from FOOD_SECURITY_TABLES, e.g. 'by_characteristic'.
+ """
+ from openbb_government_us.usda.utils.ers_food_security_in_the_united_states import (
+ FOOD_SECURITY_TABLES,
+ afetch_categories,
+ )
+
+ if table not in FOOD_SECURITY_TABLES:
+ table = "by_characteristic"
+ return [{"label": name, "value": name} for name in await afetch_categories(table)]
+
+
+router._api_router.add_api_route(
+ path="/food_security_categories",
+ endpoint=food_security_categories,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+async def food_availability_groups(data_system: str = "food_availability") -> list:
+ """List the food-group files published for a data system."""
+ from openbb_government_us.usda.utils.ers_food_availability_per_capita_data_system import (
+ DATA_SYSTEMS,
+ group_options,
+ )
+
+ return group_options(
+ data_system if data_system in DATA_SYSTEMS else "food_availability"
+ )
+
+
+router._api_router.add_api_route(
+ path="/food_availability_groups",
+ endpoint=food_availability_groups,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+async def food_availability_commodities(
+ data_system: str = "food_availability", food_group: str = "eggs"
+) -> list:
+ """List the commodity blocks published for a data system's file.
+
+ Parameters
+ ----------
+ data_system : str
+ Data system key. Unknown systems fall back to food availability.
+ food_group : str
+ Food-group file slug. Unknown slugs fall back to the system's first file.
+ """
+ from openbb_government_us.usda.utils.ers_food_availability_per_capita_data_system import (
+ CATALOG,
+ DATA_SYSTEMS,
+ afetch_blocks,
+ )
+
+ system = data_system if data_system in DATA_SYSTEMS else "food_availability"
+ groups = CATALOG[system]
+ group = food_group if food_group in groups else next(iter(groups))
+ return [
+ {"label": block, "value": block} for block in await afetch_blocks(system, group)
+ ]
+
+
+router._api_router.add_api_route(
+ path="/food_availability_commodities",
+ endpoint=food_availability_commodities,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+@router.command(
+ model="FoodAvailabilityPerCapitaDataSystem",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get the loss-adjusted rice availability budget since 1990.",
+ parameters={
+ "data_system": "loss_adjusted",
+ "food_group": "grains",
+ "commodity": "Rice: Per capita availability adjusted for loss",
+ "start_year": 1990,
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get the nutrient availability of the U.S. food supply since 2000.",
+ parameters={
+ "data_system": "nutrient",
+ "food_group": "totals",
+ "start_year": 2000,
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def food_availability_per_capita(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS Food Availability (Per Capita) Data System - per capita food supply, loss-adjusted availability, and nutrient availability of the U.S. food supply, each file pivoted to a wide layout with the measures as columns and years as rows."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+async def fmap_food_items(group: str = "Dairy") -> list:
+ """List the food items in a Tier-1 food group.
+
+ Parameters
+ ----------
+ group : str
+ Tier-1 food group. Unknown groups fall back to the default group.
+ """
+ from openbb_government_us.usda.utils.ers_food_at_home_monthly_area_prices import (
+ DEFAULT_GROUP,
+ GROUPS,
+ item_options,
+ )
+
+ return item_options(group if group in GROUPS else DEFAULT_GROUP)
+
+
+router._api_router.add_api_route(
+ path="/fmap_food_items",
+ endpoint=fmap_food_items,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+async def fmap_measures(table: str = "monthly_area_prices") -> list:
+ """List the measures a Food-at-Home Monthly Area Prices table publishes.
+
+ Parameters
+ ----------
+ table : str
+ Table key. Unknown tables fall back to the default table.
+ """
+ from openbb_government_us.usda.utils.ers_food_at_home_monthly_area_prices import (
+ DEFAULT_TABLE,
+ FMAP_TABLES,
+ measure_options,
+ )
+
+ return measure_options(table if table in FMAP_TABLES else DEFAULT_TABLE)
+
+
+router._api_router.add_api_route(
+ path="/fmap_measures",
+ endpoint=fmap_measures,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+@router.command(
+ model="FoodAtHomeMonthlyAreaPrices",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get the GEKS price index for whole milk across the 15 areas.",
+ parameters={
+ "item": "40000",
+ "measure": "price_index_geks",
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get the supplemental Fisher Ideal price index for fresh whole fruit since 2017.",
+ parameters={
+ "table": "supplemental_price_indexes",
+ "group": "Fruit",
+ "item": "30000",
+ "measure": "price_index_fisher_ideal",
+ "start_year": 2017,
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def food_at_home_monthly_area_prices(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS Food-at-Home Monthly Area Prices - monthly unit values, purchases, store counts, and price indexes for a food item and measure, spread across the national, Census-region, and metropolitan areas."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+async def rural_urban_continuum_states(vintage: str = "2023") -> list:
+ """List the states published for a rural-urban continuum code vintage.
+
+ Parameters
+ ----------
+ vintage : str
+ A vintage key. Unknown vintages fall back to the latest, 2023.
+ """
+ from openbb_government_us.usda.utils.ers_rural_urban_continuum_codes import (
+ VINTAGES,
+ afetch_states,
+ )
+
+ return await afetch_states(vintage if vintage in VINTAGES else "2023")
+
+
+router._api_router.add_api_route(
+ path="/rural_urban_continuum_states",
+ endpoint=rural_urban_continuum_states,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+@router.command(
+ model="RuralUrbanContinuumCodes",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get the 2023 Rural-Urban Continuum Codes for Wyoming.",
+ parameters={"vintage": "2023", "state": "WY", "provider": "usda"},
+ ),
+ APIEx(
+ description="Get the code-only 1974 Rural-Urban Continuum Codes"
+ " for Alabama.",
+ parameters={"vintage": "1974", "state": "AL", "provider": "usda"},
+ ),
+ ],
+)
+async def rural_urban_continuum_codes(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS Rural-Urban Continuum Codes - a county-level lookup classifying U.S. counties by metro/nonmetro status and degree of urbanization, published in vintages from 1974 to 2023."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+async def urban_influence_code_states(vintage: str = "2024") -> list:
+ """List the states published for an Urban Influence Codes vintage.
+
+ Parameters
+ ----------
+ vintage : str
+ Vintage key. Unknown vintages fall back to the 2024 revision's states.
+ """
+ from openbb_government_us.usda.utils.ers_urban_influence_codes import (
+ VINTAGES,
+ state_options,
+ )
+
+ return state_options(vintage if vintage in VINTAGES else "2024")
+
+
+router._api_router.add_api_route(
+ path="/urban_influence_code_states",
+ endpoint=urban_influence_code_states,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+@router.command(
+ model="UrbanInfluenceCodes",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get the 2024 Urban Influence Codes for Alabama counties.",
+ parameters={"vintage": "2024", "state": "AL", "provider": "usda"},
+ ),
+ APIEx(
+ description="Get the 2003 codes with population density for Wyoming.",
+ parameters={"vintage": "2003", "state": "WY", "provider": "usda"},
+ ),
+ APIEx(
+ description="Get the 1993 vintage codes for the whole United States.",
+ parameters={"vintage": "1993", "provider": "usda"},
+ ),
+ ],
+)
+async def urban_influence_codes(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS Urban Influence Codes - a county-level classification of every U.S. county by metropolitan status, size, and adjacency to metro and micro areas, published in the 1993, 2003, 2013, and 2024 vintages as a FIPS lookup table."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+@router.command(
+ model="CountyTypologyCodes",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get the 2025-edition county typology codes for Texas.",
+ parameters={"vintage": "2025", "state": "TX", "provider": "usda"},
+ ),
+ APIEx(
+ description="Get the 2004-edition county economic and policy types.",
+ parameters={"vintage": "2004", "provider": "usda"},
+ ),
+ ],
+)
+async def county_typology_codes(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS County Typology Codes - county-level economic-dependence and policy typology classifications, published in successive vintages, as a one-row-per-county lookup table scoped by edition and state."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+async def ruca_states(table: str = "tract_2020") -> list:
+ """List the states published for a rural-urban commuting area table.
+
+ Parameters
+ ----------
+ table : str
+ Table key. Unknown tables fall back to the 2020 census-tract states.
+ """
+ from openbb_government_us.usda.utils.ers_rural_urban_commuting_area_codes import (
+ TABLE_LABELS,
+ state_options,
+ )
+
+ return state_options(table if table in TABLE_LABELS else "tract_2020")
+
+
+router._api_router.add_api_route(
+ path="/ruca_states",
+ endpoint=ruca_states,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+@router.command(
+ model="RuralUrbanCommutingAreaCodes",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get the 2020 census-tract RUCA codes for Delaware.",
+ parameters={"table": "tract_2020", "state": "DE", "provider": "usda"},
+ ),
+ APIEx(
+ description="Get the 2010 ZIP-code RUCA codes for Vermont.",
+ parameters={"table": "zip_2010", "state": "VT", "provider": "usda"},
+ ),
+ APIEx(
+ description="Get the 1990 census-tract RUCA codes for Wyoming.",
+ parameters={"table": "tract_1990", "state": "WY", "provider": "usda"},
+ ),
+ ],
+)
+async def rural_urban_commuting_area_codes(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS rural-urban commuting area (RUCA) codes - primary and secondary RUCA classification codes and descriptions for U.S. census tracts and ZIP codes across the 1990, 2000, 2010, and 2020 vintages, as a per-area lookup table scoped by state."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+async def far_zip_states(year: str = "2020") -> list:
+ """List the states a frontier and remote area codes ZIP vintage publishes.
+
+ Parameters
+ ----------
+ year : str
+ Vintage key. Unknown vintages fall back to the latest vintage.
+ """
+ from openbb_government_us.usda.utils.ers_frontier_and_remote_area_codes import (
+ FAR_ZIP_FILES,
+ state_options,
+ )
+
+ return state_options(year if year in FAR_ZIP_FILES else "2020")
+
+
+router._api_router.add_api_route(
+ path="/far_zip_states",
+ endpoint=far_zip_states,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+@router.command(
+ model="FrontierAndRemoteAreaCodes",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get the 2020 frontier and remote area codes for"
+ " California ZIP Codes.",
+ parameters={"year": "2020", "state": "CA", "provider": "usda"},
+ ),
+ APIEx(
+ description="Get the 2000 vintage for Massachusetts ZIP Codes.",
+ parameters={"year": "2000", "state": "MA", "provider": "usda"},
+ ),
+ ],
+)
+async def frontier_and_remote_area_codes(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS frontier and remote (FAR) area codes - a ZIP-code geographic classification lookup at four FAR levels with grid population, land area, and population density, in a single-vintage lookup table scoped by State."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+@router.command(
+ model="CommutingZonesAndLaborMarketAreas",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get the 2020 commuting zones for Texas counties.",
+ parameters={"vintage": "2020", "state": "Texas", "provider": "usda"},
+ ),
+ APIEx(
+ description="Get the 2000 commuting-zone crosswalk for all counties.",
+ parameters={"vintage": "2000", "provider": "usda"},
+ ),
+ APIEx(
+ description="Get the 1980/1990 commuting zones and labor market areas.",
+ parameters={"vintage": "1980_1990", "provider": "usda"},
+ ),
+ ],
+)
+async def commuting_zones_and_labor_market_areas(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS commuting zones and labor market areas - county-level commuting-zone and labor-market-area classifications for the 2020, preliminary 2020, 2000, and 1980/1990 vintages, as a FIPS lookup table selectable by vintage and filterable by state."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+@router.command(
+ model="NaturalAmenitiesScale",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get the natural amenities scale for Rhode Island counties.",
+ parameters={"state": "RI", "provider": "usda"},
+ ),
+ APIEx(
+ description="Get the natural amenities scale for California counties.",
+ parameters={"state": "CA", "provider": "usda"},
+ ),
+ ],
+)
+async def natural_amenities_scale(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS natural amenities scale - a county-level measure of the physical characteristics that make a location a desirable place to live, combining six standardized climate, topography, and water-area components into a composite amenity scale and a 1-to-7 rank, as a one-row-per-county FIPS lookup scoped by state."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+async def poverty_area_measures_states(edition: str = "2025") -> list:
+ """List the states published for a poverty area measures edition.
+
+ Parameters
+ ----------
+ edition : str
+ An edition key. Unknown editions fall back to the latest, 2025.
+ """
+ from openbb_government_us.usda.utils.ers_poverty_area_measures import (
+ EDITIONS,
+ afetch_states,
+ )
+
+ return await afetch_states(edition if edition in EDITIONS else "2025")
+
+
+router._api_router.add_api_route(
+ path="/poverty_area_measures_states",
+ endpoint=poverty_area_measures_states,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+@router.command(
+ model="PovertyAreaMeasures",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get the 2025-edition county poverty-area measures for"
+ " Mississippi.",
+ parameters={"edition": "2025", "state": "MS", "provider": "usda"},
+ ),
+ APIEx(
+ description="Get the 2025-edition census-tract poverty-area measures"
+ " for Mississippi.",
+ parameters={
+ "edition": "2025",
+ "level": "tract",
+ "state": "MS",
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def poverty_area_measures(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS Poverty Area Measures - county- and census-tract-level poverty-area classification flags (high-poverty, extreme-poverty, persistent-poverty, and enduring-poverty), published in successive editions as a FIPS/tract lookup table scoped by edition, geography level, and state."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+async def area_road_ruggedness_states(vintage: str = "2020") -> list:
+ """List the states an area and road ruggedness scales vintage publishes.
+
+ Parameters
+ ----------
+ vintage : str
+ Vintage key. Unknown vintages fall back to the latest vintage.
+ """
+ from openbb_government_us.usda.utils.ers_area_and_road_ruggedness_scales import (
+ VINTAGE_CATALOG,
+ state_options,
+ )
+
+ return state_options(vintage if vintage in VINTAGE_CATALOG else "2020")
+
+
+router._api_router.add_api_route(
+ path="/area_road_ruggedness_states",
+ endpoint=area_road_ruggedness_states,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+@router.command(
+ model="AreaAndRoadRuggednessScales",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get the 2020 area and road ruggedness scales for"
+ " Colorado census tracts.",
+ parameters={"vintage": "2020", "state": "CO", "provider": "usda"},
+ ),
+ APIEx(
+ description="Get the 2010 vintage for West Virginia census tracts.",
+ parameters={"vintage": "2010", "state": "WV", "provider": "usda"},
+ ),
+ ],
+)
+async def area_and_road_ruggedness_scales(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS area and road ruggedness scales - census-tract Area Ruggedness Scale (ARS) and Road Ruggedness Scale (RRS) classifications with terrain ruggedness statistics, rurality, and population, as a single-vintage geographic lookup table scoped by State."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+@router.command(
+ model="CostEstimatesOfFoodborneIllnesses",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get the mean cost of each health outcome, by pathogen.",
+ parameters={
+ "table": "health_outcome_cost",
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def cost_estimates_of_foodborne_illnesses(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS Cost Estimates of Foodborne Illnesses - annual cases and the economic burden of major foodborne pathogens, by pathogen and by health outcome, in 2023 U.S. dollars."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+@router.command(
+ model="SnapPolicyDataSets",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get California SNAP policy variables since 2010.",
+ parameters={"state": "CA", "start_year": 2010, "provider": "usda"},
+ ),
+ APIEx(
+ description="Get Texas SNAP policy variables for 2019.",
+ parameters={
+ "state": "TX",
+ "start_year": 2019,
+ "end_year": 2019,
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def snap_policy_data_sets(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS SNAP policy data sets - a selected State's monthly SNAP policy options from January 1996 to December 2020, with the forty-five broad-based categorical eligibility, recertification period, EBT issuance, interview-waiver, fingerprinting, noncitizen eligibility, online application, outreach spending, and vehicle-asset variables laid out as columns."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+async def intl_baseline_attributes(commodity: str = "Wheat") -> list:
+ """List the measures published for an international baseline commodity.
+
+ Parameters
+ ----------
+ commodity : str
+ Commodity name. Unknown commodities fall back to wheat's measures.
+ """
+ from openbb_government_us.usda.utils.ers_international_baseline_data import (
+ COMMODITIES,
+ DEFAULT_COMMODITY,
+ afetch_records,
+ attributes_for_commodity,
+ )
+
+ name = commodity if commodity in COMMODITIES else DEFAULT_COMMODITY
+ records = await afetch_records()
+ return [
+ {"label": attribute, "value": attribute}
+ for attribute in attributes_for_commodity(records, name)
+ ]
+
+
+router._api_router.add_api_route(
+ path="/intl_baseline_attributes",
+ endpoint=intl_baseline_attributes,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+async def intl_baseline_countries(commodity: str = "Wheat") -> list:
+ """List the countries and regions published for a baseline commodity.
+
+ Parameters
+ ----------
+ commodity : str
+ Commodity name. Unknown commodities fall back to wheat's areas.
+ """
+ from openbb_government_us.usda.utils.ers_international_baseline_data import (
+ COMMODITIES,
+ DEFAULT_COMMODITY,
+ afetch_records,
+ countries_for_commodity,
+ )
+
+ name = commodity if commodity in COMMODITIES else DEFAULT_COMMODITY
+ records = await afetch_records()
+ return [
+ {"label": country, "value": country}
+ for country in countries_for_commodity(records, name)
+ ]
+
+
+router._api_router.add_api_route(
+ path="/intl_baseline_countries",
+ endpoint=intl_baseline_countries,
+ methods=["GET"],
+ openapi_extra={"widget_config": {"exclude": True}},
+)
+
+
+@router.command(
+ model="InternationalBaselineData",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get projected corn exports by country and region since 2020.",
+ parameters={
+ "commodity": "Corn",
+ "attribute": "Exports",
+ "start_year": 2020,
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get China's long-term wheat production projection.",
+ parameters={
+ "commodity": "Wheat",
+ "attribute": "Production",
+ "country": "China",
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def international_baseline_data(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS International Baseline Data - long-term projections of area, yield, production, supply, use, and trade for grains, oilseeds, cotton, and livestock, pivoted to a wide layout with the countries and regions as columns and the crop or calendar years as rows."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+@router.command(
+ model="EatingAndHealthModuleAtus",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get the 2023 fast-food purchase frequency table.",
+ parameters={
+ "table": "fast_food_purchases",
+ "year": "2023",
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get 2022 food-related activity time by BMI group.",
+ parameters={
+ "table": "activities_by_bmi_group",
+ "year": "2022",
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def eating_and_health_module_atus(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS Eating and Health Module (ATUS) tables - time spent eating, drinking, in associated and secondary eating, preparing food, grocery shopping, and buying fast food, by demographic, food-security, and body-mass-index subgroups, each kept in its published wide layout for a selected survey release year."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+@router.command(
+ model="FoodConsumptionNutrientIntakesAndDietQuality",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get daily nutrient intake by food source for males.",
+ parameters={
+ "table": "2_nutrient_intake",
+ "demographics": "Sex - Males",
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get the standard error of the mean for the food-group"
+ + " intake shares since 2003.",
+ parameters={
+ "table": "6_food_group_intake_share",
+ "statistic": "se_of_mean",
+ "start_year": 2003,
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get the 2020-2025 recommended versus 2017-2018 actual"
+ + " nutrient and food-group densities.",
+ parameters={
+ "table": "8_recommended_vs_actual_density",
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def food_consumption_nutrient_intakes_and_diet_quality(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS food consumption, nutrient intakes, and diet quality - U.S. daily nutrient and food-group intake, share, and density by food source, population-subgroup sample sizes, and 2020-2025 recommended densities, from 1977 to 2018, each pivoted to a wide layout by survey cycle (Tables 1-7) or recommended-vs-actual density (Table 8)."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+@router.command(
+ model="ResourceRequirementsOfFoodDemand",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get the resources embodied in beef demand since 2015.",
+ parameters={
+ "table": "Xf1103",
+ "start_year": 2015,
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get the household home-kitchen and transportation resource use.",
+ parameters={"table": "Xf3000", "provider": "usda"},
+ ),
+ ],
+)
+async def resource_requirements_of_food_demand(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS resource requirements of food demand - the employment, freshwater withdrawals, and energy use, total and by source, embodied across the U.S. food supply chain to meet final consumer demand for each food, beverage, and food-related category, pivoted to a wide layout with the resource-and-source measures as columns and year and supply-chain stage as rows."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+@router.command(
+ model="PurchaseToPlate",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get national average prices for grain products.",
+ parameters={"food_group": "grains", "provider": "usda"},
+ ),
+ ],
+)
+async def purchase_to_plate(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS Purchase to Plate national average prices - estimated price per 100 edible grams for NHANES foods, pivoted to a price column per biennial survey cycle."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+@router.command(
+ model="GlobalFoodAssessment",
+ examples=[
+ APIEx(parameters={"provider": "usda"}),
+ APIEx(
+ description="Get the implied additional grain supply required across"
+ " Sub-Saharan Africa's subregions.",
+ parameters={
+ "element": "Implied additional supply required",
+ "region": "Sub-Saharan Africa",
+ "provider": "usda",
+ },
+ ),
+ APIEx(
+ description="Get grain production for the Middle East and North Africa.",
+ parameters={
+ "element": "Grain production",
+ "region": "Middle East and North Africa",
+ "provider": "usda",
+ },
+ ),
+ ],
+)
+async def global_food_assessment(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+) -> OBBject:
+ """Get ERS Global Food Assessment - grain food demand, other demand, total demand, production, and the implied additional supply required to close the food gap, pivoted to a wide layout with the subregions as columns and the base year and ten-year projection horizon as rows."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+
+if not COMMODITY_INSTALLED:
+
+ @router.command(
+ model="CommodityPsdData",
+ examples=[
+ APIEx(
+ description="Get the World Crop Production Summary table.",
+ parameters={"provider": "usda"},
+ ),
+ APIEx(
+ description="Get all attributes for Coffee globally, for a single year.",
+ parameters={
+ "provider": "usda",
+ "commodity": "coffee",
+ "start_year": 2025,
+ "end_year": 2025,
+ },
+ ),
+ APIEx(
+ description="Get historical production of corn in the US from 2020.",
+ parameters={
+ "provider": "usda",
+ "commodity": "corn",
+ "country": "united_states",
+ "attribute": "production",
+ "start_year": 2020,
+ },
+ ),
+ ],
+ )
+ async def psd_data(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+ ) -> OBBject:
+ """Get data tables and historical time series from the USDA FAS Production, Supply, and Distribution (PSD) Reports."""
+ return await OBBject.from_query(OpenBBQuery(**locals()))
+
+ @router.command(
+ methods=["POST"],
+ model="CommodityPsdReport",
+ no_validate=True,
+ widget_config={
+ "name": "USDA FAS Commodity Production Supply & Distribution Reports",
+ "description": "World Markets and Trade circulars released by the USDA"
+ " Foreign Agricultural Service. Selecting a commodity offers every"
+ " historical circular for it; with none selected, the latest circular"
+ " for every commodity is offered.",
+ "type": "multi_file_viewer",
+ "refetchInterval": False,
+ "gridData": {
+ "w": 20,
+ "h": 30,
+ },
+ "category": "Commodity",
+ "subCategory": "Agriculture",
+ "source": ["USDA", "FAS"],
+ "params": [
+ {
+ "paramName": "urls",
+ "type": "endpoint",
+ "optionsEndpoint": f"{api_prefix}/usda/psd_report_urls",
+ "optionsParams": {"commodity": "$commodity"},
+ "show": False,
+ "multiSelect": True,
+ "roles": ["fileSelector"],
+ },
+ {
+ "paramName": "commodity",
+ "label": "Commodity",
+ "description": "Leave empty for the latest circular from every"
+ " commodity.",
+ "value": None,
+ "type": "endpoint",
+ "optionsEndpoint": f"{api_prefix}/usda/psd_report_commodities",
+ "multiSelect": False,
+ },
+ ],
+ },
+ examples=[
+ APIEx(
+ parameters={
+ "provider": "usda",
+ "urls": [
+ "https://apps.fas.usda.gov/PSDOnline/Circulars/2026/01/Citrus.pdf"
+ ],
+ }
+ ),
+ PythonEx(
+ description="List the latest circular for every commodity, then download them.",
+ code=[
+ "urls = [d['value'] for d in obb.usda.psd_report_urls()]",
+ "pdfs = obb.usda.psd_report(urls=urls)",
+ ],
+ ),
+ ],
+ )
+ async def psd_report(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+ ) -> OBBject:
+ """Download one, or more, USDA FAS commodity PSD circulars.
+
+ This command returns only the results portion of the OBBject response.
+ It contains a list of dictionaries where the base64 encoded content of the
+ document is under the 'content' key.
+ """
+ response = await OBBject.from_query(OpenBBQuery(**locals()))
+ return response.model_dump().get("results", {})
+
+ @router.command(
+ model="WeatherBulletin",
+ no_validate=True,
+ widget_config={"exclude": True},
+ examples=[
+ APIEx(
+ description="Get weather bulletins for the current year.",
+ parameters={"provider": "usda"},
+ ),
+ APIEx(
+ description="Get weather bulletins for May 2023, week 2.",
+ parameters={
+ "provider": "usda",
+ "year": 2023,
+ "month": 5,
+ "week": 2,
+ },
+ ),
+ PythonEx(
+ description="Get URLs for comparing versus 1 year ago and download the base64-encoded PDF content to memory.",
+ code=[
+ "from datetime import datetime",
+ "urls = []",
+ "for year in [datetime.now().year, datetime.now().year - 1]:",
+ " urls.append(obb.usda.weather_bulletins(year=year, month=5, week=2)[0]['value'])",
+ "pdfs = obb.usda.weather_bulletins_download(urls=urls)",
+ ],
+ ),
+ ],
+ )
+ async def weather_bulletins(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+ ) -> OBBject:
+ """Get current and historical weather bulletins with their PDF links.
+
+ This command returns only the results portion of the OBBject response.
+ It contains a list of dictionaries where each dictionary has 'label' and 'value' keys.
+ """
+ response = await OBBject.from_query(OpenBBQuery(**locals()))
+ return response.model_dump().get("results", {})
+
+ @router.command(
+ methods=["POST"],
+ model="WeatherBulletinDownload",
+ no_validate=True,
+ widget_config={
+ "name": "USDA Weather & Crop Bulletin",
+ "description": "Weekly Weather and Crop Bulletin from the USDA.",
+ "type": "multi_file_viewer",
+ "refetchInterval": False,
+ "gridData": {
+ "w": 20,
+ "h": 30,
+ },
+ "category": "Commodity",
+ "subCategory": "Agriculture",
+ "source": ["USDA", "WAOB"],
+ "params": [
+ {
+ "paramName": "urls",
+ "type": "endpoint",
+ "optionsEndpoint": f"{api_prefix}/usda/weather_bulletins",
+ "optionsParams": {
+ "year": "$year",
+ "month": "$month",
+ "week": "$week",
+ "provider": "usda",
+ },
+ "show": False,
+ "multiSelect": True,
+ "roles": ["fileSelector"],
+ },
+ {
+ "paramName": "year",
+ "type": "number",
+ "label": "Year",
+ "value": datetime.now().year,
+ "options": [
+ {"value": year, "label": str(year)}
+ for year in sorted(
+ list(range(1974, datetime.now().year + 1)),
+ reverse=True,
+ )
+ ],
+ },
+ {
+ "paramName": "month",
+ "type": "number",
+ "label": "Month",
+ "value": None,
+ "options": [
+ {"value": i, "label": month}
+ for i, month in enumerate(
+ [
+ "January",
+ "February",
+ "March",
+ "April",
+ "May",
+ "June",
+ "July",
+ "August",
+ "September",
+ "October",
+ "November",
+ "December",
+ ],
+ start=1,
+ )
+ ]
+ + [{"value": None, "label": "All Months"}],
+ },
+ {
+ "paramName": "week",
+ "type": "number",
+ "label": "Week",
+ "value": None,
+ "options": [
+ {"value": week, "label": str(week)} for week in range(1, 6)
+ ]
+ + [{"value": None, "label": "All Weeks"}],
+ },
+ {
+ "paramName": "provider",
+ "show": False,
+ "value": "usda",
+ "type": "text",
+ "options": [{"value": "usda", "label": "usda"}],
+ },
+ ],
+ },
+ examples=[
+ APIEx(
+ parameters={
+ "provider": "usda",
+ "urls": [
+ "https://esmis.nal.usda.gov/sites/default/release-files/cj82k728n/9w033w568/x059f4232/wwcb0125.pdf"
+ ],
+ }
+ ),
+ ],
+ )
+ async def weather_bulletins_download(
+ cc: CommandContext,
+ provider_choices: ProviderChoices,
+ standard_params: StandardParams,
+ extra_params: ExtraParams,
+ ) -> OBBject:
+ """Download one, or more, weather bulletin documents.
+
+ This command returns only the results portion of the OBBject response.
+ It contains a list of dictionaries where the base64 encoded content of the document is under the 'content' key.
+ """
+ response = await OBBject.from_query(OpenBBQuery(**locals()))
+ return response.model_dump().get("results", {})
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/__init__.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/__init__.py
new file mode 100644
index 00000000000..e9bfe601786
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/__init__.py
@@ -0,0 +1 @@
+"""USDA provider utilities."""
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_adoption_of_genetically_engineered_crops_in_the_united_states.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_adoption_of_genetically_engineered_crops_in_the_united_states.py
new file mode 100644
index 00000000000..7a4b658de01
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_adoption_of_genetically_engineered_crops_in_the_united_states.py
@@ -0,0 +1,285 @@
+"""USDA ERS adoption of genetically engineered crops catalog and parser."""
+
+import re
+from io import BytesIO
+
+BASE_URL = "https://www.ers.usda.gov"
+PRODUCT_PAGE = (
+ "data-products/adoption-of-genetically-engineered-crops-in-the-united-states"
+)
+MEDIA_PATH = (
+ "/media/5685/genetically-engineered-varieties-of-corn-upland-cotton-and"
+ "-soybeans-by-state-and-for-the-united-states-2000-25.xlsx"
+)
+
+DEFAULT_CROP = "corn"
+DEFAULT_STATE = "United States"
+
+CROPS: dict[str, dict] = {
+ "corn": {
+ "label": "Corn",
+ "sheet": "Corn",
+ "traits": ("Bt only", "HT only", "Stacked", "All GE"),
+ },
+ "cotton": {
+ "label": "Upland cotton",
+ "sheet": "Cotton",
+ "traits": ("Bt only", "HT only", "Stacked", "All GE"),
+ },
+ "soybeans": {
+ "label": "Soybeans",
+ "sheet": "Soybeans",
+ "traits": ("HT only", "All GE"),
+ },
+}
+
+TRAIT_PREFIXES: tuple[tuple[str, str], ...] = (
+ ("insect-resistant (bt) only", "Bt only"),
+ ("herbicide-tolerant (ht) only", "HT only"),
+ ("stacked gene varieties", "Stacked"),
+ ("all ge varieties", "All GE"),
+)
+
+CROP_STATES: dict[str, tuple[str, ...]] = {
+ "corn": (
+ "Illinois",
+ "Indiana",
+ "Iowa",
+ "Kansas",
+ "Michigan",
+ "Minnesota",
+ "Missouri",
+ "Nebraska",
+ "North Dakota",
+ "Ohio",
+ "South Dakota",
+ "Texas",
+ "Wisconsin",
+ "Other States",
+ "United States",
+ ),
+ "cotton": (
+ "Alabama",
+ "Arkansas",
+ "California",
+ "Georgia",
+ "Louisiana",
+ "Mississippi",
+ "Missouri",
+ "North Carolina",
+ "Tennessee",
+ "Texas",
+ "Other States",
+ "United States",
+ ),
+ "soybeans": (
+ "Arkansas",
+ "Illinois",
+ "Indiana",
+ "Iowa",
+ "Kansas",
+ "Michigan",
+ "Minnesota",
+ "Mississippi",
+ "Missouri",
+ "Nebraska",
+ "North Dakota",
+ "Ohio",
+ "South Dakota",
+ "Wisconsin",
+ "Other States",
+ "United States",
+ ),
+}
+
+STATE_CANON: dict[str, str] = {
+ state.casefold(): state for states in CROP_STATES.values() for state in states
+}
+
+STATES_ALL: tuple[str, ...] = tuple(
+ sorted({state for states in CROP_STATES.values() for state in states})
+)
+
+STATE_SUFFIX = re.compile(r"\s*\d+/\s*$")
+FOOTNOTE_MARKER = re.compile(r"^\d+/")
+
+_MISSING = object()
+_TOKEN_VALUES: dict[str, float | None] = {".": None, "*": 0.0}
+
+
+def crop_options() -> list[dict]:
+ """Build the labeled crop options for the widget's crop selector.
+
+ Returns
+ -------
+ list[dict]
+ Label/value option dictionaries, one per crop.
+ """
+ return [{"label": config["label"], "value": key} for key, config in CROPS.items()]
+
+
+def state_options(crop: str) -> list[dict]:
+ """Build the labeled state options a crop is published for.
+
+ Parameters
+ ----------
+ crop : str
+ Crop key from CROPS. Unknown crops fall back to corn.
+
+ Returns
+ -------
+ list[dict]
+ Label/value option dictionaries for the widget's state selector.
+ """
+ states = CROP_STATES.get(crop) or CROP_STATES[DEFAULT_CROP]
+ return [{"label": state, "value": state} for state in states]
+
+
+def _match_trait(label: str) -> str | None:
+ """Return the short trait name when a block-title label opens a trait block."""
+ low = label.casefold()
+ for prefix, short in TRAIT_PREFIXES:
+ if low.startswith(prefix):
+ return short
+ return None
+
+
+def _is_footnote(label: str) -> bool:
+ """Return whether a first-column label is a footnote or source line."""
+ low = label.casefold()
+ return (
+ label.startswith("*")
+ or low.startswith("note:")
+ or low.startswith("source:")
+ or bool(FOOTNOTE_MARKER.match(label))
+ )
+
+
+def _clean_state(label: str) -> str:
+ """Strip a state row's footnote suffix, e.g. 'North Dakota 2/' to 'North Dakota'."""
+ return STATE_SUFFIX.sub("", label).strip()
+
+
+def _year_columns(cells: list) -> list[tuple[int, int]]:
+ """Return the (column index, year) pairs from a 'State/Year' header row."""
+ columns: list[tuple[int, int]] = []
+ for index, cell in enumerate(cells):
+ if index == 0:
+ continue
+ text = str(cell).strip() if cell is not None else ""
+ if len(text) == 4 and text.isdigit():
+ columns.append((index, int(text)))
+ return columns
+
+
+def _coerce_value(cell) -> object:
+ """Coerce a source cell to a percent float, None for '.', or the skip sentinel.
+
+ Parameters
+ ----------
+ cell : object
+ Raw worksheet cell value.
+
+ Returns
+ -------
+ object
+ A float percent; None for the '.' not-available token; 0.0 for the '*'
+ rounds-to-less-than-half token; or the module skip sentinel for blank
+ or non-numeric cells.
+ """
+ if cell is None or isinstance(cell, bool):
+ return _MISSING
+ if isinstance(cell, (int, float)):
+ return float(cell)
+ text = str(cell).strip()
+ if not text:
+ return _MISSING
+ if text in _TOKEN_VALUES:
+ return _TOKEN_VALUES[text]
+ try:
+ return float(text)
+ except ValueError:
+ return _MISSING
+
+
+def parse_sheet(rows: list, crop: str) -> list[dict]:
+ """Parse one crop sheet's stacked trait blocks into long-format records.
+
+ Parameters
+ ----------
+ rows : list
+ The worksheet's value rows, each an ordered sequence of cell values.
+ crop : str
+ Crop key from CROPS, selecting the trait vocabulary.
+
+ Returns
+ -------
+ list[dict]
+ Records carrying crop, state, trait short name, integer year, and the
+ percent value, where '.' yields None and '*' yields 0.0. Rows outside a
+ trait block, header rows, and footnote rows are skipped.
+ """
+ valid_traits = set(CROPS[crop]["traits"])
+ records: list[dict] = []
+ trait: str | None = None
+ year_columns: list[tuple[int, int]] = []
+ for raw in rows:
+ cells = list(raw)
+ first = cells[0] if cells else None
+ label = str(first).strip() if first is not None else ""
+ if not label:
+ continue
+ matched = _match_trait(label)
+ if matched is not None:
+ trait = matched if matched in valid_traits else None
+ year_columns = []
+ continue
+ if label.casefold().startswith("state/year"):
+ year_columns = _year_columns(cells)
+ continue
+ if _is_footnote(label):
+ continue
+ if trait is None or not year_columns:
+ continue
+ state = _clean_state(label)
+ for index, year in year_columns:
+ cell = cells[index] if index < len(cells) else None
+ value = _coerce_value(cell)
+ if value is _MISSING:
+ continue
+ records.append(
+ {
+ "crop": crop,
+ "state": state,
+ "trait": trait,
+ "year": year,
+ "value": value,
+ }
+ )
+ return records
+
+
+async def afetch_crop(crop: str) -> list[dict]:
+ """Download the workbook and parse one crop's sheet through the ERS cache.
+
+ Parameters
+ ----------
+ crop : str
+ Crop key from CROPS.
+
+ Returns
+ -------
+ list[dict]
+ Long-format records from parse_sheet for the selected crop.
+ """
+ import openpyxl
+
+ from openbb_government_us.usda.utils.ers_client import afetch_ers_file
+
+ content = await afetch_ers_file(MEDIA_PATH, product=PRODUCT_PAGE)
+ workbook = openpyxl.load_workbook(BytesIO(content), read_only=True, data_only=True)
+ try:
+ rows = list(workbook[CROPS[crop]["sheet"]].iter_rows(values_only=True))
+ finally:
+ workbook.close()
+ return parse_sheet(rows, crop)
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_agricultural_and_food_research_and_development_expenditures_in_the_united_states.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_agricultural_and_food_research_and_development_expenditures_in_the_united_states.py
new file mode 100644
index 00000000000..02a26811a13
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_agricultural_and_food_research_and_development_expenditures_in_the_united_states.py
@@ -0,0 +1,120 @@
+"""USDA ERS agricultural and food R&D expenditures catalog and parser."""
+
+import csv
+import re
+from io import StringIO
+
+from openbb_government_us.utils.serializers import is_null_token
+
+BASE_URL = "https://www.ers.usda.gov"
+PRODUCT_PAGE = (
+ "data-products/agricultural-and-food-research-and-development-expenditures"
+ "-in-the-united-states"
+)
+MEDIA_PATH = (
+ "/media/5692/agricultural-and-food-research-and-development-rd-expenditures"
+ "-in-the-united-states-1970-2022.csv"
+)
+
+MEASURES = ("nominal", "real")
+
+SERIES: dict[str, str] = {
+ "Public agricultural and food R&D": "public_total",
+ "Public agricultural and food R&D performed by USDA intramural agencies": "public_usda_intramural",
+ "Public agricultural & food R&D performed by State universities and cooperating institutions": "public_state_universities",
+ "Private agriculture input industries R&D": "private_input_industries",
+ "Private food industry R&D": "private_food_industry",
+ "R&D Price Index": "rd_price_index",
+}
+
+SERIES_ORDER = (
+ "public_total",
+ "public_usda_intramural",
+ "public_state_universities",
+ "private_input_industries",
+ "private_food_industry",
+ "rd_price_index",
+)
+
+PAREN_PATTERN = re.compile(r"\s*\([^)]*\)\s*$")
+
+
+def parse_attribute(attribute: str) -> tuple[str | None, str | None]:
+ """Split a source Attribute label into its series field and measure.
+
+ Parameters
+ ----------
+ attribute : str
+ Attribute label as published, with a trailing parenthetical stating
+ the measure, e.g. 'Private food industry R&D (current U.S. dollars-
+ millions)'.
+
+ Returns
+ -------
+ tuple[str | None, str | None]
+ The mapped series field name and the measure ('nominal', 'real', or
+ None for the measure-agnostic price index). (None, None) when the
+ series is not recognized.
+ """
+ text = attribute.strip()
+ match = PAREN_PATTERN.search(text)
+ descriptor = match.group(0).lower() if match else ""
+ base = PAREN_PATTERN.sub("", text).strip()
+ field = SERIES.get(base)
+ if field is None:
+ return None, None
+ if "constant" in descriptor:
+ return field, "real"
+ if "current" in descriptor:
+ return field, "nominal"
+ return field, None
+
+
+def parse_csv(text: str) -> list[dict]:
+ """Parse the R&D expenditures CSV into long-format observation records.
+
+ Parameters
+ ----------
+ text : str
+ Decoded CSV text with Year, Attribute, and Value columns.
+
+ Returns
+ -------
+ list[dict]
+ One record per recognized (year, attribute) observation, carrying the
+ integer year, the series field, the measure, and the numeric value
+ (None for the 'NA' token).
+ """
+ records: list[dict] = []
+ for row in csv.DictReader(StringIO(text)):
+ year_label = (row.get("Year") or "").strip()
+ if len(year_label) < 4 or not year_label[:4].isdigit():
+ continue
+ field, measure = parse_attribute(row.get("Attribute") or "")
+ if field is None:
+ continue
+ raw_value = (row.get("Value") or "").strip()
+ value = None if is_null_token(raw_value) else float(raw_value)
+ records.append(
+ {
+ "year": int(year_label[:4]),
+ "series": field,
+ "measure": measure,
+ "value": value,
+ }
+ )
+ return records
+
+
+async def afetch_records() -> list[dict]:
+ """Download and parse the R&D expenditures CSV through the ERS disk cache.
+
+ Returns
+ -------
+ list[dict]
+ Long-format records from parse_csv.
+ """
+ from openbb_government_us.usda.utils.ers_client import afetch_ers_file
+
+ content = await afetch_ers_file(MEDIA_PATH, product=PRODUCT_PAGE)
+ return parse_csv(content.decode("utf-8-sig", errors="replace"))
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_agricultural_exchange_rates.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_agricultural_exchange_rates.py
new file mode 100644
index 00000000000..5d710120966
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_agricultural_exchange_rates.py
@@ -0,0 +1,191 @@
+"""USDA ERS Agricultural Exchange Rate Data Set file catalog and parsers."""
+
+import csv
+from io import StringIO
+
+BASE_URL = "https://www.ers.usda.gov"
+PRODUCT_PAGE = "data-products/agricultural-exchange-rate-data-set"
+
+MONTH_ORDER: dict[str, int] = {
+ "January": 1,
+ "February": 2,
+ "March": 3,
+ "April": 4,
+ "May": 5,
+ "June": 6,
+ "July": 7,
+ "August": 8,
+ "September": 9,
+ "October": 10,
+ "November": 11,
+ "December": 12,
+}
+
+EXCHANGE_RATE_FILES: dict[str, dict] = {
+ "real_index_annual": {
+ "media": "/media/5447/real-annual-commodity-trade-weighted-"
+ "exchange-rate-indexes.csv",
+ "kind": "index",
+ "frequency": "annual",
+ "label": "Real trade-weighted index (annual)",
+ },
+ "real_index_monthly": {
+ "media": "/media/5449/real-monthly-commodity-trade-weighted-"
+ "exchange-rate-indexes.csv",
+ "kind": "index",
+ "frequency": "monthly",
+ "label": "Real trade-weighted index (monthly)",
+ },
+ "real_bilateral_annual": {
+ "media": "/media/5457/annual-real-exchange-rates-local-currency-per-usd.csv",
+ "kind": "bilateral",
+ "frequency": "annual",
+ "label": "Real bilateral rate, local currency per USD (annual)",
+ },
+ "real_bilateral_monthly": {
+ "media": "/media/5451/monthly-real-exchange-rates-local-currency-per-usd.csv",
+ "kind": "bilateral",
+ "frequency": "monthly",
+ "label": "Real bilateral rate, local currency per USD (monthly)",
+ },
+ "nominal_bilateral_annual": {
+ "media": "/media/5453/annual-nominal-exchange-rates-local-currency-per-usd.csv",
+ "kind": "bilateral",
+ "frequency": "annual",
+ "label": "Nominal bilateral rate, local currency per USD (annual)",
+ },
+ "nominal_bilateral_monthly": {
+ "media": "/media/5455/monthly-nominal-exchange-rates-local-currency-per-usd.csv",
+ "kind": "bilateral",
+ "frequency": "monthly",
+ "label": "Nominal bilateral rate, local currency per USD (monthly)",
+ },
+}
+
+TRADE_WEIGHTS_MEDIA = (
+ "/media/5459/agricultural-trade-weights-by-commodity-or-commodity-group.csv"
+)
+
+INDEX_WEIGHTS: tuple[str, ...] = (
+ "U.S. competitors (country export weights)",
+ "U.S. markets (U.S. export weights)",
+ "U.S. suppliers (U.S. import weights)",
+)
+
+BILATERAL_REGIONS: tuple[str, ...] = (
+ "Central America",
+ "Europe",
+ "Middle East",
+ "North Africa",
+ "North America",
+ "Northeast Asia",
+ "Oceania",
+ "South America",
+ "South Asia",
+ "Southeast Asia",
+ "Sub-Saharan Africa",
+ "The Caribbean",
+)
+
+
+def media_path(table: str) -> str:
+ """Return the media path of one table's CSV file.
+
+ Parameters
+ ----------
+ table : str
+ Table key from EXCHANGE_RATE_FILES.
+
+ Returns
+ -------
+ str
+ The '/media/{id}/{slug}.csv' path of the table's CSV file.
+ """
+ return EXCHANGE_RATE_FILES[table]["media"]
+
+
+def build_url(table: str) -> str:
+ """Return the full download URL of one table's CSV file.
+
+ Parameters
+ ----------
+ table : str
+ Table key from EXCHANGE_RATE_FILES.
+
+ Returns
+ -------
+ str
+ Full URL of the table's CSV file.
+ """
+ return f"{BASE_URL}{media_path(table)}"
+
+
+def parse_table(text: str, table: str) -> list[dict]:
+ """Parse one table's CSV text into long-format observation records.
+
+ Parameters
+ ----------
+ text : str
+ Decoded CSV text of the table.
+ table : str
+ Table key from EXCHANGE_RATE_FILES.
+
+ Returns
+ -------
+ list[dict]
+ Records carrying the table key, kind, integer year, month label and
+ calendar order, the pivot series name, the index weight scheme or the
+ bilateral region, and the numeric value. Rows whose value or year is
+ blank or non-numeric are skipped.
+ """
+ config = EXCHANGE_RATE_FILES[table]
+ kind = config["kind"]
+ has_month = config["frequency"] == "monthly"
+ records: list[dict] = []
+ for row in csv.DictReader(StringIO(text)):
+ raw_value = (row.get("Value") or "").strip()
+ try:
+ value = float(raw_value)
+ except ValueError:
+ continue
+ year_label = (row.get("Year") or "").strip()
+ if not year_label.isdigit():
+ continue
+ month = (row.get("Month") or "").strip() if has_month else ""
+ record: dict = {
+ "table": table,
+ "kind": kind,
+ "year": int(year_label),
+ "month": month or None,
+ "month_order": MONTH_ORDER.get(month) if month else None,
+ "value": value,
+ }
+ if kind == "index":
+ record["weights"] = (row.get("Weights") or "").strip() or None
+ record["region"] = None
+ record["series"] = (row.get("Commodity") or "").strip()
+ else:
+ record["weights"] = None
+ record["region"] = (row.get("Region") or "").strip() or None
+ record["series"] = (row.get("Country") or "").strip()
+ records.append(record)
+ return records
+
+
+async def afetch_table(table: str, **kwargs) -> list[dict]:
+ """Download and parse one table through the ERS disk cache.
+
+ Parameters
+ ----------
+ table : str
+ Table key from EXCHANGE_RATE_FILES.
+
+ Returns
+ -------
+ list[dict]
+ Long-format records from parse_table.
+ """
+ from openbb_government_us.usda.utils.ers_client import afetch_ers_file
+
+ content = await afetch_ers_file(media_path(table), product=PRODUCT_PAGE)
+ return parse_table(content.decode("utf-8-sig", errors="replace"), table)
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_agricultural_productivity.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_agricultural_productivity.py
new file mode 100644
index 00000000000..5f99ce1a100
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_agricultural_productivity.py
@@ -0,0 +1,210 @@
+"""USDA ERS Agricultural Productivity file catalog and long-format parser."""
+
+import csv
+from io import StringIO
+
+BASE_URL = "https://www.ers.usda.gov"
+PRODUCT_PAGE = "data-products/agricultural-productivity-in-the-united-states"
+
+NATIONAL_STATE = "US"
+
+AGRICULTURAL_PRODUCTIVITY_FILES: dict[str, dict] = {
+ "national_indices": {
+ "label": "Table 1. Farm output, input, and TFP indices (U.S., 1948-2021)",
+ "media": "/media/5406/table-1-indices-of-farm-outputs-inputs-and-total-factor"
+ "-productivity-for-the-united-states-1948-2021.csv",
+ "geography": "national",
+ "year_col": "Year",
+ "series_col": "Attribute",
+ "value_col": "Value",
+ "state_col": None,
+ },
+ "national_price_quantity": {
+ "label": "Table 1a. Price indices and implicit quantities (U.S., 1948-2021)",
+ "media": "/media/5407/table-1a-price-indices-and-implicit-quantities-of-farm"
+ "-outputs-and-inputs-for-the-united-states-1948-2021.csv",
+ "geography": "national",
+ "year_col": "Year",
+ "series_col": "Attribute",
+ "value_col": "Value",
+ "state_col": None,
+ },
+ "state_relative_levels": {
+ "label": "Table 2. Relative levels of TFP, output & input indices"
+ " (48 States, 1960-2015)",
+ "media": "/media/5410/table-2-relative-levels-of-agricultural-total-factor"
+ "-productivity-tfp-and-price-and-quantity-indices-for-outputs-and-inputs-for"
+ "-the-48-contiguous-states-for-the-years-1960-2015.csv",
+ "geography": "state",
+ "year_col": "Year",
+ "series_col": "Variable Name",
+ "value_col": "Value",
+ "state_col": "State",
+ },
+}
+
+STATE_LABELS: dict[str, str] = {
+ "US": "United States",
+ "AL": "Alabama",
+ "AR": "Arkansas",
+ "AZ": "Arizona",
+ "CA": "California",
+ "CO": "Colorado",
+ "CT": "Connecticut",
+ "DE": "Delaware",
+ "FL": "Florida",
+ "GA": "Georgia",
+ "IA": "Iowa",
+ "ID": "Idaho",
+ "IL": "Illinois",
+ "IN": "Indiana",
+ "KS": "Kansas",
+ "KY": "Kentucky",
+ "LA": "Louisiana",
+ "MA": "Massachusetts",
+ "MD": "Maryland",
+ "ME": "Maine",
+ "MI": "Michigan",
+ "MN": "Minnesota",
+ "MO": "Missouri",
+ "MS": "Mississippi",
+ "MT": "Montana",
+ "NC": "North Carolina",
+ "ND": "North Dakota",
+ "NE": "Nebraska",
+ "NH": "New Hampshire",
+ "NJ": "New Jersey",
+ "NM": "New Mexico",
+ "NV": "Nevada",
+ "NY": "New York",
+ "OH": "Ohio",
+ "OK": "Oklahoma",
+ "OR": "Oregon",
+ "PA": "Pennsylvania",
+ "RI": "Rhode Island",
+ "SC": "South Carolina",
+ "SD": "South Dakota",
+ "TN": "Tennessee",
+ "TX": "Texas",
+ "UT": "Utah",
+ "VA": "Virginia",
+ "VT": "Vermont",
+ "WA": "Washington",
+ "WI": "Wisconsin",
+ "WV": "West Virginia",
+ "WY": "Wyoming",
+}
+
+CONTIGUOUS_STATES: tuple[str, ...] = tuple(
+ code for code in STATE_LABELS if code != NATIONAL_STATE
+)
+
+
+def allowed_states(table: str) -> tuple[str, ...]:
+ """Return the state codes a table is published for.
+
+ Parameters
+ ----------
+ table : str
+ Table key from AGRICULTURAL_PRODUCTIVITY_FILES.
+
+ Returns
+ -------
+ tuple[str, ...]
+ ('US',) for the national tables, otherwise the 48 contiguous-state
+ codes for the relative-levels table.
+ """
+ config = AGRICULTURAL_PRODUCTIVITY_FILES.get(table)
+ if config is None or config["geography"] == "national":
+ return (NATIONAL_STATE,)
+ return CONTIGUOUS_STATES
+
+
+def state_options(table: str) -> list[dict]:
+ """Build the labeled state options a table is published for.
+
+ Parameters
+ ----------
+ table : str
+ Table key from AGRICULTURAL_PRODUCTIVITY_FILES.
+
+ Returns
+ -------
+ list[dict]
+ Label/value option dictionaries for the widget's state selector.
+ """
+ return [
+ {"label": STATE_LABELS[code], "value": code} for code in allowed_states(table)
+ ]
+
+
+def parse_table(text: str, table: str) -> list[dict]:
+ """Parse one table's CSV text into long-format observation records.
+
+ Parameters
+ ----------
+ text : str
+ Decoded CSV text of the table's media file.
+ table : str
+ Table key from AGRICULTURAL_PRODUCTIVITY_FILES.
+
+ Returns
+ -------
+ list[dict]
+ Records carrying the table key, integer year, the state code (None
+ for the national tables), the series header, and the numeric value.
+ Rows with a blank or non-numeric value, a non-four-digit year, or a
+ blank series are skipped.
+ """
+ config = AGRICULTURAL_PRODUCTIVITY_FILES[table]
+ year_col = config["year_col"]
+ series_col = config["series_col"]
+ value_col = config["value_col"]
+ state_col = config["state_col"]
+ records: list[dict] = []
+ for row in csv.DictReader(StringIO(text)):
+ raw_value = (row.get(value_col) or "").strip()
+ try:
+ value = float(raw_value)
+ except ValueError:
+ continue
+ year_raw = (row.get(year_col) or "").strip()
+ if not (len(year_raw) == 4 and year_raw.isdigit()):
+ continue
+ series = (row.get(series_col) or "").strip()
+ if not series:
+ continue
+ records.append(
+ {
+ "table": table,
+ "year": int(year_raw),
+ "state": (
+ ((row.get(state_col) or "").strip().upper() or None)
+ if state_col
+ else None
+ ),
+ "series": series,
+ "value": value,
+ }
+ )
+ return records
+
+
+async def afetch_table(table: str, **kwargs) -> list[dict]:
+ """Download and parse one productivity table through the ERS disk cache.
+
+ Parameters
+ ----------
+ table : str
+ Table key from AGRICULTURAL_PRODUCTIVITY_FILES.
+
+ Returns
+ -------
+ list[dict]
+ Long-format records from parse_table.
+ """
+ from openbb_government_us.usda.utils.ers_client import afetch_ers_file
+
+ config = AGRICULTURAL_PRODUCTIVITY_FILES[table]
+ content = await afetch_ers_file(config["media"], product=PRODUCT_PAGE)
+ return parse_table(content.decode("utf-8-sig", errors="replace"), table)
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_agricultural_trade_multipliers.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_agricultural_trade_multipliers.py
new file mode 100644
index 00000000000..7a3356bd638
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_agricultural_trade_multipliers.py
@@ -0,0 +1,157 @@
+"""USDA ERS Agricultural Trade Multipliers file catalog and parser."""
+
+import csv
+from io import StringIO
+
+from openbb_government_us.utils.serializers import is_null_token
+
+BASE_URL = "https://www.ers.usda.gov"
+PRODUCT_PAGE = "data-products/agricultural-trade-multipliers"
+DEFAULT_TABLE = "agricultural_trade_multipliers"
+
+AGRICULTURAL_TRADE_MULTIPLIERS_FILES: dict[str, str] = {
+ "agricultural_trade_multipliers": "/media/6492/agricultural-trade-multipliers.csv",
+}
+
+AGGREGATE_COMMODITY = "All agricultural exports"
+
+VARIABLE_FIELDS: dict[str, str] = {
+ "Producer Output Multiplier": "producer_output_multiplier",
+ "Producer Employment Multiplier": "producer_employment_multiplier",
+ "Port Output Multiplier": "port_output_multiplier",
+ "Port Employment Multiplier": "port_employment_multiplier",
+ "Export Value": "export_value",
+}
+
+
+def media_path(table: str = DEFAULT_TABLE) -> str:
+ """Return the media path for one table's CSV.
+
+ Parameters
+ ----------
+ table : str
+ Table key from AGRICULTURAL_TRADE_MULTIPLIERS_FILES.
+
+ Returns
+ -------
+ str
+ The '/media/{id}/{slug}.csv' path of the table's CSV file.
+ """
+ return AGRICULTURAL_TRADE_MULTIPLIERS_FILES[table]
+
+
+def build_url(table: str = DEFAULT_TABLE) -> str:
+ """Return the download URL for one table's CSV.
+
+ Parameters
+ ----------
+ table : str
+ Table key from AGRICULTURAL_TRADE_MULTIPLIERS_FILES.
+
+ Returns
+ -------
+ str
+ Full URL of the table's CSV file.
+ """
+ return f"{BASE_URL}{media_path(table)}"
+
+
+def parse_value(raw: str | None) -> float | None:
+ """Parse a raw CSV value cell into a float.
+
+ Parameters
+ ----------
+ raw : str | None
+ Raw cell text, which may carry comma thousands separators or a null
+ token such as '' or 'NA'.
+
+ Returns
+ -------
+ float | None
+ The parsed value, or None for empty and null-token cells.
+ """
+ text = (raw or "").strip()
+ if not text or is_null_token(text):
+ return None
+ return float(text.replace(",", ""))
+
+
+def parse_commodity_id(raw: str | None) -> int | None:
+ """Parse a raw commodity id cell into an integer.
+
+ Parameters
+ ----------
+ raw : str | None
+ Raw COMMID cell text, which may be a null token on the aggregate row.
+
+ Returns
+ -------
+ int | None
+ The parsed identifier, or None for empty and null-token cells.
+ """
+ text = (raw or "").strip()
+ if not text or is_null_token(text):
+ return None
+ return int(text)
+
+
+def parse_rows(text: str) -> list[dict]:
+ """Parse the Agricultural Trade Multipliers CSV into long row records.
+
+ Parameters
+ ----------
+ text : str
+ Decoded CSV text with COMMID, CommodityName, Variable, Unit, Value, and
+ Year columns.
+
+ Returns
+ -------
+ list[dict]
+ Records with commodity_id, commodity, year, field, unit, and value
+ keys, one per commodity and variable. The aggregate row, published with
+ a null-token commodity, is relabeled to AGGREGATE_COMMODITY with a null
+ commodity_id.
+ """
+ rows: list[dict] = []
+ for row in csv.DictReader(StringIO(text)):
+ variable = (row.get("Variable") or "").strip()
+ field = VARIABLE_FIELDS.get(variable)
+ if field is None:
+ continue
+ name = (row.get("CommodityName") or "").strip()
+ if is_null_token(name):
+ commodity = AGGREGATE_COMMODITY
+ commodity_id = None
+ else:
+ commodity = name
+ commodity_id = parse_commodity_id(row.get("COMMID"))
+ rows.append(
+ {
+ "commodity_id": commodity_id,
+ "commodity": commodity,
+ "year": int((row.get("Year") or "").strip()),
+ "field": field,
+ "unit": (row.get("Unit") or "").strip() or None,
+ "value": parse_value(row.get("Value")),
+ }
+ )
+ return rows
+
+
+async def afetch_table(table: str = DEFAULT_TABLE, **kwargs) -> list[dict]:
+ """Download and parse the Agricultural Trade Multipliers CSV through the cache.
+
+ Parameters
+ ----------
+ table : str
+ Table key from AGRICULTURAL_TRADE_MULTIPLIERS_FILES.
+
+ Returns
+ -------
+ list[dict]
+ Long row records from parse_rows.
+ """
+ from openbb_government_us.usda.utils.ers_client import afetch_ers_file
+
+ content = await afetch_ers_file(media_path(table), product=PRODUCT_PAGE)
+ return parse_rows(content.decode("utf-8-sig", errors="replace"))
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_area_and_road_ruggedness_scales.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_area_and_road_ruggedness_scales.py
new file mode 100644
index 00000000000..6e6f0adf209
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_area_and_road_ruggedness_scales.py
@@ -0,0 +1,340 @@
+"""USDA ERS Area and Road Ruggedness Scales vintage catalog and census-tract lookup parsers."""
+
+import csv
+from collections import OrderedDict
+from io import StringIO
+
+from openbb_government_us.usda.utils.ers_rural_urban_continuum_codes import STATE_NAMES
+
+BASE_URL = "https://www.ers.usda.gov"
+PRODUCT_PAGE = "data-products/area-and-road-ruggedness-scales"
+
+DEFAULT_VINTAGE = "2020"
+DEFAULT_STATE = "WV"
+
+STATE_NAMES_BY_NAME: dict[str, str] = {name: code for code, name in STATE_NAMES.items()}
+
+STATES: tuple[str, ...] = (
+ "AK", "AL", "AR", "AZ", "CA", "CO", "CT", "DC", "DE", "FL", "GA", "HI",
+ "IA", "ID", "IL", "IN", "KS", "KY", "LA", "MA", "MD", "ME", "MI", "MN",
+ "MO", "MS", "MT", "NC", "ND", "NE", "NH", "NJ", "NM", "NV", "NY", "OH",
+ "OK", "OR", "PA", "RI", "SC", "SD", "TN", "TX", "UT", "VA", "VT", "WA",
+ "WI", "WV", "WY",
+) # fmt: skip
+
+ALL_STATES: frozenset[str] = frozenset(STATES)
+
+STATES_BY_VINTAGE: dict[str, tuple[str, ...]] = {"2020": STATES, "2010": STATES}
+
+CANONICAL_COLUMNS: tuple[str, ...] = (
+ "tract_fips",
+ "tract_name",
+ "county_fips",
+ "county_name",
+ "state_fips",
+ "state",
+ "state_name",
+ "rs_region",
+ "rs_region_name",
+ "primary_ruca",
+ "rurality_code",
+ "rurality_name",
+ "population",
+ "land_area",
+ "pop_density",
+ "area_tri_count",
+ "area_tri_mean",
+ "area_tri_std_dev",
+ "area_tri_median",
+ "area_tri_min",
+ "area_tri_max",
+ "area_tri_range",
+ "ars",
+ "ars_description",
+ "road_tri_count",
+ "road_tri_mean",
+ "road_tri_std_dev",
+ "road_tri_median",
+ "road_tri_min",
+ "road_tri_max",
+ "road_tri_range",
+ "rrs",
+ "rrs_description",
+)
+
+STR_COLUMNS: dict[str, str] = {
+ "rs_region": "RSRegion",
+ "rs_region_name": "RSRegionName",
+ "primary_ruca": "PrimaryRUCA",
+ "rurality_code": "RuralityCode",
+ "ars": "ARS",
+ "rrs": "RRS",
+}
+
+CAPITALIZED_COLUMNS: dict[str, str] = {
+ "rurality_name": "RuralityName",
+ "ars_description": "ARSDescription",
+ "rrs_description": "RRSDescription",
+}
+
+INT_COLUMNS: dict[str, str] = {
+ "population": "Population",
+ "area_tri_count": "AreaTRI_Count",
+ "road_tri_count": "RoadTRI_Count",
+}
+
+FLOAT_COLUMNS: dict[str, str] = {
+ "land_area": "LandArea",
+ "pop_density": "PopDensity",
+ "area_tri_mean": "AreaTRI_Mean",
+ "area_tri_std_dev": "AreaTRI_StdDev",
+ "area_tri_median": "AreaTRI_Median",
+ "area_tri_min": "AreaTRI_Min",
+ "area_tri_max": "AreaTRI_Max",
+ "area_tri_range": "AreaTRI_Range",
+ "road_tri_mean": "RoadTRI_Mean",
+ "road_tri_std_dev": "RoadTRI_StdDev",
+ "road_tri_median": "RoadTRI_Median",
+ "road_tri_min": "RoadTRI_Min",
+ "road_tri_max": "RoadTRI_Max",
+ "road_tri_range": "RoadTRI_Range",
+}
+
+VINTAGE_CATALOG: "OrderedDict[str, dict]" = OrderedDict(
+ [
+ (
+ "2020",
+ {
+ "label": "2020",
+ "media": "/media/5414/2020-area-and-road-ruggedness-scales.csv",
+ "identifiers": {
+ "tract_fips": "TractFIPS20",
+ "tract_name": "TractName20",
+ "county_fips": "CountyFIPS20",
+ "county_name": "CountyName20",
+ "state_fips": "StateFIPS20",
+ },
+ "state_column": "StateName20",
+ "state_kind": "name",
+ },
+ ),
+ (
+ "2010",
+ {
+ "label": "2010",
+ "media": "/media/5412/area-and-road-ruggedness-scales.csv",
+ "identifiers": {
+ "tract_fips": "TractFIPS",
+ "tract_name": "TractName",
+ "county_fips": "CountyFIPS",
+ "county_name": "CountyName",
+ "state_fips": "StateFIPS",
+ },
+ "state_column": "State",
+ "state_kind": "postal",
+ },
+ ),
+ ]
+)
+
+VINTAGES = tuple(VINTAGE_CATALOG)
+
+VINTAGE_OPTIONS = [
+ {"label": config["label"], "value": key} for key, config in VINTAGE_CATALOG.items()
+]
+
+
+def clean_text(value: object) -> str | None:
+ """Strip a source cell to a non-empty string, or None when blank.
+
+ Parameters
+ ----------
+ value : object
+ A raw source cell, which may be None, a missing marker rendered as
+ 'nan', or a string carrying stray leading or trailing whitespace.
+
+ Returns
+ -------
+ str | None
+ The stripped text, or None when the cell is empty or a missing marker.
+ """
+ if value is None:
+ return None
+ text = str(value).strip()
+ if not text or text.casefold() == "nan":
+ return None
+ return text
+
+
+def capitalize_text(value: object) -> str | None:
+ """Sentence-case a source label so the two vintages share one casing.
+
+ Parameters
+ ----------
+ value : object
+ A raw label cell whose casing differs between vintages, lowercase in
+ the 2010 edition and title case in the 2020 edition.
+
+ Returns
+ -------
+ str | None
+ The label capitalized to a single leading uppercase, or None when the
+ cell is blank.
+ """
+ text = clean_text(value)
+ return text.capitalize() if text else None
+
+
+def parse_population(value: object) -> int | None:
+ """Parse an integer count cell, or None when unavailable.
+
+ Parameters
+ ----------
+ value : object
+ A raw count cell, which may be None, blank, a missing marker, or a
+ digit string with optional thousands separators.
+
+ Returns
+ -------
+ int | None
+ The full integer count, or None when the cell is empty or non-numeric.
+ """
+ if value is None:
+ return None
+ text = str(value).replace(",", "").strip()
+ if not text or text.casefold() in {"nan", "na", "n/a"}:
+ return None
+ try:
+ return int(text)
+ except ValueError:
+ try:
+ return int(float(text))
+ except ValueError:
+ return None
+
+
+def parse_float(value: object) -> float | None:
+ """Parse a float cell at full precision, or None when unavailable.
+
+ Parameters
+ ----------
+ value : object
+ A raw numeric cell, which may be None, blank, a missing marker, or a
+ decimal string.
+
+ Returns
+ -------
+ float | None
+ The full-precision float, or None when the cell is empty or
+ non-numeric.
+ """
+ if value is None:
+ return None
+ text = str(value).replace(",", "").strip()
+ if not text or text.casefold() in {"nan", "na", "n/a"}:
+ return None
+ try:
+ return float(text)
+ except ValueError:
+ return None
+
+
+def parse_csv_records(text: str, vintage: str) -> list[dict]:
+ """Normalize a vintage's wide census-tract CSV into canonical records.
+
+ Parameters
+ ----------
+ text : str
+ Decoded CSV text, one row per census tract, with the vintage's source
+ column headers.
+ vintage : str
+ A vintage key from VINTAGES, selecting the identifier column map and
+ the state column and its kind.
+
+ Returns
+ -------
+ list[dict]
+ One canonical record per census tract, each carrying the fixed
+ CANONICAL_COLUMNS keys. Rows with a blank tract FIPS are skipped.
+ """
+ config = VINTAGE_CATALOG[vintage]
+ identifiers = config["identifiers"]
+ state_column = config["state_column"]
+ state_kind = config["state_kind"]
+ records: list[dict] = []
+ for row in csv.DictReader(StringIO(text)):
+ tract_fips = clean_text(row.get(identifiers["tract_fips"]))
+ if not tract_fips:
+ continue
+ record: dict = {}
+ for canonical, source in identifiers.items():
+ record[canonical] = clean_text(row.get(source))
+ if state_kind == "name":
+ state_name = clean_text(row.get(state_column))
+ record["state"] = (
+ STATE_NAMES_BY_NAME.get(state_name) if state_name else None
+ )
+ record["state_name"] = state_name
+ else:
+ state = clean_text(row.get(state_column))
+ record["state"] = state
+ record["state_name"] = STATE_NAMES.get(state) if state else None
+ for canonical, source in STR_COLUMNS.items():
+ record[canonical] = clean_text(row.get(source))
+ for canonical, source in CAPITALIZED_COLUMNS.items():
+ record[canonical] = capitalize_text(row.get(source))
+ for canonical, source in INT_COLUMNS.items():
+ record[canonical] = parse_population(row.get(source))
+ for canonical, source in FLOAT_COLUMNS.items():
+ record[canonical] = parse_float(row.get(source))
+ records.append(record)
+ return records
+
+
+def vintage_options() -> list[dict]:
+ """Build the labeled vintage options for the widget's vintage selector.
+
+ Returns
+ -------
+ list[dict]
+ Label/value option dictionaries, one per published vintage.
+ """
+ return list(VINTAGE_OPTIONS)
+
+
+def state_options(vintage: str) -> list[dict]:
+ """Build the labeled state options a vintage publishes.
+
+ Parameters
+ ----------
+ vintage : str
+ Vintage key. Unknown vintages fall back to the default vintage.
+
+ Returns
+ -------
+ list[dict]
+ Label/value option dictionaries for the widget's state selector, the
+ label the full state or district name.
+ """
+ states = STATES_BY_VINTAGE.get(vintage) or STATES_BY_VINTAGE[DEFAULT_VINTAGE]
+ return [{"label": STATE_NAMES.get(code, code), "value": code} for code in states]
+
+
+async def afetch_vintage(vintage: str) -> list[dict]:
+ """Download and parse one vintage into canonical records through the cache.
+
+ Parameters
+ ----------
+ vintage : str
+ A vintage key from VINTAGES.
+
+ Returns
+ -------
+ list[dict]
+ Canonical census-tract records for the vintage.
+ """
+ from openbb_government_us.usda.utils.ers_client import afetch_ers_file
+
+ config = VINTAGE_CATALOG[vintage]
+ content = await afetch_ers_file(config["media"], product=PRODUCT_PAGE)
+ return parse_csv_records(content.decode("utf-8-sig", errors="replace"), vintage)
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_catalog.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_catalog.py
new file mode 100644
index 00000000000..0af694f26be
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_catalog.py
@@ -0,0 +1,99 @@
+"""USDA ERS Tableau Public visualization catalog."""
+
+import json
+from datetime import datetime, timezone
+from functools import lru_cache
+from pathlib import Path
+
+CATALOG_FILE = Path(__file__).parent.parent / "assets" / "ers_viz_catalog.json"
+PROFILE_API = (
+ "https://public.tableau.com/public/apis/workbooks"
+ "?profileName=economic.research.service&start={start}&count=50"
+ "&visibility=NON_HIDDEN"
+)
+
+
+@lru_cache(maxsize=1)
+def load_catalog() -> tuple[dict, ...]:
+ """Load the packaged ERS visualization catalog.
+
+ Returns
+ -------
+ tuple[dict, ...]
+ Catalog entries with title, workbook, default_view, description,
+ and last_updated keys, sorted by title.
+ """
+ with CATALOG_FILE.open(encoding="utf-8") as file:
+ return tuple(json.load(file))
+
+
+@lru_cache(maxsize=1)
+def viz_paths() -> dict[str, dict]:
+ """Map each catalog entry's 'workbook/default_view' path to its entry."""
+ return {
+ f"{entry['workbook']}/{entry['default_view']}": entry
+ for entry in load_catalog()
+ }
+
+
+async def fetch_catalog() -> list[dict]:
+ """Fetch the current workbook catalog from the Tableau Public profile API.
+
+ Returns
+ -------
+ list[dict]
+ Catalog entries in the packaged asset's schema, sorted by title.
+ """
+ from openbb_core.provider.utils.helpers import amake_request
+
+ entries: list[dict] = []
+ start = 0
+ while True:
+ response = await amake_request(PROFILE_API.format(start=start))
+ workbooks = (
+ response.get("contents", response.get("workbooks", []))
+ if isinstance(response, dict)
+ else response
+ )
+ for workbook in workbooks or []:
+ repo_url = workbook.get("workbookRepoUrl", "")
+ default_view = workbook.get("defaultViewRepoUrl", "").rpartition(
+ "/sheets/"
+ )[2]
+ if not repo_url or not default_view:
+ continue
+ updated_ms = workbook.get("lastUpdateDate") or workbook.get(
+ "lastPublishDate"
+ )
+ entries.append(
+ {
+ "title": workbook.get("title", repo_url),
+ "workbook": repo_url,
+ "default_view": default_view,
+ "description": workbook.get("description")
+ or workbook.get("title", repo_url),
+ "last_updated": datetime.fromtimestamp(
+ updated_ms / 1000, tz=timezone.utc
+ ).strftime("%Y-%m-%d")
+ if updated_ms
+ else None,
+ }
+ )
+ next_index = response.get("nextIndex", -1) if isinstance(response, dict) else -1
+ if next_index is None or int(next_index) < 0:
+ break
+ start = int(next_index)
+ return sorted(entries, key=lambda entry: entry["title"])
+
+
+def main() -> None:
+ """Regenerate the packaged catalog asset from the live profile."""
+ import asyncio
+
+ entries = asyncio.run(fetch_catalog())
+ CATALOG_FILE.write_text(json.dumps(entries, indent=1) + "\n", encoding="utf-8")
+ print(f"Wrote {len(entries)} entries to {CATALOG_FILE}") # noqa: T201
+
+
+if __name__ == "__main__":
+ main()
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_client.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_client.py
new file mode 100644
index 00000000000..b31ec04f073
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_client.py
@@ -0,0 +1,248 @@
+"""USDA ERS file download client with disk caching."""
+
+import os
+import re
+from datetime import datetime, timezone
+from html.parser import HTMLParser
+from typing import Any
+
+CACHE_ENV_VAR = "OPENBB_USDA_CACHE_DIR"
+BASE_URL = "https://www.ers.usda.gov"
+PAGE_TTL = 86400
+DEFAULT_FILE_TTL = 86400
+MIN_FILE_TTL = 3600
+
+ITEM_CLASS = "usa-collection__item"
+HEADING_CLASS = "usa-collection__heading"
+MEDIA_PATTERN = re.compile(r"^(/media/\d+/[^?]+)")
+TIME_KIND_PATTERN = re.compile(r"(Last Updated|Next Update)")
+HEADING_PATTERN = re.compile(
+ r'.+?)\s*\((?P[^)]+)\)$")
+
+MEASURE_NAMES = {
+ "Retail price": "retail_price",
+ "Farm price": "farm_price",
+ "Farm value": "farm_value",
+ "Farm share": "farm_share",
+ "Farm-value share": "farm_share",
+ "Retail cost": "retail_cost_index",
+ "Farm-to-retail spread": "farm_to_retail_spread_index",
+}
+
+
+def build_url(item: str) -> str:
+ """Build the download URL for one item's tidy CSV.
+
+ Parameters
+ ----------
+ item : str
+ Item slug from PRICE_SPREADS_FILES.
+
+ Returns
+ -------
+ str
+ Full URL of the item's CSV file.
+ """
+ return f"{BASE_URL}{media_path(item)}"
+
+
+def media_path(item: str) -> str:
+ """Build the media path for one item's tidy CSV.
+
+ Parameters
+ ----------
+ item : str
+ Item slug from PRICE_SPREADS_FILES.
+
+ Returns
+ -------
+ str
+ The '/media/{id}/{slug}.csv' path of the item's CSV file.
+ """
+ entry = PRICE_SPREADS_FILES[item]
+ return f"/media/{entry['media_id']}/{entry['slug']}.csv"
+
+
+def parse_attribute(attribute: str) -> tuple[str, str]:
+ """Parse a published attribute string into a measure name and unit.
+
+ Parameters
+ ----------
+ attribute : str
+ Attribute string, e.g. 'Retail price (dollars/pound)'.
+
+ Returns
+ -------
+ tuple[str, str]
+ Snake-case measure name and the unit label.
+
+ Raises
+ ------
+ OpenBBError
+ If the attribute format or measure text is unrecognized.
+ """
+ match = ATTRIBUTE_PATTERN.match(attribute.strip())
+ if match is None:
+ raise OpenBBError(f"Unrecognized attribute format: '{attribute}'")
+ measure_text = match["measure"].strip()
+ unit = match["unit"].strip()
+ if unit == "cents/pounds":
+ unit = "cents/pound"
+ measure = MEASURE_NAMES.get(measure_text)
+ if measure is None:
+ raise OpenBBError(
+ f"Unrecognized measure '{measure_text}' in attribute '{attribute}'."
+ + " Expected one of: "
+ + ", ".join(sorted(MEASURE_NAMES))
+ )
+ if measure == "farm_value" and "= 100" in unit:
+ measure = "farm_value_index"
+ return measure, unit
+
+
+def parse_rows(text: str) -> list[dict]:
+ """Parse one tidy CSV's text into raw row records.
+
+ Parameters
+ ----------
+ text : str
+ Decoded CSV text with columns Commodity, Year, Attribute, Value.
+
+ Returns
+ -------
+ list[dict]
+ Records with commodity, year (raw string), attribute, and value keys,
+ skipping rows with an empty Value.
+ """
+ rows: list[dict] = []
+ for row in csv.DictReader(StringIO(text)):
+ value = (row.get("Value") or "").strip()
+ if not value:
+ continue
+ rows.append(
+ {
+ "commodity": (row.get("Commodity") or "").strip(),
+ "year": (row.get("Year") or "").strip(),
+ "attribute": (row.get("Attribute") or "").strip(),
+ "value": float(value),
+ }
+ )
+ return rows
+
+
+async def afetch_item(item: str, **kwargs) -> list[dict]:
+ """Download and parse one item's tidy CSV through the ERS disk cache.
+
+ Parameters
+ ----------
+ item : str
+ Item slug from PRICE_SPREADS_FILES.
+
+ Returns
+ -------
+ list[dict]
+ Raw row records from parse_rows.
+ """
+ from openbb_government_us.usda.utils.ers_client import afetch_ers_file
+
+ content = await afetch_ers_file(media_path(item), product=PRODUCT_PAGE)
+ return parse_rows(content.decode("utf-8-sig"))
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_publications.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_publications.py
new file mode 100644
index 00000000000..11ee6042344
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_publications.py
@@ -0,0 +1,509 @@
+"""USDA ERS publications listing and full-report resolution."""
+
+import re
+from typing import Any
+from urllib.parse import urlencode, urlparse
+
+from openbb_government_us.usda.utils.ers_client import (
+ BASE_URL,
+ HEADING_PATTERN,
+ LINK_PATTERN,
+ get_cache,
+)
+
+API_URL = f"{BASE_URL}/api/publications/v1.0"
+_MISSING = object()
+PUBLICATION_HOST = "www.ers.usda.gov"
+PUBLICATION_PATH = "/publications/"
+MAX_ITEMS_PER_PAGE = 50
+LISTING_TTL = 21600
+REPORT_URL_TTL = 604800
+REPORT_FILE_TTL = 604800
+FULL_REPORT_HEADING = "full report"
+
+SERIES_GROUPS: dict[str, list[str]] = {
+ "outlook-reports": [
+ "AES",
+ "CWS",
+ "FDS",
+ "FTS",
+ "GFA",
+ "LDPM",
+ "OCE",
+ "OCS",
+ "RCS",
+ "SSSM",
+ "TBS",
+ "VGS",
+ "WHS",
+ ],
+ "research-reports": ["AP", "CCR", "EB", "EIB", "ERR", "TB"],
+ "discontinued-reports": [
+ "AER",
+ "AGES",
+ "AH",
+ "AIB",
+ "AIS",
+ "AR",
+ "BIO",
+ "BLA",
+ "EFAN",
+ "FANRR",
+ "FAU",
+ "IUS",
+ "MP",
+ "RA",
+ "RDRR",
+ "SB",
+ "WAOB",
+ "WRS",
+ ],
+}
+
+SERIES_CODES: dict[str, str] = {
+ "AES": "Outlook for U.S. Agricultural Trade",
+ "CWS": "Cotton and Wool Outlook",
+ "FDS": "Feed Outlook",
+ "FTS": "Fruit and Tree Nuts Outlook",
+ "GFA": "Food Security Assessment Situation and Outlook",
+ "LDPM": "Livestock, Dairy, and Poultry Outlook",
+ "OCE": "USDA Agricultural Projections",
+ "OCS": "Oil Crops Outlook",
+ "RCS": "Rice Outlook",
+ "SSSM": "Sugar and Sweeteners Outlook",
+ "TBS": "Tobacco Outlook",
+ "VGS": "Vegetables and Pulses Outlook",
+ "WHS": "Wheat Outlook",
+ "AP": "Administrative Publication",
+ "CCR": "Contractor and Cooperator Reports",
+ "EB": "Economic Brief",
+ "EIB": "Economic Information Bulletin",
+ "ERR": "Economic Research Report",
+ "TB": "Technical Bulletin",
+ "AER": "Agricultural Economic Report",
+ "AGES": "AGES",
+ "AH": "Agricultural Handbook",
+ "AIB": "Agricultural Information Bulletin",
+ "AIS": "Agricultural Income and Finance Outlook",
+ "AR": "Agricultural Resources Situation and Outlook",
+ "BIO": "Bioenergy",
+ "BLA": "Bibliographies and Literature of Agriculture",
+ "EFAN": (
+ "Electronic Publications from the Food Assistance & Nutrition Research Program"
+ ),
+ "FANRR": "Food Assistance & Nutrition Research Program",
+ "FAU": "U.S. Agricultural Trade Update",
+ "IUS": ("Industrial Uses of Agricultural Materials Situation and Outlook Report"),
+ "MP": "Miscellaneous Publication",
+ "RA": "Rural America",
+ "RDRR": "Rural Development Research Report",
+ "SB": "Statistical Bulletin",
+ "WAOB": "WAOB",
+ "WRS": "International Agriculture and Trade Outlook",
+}
+
+DOWNLOAD_BLOCK_PATTERN = re.compile(
+ r'id="download".*?(.*?)',
+ re.DOTALL,
+)
+PUBLICATION_ITEM_PATTERN = re.compile(
+ r']*">.*?',
+ re.DOTALL,
+)
+
+
+def coerce_sentinel(value: Any) -> Any:
+ """Coerce the API's literal int 0 placeholder to None.
+
+ Parameters
+ ----------
+ value : Any
+ A field the API types as a string or object, which it fills with the
+ int 0 instead of null when the field is unset.
+
+ Returns
+ -------
+ Any
+ None when the value is the int 0 sentinel, otherwise the value.
+ """
+ if isinstance(value, bool):
+ return value
+ return None if isinstance(value, int) and value == 0 else value
+
+
+def resolve_series(series: str | list[str]) -> list[str]:
+ """Expand series groups to codes and reject anything unrecognized.
+
+ Parameters
+ ----------
+ series : str | list[str]
+ Group names, series codes, or a comma-separated string of either.
+
+ Returns
+ -------
+ list[str]
+ Deduplicated series codes, in the order first requested.
+
+ Raises
+ ------
+ OpenBBError
+ If a token is neither a known group nor a known series code. The API
+ silently ignores an unknown series and returns the entire corpus, so
+ an unvalidated token would masquerade as a successful query.
+ """
+ from openbb_core.app.model.abstract.error import OpenBBError
+
+ tokens = series.split(",") if isinstance(series, str) else list(series)
+ requested = [token.strip() for token in tokens if token and token.strip()]
+ if not requested:
+ raise OpenBBError(
+ "No publication series requested. Valid groups are: "
+ + ", ".join(sorted(SERIES_GROUPS))
+ )
+ codes: list[str] = []
+ unknown: list[str] = []
+ lookup = {code.casefold(): code for code in SERIES_CODES}
+ for token in requested:
+ group = SERIES_GROUPS.get(token.casefold())
+ if group is not None:
+ codes.extend(group)
+ continue
+ code = lookup.get(token.casefold())
+ if code is None:
+ unknown.append(token)
+ continue
+ codes.append(code)
+ if unknown:
+ raise OpenBBError(
+ f"Invalid publication series: {', '.join(unknown)}."
+ + " Valid groups are: "
+ + ", ".join(sorted(SERIES_GROUPS))
+ + ". Valid series codes are: "
+ + ", ".join(sorted(SERIES_CODES))
+ )
+ return list(dict.fromkeys(codes))
+
+
+def publication_page_url(row: dict) -> str:
+ """Build the canonical publication page URL of a listing row.
+
+ Parameters
+ ----------
+ row : dict
+ A listing row, carrying the site-relative 'url' key.
+
+ Returns
+ -------
+ str
+ The https page URL. The row's own baseUrl is http and redirects, so
+ the canonical https origin is used instead.
+ """
+ return f"{BASE_URL}{row['url']}"
+
+
+def normalize_row(row: dict) -> dict:
+ """Normalize a listing row, coercing every int 0 sentinel to None.
+
+ Parameters
+ ----------
+ row : dict
+ A raw listing row from the API.
+
+ Returns
+ -------
+ dict
+ A record whose optional fields are None rather than the int 0
+ sentinel, with authors coerced per element.
+ """
+ series = coerce_sentinel(row.get("series"))
+ series = series if isinstance(series, dict) else {}
+ authors = [
+ {
+ "id": author.get("id"),
+ "name": author.get("name"),
+ "url": coerce_sentinel(author.get("url")),
+ }
+ for author in row.get("authors") or []
+ ]
+ image = coerce_sentinel(row.get("newsroomImage"))
+ return {
+ "id": row["id"],
+ "title": row["title"],
+ "release_date": row["releaseDate"],
+ "series_code": coerce_sentinel(series.get("code")),
+ "series_name": coerce_sentinel(series.get("name")),
+ "series_full_name": coerce_sentinel(series.get("fullName")),
+ "pub_type": coerce_sentinel(row.get("pubType")),
+ "report_number": coerce_sentinel(row.get("reportNumber")),
+ "url": publication_page_url(row),
+ "authors": authors,
+ "description": coerce_sentinel(row.get("description")),
+ "short_description": coerce_sentinel(row.get("shortDescription")),
+ "image_url": image.get("url") if isinstance(image, dict) else None,
+ "topics": [
+ topic.get("name") for topic in row.get("relatedTopics") or [] if topic
+ ],
+ }
+
+
+def build_listing_url(code: str, page: int = 0) -> str:
+ """Build the listing request URL for one series code and page.
+
+ Parameters
+ ----------
+ code : str
+ A single series code. Codes are paginated one at a time because a
+ multi-code query has no deterministic tiebreak and both duplicates
+ and drops rows across page boundaries.
+ page : int
+ Zero-based page index.
+
+ Returns
+ -------
+ str
+ The listing URL.
+ """
+ return f"{API_URL}?" + urlencode(
+ {
+ "series": code,
+ "items_per_page": MAX_ITEMS_PER_PAGE,
+ "sort_by": "releaseDate",
+ "sort_order": "DESC",
+ "page": page,
+ }
+ )
+
+
+async def _get_listing_page(code: str, page: int) -> dict:
+ """Request one page of one series code's listing."""
+ from openbb_core.app.model.abstract.error import OpenBBError
+ from openbb_core.provider.utils.helpers import amake_request
+
+ response = await amake_request(build_listing_url(code, page))
+ if not isinstance(response, dict) or "rows" not in response:
+ raise OpenBBError(
+ f"Unexpected ERS publications response for series {code} -> {response}"
+ )
+ return response
+
+
+async def fetch_series_rows(code: str) -> list[dict]:
+ """Fetch every listing row of one series code.
+
+ Parameters
+ ----------
+ code : str
+ A single series code.
+
+ Returns
+ -------
+ list[dict]
+ The series' raw rows, deduplicated by id.
+ """
+ first = await _get_listing_page(code, 0)
+ rows: dict[str, dict] = {row["id"]: row for row in first["rows"]}
+ total = int((first.get("pager") or {}).get("total_items") or 0)
+ pages = -(-total // MAX_ITEMS_PER_PAGE)
+ for page in range(1, pages):
+ payload = await _get_listing_page(code, page)
+ for row in payload["rows"]:
+ rows[row["id"]] = row
+ return list(rows.values())
+
+
+async def fetch_publications(
+ series: str | list[str],
+ start_date: Any = None,
+ end_date: Any = None,
+ use_cache: bool = True,
+) -> list[dict]:
+ """Fetch normalized publication rows for the requested series.
+
+ Parameters
+ ----------
+ series : str | list[str]
+ Group names or series codes, validated before any request is made.
+ start_date : Any
+ Earliest release date to keep, as a date or YYYY-MM-DD string.
+ end_date : Any
+ Latest release date to keep, as a date or YYYY-MM-DD string.
+ use_cache : bool
+ Whether to read and write the listing disk cache.
+
+ Returns
+ -------
+ list[dict]
+ Normalized records, newest release first.
+ """
+ import asyncio
+
+ codes = resolve_series(series)
+
+ async def one(code: str) -> list[dict]:
+ key = f"publications:{code}"
+ if use_cache:
+ with get_cache() as cache:
+ cached = cache.get(key)
+ if cached is not None:
+ return cached
+ rows = await fetch_series_rows(code)
+ if use_cache:
+ with get_cache() as cache:
+ cache.set(key, rows, expire=LISTING_TTL)
+ return rows
+
+ fetched = await asyncio.gather(*(one(code) for code in codes))
+ records: dict[str, dict] = {}
+ for rows in fetched:
+ for row in rows:
+ record = normalize_row(row)
+ records[record["id"]] = record
+ results = list(records.values())
+ if start_date is not None:
+ results = [row for row in results if row["release_date"] >= str(start_date)]
+ if end_date is not None:
+ results = [row for row in results if row["release_date"] <= str(end_date)]
+ results.sort(key=lambda row: (row["release_date"], row["id"]), reverse=True)
+ return results
+
+
+def validate_publication_url(url: str) -> str:
+ """Validate that a URL addresses an ERS publication page.
+
+ Parameters
+ ----------
+ url : str
+ The candidate publication page URL.
+
+ Returns
+ -------
+ str
+ The URL, stripped of surrounding whitespace.
+
+ Raises
+ ------
+ OpenBBError
+ If the URL is not an ERS publication page.
+ """
+ from openbb_core.app.model.abstract.error import OpenBBError
+
+ target = url.strip()
+ parsed = urlparse(target)
+ if parsed.netloc != PUBLICATION_HOST or not parsed.path.startswith(
+ PUBLICATION_PATH
+ ):
+ raise OpenBBError(
+ f"Invalid ERS publication URL -> {url}."
+ f" Expected a https://{PUBLICATION_HOST}{PUBLICATION_PATH}"
+ "{id} page URL."
+ )
+ return target
+
+
+def parse_full_report_path(html: str) -> str | None:
+ """Find the Full Report PDF path in a publication page's download block.
+
+ Parameters
+ ----------
+ html : str
+ The publication page's HTML.
+
+ Returns
+ -------
+ str | None
+ The '/media/{id}/{name}.pdf' path of the item headed 'Full Report',
+ or None when the page offers no such PDF. The block also lists
+ summaries, frontmatter, appendices and spreadsheet companions, and a
+ 'Report Summary' PDF can precede the full report, so the heading is
+ matched rather than the first PDF taken.
+ """
+ block = DOWNLOAD_BLOCK_PATTERN.search(html)
+ if block is None:
+ return None
+ for item in PUBLICATION_ITEM_PATTERN.finditer(block.group(1)):
+ text = item.group(0)
+ heading = HEADING_PATTERN.search(text)
+ if heading is None or heading.group(1).strip().casefold() != (
+ FULL_REPORT_HEADING
+ ):
+ continue
+ for path in LINK_PATTERN.findall(text):
+ if path.casefold().endswith(".pdf"):
+ return path
+ return None
+
+
+async def resolve_full_report(page_url: str, use_cache: bool = True) -> str | None:
+ """Resolve a publication page to its Full Report PDF URL.
+
+ Parameters
+ ----------
+ page_url : str
+ An ERS publication page URL.
+ use_cache : bool
+ Whether to read and write the resolution disk cache.
+
+ Returns
+ -------
+ str | None
+ The full https media URL of the Full Report PDF, or None when the
+ page offers no full-report PDF. The listing carries no PDF field and
+ the media id is not derivable from the publication id, so the page
+ must be parsed.
+ """
+ from openbb_government_us.usda.utils import ers_client
+
+ target = validate_publication_url(page_url)
+ key = f"publication_report:{target}"
+ if use_cache:
+ with get_cache() as cache:
+ cached = cache.get(key, default=_MISSING)
+ if cached is not _MISSING:
+ return cached
+ html = (await ers_client._download(target)).decode("utf-8", errors="replace")
+ path = parse_full_report_path(html)
+ report_url = f"{BASE_URL}{path}" if path else None
+ if use_cache:
+ with get_cache() as cache:
+ cache.set(key, report_url, expire=REPORT_URL_TTL)
+ return report_url
+
+
+async def afetch_publication(page_url: str) -> tuple[bytes, str]:
+ """Fetch a publication's Full Report PDF from its page URL.
+
+ Parameters
+ ----------
+ page_url : str
+ An ERS publication page URL.
+
+ Returns
+ -------
+ tuple[bytes, str]
+ The PDF content and its file name.
+
+ Raises
+ ------
+ OpenBBError
+ If the page offers no Full Report PDF, if the resolved media URL is
+ not served by the ERS host, or if the response is not a PDF.
+ """
+ from openbb_core.app.model.abstract.error import OpenBBError
+
+ from openbb_government_us.usda.utils.ers_client import afetch_ers_file
+
+ report_url = await resolve_full_report(page_url)
+ if report_url is None:
+ raise OpenBBError(f"No Full Report PDF is published for {page_url}.")
+ parsed = urlparse(report_url)
+ if parsed.netloc != PUBLICATION_HOST:
+ raise OpenBBError(
+ f"Refusing to download {report_url}, which is not served by"
+ f" {PUBLICATION_HOST}."
+ )
+ content = await afetch_ers_file(parsed.path, ttl=REPORT_FILE_TTL)
+ if not content.startswith(b"%PDF"):
+ raise OpenBBError(
+ f"ERS returned {len(content)} bytes for {report_url} that are not a PDF."
+ )
+ return content, parsed.path.rsplit("/", 1)[-1]
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_purchase_to_plate.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_purchase_to_plate.py
new file mode 100644
index 00000000000..b6de00a6f2f
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_purchase_to_plate.py
@@ -0,0 +1,226 @@
+"""USDA ERS Purchase to Plate national average prices catalog, fetch, and parser."""
+
+import csv
+import io
+import zipfile
+from collections import OrderedDict
+from io import StringIO
+
+BASE_URL = "https://www.ers.usda.gov"
+PRODUCT_PAGE = "data-products/purchase-to-plate"
+
+CATALOG: dict[str, dict] = {
+ "national_average_prices": {
+ "media_path": (
+ "/media/6557/purchase-to-plate-national-average-prices"
+ "-for-nhanes-csv-format.zip"
+ ),
+ "member": "pp_national_average_prices_csv.csv",
+ },
+}
+
+DEFAULT_TABLE = "national_average_prices"
+
+CYCLE_BY_YEAR: "OrderedDict[str, str]" = OrderedDict(
+ [
+ ("2011/2012", "cycle_2011_2012"),
+ ("2013/2014", "cycle_2013_2014"),
+ ("2015/2016", "cycle_2015_2016"),
+ ("2017/2018", "cycle_2017_2018"),
+ ]
+)
+
+CYCLE_ORDER: tuple[str, ...] = tuple(CYCLE_BY_YEAR.values())
+
+CYCLE_HEADERS: "OrderedDict[str, str]" = OrderedDict(
+ [(field, year) for year, field in CYCLE_BY_YEAR.items()]
+)
+
+FOOD_GROUPS: "OrderedDict[str, dict]" = OrderedDict(
+ [
+ ("milk", {"digit": "1", "label": "Milk and milk products"}),
+ ("meat", {"digit": "2", "label": "Meat, poultry, fish, and mixtures"}),
+ ("eggs", {"digit": "3", "label": "Eggs"}),
+ ("legumes", {"digit": "4", "label": "Legumes, nuts, and seeds"}),
+ ("grains", {"digit": "5", "label": "Grain products"}),
+ ("fruits", {"digit": "6", "label": "Fruits"}),
+ ("vegetables", {"digit": "7", "label": "Vegetables"}),
+ ("fats", {"digit": "8", "label": "Fats, oils, and salad dressings"}),
+ ("sugars", {"digit": "9", "label": "Sugars, sweets, and beverages"}),
+ ]
+)
+
+DEFAULT_FOOD_GROUP = "milk"
+
+DIGIT_TO_GROUP: dict[str, str] = {
+ meta["digit"]: slug for slug, meta in FOOD_GROUPS.items()
+}
+
+MOD_NULL_TOKENS = frozenset({"0", "na", "n/a", ""})
+
+VALUE_NULL_TOKENS = frozenset({"na", "n/a", ""})
+
+
+def media_path(table: str) -> str:
+ """Return a table's zip media path.
+
+ Parameters
+ ----------
+ table : str
+ Table key from CATALOG.
+
+ Returns
+ -------
+ str
+ The '/media/{id}/{name}.zip' path of the table's archive.
+ """
+ return CATALOG[table]["media_path"]
+
+
+def build_url(table: str) -> str:
+ """Return a table's full download URL."""
+ return f"{BASE_URL}{media_path(table)}"
+
+
+def group_options() -> list[dict]:
+ """Build the labeled FNDDS food-group options."""
+ return [
+ {"label": meta["label"], "value": slug} for slug, meta in FOOD_GROUPS.items()
+ ]
+
+
+def food_group_slug(food_code: str) -> str | None:
+ """Map an eight-digit FNDDS food code to its food-group slug.
+
+ Parameters
+ ----------
+ food_code : str
+ Eight-digit FNDDS food code; its leading digit names the food group.
+
+ Returns
+ -------
+ str | None
+ Food-group slug, or None when the leading digit is unmapped.
+ """
+ return DIGIT_TO_GROUP.get(food_code[:1])
+
+
+def normalize_mod(mod_code: str | None) -> str:
+ """Normalize a modification code, mapping absent codes to the base '0'.
+
+ Parameters
+ ----------
+ mod_code : str | None
+ Raw modification code; '0' on 2011/2012 rows, 'NA' on later cycles
+ whose sheet lacks the column.
+
+ Returns
+ -------
+ str
+ The base code '0' for unmodified items, else the modification code.
+ """
+ text = (mod_code or "").strip()
+ return "0" if text.casefold() in MOD_NULL_TOKENS else text
+
+
+def clean_token(value: str | None) -> str | None:
+ """Strip a raw cell, mapping empty text and the 'NA' sentinel to None."""
+ text = (value or "").strip()
+ return None if text.casefold() in VALUE_NULL_TOKENS else text
+
+
+def parse_price(value: str | None) -> float | None:
+ """Parse a raw price cell to a float, keeping full precision.
+
+ Parameters
+ ----------
+ value : str | None
+ Raw price_100gm cell, nominal dollars per 100 edible grams, or 'NA'.
+
+ Returns
+ -------
+ float | None
+ The raw price as a float, or None for an 'NA' or empty cell.
+ """
+ text = (value or "").strip()
+ if text.casefold() in VALUE_NULL_TOKENS:
+ return None
+ return float(text)
+
+
+def extract_csv_text(zip_bytes: bytes, member: str) -> str:
+ """Extract and decode one CSV member from a table's zip archive.
+
+ Parameters
+ ----------
+ zip_bytes : bytes
+ Raw bytes of the table's zip archive.
+ member : str
+ Member CSV file name to read.
+
+ Returns
+ -------
+ str
+ The decoded CSV text of the member, tolerating stray cp1252 bytes.
+ """
+ with zipfile.ZipFile(io.BytesIO(zip_bytes)) as archive:
+ raw = archive.read(member)
+ return raw.decode("utf-8-sig", errors="replace")
+
+
+def parse_rows(text: str) -> list[dict]:
+ """Parse the national-average-prices CSV into long, normalized records.
+
+ Parameters
+ ----------
+ text : str
+ Decoded CSV text with columns year, food_code, mod_code,
+ food_description, method, method_description, nhanes, price_100gm.
+
+ Returns
+ -------
+ list[dict]
+ Records with food_code, mod_code, food_group, cycle, cycle_index,
+ food_description, method_description, nhanes, and price keys, one per
+ source row of a recognized survey cycle.
+ """
+ records: list[dict] = []
+ for row in csv.DictReader(StringIO(text)):
+ cycle = CYCLE_BY_YEAR.get((row.get("year") or "").strip())
+ if cycle is None:
+ continue
+ food_code = (row.get("food_code") or "").strip()
+ records.append(
+ {
+ "food_code": food_code,
+ "mod_code": normalize_mod(row.get("mod_code")),
+ "food_group": food_group_slug(food_code),
+ "cycle": cycle,
+ "cycle_index": CYCLE_ORDER.index(cycle),
+ "food_description": (row.get("food_description") or "").strip(),
+ "method_description": clean_token(row.get("method_description")),
+ "nhanes": clean_token(row.get("nhanes")),
+ "price": parse_price(row.get("price_100gm")),
+ }
+ )
+ return records
+
+
+async def afetch_table(table: str = DEFAULT_TABLE) -> list[dict]:
+ """Download and parse a table's CSV through the ERS disk cache.
+
+ Parameters
+ ----------
+ table : str
+ Table key from CATALOG.
+
+ Returns
+ -------
+ list[dict]
+ Long, normalized records from parse_rows.
+ """
+ from openbb_government_us.usda.utils.ers_client import afetch_ers_file
+
+ config = CATALOG[table]
+ content = await afetch_ers_file(config["media_path"], product=PRODUCT_PAGE)
+ return parse_rows(extract_csv_text(content, config["member"]))
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_resource_requirements_of_food_demand.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_resource_requirements_of_food_demand.py
new file mode 100644
index 00000000000..0308f0751e4
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_resource_requirements_of_food_demand.py
@@ -0,0 +1,209 @@
+"""USDA ERS Resource Requirements of Food Demand catalog and long parser."""
+
+import csv
+import io
+import zipfile
+from collections import OrderedDict
+
+PRODUCT_PAGE = "data-products/resource-requirements-of-food-demand"
+MEDIA_PATH = "/media/7066/resource-requirements-of-food-demand.zip"
+CSV_MEMBER = "resource-requirements-food-demand-data.csv"
+
+TABLES: "OrderedDict[str, str]" = OrderedDict(
+ [
+ ("Xf0000", "Food and food-related"),
+ ("Xf1000", "Food"),
+ ("Xf1100", "Food at home"),
+ ("Xf1101", "Cereals"),
+ ("Xf1102", "Bakery products"),
+ ("Xf1103", "Beef"),
+ ("Xf1104", "Pork"),
+ ("Xf1105", "Other meats"),
+ ("Xf1106", "Poultry"),
+ ("Xf1107", "Fish and seafood"),
+ ("Xf1108", "Fresh milk"),
+ ("Xf1109", "Processed dairy products"),
+ ("Xf1110", "Fresh eggs"),
+ ("Xf1111", "Processed eggs"),
+ ("Xf1112", "Fats and oils"),
+ ("Xf1113", "Fresh fruits"),
+ ("Xf1114", "Fresh vegetables"),
+ ("Xf1115", "Canned, frozen, and dried fruits and vegetables"),
+ ("Xf1116", "Sugar and sweets"),
+ ("Xf1117", "Snack foods"),
+ ("Xf1118", "Frozen prepared foods"),
+ ("Xf1119", "Processed fruit and vegetable canning and drying"),
+ ("Xf1120", "Seasonings, sauces, and dressings"),
+ ("Xf1121", "Dry, condensed, and evaporated dairy and non-dairy products"),
+ ("Xf1122", "Tree nuts and peanuts"),
+ ("Xf1123", "Fresh cut produce plus grab-and-go foods"),
+ ("Xf1124", "Miscellaneous foods and ingredients"),
+ ("Xf1125", "Fruit and vegetable juices"),
+ ("Xf1126", "Food consumed on farms"),
+ ("Xf1127", "Coffee, tea, and beverage materials"),
+ ("Xf1128", "Soft drinks and bottled water"),
+ ("Xf1200", "Food away from home"),
+ ("Xf1201", "Food at full-service restaurants"),
+ ("Xf1202", "Food at limited-service restaurants"),
+ ("Xf1203", "Food at other food and drinking places"),
+ ("Xf1204", "Food at schools and colleges"),
+ ("Xf1205", "Food furnished to employees (including military)"),
+ ("Xf1206", "Institutional and employer furnished meals plus food assistance"),
+ (
+ "Xf1207",
+ "Food and non-alcoholic beverages at work (per diem and expensing)",
+ ),
+ ("Xf2000", "Alcohol"),
+ ("Xf2100", "Alcohol at home"),
+ ("Xf2101", "Beer"),
+ ("Xf2102", "Wine"),
+ ("Xf2103", "Spirits"),
+ ("Xf2200", "Alcohol away from home"),
+ ("Xf2201", "Alcohol at full-service restaurants"),
+ ("Xf2202", "Alcohol at limited-service restaurants"),
+ ("Xf2203", "Alcohol at other food and drinking places"),
+ ("Xf3000", "Households"),
+ ]
+)
+
+ENERGY_SOURCE_COLUMNS: dict[str, str] = {
+ "TOTAL": "energy_total",
+ "PA": "energy_pa",
+ "NG": "energy_ng",
+ "ES": "energy_es",
+ "CL": "energy_cl",
+ "LO": "energy_lo",
+ "BF": "energy_bf",
+ "WW": "energy_ww",
+ "WD": "energy_wd",
+ "HY": "energy_hy",
+ "GE": "energy_ge",
+ "SO": "energy_so",
+ "WY": "energy_wy",
+ "CC": "energy_cc",
+ "SF": "energy_sf",
+}
+
+MEASURE_COLUMNS: list[str] = [
+ "employment",
+ "water",
+ "energy_total",
+ "energy_pa",
+ "energy_ng",
+ "energy_es",
+ "energy_cl",
+ "energy_lo",
+ "energy_bf",
+ "energy_ww",
+ "energy_wd",
+ "energy_hy",
+ "energy_ge",
+ "energy_so",
+ "energy_wy",
+ "energy_cc",
+ "energy_sf",
+]
+
+
+def measure_column(resource: str, source_code: str) -> str | None:
+ """Map a resource and source code to a wide measure-column name.
+
+ Parameters
+ ----------
+ resource : str
+ Resource dimension, one of 'Employment', 'Water', or 'Energy'.
+ source_code : str
+ Source code within the resource; 'TOTAL' or a two-letter energy
+ source such as 'PA' or 'NG'.
+
+ Returns
+ -------
+ str | None
+ The wide-column name, or None when the resource and source do not
+ map to a known measure column.
+ """
+ name = (resource or "").strip()
+ code = (source_code or "").strip().upper()
+ if name == "Employment":
+ column = "employment"
+ elif name == "Water":
+ column = "water"
+ elif name == "Energy":
+ column = ENERGY_SOURCE_COLUMNS.get(code)
+ else:
+ column = None
+ return column if column in MEASURE_COLUMNS else None
+
+
+def parse_table(text: str, table: str) -> list[dict]:
+ """Parse the linearized CSV into one table's long-format observations.
+
+ Parameters
+ ----------
+ text : str
+ Decoded text of the resource-requirements data CSV.
+ table : str
+ Table number to keep, e.g. 'Xf0000'.
+
+ Returns
+ -------
+ list[dict]
+ Records carrying the table number, integer year, the supply-chain
+ stage label and ordinal, the wide measure-column name, the numeric
+ value, and source order. Rows outside the table, with an unmapped
+ resource, a blank year, or a non-numeric value are skipped.
+ """
+ records: list[dict] = []
+ for order, row in enumerate(csv.DictReader(io.StringIO(text))):
+ if (row.get("TableNumber") or "").strip() != table:
+ continue
+ column = measure_column(row.get("Resource", ""), row.get("SourceCode", ""))
+ if column is None:
+ continue
+ raw_value = (row.get("Data") or "").strip()
+ try:
+ value = float(raw_value)
+ except ValueError:
+ continue
+ year_raw = (row.get("Year") or "").strip()
+ if not (len(year_raw) == 4 and year_raw.isdigit()):
+ continue
+ try:
+ stage_ord = int((row.get("SupplyChainStageNumber") or "").strip())
+ except ValueError:
+ stage_ord = 0
+ records.append(
+ {
+ "table": table,
+ "year": int(year_raw),
+ "stage": (row.get("SupplyChainStageDescription") or "").strip() or None,
+ "stage_ord": stage_ord,
+ "column": column,
+ "value": value,
+ }
+ )
+ return records
+
+
+async def afetch_table(table: str, **kwargs) -> list[dict]:
+ """Download and parse one table through the ERS disk cache.
+
+ Parameters
+ ----------
+ table : str
+ Table number from TABLES.
+
+ Returns
+ -------
+ list[dict]
+ Long-format records from parse_table.
+ """
+ from openbb_government_us.usda.utils.ers_client import afetch_ers_file
+
+ content = await afetch_ers_file(MEDIA_PATH, product=PRODUCT_PAGE)
+ text = (
+ zipfile.ZipFile(io.BytesIO(content))
+ .read(CSV_MEMBER)
+ .decode("utf-8-sig", errors="replace")
+ )
+ return parse_table(text, table)
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_rice_yearbook.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_rice_yearbook.py
new file mode 100644
index 00000000000..7fb1d685aa7
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_rice_yearbook.py
@@ -0,0 +1,500 @@
+"""USDA ERS Rice Yearbook file catalog, long-format parser, and wide pivots."""
+
+import csv
+from io import StringIO
+
+BASE_URL = "https://www.ers.usda.gov"
+PRODUCT_PAGE = "data-products/rice-yearbook"
+
+MEDIA_ACREAGE = "/media/5670/us-rice-acreage-production-and-yield.csv"
+MEDIA_SUPPLY = "/media/5676/us-supply-disappearance-and-stocks.csv"
+MEDIA_STOCKS = "/media/5678/rice-stocks-rough-and-milled-1983-to-present.csv"
+MEDIA_EXPORTS = (
+ "/media/5672/us-rice-exports-by-type-and-top-10-us-rice-export-markets.csv"
+)
+MEDIA_IMPORTS = "/media/5674/us-rice-imports-by-origin-market-years.csv"
+MEDIA_PRICES = (
+ "/media/5664/us-rough-and-milled-rice-prices-monthly-and-marketing-year.csv"
+)
+MEDIA_PROGRAM = "/media/5666/program-payment-rates.csv"
+MEDIA_WORLD_SUPPLY = (
+ "/media/5680/world-rice-supply-and-utilization-196061-to-present.csv"
+)
+MEDIA_WORLD_TRADE = (
+ "/media/5682/"
+ "world-rice-trade-milled-basis-exports-and-imports-of-selected-countries-or-regions.csv"
+)
+MEDIA_ASIA_PRICES = (
+ "/media/5668/export-prices-for-thailand-vietnam-india-and-pakistan.csv"
+)
+
+ANNUAL = "Annual"
+MONTHLY = "Monthly"
+WEEKLY = "Weekly"
+POINT_IN_TIME = "Point-in-time"
+FREQUENCY_ORDER = (MONTHLY, WEEKLY, POINT_IN_TIME, ANNUAL)
+
+MONTHS = (
+ "JANUARY",
+ "FEBRUARY",
+ "MARCH",
+ "APRIL",
+ "MAY",
+ "JUNE",
+ "JULY",
+ "AUGUST",
+ "SEPTEMBER",
+ "OCTOBER",
+ "NOVEMBER",
+ "DECEMBER",
+)
+MONTH_SET = frozenset(MONTHS)
+CALENDAR_MONTH_ORDER = {month: rank for rank, month in enumerate(MONTHS, start=1)}
+MARKETING_MONTH_ORDER = {
+ month: rank
+ for rank, month in enumerate(
+ (
+ "AUGUST",
+ "SEPTEMBER",
+ "OCTOBER",
+ "NOVEMBER",
+ "DECEMBER",
+ "JANUARY",
+ "FEBRUARY",
+ "MARCH",
+ "APRIL",
+ "MAY",
+ "JUNE",
+ "JULY",
+ ),
+ start=1,
+ )
+}
+
+RICE_TABLES: dict[str, dict] = {
+ "us_area_planted_by_class": {
+ "label": "U.S. rice area planted, by class and State",
+ "media": MEDIA_ACREAGE,
+ "table_number": "1",
+ "layout": "engine1",
+ "pivot_field": "location",
+ "frequencies": (ANNUAL,),
+ },
+ "us_state_yields": {
+ "label": "U.S. rice yield, by State",
+ "media": MEDIA_ACREAGE,
+ "table_number": "2",
+ "layout": "engine1",
+ "pivot_field": "location",
+ "frequencies": (ANNUAL,),
+ },
+ "us_acreage_yield_production": {
+ "label": "U.S. rice acreage, yield, and production",
+ "media": MEDIA_ACREAGE,
+ "table_number": "3",
+ "layout": "engine1",
+ "pivot_field": "series",
+ "frequencies": (ANNUAL,),
+ },
+ "us_production_by_class": {
+ "label": "U.S. rice production, by class and State",
+ "media": MEDIA_ACREAGE,
+ "table_number": "4",
+ "layout": "engine1",
+ "pivot_field": "location",
+ "frequencies": (ANNUAL,),
+ },
+ "us_state_acreage_yield_production_by_class": {
+ "label": "U.S. rice acreage, yield, and production, by class and State",
+ "media": MEDIA_ACREAGE,
+ "table_number": "5",
+ "layout": "engine1",
+ "pivot_field": "series",
+ "frequencies": (ANNUAL,),
+ },
+ "us_production_class_distribution": {
+ "label": "U.S. rice production and class distribution",
+ "media": MEDIA_ACREAGE,
+ "table_number": "6",
+ "layout": "engine1",
+ "pivot_field": "rice_class",
+ "frequencies": (ANNUAL,),
+ },
+ "us_supply_use_by_class": {
+ "label": "U.S. rice supply and use, by class and region",
+ "media": MEDIA_SUPPLY,
+ "table_number": "7",
+ "layout": "engine2",
+ "pivot_field": "series",
+ "frequencies": (ANNUAL,),
+ "default_scope": {"rice_class": "ALL CLASSES", "location": "U.S. TOTAL"},
+ "scope_fields": ("rice_class", "location"),
+ },
+ "us_supply_disappearance_price": {
+ "label": "U.S. rice supply, disappearance, and price",
+ "media": MEDIA_SUPPLY,
+ "table_number": "8",
+ "layout": "engine2",
+ "pivot_field": "series",
+ "frequencies": (ANNUAL,),
+ },
+ "long_grain_supply_disappearance_price": {
+ "label": "U.S. long-grain rice supply, disappearance, and price",
+ "media": MEDIA_SUPPLY,
+ "table_number": "9",
+ "layout": "engine2",
+ "pivot_field": "series",
+ "frequencies": (ANNUAL,),
+ },
+ "medium_short_grain_supply_disappearance_price": {
+ "label": "U.S. medium- and short-grain rice supply, disappearance, and price",
+ "media": MEDIA_SUPPLY,
+ "table_number": "10",
+ "layout": "engine2",
+ "pivot_field": "series",
+ "frequencies": (ANNUAL,),
+ "default_scope": {"location": "U.S. TOTAL"},
+ "scope_fields": ("location",),
+ },
+ "us_stocks_rough_and_milled": {
+ "label": "U.S. rice stocks, rough and milled",
+ "media": MEDIA_STOCKS,
+ "table_number": "11",
+ "layout": "engine1",
+ "pivot_field": "series",
+ "frequencies": (POINT_IN_TIME,),
+ },
+ "us_exports_by_type": {
+ "label": "U.S. rice exports, by type",
+ "media": MEDIA_EXPORTS,
+ "table_number": "12",
+ "layout": "engine2",
+ "pivot_field": "rice_class",
+ "frequencies": (ANNUAL,),
+ },
+ "us_top_export_markets": {
+ "label": "Top 10 U.S. rice export markets",
+ "media": MEDIA_EXPORTS,
+ "table_number": "13",
+ "layout": "engine1",
+ "pivot_field": "series",
+ "frequencies": (ANNUAL,),
+ "chrono_first": True,
+ },
+ "us_imports_by_origin": {
+ "label": "U.S. rice imports, by origin",
+ "media": MEDIA_IMPORTS,
+ "table_number": "14",
+ "layout": "engine2",
+ "pivot_field": "location",
+ "frequencies": (ANNUAL,),
+ },
+ "us_rough_price_by_month": {
+ "label": "U.S. rough rice prices received, by month and State",
+ "media": MEDIA_PRICES,
+ "table_number": "15",
+ "layout": "engine1",
+ "pivot_field": "series",
+ "frequencies": (MONTHLY, ANNUAL),
+ },
+ "us_rough_price_by_month_and_class": {
+ "label": "U.S. rough rice prices received, by month and class",
+ "media": MEDIA_PRICES,
+ "table_number": "16",
+ "layout": "engine1",
+ "pivot_field": "rice_class",
+ "frequencies": (MONTHLY, ANNUAL),
+ },
+ "us_milled_price_milling_centers": {
+ "label": "U.S. milled rice prices at milling centers",
+ "media": MEDIA_PRICES,
+ "table_number": "17",
+ "layout": "engine1",
+ "pivot_field": "location",
+ "frequencies": (MONTHLY, ANNUAL),
+ },
+ "us_byproduct_prices": {
+ "label": "U.S. rice byproduct prices",
+ "media": MEDIA_PRICES,
+ "table_number": "18",
+ "layout": "engine1",
+ "pivot_field": "rice_class",
+ "frequencies": (MONTHLY, ANNUAL),
+ },
+ "us_brewers_rice_prices": {
+ "label": "U.S. brewers' rice prices",
+ "media": MEDIA_PRICES,
+ "table_number": "19",
+ "layout": "engine1",
+ "pivot_field": "series",
+ "frequencies": (MONTHLY, ANNUAL),
+ },
+ "us_prices_and_payment_rates": {
+ "label": "U.S. rice prices and program payment rates",
+ "media": MEDIA_PROGRAM,
+ "table_number": "20",
+ "layout": "engine1",
+ "pivot_field": "series",
+ "frequencies": (ANNUAL,),
+ },
+ "us_class_loan_values": {
+ "label": "U.S. rice loan rates, by class",
+ "media": MEDIA_PROGRAM,
+ "table_number": "21",
+ "layout": "engine1",
+ "pivot_field": "rice_class",
+ "frequencies": (ANNUAL,),
+ "default_scope": {"statistic": "LOAN RATE"},
+ "scope_fields": ("statistic",),
+ },
+ "world_market_prices_loan_basis": {
+ "label": "World rice market prices, loan basis",
+ "media": MEDIA_PROGRAM,
+ "table_number": "22",
+ "layout": "engine1",
+ "pivot_field": "rice_class",
+ "frequencies": (WEEKLY,),
+ "default_scope": {"statistic": "MILLED-KERNEL RATE"},
+ "scope_fields": ("statistic",),
+ },
+ "world_supply_and_utilization": {
+ "label": "World rice supply and utilization",
+ "media": MEDIA_WORLD_SUPPLY,
+ "table_number": "23",
+ "layout": "engine2",
+ "pivot_field": "series",
+ "frequencies": (ANNUAL,),
+ },
+ "world_trade_milled_basis": {
+ "label": "World rice trade, milled basis",
+ "media": MEDIA_WORLD_TRADE,
+ "table_number": "24",
+ "layout": "engine2",
+ "pivot_field": "location",
+ "frequencies": (ANNUAL,),
+ "default_scope": {"statistic": "IMPORTS"},
+ "scope_fields": ("statistic",),
+ },
+ "thailand_export_prices": {
+ "label": "Thailand rice export prices",
+ "media": MEDIA_ASIA_PRICES,
+ "table_number": "25",
+ "layout": "engine1",
+ "pivot_field": "rice_class",
+ "frequencies": (MONTHLY, ANNUAL),
+ },
+ "vietnam_export_prices": {
+ "label": "Vietnam rice export prices",
+ "media": MEDIA_ASIA_PRICES,
+ "table_number": "26",
+ "layout": "engine1",
+ "pivot_field": "rice_class",
+ "frequencies": (MONTHLY, ANNUAL),
+ },
+ "india_export_prices": {
+ "label": "India rice export prices",
+ "media": MEDIA_ASIA_PRICES,
+ "table_number": "27",
+ "layout": "engine1",
+ "pivot_field": "rice_class",
+ "frequencies": (MONTHLY, ANNUAL),
+ },
+ "pakistan_export_prices": {
+ "label": "Pakistan rice export prices",
+ "media": MEDIA_ASIA_PRICES,
+ "table_number": "28",
+ "layout": "engine1",
+ "pivot_field": "rice_class",
+ "frequencies": (MONTHLY, ANNUAL),
+ },
+}
+
+NULL_TOKENS = frozenset({"", "-", "--", "n/a", "na", "null", "none", "nan"})
+
+
+def clean_token(value: str | None) -> str | None:
+ """Strip a cell and coerce blank or placeholder tokens to None.
+
+ Parameters
+ ----------
+ value : str | None
+ Raw cell value as read from the CSV.
+
+ Returns
+ -------
+ str | None
+ The stripped text, or None when blank or a placeholder such as 'NA'.
+ """
+ text = (value or "").strip()
+ if not text or text.casefold() in NULL_TOKENS:
+ return None
+ return text
+
+
+def classify_frequency(period: str | None) -> str:
+ """Classify a reference-period token into its observation frequency.
+
+ Parameters
+ ----------
+ period : str | None
+ Reference-period token as published.
+
+ Returns
+ -------
+ str
+ One of Monthly, Annual, Point-in-time, or Weekly.
+ """
+ if period is None:
+ return ANNUAL
+ upper = period.upper()
+ if upper in MONTH_SET:
+ return MONTHLY
+ if (
+ upper.startswith("CROP YEAR")
+ or upper.startswith("MARKETING YEAR")
+ or upper in ("CALENDAR YEAR", "MARKETING")
+ ):
+ return ANNUAL
+ parts = period.split()
+ if len(parts) == 2 and parts[0].upper() in MONTH_SET and parts[1].isdigit():
+ return POINT_IN_TIME
+ return WEEKLY
+
+
+def display_period(period: str | None) -> str | None:
+ """Return the period token for display, title-casing bare month names.
+
+ Parameters
+ ----------
+ period : str | None
+ Reference-period token as published.
+
+ Returns
+ -------
+ str | None
+ The token with bare month names title-cased; other tokens unchanged.
+ """
+ if period is None:
+ return None
+ if period.upper() in MONTH_SET:
+ return period.title()
+ return period
+
+
+def _weekly_sort_key(display: str) -> int:
+ """Return the calendar month-and-day rank of a weekly period token."""
+ month = day = 0
+ for token in display.split(" - ", maxsplit=1)[0].replace("-", " ").split():
+ if token.isdigit():
+ day = int(token)
+ continue
+ name = token.upper()
+ if len(name) < 3:
+ continue
+ month = next(
+ (
+ rank
+ for label, rank in CALENDAR_MONTH_ORDER.items()
+ if label.startswith(name)
+ ),
+ month,
+ )
+ return month * 100 + day
+
+
+def _period_sort_key(display: str | None, frequency: str) -> int:
+ """Return the within-year rank of a sub-annual period, else zero."""
+ if display is None:
+ return 0
+ if frequency == WEEKLY:
+ return _weekly_sort_key(display)
+ if frequency in (MONTHLY, POINT_IN_TIME):
+ return MARKETING_MONTH_ORDER.get(display.split()[0].upper(), 0)
+ return 0
+
+
+async def table_frequencies(table: str, **kwargs) -> list[str]:
+ """List the frequencies present in a table's data, annual first.
+
+ Parameters
+ ----------
+ table : str
+ Table key from RICE_TABLES.
+
+ Returns
+ -------
+ list[str]
+ Distinct frequencies present, ordered Annual, Point-in-time, Weekly,
+ Monthly.
+ """
+ records = await afetch_table(table)
+ present = {classify_frequency(record["period"]) for record in records}
+ return [label for label in reversed(FREQUENCY_ORDER) if label in present]
+
+
+def parse_table(text: str, table: str) -> list[dict]:
+ """Parse one numbered table's rows from its parent CSV into long records.
+
+ Parameters
+ ----------
+ text : str
+ Decoded CSV text of the parent media file.
+ table : str
+ Table key from RICE_TABLES.
+
+ Returns
+ -------
+ list[dict]
+ Records carrying the table key, integer year, period, the row-dimension
+ fields, the statistic name, its unit, and the numeric value. Rows
+ outside the table number, or whose value is blank or non-numeric, or
+ whose year is not a four-digit number, are skipped.
+ """
+ table_number = RICE_TABLES[table]["table_number"]
+ records: list[dict] = []
+ for row in csv.DictReader(StringIO(text)):
+ if (row.get("TABLE_NUMBER") or "").strip() != table_number:
+ continue
+ raw_value = (row.get("VALUE") or "").strip()
+ try:
+ value = float(raw_value)
+ except ValueError:
+ continue
+ year_label = (row.get("YEAR") or "").strip()
+ if not year_label[:4].isdigit():
+ continue
+ records.append(
+ {
+ "table": table,
+ "year": int(year_label[:4]),
+ "period": clean_token(row.get("REFERENCE_PERIOD_DESCRIPTION")),
+ "rice_class": clean_token(row.get("CLASS_DESCRIPTION")),
+ "location": clean_token(row.get("LOCATION_DESCRIPTION")),
+ "rank": clean_token(row.get("RANK")),
+ "aggregate_level": clean_token(row.get("AGGREGATE_LEVEL_DESCRIPTION")),
+ "series": (row.get("STATISTIC_DESCRIPTION") or "").strip(),
+ "unit": clean_token(row.get("UNIT_DESCRIPTION")),
+ "value": value,
+ }
+ )
+ return records
+
+
+async def afetch_table(table: str, **kwargs) -> list[dict]:
+ """Download and parse one rice-yearbook table through the ERS disk cache.
+
+ Parameters
+ ----------
+ table : str
+ Table key from RICE_TABLES.
+
+ Returns
+ -------
+ list[dict]
+ Long-format records from parse_table.
+ """
+ from openbb_government_us.usda.utils.ers_client import afetch_ers_file
+
+ config = RICE_TABLES[table]
+ content = await afetch_ers_file(config["media"], product=PRODUCT_PAGE)
+ return parse_table(content.decode("utf-8-sig", errors="replace"), table)
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_rural_urban_commuting_area_codes.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_rural_urban_commuting_area_codes.py
new file mode 100644
index 00000000000..14d3d21e101
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_rural_urban_commuting_area_codes.py
@@ -0,0 +1,593 @@
+"""USDA ERS Rural-Urban Commuting Area Codes catalog, fetch, and parsers."""
+
+import csv
+import io
+from io import StringIO
+
+BASE_URL = "https://www.ers.usda.gov"
+PRODUCT_PAGE = "data-products/rural-urban-commuting-area-codes"
+
+TABLE_CATALOG: dict[str, tuple[str, str]] = {
+ "tract_2020": (
+ "/media/5443/2020-rural-urban-commuting-area-codes-census-tracts.csv",
+ "csv",
+ ),
+ "tract_2010": (
+ "/media/5438/2010-rural-urban-commuting-area-codes-revised-732019.xlsx",
+ "xlsx",
+ ),
+ "tract_2000": (
+ "/media/5437/2000-rural-urban-commuting-area-codes.xls",
+ "xls",
+ ),
+ "tract_1990": (
+ "/media/5436/1990-rural-urban-commuting-area-codes.xls",
+ "xls",
+ ),
+ "zip_2020": (
+ "/media/5444/2020-rural-urban-commuting-area-codes-zip-codes.csv",
+ "csv",
+ ),
+ "zip_2010": (
+ "/media/5440/2010-rural-urban-commuting-area-codes-zip-code-file.csv",
+ "csv",
+ ),
+}
+
+TABLE_LABELS: dict[str, str] = {
+ "tract_2020": "Census tracts, 2020",
+ "tract_2010": "Census tracts, 2010",
+ "tract_2000": "Census tracts, 2000",
+ "tract_1990": "Census tracts, 1990",
+ "zip_2020": "ZIP codes, 2020",
+ "zip_2010": "ZIP codes, 2010",
+}
+
+DEFAULT_TABLE = "tract_2020"
+DEFAULT_STATE = "DE"
+
+PRIMARY_DESCRIPTIONS: dict[str, str] = {
+ "1": "Metropolitan core",
+ "2": "Metropolitan high commuting",
+ "3": "Metropolitan low commuting",
+ "4": "Micropolitan core",
+ "5": "Micropolitan high commuting",
+ "6": "Micropolitan low commuting",
+ "7": "Small town core",
+ "8": "Small town high commuting",
+ "9": "Small town low commuting",
+ "10": "Rural area",
+ "99": "Not coded",
+}
+
+SECONDARY_DESCRIPTIONS: dict[str, str] = {
+ "1": "Metropolitan core, no addtional code",
+ "1.1": "Metropolitan core, secondary flow to larger UA",
+ "2": "Metropolitan high commuting, no additional code",
+ "2.1": "Metropolitan high commuting, seconary flow to larger UA",
+ "3": "Metropolitan low commuting, no additional code",
+ "4": "Micropolitan core, no additional code",
+ "4.1": "Micropolitan core, secondary flow to metro UA",
+ "5": "Micropolitan high commuting, no additional code",
+ "5.1": "Micropolitan high commuting, seconary flow to metro UA",
+ "6": "Micropolitan low commuting, no additional code",
+ "7": "Small town core, no additional code",
+ "7.1": "Small town core, secondary flow to metro UA",
+ "7.2": "Small town core, secondary flow to micro UA",
+ "8": "Small town high commuting, no additional code",
+ "8.1": "Small town high commuting, seconary flow to metro UA",
+ "8.2": "Small town high commuting, secondary flow to micro UA",
+ "9": "Small town low commuting, no additional code",
+ "10": "Rural area, no additional code",
+ "10.1": "Rural area, seconary flow to metro UA",
+ "10.2": "Rural area, secondary flow to micro UA",
+ "10.3": "Rural area, secondary flow to small town UA",
+ "99": "Not coded",
+}
+
+FIPS_TO_STATE: dict[str, str] = {
+ "01": "AL",
+ "02": "AK",
+ "04": "AZ",
+ "05": "AR",
+ "06": "CA",
+ "08": "CO",
+ "09": "CT",
+ "10": "DE",
+ "11": "DC",
+ "12": "FL",
+ "13": "GA",
+ "15": "HI",
+ "16": "ID",
+ "17": "IL",
+ "18": "IN",
+ "19": "IA",
+ "20": "KS",
+ "21": "KY",
+ "22": "LA",
+ "23": "ME",
+ "24": "MD",
+ "25": "MA",
+ "26": "MI",
+ "27": "MN",
+ "28": "MS",
+ "29": "MO",
+ "30": "MT",
+ "31": "NE",
+ "32": "NV",
+ "33": "NH",
+ "34": "NJ",
+ "35": "NM",
+ "36": "NY",
+ "37": "NC",
+ "38": "ND",
+ "39": "OH",
+ "40": "OK",
+ "41": "OR",
+ "42": "PA",
+ "44": "RI",
+ "45": "SC",
+ "46": "SD",
+ "47": "TN",
+ "48": "TX",
+ "49": "UT",
+ "50": "VT",
+ "51": "VA",
+ "53": "WA",
+ "54": "WV",
+ "55": "WI",
+ "56": "WY",
+ "60": "AS",
+ "66": "GU",
+ "69": "MP",
+ "72": "PR",
+ "78": "VI",
+}
+
+STATE_NAMES: dict[str, str] = {
+ "AL": "Alabama",
+ "AK": "Alaska",
+ "AZ": "Arizona",
+ "AR": "Arkansas",
+ "CA": "California",
+ "CO": "Colorado",
+ "CT": "Connecticut",
+ "DE": "Delaware",
+ "DC": "District of Columbia",
+ "FL": "Florida",
+ "GA": "Georgia",
+ "HI": "Hawaii",
+ "ID": "Idaho",
+ "IL": "Illinois",
+ "IN": "Indiana",
+ "IA": "Iowa",
+ "KS": "Kansas",
+ "KY": "Kentucky",
+ "LA": "Louisiana",
+ "ME": "Maine",
+ "MD": "Maryland",
+ "MA": "Massachusetts",
+ "MI": "Michigan",
+ "MN": "Minnesota",
+ "MS": "Mississippi",
+ "MO": "Missouri",
+ "MT": "Montana",
+ "NE": "Nebraska",
+ "NV": "Nevada",
+ "NH": "New Hampshire",
+ "NJ": "New Jersey",
+ "NM": "New Mexico",
+ "NY": "New York",
+ "NC": "North Carolina",
+ "ND": "North Dakota",
+ "OH": "Ohio",
+ "OK": "Oklahoma",
+ "OR": "Oregon",
+ "PA": "Pennsylvania",
+ "RI": "Rhode Island",
+ "SC": "South Carolina",
+ "SD": "South Dakota",
+ "TN": "Tennessee",
+ "TX": "Texas",
+ "UT": "Utah",
+ "VT": "Vermont",
+ "VA": "Virginia",
+ "WA": "Washington",
+ "WV": "West Virginia",
+ "WI": "Wisconsin",
+ "WY": "Wyoming",
+ "AS": "American Samoa",
+ "GU": "Guam",
+ "MP": "Northern Mariana Islands",
+ "PR": "Puerto Rico",
+ "VI": "U.S. Virgin Islands",
+}
+
+_STATES_50_DC: tuple[str, ...] = tuple(
+ sorted(code for code in STATE_NAMES if code not in {"AS", "GU", "MP", "PR", "VI"})
+)
+_STATES_50_DC_PR: tuple[str, ...] = tuple(sorted((*_STATES_50_DC, "PR")))
+_STATES_ALL: tuple[str, ...] = tuple(sorted(STATE_NAMES))
+
+TABLE_STATES: dict[str, tuple[str, ...]] = {
+ "tract_2020": _STATES_ALL,
+ "zip_2020": _STATES_ALL,
+ "tract_2010": _STATES_50_DC_PR,
+ "zip_2010": _STATES_50_DC_PR,
+ "tract_2000": _STATES_50_DC,
+ "tract_1990": _STATES_50_DC,
+}
+
+
+def normalize_code(value) -> str | None:
+ """Normalize a RUCA code to its canonical string form.
+
+ Parameters
+ ----------
+ value : Any
+ A code as a string, integer, or float across the source vintages,
+ e.g. '1', 1, 2.0, '2.1', or '99.0'.
+
+ Returns
+ -------
+ str | None
+ The code with any trailing '.0' dropped, e.g. '2', '2.1', '10', or
+ '99'; None for a blank or non-numeric value.
+ """
+ if value is None:
+ return None
+ if isinstance(value, str):
+ text = value.strip()
+ if not text:
+ return None
+ try:
+ number = float(text)
+ except ValueError:
+ return text
+ else:
+ try:
+ number = float(value)
+ except (TypeError, ValueError):
+ return None
+ if number.is_integer():
+ return str(int(number))
+ return str(number)
+
+
+def primary_description(code: str | None) -> str | None:
+ """Return the canonical primary RUCA label for a normalized code."""
+ if code is None:
+ return None
+ return PRIMARY_DESCRIPTIONS.get(code)
+
+
+def secondary_description(code: str | None) -> str | None:
+ """Return the secondary RUCA label for a normalized code.
+
+ Parameters
+ ----------
+ code : str | None
+ A normalized secondary code, e.g. '2.1' or '5.2'.
+
+ Returns
+ -------
+ str | None
+ The 2020 secondary label when the exact code is published, otherwise
+ the primary label of the code's integer part so granular sub-codes of
+ the older vintages still carry a description.
+ """
+ if code is None:
+ return None
+ if code in SECONDARY_DESCRIPTIONS:
+ return SECONDARY_DESCRIPTIONS[code]
+ return PRIMARY_DESCRIPTIONS.get(code.split(".")[0])
+
+
+def allowed_states(table: str) -> tuple[str, ...]:
+ """Return the two-letter state codes a table publishes, led alphabetically."""
+ return TABLE_STATES.get(table, _STATES_ALL)
+
+
+def state_options(table: str) -> list[dict]:
+ """Build the labeled state options a table publishes for the selector."""
+ return [
+ {"label": STATE_NAMES[code], "value": code} for code in allowed_states(table)
+ ]
+
+
+def _to_int(value) -> int | None:
+ """Parse a population count to an int, keeping the exact value."""
+ if value is None:
+ return None
+ if isinstance(value, str):
+ text = value.strip()
+ if not text:
+ return None
+ try:
+ return int(float(text))
+ except ValueError:
+ return None
+ try:
+ return int(value)
+ except (TypeError, ValueError):
+ return None
+
+
+def _to_float(value) -> float | None:
+ """Parse a measure to a float, keeping full precision."""
+ if value is None:
+ return None
+ if isinstance(value, str):
+ text = value.strip()
+ if not text:
+ return None
+ try:
+ return float(text)
+ except ValueError:
+ return None
+ try:
+ return float(value)
+ except (TypeError, ValueError):
+ return None
+
+
+def _fips_str(value, width: int) -> str | None:
+ """Return a zero-padded FIPS or ZIP string, preserving leading zeros.
+
+ Parameters
+ ----------
+ value : Any
+ The raw identifier as text or a number.
+ width : int
+ The fixed identifier width, 11 for a census tract or 5 for a ZIP.
+
+ Returns
+ -------
+ str | None
+ The identifier as a fixed-width digit string, or the stripped text
+ when it is not purely numeric; None when blank.
+ """
+ if value is None:
+ return None
+ if isinstance(value, str):
+ text = value.strip()
+ else:
+ number = _to_int(value)
+ text = "" if number is None else str(number)
+ if not text:
+ return None
+ if text.isdigit():
+ return text.zfill(width)
+ return text
+
+
+def _mapped_record(
+ area_fips: str | None,
+ state: str | None,
+ primary: str | None,
+ secondary: str | None,
+ *,
+ county: str | None = None,
+ area_name: str | None = None,
+ area_type: str | None = None,
+ population: int | None = None,
+ land_area: float | None = None,
+ population_density: float | None = None,
+ primary_desc: str | None = None,
+ secondary_desc: str | None = None,
+) -> dict:
+ """Assemble one unified lookup record with canonical descriptions."""
+ return {
+ "area_fips": area_fips,
+ "state": state,
+ "area_name": area_name,
+ "county": county,
+ "area_type": area_type,
+ "primary_ruca": primary,
+ "primary_ruca_description": (
+ primary_desc if primary_desc else primary_description(primary)
+ ),
+ "secondary_ruca": secondary,
+ "secondary_ruca_description": (
+ secondary_desc if secondary_desc else secondary_description(secondary)
+ ),
+ "population": population,
+ "land_area": land_area,
+ "population_density": population_density,
+ }
+
+
+def parse_tract_2020(text: str, state: str | None) -> list[dict]:
+ """Parse the 2020 census-tract CSV into unified lookup records."""
+ records: list[dict] = []
+ for row in csv.DictReader(StringIO(text)):
+ code = (row.get("StateFIPS20") or "").strip()
+ row_state = FIPS_TO_STATE.get(code)
+ if state is not None and row_state != state:
+ continue
+ records.append(
+ _mapped_record(
+ _fips_str(row.get("TractFIPS20"), 11),
+ row_state,
+ normalize_code(row.get("PrimaryRUCA")),
+ normalize_code(row.get("SecondaryRUCA")),
+ county=(row.get("CountyName20") or "").strip() or None,
+ area_name=(row.get("TractName20") or "").strip() or None,
+ population=_to_int(row.get("Population")),
+ land_area=_to_float(row.get("LandArea")),
+ population_density=_to_float(row.get("PopDensity")),
+ primary_desc=(row.get("PrimaryRUCADescription") or "").strip() or None,
+ secondary_desc=(row.get("SecondaryRUCADescription") or "").strip()
+ or None,
+ )
+ )
+ return records
+
+
+def parse_zip_2020(text: str, state: str | None) -> list[dict]:
+ """Parse the 2020 ZIP-code CSV into unified lookup records."""
+ records: list[dict] = []
+ for row in csv.DictReader(StringIO(text)):
+ row_state = (row.get("State") or "").strip().upper() or None
+ if state is not None and row_state != state:
+ continue
+ records.append(
+ _mapped_record(
+ _fips_str(row.get("ZIPCode"), 5),
+ row_state,
+ normalize_code(row.get("PrimaryRUCA")),
+ normalize_code(row.get("SecondaryRUCA")),
+ area_name=(row.get("POName") or "").strip() or None,
+ area_type=(row.get("ZIPCodeType") or "").strip() or None,
+ )
+ )
+ return records
+
+
+def parse_zip_2010(text: str, state: str | None) -> list[dict]:
+ """Parse the 2010 ZIP-code CSV into unified lookup records."""
+ records: list[dict] = []
+ for row in csv.DictReader(StringIO(text)):
+ row_state = (row.get("STATE") or "").strip().upper() or None
+ if state is not None and row_state != state:
+ continue
+ raw_zip = (row.get("''ZIP_CODE''") or "").strip().strip("'")
+ records.append(
+ _mapped_record(
+ _fips_str(raw_zip, 5),
+ row_state,
+ normalize_code(row.get("RUCA1")),
+ normalize_code(row.get("RUCA2")),
+ area_type=(row.get("ZIP_TYPE") or "").strip() or None,
+ )
+ )
+ return records
+
+
+def _xlsx_data_rows(content: bytes) -> list[tuple]:
+ """Read the 'Data' worksheet of an XLSX workbook into value rows."""
+ import openpyxl
+
+ workbook = openpyxl.load_workbook(
+ io.BytesIO(content), data_only=True, read_only=True
+ )
+ rows = list(workbook["Data"].iter_rows(values_only=True))
+ workbook.close()
+ return rows
+
+
+def _xls_data_rows(content: bytes) -> list[list]:
+ """Read the 'Data' worksheet of a legacy XLS workbook into value rows."""
+ import xlrd
+
+ sheet = xlrd.open_workbook(file_contents=content).sheet_by_name("Data")
+ return [
+ [sheet.cell_value(r, c) for c in range(sheet.ncols)] for r in range(sheet.nrows)
+ ]
+
+
+def parse_tract_2010(content: bytes, state: str | None) -> list[dict]:
+ """Parse the 2010 census-tract XLSX into unified lookup records."""
+ records: list[dict] = []
+ for row in _xlsx_data_rows(content)[2:]:
+ row_state = (str(row[1]).strip().upper() or None) if row[1] else None
+ if state is not None and row_state != state:
+ continue
+ records.append(
+ _mapped_record(
+ _fips_str(row[3], 11),
+ row_state,
+ normalize_code(row[4]),
+ normalize_code(row[5]),
+ county=(str(row[2]).strip() or None) if row[2] else None,
+ population=_to_int(row[6]),
+ land_area=_to_float(row[7]),
+ population_density=_to_float(row[8]),
+ )
+ )
+ return records
+
+
+def parse_tract_2000(content: bytes, state: str | None) -> list[dict]:
+ """Parse the 2000 census-tract XLS into unified lookup records."""
+ records: list[dict] = []
+ for row in _xls_data_rows(content)[1:]:
+ row_state = (str(row[1]).strip().upper() or None) if row[1] else None
+ if state is not None and row_state != state:
+ continue
+ records.append(
+ _mapped_record(
+ _fips_str(row[3], 11),
+ row_state,
+ normalize_code(row[4]),
+ normalize_code(row[5]),
+ county=(str(row[2]).strip() or None) if row[2] else None,
+ population=_to_int(row[6]),
+ )
+ )
+ return records
+
+
+def parse_tract_1990(content: bytes, state: str | None) -> list[dict]:
+ """Parse the 1990 census-tract XLS into unified lookup records.
+
+ The 1990 file carries a single combined code, so the integer part becomes
+ the primary code and the full code becomes the secondary code, and the
+ FIPS is reformatted from '010010201.00' to the 11-digit '01001020100'.
+ """
+ records: list[dict] = []
+ for row in _xls_data_rows(content)[2:]:
+ area_fips = _fips_str(str(row[0]).replace(".", ""), 11)
+ row_state = FIPS_TO_STATE.get(area_fips[:2]) if area_fips else None
+ if state is not None and row_state != state:
+ continue
+ secondary = normalize_code(row[1])
+ primary = secondary.split(".")[0] if secondary else None
+ records.append(
+ _mapped_record(
+ area_fips,
+ row_state,
+ primary,
+ secondary,
+ population=_to_int(row[2]),
+ land_area=_to_float(row[3]),
+ )
+ )
+ return records
+
+
+CSV_PARSERS = {
+ "tract_2020": parse_tract_2020,
+ "zip_2020": parse_zip_2020,
+ "zip_2010": parse_zip_2010,
+}
+
+BINARY_PARSERS = {
+ "tract_2010": parse_tract_2010,
+ "tract_2000": parse_tract_2000,
+ "tract_1990": parse_tract_1990,
+}
+
+
+async def afetch_table(table: str, state: str | None) -> list[dict]:
+ """Download one vintage file and parse it into unified lookup records.
+
+ Parameters
+ ----------
+ table : str
+ Table key from TABLE_CATALOG.
+ state : str | None
+ Two-letter state code to keep, or None for every area.
+
+ Returns
+ -------
+ list[dict]
+ Unified lookup records for the selected table and state filter.
+ """
+ from openbb_government_us.usda.utils.ers_client import afetch_ers_file
+
+ media_path, kind = TABLE_CATALOG[table]
+ content = await afetch_ers_file(media_path, product=PRODUCT_PAGE)
+ if kind == "csv":
+ return CSV_PARSERS[table](content.decode("utf-8-sig", errors="replace"), state)
+ return BINARY_PARSERS[table](content, state)
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_rural_urban_continuum_codes.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_rural_urban_continuum_codes.py
new file mode 100644
index 00000000000..1ad3bdb067e
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_rural_urban_continuum_codes.py
@@ -0,0 +1,345 @@
+"""USDA ERS Rural-Urban Continuum Codes vintage catalog and county-lookup parsers."""
+
+import csv
+from collections import OrderedDict
+from io import BytesIO, StringIO
+
+BASE_URL = "https://www.ers.usda.gov"
+PRODUCT_PAGE = "data-products/rural-urban-continuum-codes"
+
+DEFAULT_VINTAGE = "2023"
+
+STATE_NAMES: dict[str, str] = {
+ "AL": "Alabama",
+ "AK": "Alaska",
+ "AZ": "Arizona",
+ "AR": "Arkansas",
+ "CA": "California",
+ "CO": "Colorado",
+ "CT": "Connecticut",
+ "DE": "Delaware",
+ "DC": "District of Columbia",
+ "FL": "Florida",
+ "GA": "Georgia",
+ "HI": "Hawaii",
+ "ID": "Idaho",
+ "IL": "Illinois",
+ "IN": "Indiana",
+ "IA": "Iowa",
+ "KS": "Kansas",
+ "KY": "Kentucky",
+ "LA": "Louisiana",
+ "ME": "Maine",
+ "MD": "Maryland",
+ "MA": "Massachusetts",
+ "MI": "Michigan",
+ "MN": "Minnesota",
+ "MS": "Mississippi",
+ "MO": "Missouri",
+ "MT": "Montana",
+ "NE": "Nebraska",
+ "NV": "Nevada",
+ "NH": "New Hampshire",
+ "NJ": "New Jersey",
+ "NM": "New Mexico",
+ "NY": "New York",
+ "NC": "North Carolina",
+ "ND": "North Dakota",
+ "OH": "Ohio",
+ "OK": "Oklahoma",
+ "OR": "Oregon",
+ "PA": "Pennsylvania",
+ "RI": "Rhode Island",
+ "SC": "South Carolina",
+ "SD": "South Dakota",
+ "TN": "Tennessee",
+ "TX": "Texas",
+ "UT": "Utah",
+ "VT": "Vermont",
+ "VA": "Virginia",
+ "WA": "Washington",
+ "WV": "West Virginia",
+ "WI": "Wisconsin",
+ "WY": "Wyoming",
+ "PR": "Puerto Rico",
+ "AS": "American Samoa",
+ "GU": "Guam",
+ "MP": "Northern Mariana Islands",
+ "VI": "U.S. Virgin Islands",
+}
+
+VINTAGE_CATALOG: "OrderedDict[str, dict]" = OrderedDict(
+ [
+ (
+ "2023",
+ {
+ "label": "2023",
+ "format": "csv",
+ "media": "/media/5768/2023-rural-urban-continuum-codes.csv",
+ "code_attr": "RUCC_2023",
+ "description_attr": "Description",
+ "population_attr": "Population_2020",
+ },
+ ),
+ (
+ "2013",
+ {
+ "label": "2013",
+ "format": "xls",
+ "parts": [
+ {
+ "media": "/media/5769/2013-rural-urban-continuum-codes.xls",
+ "sheet": "Rural-urban Continuum Code 2013",
+ "fips": "FIPS",
+ "state": "State",
+ "name": "County_Name",
+ "code": "RUCC_2013",
+ "description": "Description",
+ "population": "Population_2010",
+ }
+ ],
+ },
+ ),
+ (
+ "2003",
+ {
+ "label": "2003",
+ "format": "xls",
+ "parts": [
+ {
+ "media": "/media/5770/2003-rural-urban-continuum-codes.xls",
+ "sheet": "beale03",
+ "fips": "FIPS Code",
+ "state": "State",
+ "name": "County Name",
+ "code": "2003 Rural-urban Continuum Code",
+ "description": "Description for 2003 codes",
+ "population": "2000 Population",
+ },
+ {
+ "media": "/media/5771/2003-rural-urban-continuum-codes"
+ "-codes-for-puerto-rico.xls",
+ "sheet": "pr2003",
+ "fips": "FIPS Code",
+ "state": "State",
+ "name": "Municipio Name",
+ "code": "Rural-urban Continuum Code, 2003",
+ "description": "Description of the 2003 Code",
+ "population": "Population 2003",
+ },
+ ],
+ },
+ ),
+ ]
+)
+
+VINTAGES = tuple(VINTAGE_CATALOG)
+
+VINTAGE_OPTIONS = [
+ {"label": config["label"], "value": key} for key, config in VINTAGE_CATALOG.items()
+]
+
+
+def clean_text(value: object) -> str | None:
+ """Strip a source cell to a non-empty string, or None when blank.
+
+ Parameters
+ ----------
+ value : object
+ A raw source cell, which may be None, a pandas missing marker rendered
+ as 'nan', or a string carrying stray leading or trailing whitespace.
+
+ Returns
+ -------
+ str | None
+ The stripped text, or None when the cell is empty or a missing marker.
+ """
+ if value is None:
+ return None
+ text = str(value).strip()
+ if not text or text.casefold() == "nan":
+ return None
+ return text
+
+
+def parse_population(value: object) -> int | None:
+ """Parse a population cell into an integer count, or None when unavailable.
+
+ Parameters
+ ----------
+ value : object
+ A raw population cell, which may be None, blank, a missing marker, or a
+ digit string with optional thousands separators.
+
+ Returns
+ -------
+ int | None
+ The full integer population, or None when the cell is empty or
+ non-numeric.
+ """
+ if value is None:
+ return None
+ text = str(value).replace(",", "").strip()
+ if not text or text.casefold() in {"nan", "na", "n/a"}:
+ return None
+ try:
+ return int(text)
+ except ValueError:
+ try:
+ return int(float(text))
+ except ValueError:
+ return None
+
+
+def parse_csv_records(text: str, config: dict, vintage: str) -> list[dict]:
+ """Pivot the 2023 melted CSV into one county-lookup record per FIPS.
+
+ Parameters
+ ----------
+ text : str
+ Decoded CSV text with columns FIPS, State, County_Name, Attribute,
+ Value, one row per county and attribute.
+ config : dict
+ The vintage config, naming the code, description, and population
+ attribute tokens.
+ vintage : str
+ The vintage key carried onto each record.
+
+ Returns
+ -------
+ list[dict]
+ One record per FIPS in first-seen order, with vintage, fips, state,
+ county_name, rucc_code, description, and population keys.
+ """
+ code_attr = config["code_attr"]
+ description_attr = config["description_attr"]
+ population_attr = config["population_attr"]
+ rows: OrderedDict[str, dict] = OrderedDict()
+ for row in csv.DictReader(StringIO(text)):
+ fips = (row.get("FIPS") or "").strip()
+ if not fips:
+ continue
+ entry = rows.get(fips)
+ if entry is None:
+ entry = {
+ "vintage": vintage,
+ "fips": fips,
+ "state": clean_text(row.get("State")),
+ "county_name": clean_text(row.get("County_Name")),
+ "rucc_code": None,
+ "description": None,
+ "population": None,
+ }
+ rows[fips] = entry
+ attribute = (row.get("Attribute") or "").strip()
+ value = (row.get("Value") or "").strip()
+ if attribute == code_attr:
+ entry["rucc_code"] = value or None
+ elif attribute == description_attr:
+ entry["description"] = value or None
+ elif attribute == population_attr:
+ entry["population"] = parse_population(value)
+ return list(rows.values())
+
+
+def parse_xls_records(content: bytes, part: dict, vintage: str) -> list[dict]:
+ """Parse one vintage workbook sheet into county-lookup records.
+
+ Parameters
+ ----------
+ content : bytes
+ Raw legacy .xls bytes of the vintage file.
+ part : dict
+ The part config, naming the sheet and the source columns for fips,
+ state, name, code, description, and population.
+ vintage : str
+ The vintage key carried onto each record.
+
+ Returns
+ -------
+ list[dict]
+ One record per county row, with vintage, fips, state, county_name,
+ rucc_code, description, and population keys. Description and population
+ are None on the code-only vintages that omit those columns.
+ """
+ import pandas as pd
+
+ frame = pd.read_excel(
+ BytesIO(content), sheet_name=part["sheet"], engine="xlrd", dtype=str
+ )
+ frame.columns = [str(column).strip() for column in frame.columns]
+ description_column = part["description"]
+ population_column = part["population"]
+ records: list[dict] = []
+ for raw in frame.to_dict("records"):
+ fips = clean_text(raw.get(part["fips"]))
+ if not fips:
+ continue
+ records.append(
+ {
+ "vintage": vintage,
+ "fips": fips,
+ "state": clean_text(raw.get(part["state"])),
+ "county_name": clean_text(raw.get(part["name"])),
+ "rucc_code": clean_text(raw.get(part["code"])),
+ "description": (
+ clean_text(raw.get(description_column))
+ if description_column
+ else None
+ ),
+ "population": (
+ parse_population(raw.get(population_column))
+ if population_column
+ else None
+ ),
+ }
+ )
+ return records
+
+
+async def afetch_vintage(vintage: str) -> list[dict]:
+ """Download and parse one vintage into county-lookup records through the cache.
+
+ Parameters
+ ----------
+ vintage : str
+ A vintage key from VINTAGES.
+
+ Returns
+ -------
+ list[dict]
+ County-lookup records for the vintage; the 2003 vintage merges the
+ mainland file with the separate Puerto Rico file.
+ """
+ from openbb_government_us.usda.utils.ers_client import afetch_ers_file
+
+ config = VINTAGE_CATALOG[vintage]
+ if config["format"] == "csv":
+ content = await afetch_ers_file(config["media"], product=PRODUCT_PAGE)
+ return parse_csv_records(
+ content.decode("utf-8-sig", errors="replace"), config, vintage
+ )
+ records: list[dict] = []
+ for part in config["parts"]:
+ content = await afetch_ers_file(part["media"], product=PRODUCT_PAGE)
+ records.extend(parse_xls_records(content, part, vintage))
+ return records
+
+
+async def afetch_states(vintage: str) -> list[dict]:
+ """List a vintage's states as label/value options in sorted code order.
+
+ Parameters
+ ----------
+ vintage : str
+ A vintage key from VINTAGES.
+
+ Returns
+ -------
+ list[dict]
+ One option per distinct state code present in the vintage, its label
+ the full state or territory name.
+ """
+ records = await afetch_vintage(vintage)
+ codes = sorted({record["state"] for record in records if record["state"]})
+ return [{"label": STATE_NAMES.get(code, code), "value": code} for code in codes]
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_season_average_price_forecasts.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_season_average_price_forecasts.py
new file mode 100644
index 00000000000..a456cbe6b5b
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_season_average_price_forecasts.py
@@ -0,0 +1,104 @@
+"""USDA ERS Season-Average Price Forecasts file catalog and CSV parser."""
+
+import csv
+from io import StringIO
+
+BASE_URL = "https://www.ers.usda.gov"
+PRODUCT_PAGE = "data-products/season-average-price-forecasts"
+
+SEASON_AVERAGE_FILES: dict[str, str] = {
+ "output_forecast": "/media/6497/output-forecast-csv-file.csv",
+ "input_data": "/media/6498/input-data-csv-file.csv",
+}
+
+COMMODITIES: tuple[str, ...] = ("Corn", "Cotton", "Soybeans", "Wheat")
+
+OUTPUT_FORECAST_COLUMNS: tuple[str, ...] = (
+ "model_forecast_date",
+ "commodity",
+ "futures_model_type",
+ "futures_exchange",
+ "marketing_year",
+ "mya_price_model_forecast",
+ "mya_price_wasde_forecast",
+ "wasde_forecast_date",
+ "target_price",
+ "national_loan_rate",
+ "direct_payment_rate",
+ "reference_price",
+ "ccp_effective_price_model_forecast",
+ "ccp_rate_model_forecast",
+ "ccp_effective_price_wasde_forecast",
+ "ccp_rate_wasde_forecast",
+ "historical_mya_price_year1",
+ "historical_mya_price_year2",
+ "historical_mya_price_year3",
+ "historical_mya_price_year4",
+ "historical_mya_price_year5",
+ "historical_mya_5year_range",
+ "historical_mya_price_5year_olympic_average",
+ "effective_reference_price",
+ "effective_price_model_forecast",
+ "plc_payment_rate_model_forecast",
+ "effective_price_wasde_forecast",
+ "plc_payment_rate_wasde_forecast",
+ "maximum_plc_payment_rate",
+ "arc_annual_benchmark_price_year1",
+ "arc_annual_benchmark_price_year2",
+ "arc_annual_benchmark_price_year3",
+ "arc_annual_benchmark_price_year4",
+ "arc_annual_benchmark_price_year5",
+ "arc_annual_benchmark_price_5year_range",
+ "arc_co_benchmark_price",
+ "arc_co_price_model_forecast",
+ "arc_co_price_wasde_forecast",
+ "arc_ic_price_model_forecast",
+ "arc_ic_price_wasde_forecast",
+ "actual_mya_price",
+ "price_unit",
+)
+
+
+def parse_output_forecast(text: str) -> list[dict]:
+ """Parse the output-forecast CSV text into wide observation records.
+
+ Parameters
+ ----------
+ text : str
+ Decoded CSV text of the output-forecast file. Each row is one
+ already-wide observation keyed by model forecast date, commodity,
+ futures model type, futures exchange, and marketing year, with the
+ forecast measures spread across a fixed set of columns.
+
+ Returns
+ -------
+ list[dict]
+ One record per row, values stripped of surrounding whitespace, with
+ rows lacking a commodity skipped.
+ """
+ records: list[dict] = []
+ for row in csv.DictReader(StringIO(text)):
+ record = {
+ key.lower(): (value.strip() if isinstance(value, str) else value)
+ for key, value in row.items()
+ }
+ if not record.get("commodity"):
+ continue
+ records.append(record)
+ return records
+
+
+async def afetch_output_forecast() -> list[dict]:
+ """Download and parse the output-forecast CSV through the ERS disk cache.
+
+ Returns
+ -------
+ list[dict]
+ Wide observation records from parse_output_forecast.
+ """
+ from openbb_government_us.usda.utils.ers_client import afetch_ers_file
+
+ content = await afetch_ers_file(
+ SEASON_AVERAGE_FILES["output_forecast"], product=PRODUCT_PAGE
+ )
+ return parse_output_forecast(content.decode("utf-8-sig", errors="replace"))
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_snap_policy_data_sets.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_snap_policy_data_sets.py
new file mode 100644
index 00000000000..00977a4218e
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_snap_policy_data_sets.py
@@ -0,0 +1,276 @@
+"""USDA ERS SNAP Policy Database catalog, fetch, and CSV parser."""
+
+import csv
+import io
+import zipfile
+from io import StringIO
+
+PRODUCT_PAGE = "data-products/snap-policy-data-sets"
+MEDIA_ZIP = "/media/6473/snap-policy-database.zip"
+CSV_MEMBER = "SNAPPolicyDatabase.csv"
+
+STATE_LABELS: dict[str, str] = {
+ "AL": "Alabama",
+ "AK": "Alaska",
+ "AZ": "Arizona",
+ "AR": "Arkansas",
+ "CA": "California",
+ "CO": "Colorado",
+ "CT": "Connecticut",
+ "DE": "Delaware",
+ "DC": "District of Columbia",
+ "FL": "Florida",
+ "GA": "Georgia",
+ "HI": "Hawaii",
+ "ID": "Idaho",
+ "IL": "Illinois",
+ "IN": "Indiana",
+ "IA": "Iowa",
+ "KS": "Kansas",
+ "KY": "Kentucky",
+ "LA": "Louisiana",
+ "ME": "Maine",
+ "MD": "Maryland",
+ "MA": "Massachusetts",
+ "MI": "Michigan",
+ "MN": "Minnesota",
+ "MS": "Mississippi",
+ "MO": "Missouri",
+ "MT": "Montana",
+ "NE": "Nebraska",
+ "NV": "Nevada",
+ "NH": "New Hampshire",
+ "NJ": "New Jersey",
+ "NM": "New Mexico",
+ "NY": "New York",
+ "NC": "North Carolina",
+ "ND": "North Dakota",
+ "OH": "Ohio",
+ "OK": "Oklahoma",
+ "OR": "Oregon",
+ "PA": "Pennsylvania",
+ "RI": "Rhode Island",
+ "SC": "South Carolina",
+ "SD": "South Dakota",
+ "TN": "Tennessee",
+ "TX": "Texas",
+ "UT": "Utah",
+ "VT": "Vermont",
+ "VA": "Virginia",
+ "WA": "Washington",
+ "WV": "West Virginia",
+ "WI": "Wisconsin",
+ "WY": "Wyoming",
+}
+
+STATE_CODES: tuple[str, ...] = tuple(STATE_LABELS)
+
+POLICY_FIELDS: tuple[str, ...] = (
+ "bbce",
+ "bbce_asset",
+ "bbce_a_amt",
+ "bbce_a_veh",
+ "bbce_inclmt",
+ "bbce_child",
+ "bbce_elddisinclmt",
+ "bbce_multiple",
+ "call_any",
+ "cap",
+ "certearn_0103",
+ "certearn_0406",
+ "certearn_0712",
+ "certearn_1399",
+ "certearnavg",
+ "certearnmed",
+ "certeld_0103",
+ "certeld_0406",
+ "certeld_0712",
+ "certeld_1399",
+ "certeldavg",
+ "certeldmed",
+ "certnonearn_0103",
+ "certnonearn_0406",
+ "certnonearn_0712",
+ "certnonearn_1399",
+ "certnonearnavg",
+ "certnonearnmed",
+ "ebtissuance",
+ "faceini",
+ "facerec",
+ "fingerprint",
+ "noncitadultfull",
+ "noncitadultpart",
+ "noncitchildfull",
+ "noncitchildpart",
+ "nonciteldfull",
+ "nonciteldpart",
+ "oapp",
+ "outreach",
+ "reportsimple",
+ "transben",
+ "vehexclall",
+ "vehexclamt",
+ "vehexclone",
+)
+
+BBCE_SENTINEL_FIELDS: frozenset[str] = frozenset(
+ {
+ "bbce_asset",
+ "bbce_a_amt",
+ "bbce_a_veh",
+ "bbce_inclmt",
+ "bbce_child",
+ "bbce_elddisinclmt",
+ "bbce_multiple",
+ }
+)
+
+FIELD_TO_CSV: dict[str, str] = {
+ "certearn_0103": "certearn0103",
+ "certearn_0406": "certearn0406",
+ "certearn_0712": "certearn0712",
+ "certearn_1399": "certearn1399",
+ "certeld_0103": "certeld0103",
+ "certeld_0406": "certeld0406",
+ "certeld_0712": "certeld0712",
+ "certeld_1399": "certeld1399",
+ "certnonearn_0103": "certnonearn0103",
+ "certnonearn_0406": "certnonearn0406",
+ "certnonearn_0712": "certnonearn0712",
+ "certnonearn_1399": "certnonearn1399",
+}
+
+
+def csv_column(field: str) -> str:
+ """Return the source CSV header for a model field name.
+
+ Parameters
+ ----------
+ field : str
+ Model field name from POLICY_FIELDS.
+
+ Returns
+ -------
+ str
+ The un-split CSV header, e.g. 'certearn0103' for 'certearn_0103'.
+ """
+ return FIELD_TO_CSV.get(field, field)
+
+
+def policy_value(field: str, raw: str | None) -> str | None:
+ """Normalize a raw policy cell, blanking missing and not-applicable codes.
+
+ Parameters
+ ----------
+ field : str
+ Model field name from POLICY_FIELDS.
+ raw : str | None
+ The raw CSV cell value.
+
+ Returns
+ -------
+ str | None
+ The stripped value, or None for a blank cell or the ``-9`` not-applicable
+ sentinel on a BBCE sub-field (which means the state does not use
+ broad-based categorical eligibility). The ``-8`` and ``-7`` policy codes
+ are preserved.
+ """
+ if raw is None:
+ return None
+ value = raw.strip()
+ if not value:
+ return None
+ if value == "-9" and field in BBCE_SENTINEL_FIELDS:
+ return None
+ return value
+
+
+def state_options() -> list[dict]:
+ """Build the labeled state options for the widget's state selector.
+
+ Returns
+ -------
+ list[dict]
+ Label/value option dictionaries for the fifty states and the
+ District of Columbia.
+ """
+ return [{"label": STATE_LABELS[code], "value": code} for code in STATE_CODES]
+
+
+def extract_csv_text(zip_bytes: bytes) -> str:
+ """Extract and decode the SNAP policy CSV member from the zip bytes.
+
+ Parameters
+ ----------
+ zip_bytes : bytes
+ Raw bytes of the snap-policy-database zip archive.
+
+ Returns
+ -------
+ str
+ The decoded CSV text of the SNAPPolicyDatabase.csv member.
+ """
+ with zipfile.ZipFile(io.BytesIO(zip_bytes)) as archive:
+ raw = archive.read(CSV_MEMBER)
+ return raw.decode("utf-8-sig", errors="replace")
+
+
+def parse_rows(text: str, state: str) -> list[dict]:
+ """Parse the wide CSV into per-month records for one state.
+
+ Parameters
+ ----------
+ text : str
+ Decoded CSV text of the SNAP policy database.
+ state : str
+ Two-letter state postal code to keep.
+
+ Returns
+ -------
+ list[dict]
+ Records with state (full name), yearmonth (int), and the raw string
+ value of each of the forty-five policy fields, in source file order.
+ """
+ records: list[dict] = []
+ for row in csv.DictReader(StringIO(text)):
+ if (row.get("state_pc") or "").strip().upper() != state:
+ continue
+ record: dict = {
+ "state": (row.get("statename") or "").strip(),
+ "yearmonth": int((row.get("yearmonth") or "").strip()),
+ }
+ for field in POLICY_FIELDS:
+ record[field] = policy_value(field, row.get(csv_column(field)))
+ records.append(record)
+ return records
+
+
+async def afetch_dataset() -> str:
+ """Download and decode the SNAP policy CSV through the ERS disk cache.
+
+ Returns
+ -------
+ str
+ The decoded CSV text of the current release.
+ """
+ from openbb_government_us.usda.utils.ers_client import afetch_ers_file
+
+ content = await afetch_ers_file(MEDIA_ZIP, product=PRODUCT_PAGE)
+ return extract_csv_text(content)
+
+
+async def afetch_state(state: str) -> list[dict]:
+ """Download the SNAP policy CSV and parse one state's monthly records.
+
+ Parameters
+ ----------
+ state : str
+ Two-letter state postal code.
+
+ Returns
+ -------
+ list[dict]
+ Row records from parse_rows for the selected state.
+ """
+ text = await afetch_dataset()
+ return parse_rows(text, state)
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_state_agricultural_trade.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_state_agricultural_trade.py
new file mode 100644
index 00000000000..3662a64e857
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_state_agricultural_trade.py
@@ -0,0 +1,332 @@
+"""USDA ERS State Agricultural Trade catalog, fetch, and CSV parsers."""
+
+import csv
+from io import StringIO
+
+BASE_URL = "https://www.ers.usda.gov"
+PRODUCT_PAGE = "data-products/state-agricultural-trade-data"
+
+ALL_DATA_CSV = "/media/5420/csv-comma-separated-values-format-of-all-data.csv"
+TOP_EXPORTS_CSV = (
+ "/media/5422/top-5-us-agricultural-export-commodities-by-state-fiscal-year.csv"
+)
+TOP_IMPORTS_CSV = (
+ "/media/5424/top-5-us-agricultural-import-commodities-by-state-fiscal-year.csv"
+)
+
+STATE_AG_TRADE_FILES: dict[str, tuple[str, str]] = {
+ "exports_by_commodity": (ALL_DATA_CSV, PRODUCT_PAGE),
+ "exports_by_state": (ALL_DATA_CSV, PRODUCT_PAGE),
+ "top_exports": (TOP_EXPORTS_CSV, PRODUCT_PAGE),
+ "top_imports": (TOP_IMPORTS_CSV, PRODUCT_PAGE),
+}
+
+TABLE_LABELS: dict[str, str] = {
+ "exports_by_commodity": "Exports by commodity (state, calendar year)",
+ "exports_by_state": "Exports by state (commodity, calendar year)",
+ "top_exports": "Top export commodities (state, fiscal year)",
+ "top_imports": "Top import commodities (state, fiscal year)",
+}
+
+STATE_SCOPED_TABLES: frozenset[str] = frozenset(
+ {"exports_by_commodity", "top_exports", "top_imports"}
+)
+DATASET_A_TABLES: frozenset[str] = frozenset(
+ {"exports_by_commodity", "exports_by_state"}
+)
+DATASET_B_TABLES: frozenset[str] = frozenset({"top_exports", "top_imports"})
+
+TABLE_UNITS: dict[str, str] = {
+ "exports_by_commodity": "Million dollars",
+ "exports_by_state": "Million dollars",
+ "top_exports": "Dollars",
+ "top_imports": "Dollars",
+}
+
+STATE_CODE_TO_NAME: dict[str, str] = {
+ "US": "United States",
+ "AL": "Alabama",
+ "AK": "Alaska",
+ "AZ": "Arizona",
+ "AR": "Arkansas",
+ "CA": "California",
+ "CO": "Colorado",
+ "CT": "Connecticut",
+ "DE": "Delaware",
+ "DC": "District of Columbia",
+ "FL": "Florida",
+ "GA": "Georgia",
+ "HI": "Hawaii",
+ "ID": "Idaho",
+ "IL": "Illinois",
+ "IN": "Indiana",
+ "IA": "Iowa",
+ "KS": "Kansas",
+ "KY": "Kentucky",
+ "LA": "Louisiana",
+ "ME": "Maine",
+ "MD": "Maryland",
+ "MA": "Massachusetts",
+ "MI": "Michigan",
+ "MN": "Minnesota",
+ "MS": "Mississippi",
+ "MO": "Missouri",
+ "MT": "Montana",
+ "NE": "Nebraska",
+ "NV": "Nevada",
+ "NH": "New Hampshire",
+ "NJ": "New Jersey",
+ "NM": "New Mexico",
+ "NY": "New York",
+ "NC": "North Carolina",
+ "ND": "North Dakota",
+ "OH": "Ohio",
+ "OK": "Oklahoma",
+ "OR": "Oregon",
+ "PA": "Pennsylvania",
+ "PR": "Puerto Rico",
+ "RI": "Rhode Island",
+ "SC": "South Carolina",
+ "SD": "South Dakota",
+ "TN": "Tennessee",
+ "TX": "Texas",
+ "UT": "Utah",
+ "VT": "Vermont",
+ "VA": "Virginia",
+ "VI": "Virgin Islands",
+ "WA": "Washington",
+ "WV": "West Virginia",
+ "WI": "Wisconsin",
+ "WY": "Wyoming",
+}
+
+STATE_NAME_TO_CODE: dict[str, str] = {
+ name: code for code, name in STATE_CODE_TO_NAME.items()
+}
+
+DATASET_A_CODES: frozenset[str] = frozenset(STATE_CODE_TO_NAME) - {"DC", "PR", "VI"}
+DATASET_B_CODES: frozenset[str] = frozenset(STATE_CODE_TO_NAME)
+
+NATIONAL_NAME = "United States"
+
+COMMODITIES: tuple[str, ...] = (
+ "Total agricultural exports",
+ "Total animal products",
+ "Total plant products",
+ "Beef and veal",
+ "Broiler meat",
+ "Corn",
+ "Cotton",
+ "Dairy products",
+ "Feeds and other feed grains",
+ "Fruits, fresh",
+ "Fruits, processed",
+ "Grain products",
+ "Hides and skins",
+ "Other livestock products",
+ "Other oilseeds and products",
+ "Other plant products",
+ "Other poultry products",
+ "Pork",
+ "Rice",
+ "Soybean meal",
+ "Soybeans",
+ "Tobacco",
+ "Tree nuts",
+ "Vegetable oils",
+ "Vegetables, fresh",
+ "Vegetables, processed",
+ "Wheat",
+)
+
+DEFAULT_TABLE = "exports_by_commodity"
+DEFAULT_STATE = "California"
+DEFAULT_COMMODITY = "Total agricultural exports"
+
+
+def _state_names(codes: frozenset[str]) -> tuple[str, ...]:
+ """Order a code set's names with the national total first, then alphabetically.
+
+ Parameters
+ ----------
+ codes : frozenset[str]
+ Two-letter state codes to render as labeled names.
+
+ Returns
+ -------
+ tuple[str, ...]
+ Full state names led by 'United States', the remaining names sorted.
+ """
+ others = sorted(STATE_CODE_TO_NAME[code] for code in codes if code != "US")
+ return (NATIONAL_NAME, *others)
+
+
+DATASET_A_STATE_NAMES: tuple[str, ...] = _state_names(DATASET_A_CODES)
+DATASET_B_STATE_NAMES: tuple[str, ...] = _state_names(DATASET_B_CODES)
+
+
+def allowed_state_names(table: str) -> tuple[str, ...]:
+ """Return the state names published for a table's underlying dataset.
+
+ Parameters
+ ----------
+ table : str
+ Table key from TABLE_LABELS.
+
+ Returns
+ -------
+ tuple[str, ...]
+ The 51 calendar-year state names for the all-data tables, or the 54
+ fiscal-year state names for the top-commodity tables.
+ """
+ return DATASET_B_STATE_NAMES if table in DATASET_B_TABLES else DATASET_A_STATE_NAMES
+
+
+def state_options(table: str) -> list[dict]:
+ """Build the labeled state options for a table's scoping selector.
+
+ Parameters
+ ----------
+ table : str
+ Table key from TABLE_LABELS.
+
+ Returns
+ -------
+ list[dict]
+ Label/value option dictionaries whose value is the full state name.
+ """
+ return [{"label": name, "value": name} for name in allowed_state_names(table)]
+
+
+def commodity_options() -> list[dict]:
+ """Build the labeled commodity options for the exports-by-state selector."""
+ return [{"label": name, "value": name} for name in COMMODITIES]
+
+
+def parse_value(raw: str | None) -> float | None:
+ """Parse a raw numeric cell to a float, keeping full precision.
+
+ Parameters
+ ----------
+ raw : str | None
+ Raw value cell.
+
+ Returns
+ -------
+ float | None
+ The numeric value, or None when the cell is blank or non-numeric.
+ """
+ text = (raw or "").strip()
+ if not text:
+ return None
+ try:
+ return float(text)
+ except ValueError:
+ return None
+
+
+def parse_dataset_a(text: str) -> list[dict]:
+ """Parse the all-data export CSV into tidy calendar-year records.
+
+ Parameters
+ ----------
+ text : str
+ Decoded CSV text of the all-data file.
+
+ Returns
+ -------
+ list[dict]
+ Records with commodity, state (full name), year, unit, and value keys.
+ Rows whose year token is non-numeric are skipped.
+ """
+ records: list[dict] = []
+ for row in csv.DictReader(StringIO(text)):
+ year_token = (row.get("Year") or "").strip()
+ if not year_token.isdigit():
+ continue
+ records.append(
+ {
+ "commodity": (row.get("Commodity") or "").strip(),
+ "state": (row.get("State") or "").strip(),
+ "year": int(year_token),
+ "unit": (row.get("Units") or "").strip(),
+ "value": parse_value(row.get("Value")),
+ }
+ )
+ return records
+
+
+def parse_dataset_b(text: str) -> list[dict]:
+ """Parse a top-commodity fiscal-year CSV into all-destination annual totals.
+
+ Parameters
+ ----------
+ text : str
+ Decoded CSV text of a top-export or top-import file.
+
+ Returns
+ -------
+ list[dict]
+ Records with state (two-letter code), year (fiscal), commodity, and
+ value keys, kept only for the World destination and the fiscal-year
+ total quarter, and only for numeric fiscal years.
+ """
+ records: list[dict] = []
+ for row in csv.DictReader(StringIO(text)):
+ if (row.get("Country") or "").strip() != "World":
+ continue
+ if (row.get("Fiscal quarter") or "").strip() != "0":
+ continue
+ year_token = (row.get("Fiscal year") or "").strip()
+ if not year_token.isdigit():
+ continue
+ records.append(
+ {
+ "state": (row.get("State") or "").strip(),
+ "year": int(year_token),
+ "commodity": (row.get("Commodity name") or "").strip(),
+ "value": parse_value(row.get("Dollar value")),
+ }
+ )
+ return records
+
+
+async def _afetch_csv(media_path: str) -> str:
+ """Fetch and decode an ERS media CSV through the disk cache.
+
+ Parameters
+ ----------
+ media_path : str
+ Media path of the CSV to fetch.
+
+ Returns
+ -------
+ str
+ The decoded CSV text.
+ """
+ from openbb_government_us.usda.utils.ers_client import afetch_ers_file
+
+ content = await afetch_ers_file(media_path, product=PRODUCT_PAGE)
+ return content.decode("utf-8-sig", errors="replace")
+
+
+async def afetch_table(table: str) -> list[dict]:
+ """Download and parse the long-format records backing a table.
+
+ Parameters
+ ----------
+ table : str
+ Table key from TABLE_LABELS.
+
+ Returns
+ -------
+ list[dict]
+ Calendar-year records from parse_dataset_a for the all-data tables, or
+ fiscal-year all-destination records from parse_dataset_b for the
+ top-commodity tables.
+ """
+ media_path = STATE_AG_TRADE_FILES[table][0]
+ text = await _afetch_csv(media_path)
+ if table in DATASET_A_TABLES:
+ return parse_dataset_a(text)
+ return parse_dataset_b(text)
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_sugar_sweeteners_yearbook.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_sugar_sweeteners_yearbook.py
new file mode 100644
index 00000000000..54331164406
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_sugar_sweeteners_yearbook.py
@@ -0,0 +1,536 @@
+"""USDA ERS Sugar and Sweeteners Yearbook file catalog and long-format parser."""
+
+import csv
+from io import StringIO
+
+BASE_URL = "https://www.ers.usda.gov"
+PRODUCT_PAGE = "data-products/sugar-and-sweeteners-yearbook-tables"
+
+DIMENSION_FIELDS: tuple[tuple[str, str], ...] = (
+ ("Commodity_desc", "commodity_desc"),
+ ("Commodity_desc2", "commodity_desc2"),
+ ("Geographic_extent", "geographic_extent"),
+ ("Geographic_extent2", "geographic_extent2"),
+ ("Source_or_destination", "source_or_destination"),
+ ("Fiscal_year_entered", "fiscal_year_entered"),
+ ("Fiscal_year_quota", "fiscal_year_quota"),
+ ("Forecast_yearmonth", "forecast_yearmonth"),
+)
+
+DIMENSION_OUTPUT_FIELDS: tuple[str, ...] = tuple(out for _, out in DIMENSION_FIELDS)
+
+SUGAR_SWEETENERS_FILES: dict[str, dict] = {
+ "table_2": {
+ "media": "/media/5170/world-us-and-mexican-sugar-and-corn-sweetener-prices.csv",
+ "number": "2",
+ "label": "Table 2: World white (refined) sugar nearby futures price, ICE Contract Number 5, monthly, quarterly, and by calendar and fiscal year, since 1980, cents per pound",
+ },
+ "table_3a": {
+ "media": "/media/5170/world-us-and-mexican-sugar-and-corn-sweetener-prices.csv",
+ "number": "3a",
+ "label": "Table 3a: World raw sugar price, monthly, quarterly, and by calendar and fiscal year, 1960 to 2011, cents per pound, discontinued as of July 1, 2011",
+ },
+ "table_3b": {
+ "media": "/media/5170/world-us-and-mexican-sugar-and-corn-sweetener-prices.csv",
+ "number": "3b",
+ "label": "Table 3b: World raw sugar nearby futures price, ICE Contract Number 11, monthly, quarterly, and by calendar and fiscal year, since 1989, cents per pound",
+ },
+ "table_4": {
+ "media": "/media/5170/world-us-and-mexican-sugar-and-corn-sweetener-prices.csv",
+ "number": "4",
+ "label": "Table 4: U.S. raw sugar nearby futures price, ICE Contract Number 16, monthly, quarterly, and by calendar and fiscal year, since 1960, cents per pound",
+ },
+ "table_5": {
+ "media": "/media/5170/world-us-and-mexican-sugar-and-corn-sweetener-prices.csv",
+ "number": "5",
+ "label": "Table 5: U.S. spot price for bulk refined beet sugar, Midwest markets, monthly, quarterly, and by calendar and fiscal year, since 1960, cents per pound",
+ },
+ "table_5a": {
+ "media": "/media/5170/world-us-and-mexican-sugar-and-corn-sweetener-prices.csv",
+ "number": "5a",
+ "label": "Table 5a: U.S. calendar price for bulk refined cane sugar, Northeast markets, monthly, quarterly, and by calendar and fiscal year, since 1999, cents per pound",
+ },
+ "table_6": {
+ "media": "/media/5170/world-us-and-mexican-sugar-and-corn-sweetener-prices.csv",
+ "number": "6",
+ "label": "Table 6: U.S. retail refined sugar price, monthly, quarterly, and by calendar and fiscal year, since 1960, cents per pound",
+ },
+ "table_7": {
+ "media": "/media/5170/world-us-and-mexican-sugar-and-corn-sweetener-prices.csv",
+ "number": "7",
+ "label": "Table 7: U.S. spot price for bulk glucose syrup, Midwest markets, monthly, quarterly, and by calendar and fiscal year, since 1975, cents per pound (dry weight)",
+ },
+ "table_8": {
+ "media": "/media/5170/world-us-and-mexican-sugar-and-corn-sweetener-prices.csv",
+ "number": "8",
+ "label": "Table 8: U.S. spot price for bulk dextrose, Midwest markets, monthly, quarterly, and by calendar and fiscal year, since 1975, cents per pound (dry weight)",
+ },
+ "table_9": {
+ "media": "/media/5170/world-us-and-mexican-sugar-and-corn-sweetener-prices.csv",
+ "number": "9",
+ "label": "Table 9: U.S. price for bulk high-fructose corn syrup, Midwest markets, monthly, quarterly, and by calendar and fiscal year, since 1994, cents per pound (dry weight)",
+ },
+ "table_10": {
+ "media": "/media/5170/world-us-and-mexican-sugar-and-corn-sweetener-prices.csv",
+ "number": "10",
+ "label": "Table 10: U.S. producer price index for corn sweeteners and sugar, monthly, not seasonally adjusted, since 1967",
+ },
+ "table_11": {
+ "media": "/media/5170/world-us-and-mexican-sugar-and-corn-sweetener-prices.csv",
+ "number": "11",
+ "label": "Table 11: U.S. consumer price index for sugar and selected sweetener-containing products, monthly and by calendar year, in U.S. city average, all urban consumers, not seasonally adjusted, since 1978",
+ },
+ "table_12": {
+ "media": "/media/5170/world-us-and-mexican-sugar-and-corn-sweetener-prices.csv",
+ "number": "12",
+ "label": "Table 12: Sugarbeet price per ton, by State and United States, since 1972/73, dollars per ton",
+ },
+ "table_13": {
+ "media": "/media/5170/world-us-and-mexican-sugar-and-corn-sweetener-prices.csv",
+ "number": "13",
+ "label": "Table 13: Sugarcane for sugar: price per ton, by State, since 1972/73, dollars per ton",
+ },
+ "table_31a": {
+ "media": "/media/5170/world-us-and-mexican-sugar-and-corn-sweetener-prices.csv",
+ "number": "31a",
+ "label": "Table 31a: Net cost of corn starch to U.S. wet-millers, Midwest markets, quarterly, since 1990",
+ },
+ "table_31b": {
+ "media": "/media/5170/world-us-and-mexican-sugar-and-corn-sweetener-prices.csv",
+ "number": "31b",
+ "label": "Table 31b: Net cost of corn starch to U.S. wet-millers, Midwest markets, by calendar and fiscal year, since 1990",
+ },
+ "table_54": {
+ "media": "/media/5170/world-us-and-mexican-sugar-and-corn-sweetener-prices.csv",
+ "number": "54",
+ "label": "Table 54: Mexico's price of estándar (standard) sugar, wholesale center, Mexico City, monthly and by calendar and fiscal year, since 1994",
+ },
+ "table_55": {
+ "media": "/media/5170/world-us-and-mexican-sugar-and-corn-sweetener-prices.csv",
+ "number": "55",
+ "label": "Table 55: Mexico's price of refinada (refined) sugar, wholesale center, Mexico City, monthly and by calendar and fiscal year, since 1994",
+ },
+ "table_14": {
+ "media": "/media/7096/us-sugar-crop-production-and-sugar-production-deliveries-and-stocks.csv",
+ "number": "14",
+ "label": "Table 14: U.S. sugarbeet: area planted (1,000 acres), area harvested (1,000 acres), yield per acre (short tons per acre), and production (1,000 short tons), by State and region, by crop year, since 1980/81",
+ },
+ "table_15": {
+ "media": "/media/7096/us-sugar-crop-production-and-sugar-production-deliveries-and-stocks.csv",
+ "number": "15",
+ "label": "Table 15: U.S. sugarcane: area, yield, production, sugar output, recovery rate, and sugar yield per acre, by crop year and fiscal year (Louisiana only)",
+ },
+ "table_16": {
+ "media": "/media/7096/us-sugar-crop-production-and-sugar-production-deliveries-and-stocks.csv",
+ "number": "16",
+ "label": "Table 16: U.S. beet and cane sugar production (including Puerto Rico), by fiscal year and share of total, since 1969/70",
+ },
+ "table_17": {
+ "media": "/media/7096/us-sugar-crop-production-and-sugar-production-deliveries-and-stocks.csv",
+ "number": "17",
+ "label": "Table 17: U.S. sugarbeet area, yield, and production, by crop year and fiscal year, since 1980/81",
+ },
+ "table_18": {
+ "media": "/media/7096/us-sugar-crop-production-and-sugar-production-deliveries-and-stocks.csv",
+ "number": "18",
+ "label": "Table 18: U.S. production of beet sugar and cane sugar by State, monthly, quarterly, and by calendar year and fiscal year, since 1992 (1,000 short tons, raw value)",
+ },
+ "table_19": {
+ "media": "/media/7096/us-sugar-crop-production-and-sugar-production-deliveries-and-stocks.csv",
+ "number": "19",
+ "label": "Table 19: U.S. cane and beet sugar deliveries and exports, monthly, quarterly, and by calendar year and fiscal year, since 1992 (1,000 short tons, raw value)",
+ },
+ "table_20a": {
+ "media": "/media/7096/us-sugar-crop-production-and-sugar-production-deliveries-and-stocks.csv",
+ "number": "20a",
+ "label": "Table 20a: U.S. sugar deliveries for human consumption by type of user, by calendar year, since 1949 (1,000 short tons, refined value)",
+ },
+ "table_20b": {
+ "media": "/media/7096/us-sugar-crop-production-and-sugar-production-deliveries-and-stocks.csv",
+ "number": "20b",
+ "label": "Table 20b: U.S. sugar deliveries for human consumption by type of user, monthly and quarterly, since 2001 (1,000 short tons, refined value)",
+ },
+ "table_21": {
+ "media": "/media/7096/us-sugar-crop-production-and-sugar-production-deliveries-and-stocks.csv",
+ "number": "21",
+ "label": "Table 21: U.S. sugar deliveries: industrial and nonindustrial uses by region, monthly, quarterly, and by calendar year (1,000 short tons, refined value)",
+ },
+ "table_22": {
+ "media": "/media/7096/us-sugar-crop-production-and-sugar-production-deliveries-and-stocks.csv",
+ "number": "22",
+ "label": "Table 22: U.S. sugar stocks held by primary distributors, quarterly (1,000 short tons, raw value)",
+ },
+ "table_46": {
+ "media": "/media/5151/us-honey-production-and-trade.csv",
+ "number": "46",
+ "label": "Table 46: U.S. honey production, imports, exports, stocks, and average price, by calendar year, since 1986",
+ },
+ "table_47": {
+ "media": "/media/5151/us-honey-production-and-trade.csv",
+ "number": "47",
+ "label": "Table 47: Honey: number of colonies, yield, production, stocks, price, and value by top producing States and United States, by calendar year, since 1986",
+ },
+ "table_48a": {
+ "media": "/media/5151/us-honey-production-and-trade.csv",
+ "number": "48a",
+ "label": "Table 48a: U.S. honey imports, by country of source, calendar year, since 1989, metric tons",
+ },
+ "table_48b": {
+ "media": "/media/5151/us-honey-production-and-trade.csv",
+ "number": "48b",
+ "label": "Table 48b: U.S. honey imports, by country of source, calendar year, since 1989, million pounds",
+ },
+ "table_49": {
+ "media": "/media/7144/us-consumption-of-caloric-sweeteners.csv",
+ "number": "49",
+ "label": "Table 49: U.S. total estimated deliveries of caloric sweeteners for domestic food and beverage use (1,000 short tons, dry basis), by calendar year, since 1966",
+ },
+ "table_50": {
+ "media": "/media/7144/us-consumption-of-caloric-sweeteners.csv",
+ "number": "50",
+ "label": "Table 50: U.S. per capita caloric sweeteners estimated deliveries for domestic food and beverage use (pounds, dry basis), by calendar year, since 1966",
+ },
+ "table_51": {
+ "media": "/media/7144/us-consumption-of-caloric-sweeteners.csv",
+ "number": "51",
+ "label": "Table 51: Refined cane and beet sugar: estimated number of per capita calories consumed daily, by calendar year, since 1970",
+ },
+ "table_52": {
+ "media": "/media/7144/us-consumption-of-caloric-sweeteners.csv",
+ "number": "52",
+ "label": "Table 52: High-fructose corn syrup: estimated number of per capita calories consumed daily, by calendar year, since 1970",
+ },
+ "table_53": {
+ "media": "/media/7144/us-consumption-of-caloric-sweeteners.csv",
+ "number": "53",
+ "label": "Table 53: Other sweeteners: estimated number of per capita calories consumed daily, by calendar year, since 1970",
+ },
+ "table_24a": {
+ "media": "/media/7097/the-machine-readable-file-combines-the-data-from-the-3-excel-tables-us-and-mexico-fiscal-year-sugar-supply-and-use-table-25-us-monthly-estimates-of-sugar-supply-and-use-and-table-26-monthly-estimates-of-mexican-sugar-supply-and-use.csv",
+ "number": "24a",
+ "label": "Table 24a: U.S. sugar: supply and use (including Puerto Rico), by fiscal year, 1,000 short tons, raw value, since 2000/01",
+ },
+ "table_24b": {
+ "media": "/media/7097/the-machine-readable-file-combines-the-data-from-the-3-excel-tables-us-and-mexico-fiscal-year-sugar-supply-and-use-table-25-us-monthly-estimates-of-sugar-supply-and-use-and-table-26-monthly-estimates-of-mexican-sugar-supply-and-use.csv",
+ "number": "24b",
+ "label": "Table 24b: U.S. sugar: supply and use (including Puerto Rico), by fiscal year, 1,000 metric tons, raw value, since 2000/01",
+ },
+ "table_25": {
+ "media": "/media/7097/the-machine-readable-file-combines-the-data-from-the-3-excel-tables-us-and-mexico-fiscal-year-sugar-supply-and-use-table-25-us-monthly-estimates-of-sugar-supply-and-use-and-table-26-monthly-estimates-of-mexican-sugar-supply-and-use.csv",
+ "number": "25",
+ "label": "Table 25: Monthly estimates of U.S. sugar supply and use (1,000 short tons, raw value)",
+ },
+ "table_26": {
+ "media": "/media/7097/the-machine-readable-file-combines-the-data-from-the-3-excel-tables-us-and-mexico-fiscal-year-sugar-supply-and-use-table-25-us-monthly-estimates-of-sugar-supply-and-use-and-table-26-monthly-estimates-of-mexican-sugar-supply-and-use.csv",
+ "number": "26",
+ "label": "Table 26: Monthly estimates of Mexican sugar supply and use (1,000 metric tons, actual weight)",
+ },
+ "table_56a": {
+ "media": "/media/7097/the-machine-readable-file-combines-the-data-from-the-3-excel-tables-us-and-mexico-fiscal-year-sugar-supply-and-use-table-25-us-monthly-estimates-of-sugar-supply-and-use-and-table-26-monthly-estimates-of-mexican-sugar-supply-and-use.csv",
+ "number": "56a",
+ "label": "Table 56a: Mexico: sugar production and supply, and sugar and high-fructose corn syrup utilization, by fiscal year, 1,000 metric tons, tel quel, since 1995/96",
+ },
+ "table_56b": {
+ "media": "/media/7097/the-machine-readable-file-combines-the-data-from-the-3-excel-tables-us-and-mexico-fiscal-year-sugar-supply-and-use-table-25-us-monthly-estimates-of-sugar-supply-and-use-and-table-26-monthly-estimates-of-mexican-sugar-supply-and-use.csv",
+ "number": "56b",
+ "label": "Table 56b: Mexico: sugar production and supply, and sugar and high-fructose corn syrup utilization, by fiscal year, 1,000 metric tons, raw value, since 1995/96",
+ },
+ "table_27": {
+ "media": "/media/7095/the-machine-readable-file-combines-the-data-from-the-2-excel-tables-us-corn-sweetener-supply-and-use-and-corn-sweetener-trade.csv",
+ "number": "27",
+ "label": "Table 27: U.S. use of field corn, by marketing year, since 1990/91 (million bushels)",
+ },
+ "table_28": {
+ "media": "/media/7095/the-machine-readable-file-combines-the-data-from-the-2-excel-tables-us-corn-sweetener-supply-and-use-and-corn-sweetener-trade.csv",
+ "number": "28",
+ "label": "Table 28: U.S. high-fructose corn syrup (HFCS) deliveries, quarterly, by fiscal and calendar year, since 1992 (1,000 short tons, dry weight)",
+ },
+ "table_29": {
+ "media": "/media/7095/the-machine-readable-file-combines-the-data-from-the-2-excel-tables-us-corn-sweetener-supply-and-use-and-corn-sweetener-trade.csv",
+ "number": "29",
+ "label": "Table 29: U.S. high-fructose corn syrup (HFCS) production, quarterly, by fiscal and calendar year, since 1992 (1,000 short tons, dry weight)",
+ },
+ "table_30": {
+ "media": "/media/7095/the-machine-readable-file-combines-the-data-from-the-2-excel-tables-us-corn-sweetener-supply-and-use-and-corn-sweetener-trade.csv",
+ "number": "30",
+ "label": "Table 30: U.S. high-fructose corn syrup supply and use, by calendar and fiscal year, since 1992 (1,000 short tons, dry weight)",
+ },
+ "table_34a": {
+ "media": "/media/7095/the-machine-readable-file-combines-the-data-from-the-2-excel-tables-us-corn-sweetener-supply-and-use-and-corn-sweetener-trade.csv",
+ "number": "34a",
+ "label": "Table 34a: U.S. exports of high-fructose corn syrup to Mexico, since 1995 (metric tons, dry weight basis)",
+ },
+ "table_34b": {
+ "media": "/media/7095/the-machine-readable-file-combines-the-data-from-the-2-excel-tables-us-corn-sweetener-supply-and-use-and-corn-sweetener-trade.csv",
+ "number": "34b",
+ "label": "Table 34b: U.S. exports of high-fructose corn syrup to all countries, since 1995 (metric tons, dry weight basis)",
+ },
+ "table_35a": {
+ "media": "/media/7095/the-machine-readable-file-combines-the-data-from-the-2-excel-tables-us-corn-sweetener-supply-and-use-and-corn-sweetener-trade.csv",
+ "number": "35a",
+ "label": "Table 35a: Mexican imports of high-fructose corn syrup from the United States, since 1995 (metric tons, dry weight basis)",
+ },
+ "table_35b": {
+ "media": "/media/7095/the-machine-readable-file-combines-the-data-from-the-2-excel-tables-us-corn-sweetener-supply-and-use-and-corn-sweetener-trade.csv",
+ "number": "35b",
+ "label": "Table 35b: Mexican imports of high-fructose corn syrup from all countries, since 1995 (metric tons, dry weight basis)",
+ },
+ "table_36": {
+ "media": "/media/7095/the-machine-readable-file-combines-the-data-from-the-2-excel-tables-us-corn-sweetener-supply-and-use-and-corn-sweetener-trade.csv",
+ "number": "36",
+ "label": "Table 36: U.S. corn refinery exports, by calendar year, since 1980 (metric tons)",
+ },
+ "table_37": {
+ "media": "/media/7095/the-machine-readable-file-combines-the-data-from-the-2-excel-tables-us-corn-sweetener-supply-and-use-and-corn-sweetener-trade.csv",
+ "number": "37",
+ "label": "Table 37: U.S. dextrose supply and use, by calendar year, since 1964 (1,000 short tons, dry weight)",
+ },
+ "table_38": {
+ "media": "/media/7095/the-machine-readable-file-combines-the-data-from-the-2-excel-tables-us-corn-sweetener-supply-and-use-and-corn-sweetener-trade.csv",
+ "number": "38",
+ "label": "Table 38: U.S. glucose supply and use, by calendar year, since 1964 (1,000 short tons, dry weight)",
+ },
+ "table_57": {
+ "media": "/media/7098/the-machine-readable-file-combines-the-data-from-the-5-excel-tables-table-57-to-table-61.csv",
+ "number": "57",
+ "label": "Table 57: U.S. raw sugar tariff-rate quota World Trade Organization allocations, entries, and shortfalls, by fiscal year quota, since 1996 (metric tons, raw value)",
+ },
+ "table_58a": {
+ "media": "/media/7098/the-machine-readable-file-combines-the-data-from-the-5-excel-tables-table-57-to-table-61.csv",
+ "number": "58a",
+ "label": "Table 58a: U.S. refined sugar tariff-rate quota World Trade Organization allocations and entries by month, since fiscal year quota 2008 (metric tons, raw value)",
+ },
+ "table_58b": {
+ "media": "/media/7098/the-machine-readable-file-combines-the-data-from-the-5-excel-tables-table-57-to-table-61.csv",
+ "number": "58b",
+ "label": "Table 58b: U.S. refined sugar tariff-rate quota World Trade Organization allocations and entries, fiscal year 2007 (metric tons, raw value)",
+ },
+ "table_58c": {
+ "media": "/media/7098/the-machine-readable-file-combines-the-data-from-the-5-excel-tables-table-57-to-table-61.csv",
+ "number": "58c",
+ "label": "Table 58c: U.S. refined sugar tariff-rate quota World Trade Organization allocations and entries, fiscal year 2006 (metric tons, raw value)",
+ },
+ "table_59a": {
+ "media": "/media/7098/the-machine-readable-file-combines-the-data-from-the-5-excel-tables-table-57-to-table-61.csv",
+ "number": "59a",
+ "label": "Table 59a: U.S. sugar tariff-rate quota allocations and entries by month under free trade agreements, since fiscal year 2008 (metric tons, raw value)",
+ },
+ "table_59b": {
+ "media": "/media/7098/the-machine-readable-file-combines-the-data-from-the-5-excel-tables-table-57-to-table-61.csv",
+ "number": "59b",
+ "label": "Table 59b: U.S. sugar tariff-rate quota annual allocations and entries under free trade agreements, fiscal years 2006 and 2007 (metric tons, raw value)",
+ },
+ "table_60a": {
+ "media": "/media/7098/the-machine-readable-file-combines-the-data-from-the-5-excel-tables-table-57-to-table-61.csv",
+ "number": "60a",
+ "label": "Table 60a: U.S. imports of sugar from Mexico, monthly, since fiscal year 2011, metric tons, commercial weight",
+ },
+ "table_60b": {
+ "media": "/media/7098/the-machine-readable-file-combines-the-data-from-the-5-excel-tables-table-57-to-table-61.csv",
+ "number": "60b",
+ "label": "Table 60b: U.S. imports of sugar from Mexico, monthly, since fiscal year 2011, metric tons, raw value",
+ },
+ "table_60c": {
+ "media": "/media/7098/the-machine-readable-file-combines-the-data-from-the-5-excel-tables-table-57-to-table-61.csv",
+ "number": "60c",
+ "label": "Table 60c: U.S. imports of sugar from Mexico, monthly, fiscal years 2008 to 2010, metric tons, raw value",
+ },
+ "table_60d": {
+ "media": "/media/7098/the-machine-readable-file-combines-the-data-from-the-5-excel-tables-table-57-to-table-61.csv",
+ "number": "60d",
+ "label": "Table 60d: U.S. imports of sugar from Mexico by port, monthly, since fiscal year 2011, metric tons, commercial weight",
+ },
+ "table_60e": {
+ "media": "/media/7098/the-machine-readable-file-combines-the-data-from-the-5-excel-tables-table-57-to-table-61.csv",
+ "number": "60e",
+ "label": "Table 60e: U.S. imports of sugar from Mexico by port, fiscal year, since fiscal year 2011, metric tons, commercial weight",
+ },
+ "table_61a": {
+ "media": "/media/7098/the-machine-readable-file-combines-the-data-from-the-5-excel-tables-table-57-to-table-61.csv",
+ "number": "61a",
+ "label": "Table 61a: U.S. monthly sugar imports by source, since fiscal year 2008 (metric tons, raw value)",
+ },
+ "table_61b": {
+ "media": "/media/7098/the-machine-readable-file-combines-the-data-from-the-5-excel-tables-table-57-to-table-61.csv",
+ "number": "61b",
+ "label": "Table 61b: U.S. monthly sugar imports by source, since fiscal year 2008 (short tons, raw value)",
+ },
+}
+
+
+FOLD_DIM_FIELDS: tuple[str, ...] = (
+ "commodity_desc",
+ "commodity_desc2",
+ "geographic_extent",
+ "geographic_extent2",
+ "source_or_destination",
+ "fiscal_year_entered",
+ "fiscal_year_quota",
+)
+
+
+def frequency_rank(period_cat: str | None) -> int:
+ """Rank a period category from annual (0) to quarterly (1) to monthly (2)."""
+ label = (period_cat or "").casefold()
+ if "quarter" in label:
+ return 1
+ if "month" in label:
+ return 2
+ return 0
+
+
+def within_year_label(period_cat: str | None, period_desc: str | None) -> str:
+ """Return the within-year period label for a sub-annual row, else empty.
+
+ Parameters
+ ----------
+ period_cat : str | None
+ Published Period_cat value, e.g. 'Month' or 'Calendar year quarter'.
+ period_desc : str | None
+ Published Period_desc value, e.g. 'Jan', 'Q1 (Jan-Mar)', or 'Dec_31'.
+
+ Returns
+ -------
+ str
+ The month name, quarter code, or month-day label for sub-annual rows,
+ or an empty string for annual rows.
+ """
+ if frequency_rank(period_cat) == 0:
+ return ""
+ desc = period_desc or ""
+ if "quarter" in (period_cat or "").casefold():
+ return desc.split(" ")[0]
+ return desc
+
+
+async def table_frequencies(table: str, **kwargs) -> list[str]:
+ """List the period categories a table publishes, annual bases first.
+
+ Parameters
+ ----------
+ table : str
+ Table key from SUGAR_SWEETENERS_FILES.
+
+ Returns
+ -------
+ list[str]
+ Distinct Period_cat values present in the table, ordered from annual
+ bases through quarters to months.
+ """
+ records = await afetch_table(table)
+ cats = {record["period_cat"] for record in records if record["period_cat"]}
+ return sorted(cats, key=lambda cat: (frequency_rank(cat), cat))
+
+
+def period_sort_rank(period_cat: str | None) -> int:
+ """Return the intra-year granularity rank of a period category.
+
+ Parameters
+ ----------
+ period_cat : str | None
+ Published Period_cat value, e.g. 'Month', 'Calendar year quarter',
+ or 'Fiscal year'.
+
+ Returns
+ -------
+ int
+ 0 for annual categories, 1 for quarters, 2 for months, ordering the
+ rows within a year from the annual summary down to the finest period.
+ """
+ label = (period_cat or "").casefold()
+ if "quarter" in label:
+ return 1
+ if "month" in label:
+ return 2
+ return 0
+
+
+def period_sort_num(period: str | None) -> int:
+ """Return the numeric ordinal of a period token, 0 when non-numeric.
+
+ Parameters
+ ----------
+ period : str | None
+ Published Period value, e.g. '1' for January, '4' for a quarter, or
+ a four-digit year for an annual row.
+
+ Returns
+ -------
+ int
+ The integer value of the token, or 0 when it is not an integer.
+ """
+ try:
+ return int(period or "")
+ except ValueError:
+ return 0
+
+
+def parse_table(text: str, table: str) -> list[dict]:
+ """Parse one table's rows from a combined CSV into long-format records.
+
+ Parameters
+ ----------
+ text : str
+ Decoded CSV text of the file that carries the table.
+ table : str
+ Table key from SUGAR_SWEETENERS_FILES.
+
+ Returns
+ -------
+ list[dict]
+ One record per observation of the selected table, carrying the
+ integer year, the year and period labels, the eight dimension
+ fields, the attribute name, the unit, and the numeric value. Rows
+ for other tables, blank or non-numeric values, and non-numeric year
+ labels are skipped.
+ """
+ number = SUGAR_SWEETENERS_FILES[table]["number"]
+ records: list[dict] = []
+ for row in csv.DictReader(StringIO(text)):
+ if (row.get("Table_number") or "").strip() != number:
+ continue
+ raw_value = (row.get("Value") or "").strip()
+ try:
+ value = float(raw_value)
+ except ValueError:
+ continue
+ year_label = (row.get("Year") or "").strip()
+ if not year_label[:4].isdigit():
+ continue
+ record: dict = {
+ "table": table,
+ "number": number,
+ "year": int(year_label[:4]),
+ "year_cat": (row.get("Year_cat") or "").strip() or None,
+ "year_desc": (row.get("Year_desc") or "").strip() or None,
+ "period": (row.get("Period") or "").strip() or None,
+ "period_cat": (row.get("Period_cat") or "").strip() or None,
+ "period_desc": (row.get("Period_desc") or "").strip() or None,
+ "attribute": (row.get("Attribute_desc") or "").strip(),
+ "unit": (row.get("Unit") or "").strip(),
+ "value": value,
+ }
+ for source_col, output_field in DIMENSION_FIELDS:
+ record[output_field] = (row.get(source_col) or "").strip() or None
+ records.append(record)
+ return records
+
+
+async def afetch_table(table: str, **kwargs) -> list[dict]:
+ """Download and parse one yearbook table through the ERS disk cache.
+
+ Parameters
+ ----------
+ table : str
+ Table key from SUGAR_SWEETENERS_FILES.
+
+ Returns
+ -------
+ list[dict]
+ Long-format records from parse_table.
+ """
+ from openbb_government_us.usda.utils.ers_client import afetch_ers_file
+
+ config = SUGAR_SWEETENERS_FILES[table]
+ content = await afetch_ers_file(config["media"], product=PRODUCT_PAGE)
+ return parse_table(content.decode("utf-8-sig", errors="replace"), table)
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_urban_influence_codes.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_urban_influence_codes.py
new file mode 100644
index 00000000000..b22d4144eac
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_urban_influence_codes.py
@@ -0,0 +1,399 @@
+"""USDA ERS Urban Influence Codes catalog, fetch, and per-vintage parsers."""
+
+import csv
+import math
+from io import BytesIO, StringIO
+from typing import Any
+
+from openbb_core.app.model.abstract.error import OpenBBError
+
+PRODUCT_PAGE = "data-products/urban-influence-codes"
+
+VINTAGES: tuple[str, ...] = ("2024", "2013", "2003", "1993")
+
+VINTAGE_LABELS: dict[str, str] = {
+ "2024": "2024 revision (9-code scheme)",
+ "2013": "2013 (12-code scheme)",
+ "2003": "2003 (12-code scheme)",
+ "1993": "1993 (9-code scheme)",
+}
+
+MEDIA_2024_CSV = "/media/6182/2024-urban-influence-codes.csv"
+MEDIA_2013_XLS = "/media/6183/2013-urban-influence-codes.xls"
+MEDIA_2003_1993_XLS = (
+ "/media/6184/2003-and-1993-urban-influence-codes-for-us-counties.xls"
+)
+MEDIA_2003_PR_XLS = "/media/6185/2003-urban-influence-codes-for-puerto-rico.xls"
+
+SHEET_2013 = "Urban Influence Codes 2013"
+SHEET_2003_1993 = "Urban Influence Codes"
+SHEET_2003_PR = "pr2003UrbInf"
+
+STATE_LABELS: dict[str, str] = {
+ "AK": "Alaska",
+ "AL": "Alabama",
+ "AR": "Arkansas",
+ "AS": "American Samoa",
+ "AZ": "Arizona",
+ "CA": "California",
+ "CO": "Colorado",
+ "CT": "Connecticut",
+ "DC": "District of Columbia",
+ "DE": "Delaware",
+ "FL": "Florida",
+ "GA": "Georgia",
+ "GU": "Guam",
+ "HI": "Hawaii",
+ "IA": "Iowa",
+ "ID": "Idaho",
+ "IL": "Illinois",
+ "IN": "Indiana",
+ "KS": "Kansas",
+ "KY": "Kentucky",
+ "LA": "Louisiana",
+ "MA": "Massachusetts",
+ "MD": "Maryland",
+ "ME": "Maine",
+ "MI": "Michigan",
+ "MN": "Minnesota",
+ "MO": "Missouri",
+ "MP": "Northern Mariana Islands",
+ "MS": "Mississippi",
+ "MT": "Montana",
+ "NC": "North Carolina",
+ "ND": "North Dakota",
+ "NE": "Nebraska",
+ "NH": "New Hampshire",
+ "NJ": "New Jersey",
+ "NM": "New Mexico",
+ "NV": "Nevada",
+ "NY": "New York",
+ "OH": "Ohio",
+ "OK": "Oklahoma",
+ "OR": "Oregon",
+ "PA": "Pennsylvania",
+ "PR": "Puerto Rico",
+ "RI": "Rhode Island",
+ "SC": "South Carolina",
+ "SD": "South Dakota",
+ "TN": "Tennessee",
+ "TX": "Texas",
+ "UT": "Utah",
+ "VA": "Virginia",
+ "VI": "U.S. Virgin Islands",
+ "VT": "Vermont",
+ "WA": "Washington",
+ "WI": "Wisconsin",
+ "WV": "West Virginia",
+ "WY": "Wyoming",
+}
+
+ALL_TERRITORIES: frozenset[str] = frozenset({"AS", "GU", "MP", "PR", "VI"})
+
+VINTAGE_TERRITORIES: dict[str, tuple[str, ...]] = {
+ "2024": ("AS", "GU", "MP", "PR", "VI"),
+ "2013": ("PR",),
+ "2003": ("PR",),
+ "1993": (),
+}
+
+
+def allowed_states(vintage: str) -> tuple[str, ...]:
+ """Return the state codes an Urban Influence Codes vintage is published for.
+
+ Parameters
+ ----------
+ vintage : str
+ Vintage key from VINTAGES.
+
+ Returns
+ -------
+ tuple[str, ...]
+ The fifty states and the District of Columbia, plus the territories
+ carried by the vintage, in alphabetical-by-code order.
+ """
+ territories = set(VINTAGE_TERRITORIES.get(vintage, ()))
+ return tuple(
+ code
+ for code in STATE_LABELS
+ if code not in ALL_TERRITORIES or code in territories
+ )
+
+
+def state_options(vintage: str) -> list[dict]:
+ """Build the labeled state options a vintage is published for.
+
+ Parameters
+ ----------
+ vintage : str
+ Vintage key from VINTAGES.
+
+ Returns
+ -------
+ list[dict]
+ Label/value option dictionaries for the widget's state selector.
+ """
+ return [
+ {"label": STATE_LABELS[code], "value": code} for code in allowed_states(vintage)
+ ]
+
+
+def _fips5(value) -> str:
+ """Return a FIPS code as a zero-padded five-character string."""
+ text = str(value).strip()
+ if text.endswith(".0"):
+ text = text[:-2]
+ return text.zfill(5)
+
+
+def _state(value) -> str:
+ """Return a two-letter state code stripped of whitespace."""
+ return str(value).strip()
+
+
+def _clean_text(value) -> str | None:
+ """Return text with runs of whitespace collapsed, or None when empty."""
+ if value is None or (isinstance(value, float) and math.isnan(value)):
+ return None
+ text = " ".join(str(value).split())
+ return text or None
+
+
+def _text_int(value) -> int | None:
+ """Return a CSV integer string as an int, or None when blank."""
+ text = (value or "").strip()
+ if not text:
+ return None
+ try:
+ return int(text)
+ except ValueError:
+ return None
+
+
+def _cell_number(value) -> int | float | None:
+ """Return a spreadsheet cell as a native number, or None when blank or NaN."""
+ if value is None or (isinstance(value, float) and math.isnan(value)):
+ return None
+ return value.item() if hasattr(value, "item") else value
+
+
+def _code(value) -> str | None:
+ """Return an ordinal classification code as its bare string, or None."""
+ if value is None or (isinstance(value, float) and math.isnan(value)):
+ return None
+ if isinstance(value, str):
+ text = value.strip()
+ return text or None
+ number = value.item() if hasattr(value, "item") else value
+ if isinstance(number, float) and number.is_integer():
+ number = int(number)
+ return str(number)
+
+
+def parse_2024_csv(text: str) -> list[dict]:
+ """Pivot the long 2024 revision CSV into one record per county FIPS.
+
+ Parameters
+ ----------
+ text : str
+ Decoded CSV text with columns FIPS-UIC, State, County_Name, Attribute,
+ and Value, where Attribute is one of Population_2020, UIC_2024, or
+ Description.
+
+ Returns
+ -------
+ list[dict]
+ Unified records with fips, state, county_name, urban_influence_code,
+ description, population, and population_density keys; population_density
+ is None because the 2024 file carries no density.
+ """
+ grouped: dict[str, dict[str, Any]] = {}
+ for row in csv.DictReader(StringIO(text)):
+ fips = _fips5(row["FIPS-UIC"])
+ entry = grouped.get(fips)
+ if entry is None:
+ entry: dict[str, Any] = {
+ "fips": fips,
+ "state": _state(row.get("State")),
+ "county_name": _clean_text(row.get("County_Name")),
+ "urban_influence_code": None,
+ "description": None,
+ "population": None,
+ "population_density": None,
+ }
+ grouped[fips] = entry
+ attribute = (row.get("Attribute") or "").strip()
+ value = row.get("Value")
+ if attribute == "UIC_2024":
+ entry["urban_influence_code"] = _code(value)
+ elif attribute == "Description":
+ entry["description"] = _clean_text(value)
+ elif attribute == "Population_2020":
+ entry["population"] = _text_int(value)
+ return list(grouped.values())
+
+
+def parse_2013_xls(content: bytes) -> list[dict]:
+ """Parse the wide 2013 workbook into unified county records.
+
+ Parameters
+ ----------
+ content : bytes
+ Raw legacy .xls bytes of the 2013 Urban Influence Codes workbook.
+
+ Returns
+ -------
+ list[dict]
+ Unified records; population is the 2010 census count and
+ population_density is None because the 2013 file carries no density.
+ """
+ import pandas as pd
+
+ frame = pd.read_excel(BytesIO(content), sheet_name=SHEET_2013, header=0)
+ return [
+ {
+ "fips": _fips5(row["FIPS"]),
+ "state": _state(row["State"]),
+ "county_name": _clean_text(row["County_Name"]),
+ "urban_influence_code": _code(row["UIC_2013"]),
+ "description": _clean_text(row["Description"]),
+ "population": _cell_number(row["Population_2010"]),
+ "population_density": None,
+ }
+ for row in frame.to_dict("records")
+ ]
+
+
+def parse_2003_combined(content: bytes) -> list[dict]:
+ """Parse the 2003 columns of the combined U.S. counties workbook.
+
+ Parameters
+ ----------
+ content : bytes
+ Raw legacy .xls bytes of the combined 2003-and-1993 U.S. counties
+ workbook.
+
+ Returns
+ -------
+ list[dict]
+ Unified records using the 2003 code and description columns; population
+ and population_density are the 2000 census columns.
+ """
+ import pandas as pd
+
+ frame = pd.read_excel(BytesIO(content), sheet_name=SHEET_2003_1993, header=0)
+ return [
+ {
+ "fips": _fips5(row["FIPS Code"]),
+ "state": _state(row["State"]),
+ "county_name": _clean_text(row["County name"]),
+ "urban_influence_code": _code(row["2003 Urban Influence Code"]),
+ "description": _clean_text(row["2003 Urban Influence Code description"]),
+ "population": _cell_number(row["2000 Population"]),
+ "population_density": _cell_number(row["2000 Persons per square mile"]),
+ }
+ for row in frame.to_dict("records")
+ ]
+
+
+def parse_2003_pr(content: bytes) -> list[dict]:
+ """Parse the 2003 Puerto Rico municipios workbook into unified records.
+
+ Parameters
+ ----------
+ content : bytes
+ Raw legacy .xls bytes of the 2003 Puerto Rico workbook.
+
+ Returns
+ -------
+ list[dict]
+ Unified records for the 78 municipios; population_density is None
+ because the Puerto Rico file carries no density.
+ """
+ import pandas as pd
+
+ frame = pd.read_excel(BytesIO(content), sheet_name=SHEET_2003_PR, header=0)
+ return [
+ {
+ "fips": _fips5(row["FIPS Code"]),
+ "state": _state(row["State"]),
+ "county_name": _clean_text(row["Municipio Name"]),
+ "urban_influence_code": _code(row["Urban Influence Code, 2003"]),
+ "description": _clean_text(row["Description of the 2003 Code"]),
+ "population": _cell_number(row["Population 2003 "]),
+ "population_density": None,
+ }
+ for row in frame.to_dict("records")
+ ]
+
+
+def parse_1993_combined(content: bytes) -> list[dict]:
+ """Parse the 1993 columns of the combined U.S. counties workbook.
+
+ Parameters
+ ----------
+ content : bytes
+ Raw legacy .xls bytes of the combined 2003-and-1993 U.S. counties
+ workbook.
+
+ Returns
+ -------
+ list[dict]
+ Unified records using the 1993 code and description columns; the
+ population and population_density carried by the file are the 2000
+ census columns, as no 1993-vintage population is published.
+ """
+ import pandas as pd
+
+ frame = pd.read_excel(BytesIO(content), sheet_name=SHEET_2003_1993, header=0)
+ return [
+ {
+ "fips": _fips5(row["FIPS Code"]),
+ "state": _state(row["State"]),
+ "county_name": _clean_text(row["County name"]),
+ "urban_influence_code": _code(row["1993 Urban Influence Code"]),
+ "description": _clean_text(row["1993 Urban Influence Code description"]),
+ "population": _cell_number(row["2000 Population"]),
+ "population_density": _cell_number(row["2000 Persons per square mile"]),
+ }
+ for row in frame.to_dict("records")
+ ]
+
+
+async def afetch_vintage(vintage: str) -> list[dict]:
+ """Download and parse an Urban Influence Codes vintage through the cache.
+
+ Parameters
+ ----------
+ vintage : str
+ Vintage key from VINTAGES.
+
+ Returns
+ -------
+ list[dict]
+ Unified county records for the vintage; the 2003 vintage appends the
+ Puerto Rico municipios to the U.S. counties file.
+
+ Raises
+ ------
+ OpenBBError
+ If the vintage is not one of the published vintages.
+ """
+ from openbb_government_us.usda.utils.ers_client import afetch_ers_file
+
+ if vintage == "2024":
+ content = await afetch_ers_file(MEDIA_2024_CSV, product=PRODUCT_PAGE)
+ return parse_2024_csv(content.decode("utf-8-sig", errors="replace"))
+ if vintage == "2013":
+ content = await afetch_ers_file(MEDIA_2013_XLS, product=PRODUCT_PAGE)
+ return parse_2013_xls(content)
+ if vintage == "2003":
+ us = await afetch_ers_file(MEDIA_2003_1993_XLS, product=PRODUCT_PAGE)
+ pr = await afetch_ers_file(MEDIA_2003_PR_XLS, product=PRODUCT_PAGE)
+ return parse_2003_combined(us) + parse_2003_pr(pr)
+ if vintage == "1993":
+ us = await afetch_ers_file(MEDIA_2003_1993_XLS, product=PRODUCT_PAGE)
+ return parse_1993_combined(us)
+ raise OpenBBError(
+ f"Invalid vintage: {vintage}. Valid vintages are: " + ", ".join(VINTAGES)
+ )
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_us_bioenergy_statistics.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_us_bioenergy_statistics.py
new file mode 100644
index 00000000000..cf4d8f8ed6b
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_us_bioenergy_statistics.py
@@ -0,0 +1,284 @@
+"""USDA ERS U.S. Bioenergy Statistics file catalog and long-format parser."""
+
+import csv
+from io import StringIO
+
+from openbb_government_us.utils.serializers import is_null_token
+
+BASE_URL = "https://www.ers.usda.gov"
+PRODUCT_PAGE = "data-products/us-bioenergy-statistics"
+MEDIA_PATH = "/media/6478/us-bioenergy-statistics.csv"
+
+BIOENERGY_TABLES: dict[str, dict] = {
+ "ethanol_supply_marketing_year": {
+ "csv_table_key": "1",
+ "label": "Fuel ethanol supply and disappearance and grain crushings"
+ + " for fuel ethanol by marketing year and quarter",
+ "series_fields": ("commodity", "data_item"),
+ "row_location": False,
+ "cadence": "Marketing year and quarter",
+ },
+ "ethanol_supply_calendar_year": {
+ "csv_table_key": "2",
+ "label": "Fuel ethanol supply and disappearance and grain crushings"
+ + " for fuel ethanol by calendar year",
+ "series_fields": ("commodity", "data_item"),
+ "row_location": False,
+ "cadence": "Calendar year",
+ },
+ "ethanol_supply_monthly": {
+ "csv_table_key": "3",
+ "label": "Fuel ethanol supply and disappearance and grain crushings"
+ + " for fuel ethanol by month",
+ "series_fields": ("commodity", "data_item"),
+ "row_location": False,
+ "cadence": "Monthly",
+ },
+ "biodiesel_supply_marketing_year": {
+ "csv_table_key": "4.1",
+ "label": "Biodiesel supply and disappearance by soybean oil marketing year",
+ "series_fields": ("data_item",),
+ "row_location": False,
+ "cadence": "Marketing year",
+ },
+ "biodiesel_supply_calendar_year": {
+ "csv_table_key": "4.2",
+ "label": "Biodiesel supply and disappearance by calendar year",
+ "series_fields": ("data_item",),
+ "row_location": False,
+ "cadence": "Calendar year",
+ },
+ "renewable_diesel_supply_marketing_year": {
+ "csv_table_key": "4.3",
+ "label": "Renewable diesel supply and disappearance by soybean oil"
+ + " marketing year",
+ "series_fields": ("data_item",),
+ "row_location": False,
+ "cadence": "Marketing year",
+ },
+ "renewable_diesel_supply_calendar_year": {
+ "csv_table_key": "4.4",
+ "label": "Renewable diesel supply and disappearance by calendar year",
+ "series_fields": ("data_item",),
+ "row_location": False,
+ "cadence": "Calendar year",
+ },
+ "corn_supply_and_ethanol_share": {
+ "csv_table_key": "5",
+ "label": "Corn supply, disappearance, and share of total corn used"
+ + " for ethanol",
+ "series_fields": ("data_item",),
+ "row_location": False,
+ "cadence": "Marketing year and quarter",
+ },
+ "soybean_oil_supply_and_biofuel_share": {
+ "csv_table_key": "6",
+ "label": "Soybean oil supply, disappearance, and share used for biofuel",
+ "series_fields": ("data_item",),
+ "row_location": False,
+ "cadence": "Marketing year",
+ },
+ "oils_and_fats_supply_and_prices": {
+ "csv_table_key": "7",
+ "label": "Oils and fats supply and prices",
+ "series_fields": ("commodity", "data_item", "data_item_desc"),
+ "row_location": False,
+ "cadence": "Annual",
+ },
+ "distillers_grains_supply": {
+ "csv_table_key": "8.1",
+ "label": "Distillers grains supply and disappearance",
+ "series_fields": ("data_item",),
+ "row_location": False,
+ "cadence": "Marketing year",
+ },
+ "corn_gluten_meal_supply": {
+ "csv_table_key": "8.2",
+ "label": "Corn gluten meal supply and disappearance",
+ "series_fields": ("data_item",),
+ "row_location": False,
+ "cadence": "Marketing year",
+ },
+ "corn_gluten_feed_supply": {
+ "csv_table_key": "8.3",
+ "label": "Corn gluten feed supply and disappearance",
+ "series_fields": ("data_item",),
+ "row_location": False,
+ "cadence": "Marketing year",
+ },
+ "distillers_dried_grains_price": {
+ "csv_table_key": "9",
+ "label": "Distillers dried grains price",
+ "series_fields": ("data_item",),
+ "row_location": True,
+ "cadence": "Monthly",
+ },
+ "ethanol_capacity_and_utilization": {
+ "csv_table_key": "10",
+ "label": "Fuel ethanol production facilities capacity, production,"
+ + " and utilization ratios",
+ "series_fields": ("data_item",),
+ "row_location": False,
+ "cadence": "Annual",
+ },
+ "ethanol_capacity_by_state": {
+ "csv_table_key": "11",
+ "label": "Fuel ethanol production facilities capacity, by State",
+ "series_fields": ("data_item",),
+ "row_location": True,
+ "cadence": "Annual snapshot",
+ },
+ "biodiesel_renewable_diesel_plants_by_state": {
+ "csv_table_key": "12",
+ "label": "Biodiesel and renewable diesel production: Number of plants"
+ + " and capacity by State",
+ "series_fields": ("commodity", "data_item"),
+ "row_location": True,
+ "cadence": "Annual snapshot",
+ },
+ "alternative_fuel_stations": {
+ "csv_table_key": "13",
+ "label": "Alternative fuel stations",
+ "series_fields": ("commodity",),
+ "row_location": False,
+ "cadence": "Calendar year",
+ },
+ "corn_ethanol_gasoline_prices_monthly": {
+ "csv_table_key": "14",
+ "label": "Monthly prices for corn, fuel ethanol, and gasoline",
+ "series_fields": ("commodity", "data_item", "data_item_desc"),
+ "row_location": False,
+ "cadence": "Monthly",
+ },
+ "ethanol_gasoline_consumption_and_share": {
+ "csv_table_key": "15",
+ "label": "Fuel ethanol and finished motor gasoline consumption and"
+ + " fuel ethanol market share",
+ "series_fields": ("commodity", "data_item"),
+ "row_location": False,
+ "cadence": "Calendar year",
+ },
+ "biodiesel_and_diesel_prices": {
+ "csv_table_key": "16",
+ "label": "Biodiesel and diesel prices",
+ "series_fields": ("commodity",),
+ "row_location": False,
+ "cadence": "Monthly",
+ },
+ "retail_diesel_prices_monthly": {
+ "csv_table_key": "17",
+ "label": "Monthly average retail diesel prices",
+ "series_fields": ("data_item",),
+ "row_location": False,
+ "cadence": "Monthly",
+ },
+}
+
+
+def series_label(components: list[str], units: str) -> str:
+ """Build a wide-column header from series-key components and the unit.
+
+ Parameters
+ ----------
+ components : list[str]
+ Ordered series-key values, e.g. ['Fuel ethanol', 'Production'].
+ Placeholder and empty values are dropped, and an immediately
+ repeated value is collapsed.
+ units : str
+ Unit of the value, appended in parentheses unless it is a
+ placeholder or already appears as a component.
+
+ Returns
+ -------
+ str
+ The column header, e.g. 'Fuel ethanol - Production (1,000 gallons)'.
+ """
+ parts: list[str] = []
+ for component in components:
+ value = (component or "").strip()
+ if not value or is_null_token(value):
+ continue
+ if parts and parts[-1] == value:
+ continue
+ parts.append(value)
+ label = " - ".join(parts)
+ unit = (units or "").strip()
+ if unit and not is_null_token(unit) and unit != label and unit not in parts:
+ label = f"{label} ({unit})" if label else unit
+ return label
+
+
+def parse_table(text: str, table: str) -> list[dict]:
+ """Parse the tidy CSV into one table's long-format observation records.
+
+ Parameters
+ ----------
+ text : str
+ Decoded text of the whole us-bioenergy-statistics CSV.
+ table : str
+ Table slug from BIOENERGY_TABLES.
+
+ Returns
+ -------
+ list[dict]
+ Records carrying the table slug, integer year, period label and
+ ordinal, the row location (only for state and city tables), the
+ wide-column series header, the numeric value, and source order.
+ Rows outside the table or with a blank year or non-numeric value
+ are skipped.
+ """
+ config = BIOENERGY_TABLES[table]
+ key = config["csv_table_key"]
+ series_fields = config["series_fields"]
+ row_location = config["row_location"]
+ records: list[dict] = []
+ for order, row in enumerate(csv.DictReader(StringIO(text))):
+ if (row.get("table") or "").strip() != key:
+ continue
+ raw_value = (row.get("value") or "").strip()
+ try:
+ value = float(raw_value)
+ except ValueError:
+ continue
+ year_raw = (row.get("year") or "").strip()
+ if not (len(year_raw) == 4 and year_raw.isdigit()):
+ continue
+ try:
+ period_ord = int((row.get("period") or "").strip())
+ except ValueError:
+ period_ord = 0
+ location = (row.get("location") or "").strip()
+ records.append(
+ {
+ "table": table,
+ "year": int(year_raw),
+ "period": (row.get("period_desc") or "").strip() or None,
+ "period_ord": period_ord,
+ "location": (location or None) if row_location else None,
+ "series": series_label(
+ [row.get(field, "") for field in series_fields],
+ row.get("units", ""),
+ ),
+ "value": value,
+ }
+ )
+ return records
+
+
+async def afetch_table(table: str, **kwargs) -> list[dict]:
+ """Download and parse one bioenergy table through the ERS disk cache.
+
+ Parameters
+ ----------
+ table : str
+ Table slug from BIOENERGY_TABLES.
+
+ Returns
+ -------
+ list[dict]
+ Long-format records from parse_table.
+ """
+ from openbb_government_us.usda.utils.ers_client import afetch_ers_file
+
+ content = await afetch_ers_file(MEDIA_PATH, product=PRODUCT_PAGE)
+ return parse_table(content.decode("utf-8-sig", errors="replace"), table)
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_us_food_imports.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_us_food_imports.py
new file mode 100644
index 00000000000..22aa000c774
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_us_food_imports.py
@@ -0,0 +1,234 @@
+"""USDA ERS U.S. Food Imports file catalog, parser, and pivot helpers."""
+
+import csv
+from collections import OrderedDict
+from io import StringIO
+
+BASE_URL = "https://www.ers.usda.gov"
+PRODUCT_PAGE = "data-products/us-food-imports"
+MEDIA_PATH = (
+ "/media/6495/summary-data-on-annual-food-imports-values-and-volume"
+ "-by-food-category-and-source-country.csv"
+)
+
+FOOD_IMPORTS_FILES: dict[str, tuple[str, str]] = {"summary": (MEDIA_PATH, PRODUCT_PAGE)}
+
+AGGREGATE_CATEGORIES = ("Food dollars", "Food volume", "Prices")
+
+FOOD_GROUPS: "OrderedDict[str, str]" = OrderedDict(
+ [
+ ("Animals", "Live animals"),
+ ("Meats", "Meats"),
+ ("Fish", "Fish and shellfish"),
+ ("Dairy", "Dairy"),
+ ("Vegetables", "Vegetables"),
+ ("Fruits", "Fruits"),
+ ("Nuts", "Nuts"),
+ ("Coffee", "Coffee, tea, and spices"),
+ ("Grains", "Grains"),
+ ("VegetablesOil", "Vegetable oils"),
+ ("Sweets", "Sugar and candy"),
+ ("Cocoa", "Cocoa and chocolate"),
+ ("Other", "Other edible products"),
+ ("Beverages", "Beverages"),
+ ]
+)
+
+DEFAULT_FOOD_GROUP = "Fruits"
+DEFAULT_COMMODITY = "Total fruit and preparations"
+
+AGG_COLUMN_LABELS = {
+ "U.S. imports": "Total foods",
+ "Live meat animals": "Live animals",
+ "Fish and shellfish": "Fish",
+}
+
+MEASURES: dict[str, dict] = {
+ "value": {
+ "label": "Import value (million $)",
+ "category": "Food dollars",
+ "subcategories": ("Total foods", "Foods"),
+ "percent": False,
+ "uniform_unit": "Million $",
+ },
+ "value_change": {
+ "label": "Import value, year-over-year change (percent)",
+ "category": "Food dollars",
+ "subcategories": ("Total foods", "Foods"),
+ "percent": True,
+ "uniform_unit": "percent",
+ },
+ "volume": {
+ "label": "Import volume",
+ "category": "Food volume",
+ "subcategories": ("Foods",),
+ "percent": False,
+ "uniform_unit": None,
+ },
+ "volume_change": {
+ "label": "Import volume, year-over-year change (percent)",
+ "category": "Food volume",
+ "subcategories": ("Foods",),
+ "percent": True,
+ "uniform_unit": "percent",
+ },
+ "unit_price": {
+ "label": "Import unit price",
+ "category": "Prices",
+ "subcategories": ("Imported food prices",),
+ "percent": False,
+ "uniform_unit": None,
+ },
+ "inflation": {
+ "label": "Import price inflation (percent)",
+ "category": "Prices",
+ "subcategories": ("Import price inflation",),
+ "percent": True,
+ "uniform_unit": "percent",
+ },
+}
+
+SOURCE_MEASURES = ("value", "volume")
+
+SMALL_WORDS = frozenset({"and", "of", "or", "the"})
+
+COUNTRY_FIXES = {
+ "GERMANY, FED. REPUBLIC": "Germany",
+ "COTE D'IVOIRE": "Cote d'Ivoire",
+ "REST OF WORLD": "Rest of world",
+ "WORLD": "World",
+ "WORLD (QUANTITY)": "World",
+}
+
+UNIT_LABELS = {"1,000": "1,000 head"}
+
+
+def build_url() -> str:
+ """Build the download URL of the summary CSV."""
+ return f"{BASE_URL}{MEDIA_PATH}"
+
+
+def clean_country(raw: str) -> str:
+ """Clean a source-country label to title case with special-case fixes.
+
+ Parameters
+ ----------
+ raw : str
+ Country label as published, uppercase and sometimes carrying a
+ replacement character for a curly apostrophe or a trailing space.
+
+ Returns
+ -------
+ str
+ The title-cased label, with 'GERMANY, FED. REPUBLIC', Cote d'Ivoire,
+ and the world aggregates mapped to their canonical forms.
+ """
+ text = raw.replace("�", "'").strip()
+ key = text.upper()
+ if key in COUNTRY_FIXES:
+ return COUNTRY_FIXES[key]
+ words = text.split()
+ parts: list[str] = []
+ for index, word in enumerate(words):
+ lower = word.lower()
+ if index and lower in SMALL_WORDS:
+ parts.append(lower)
+ else:
+ parts.append(lower[:1].upper() + lower[1:])
+ return " ".join(parts)
+
+
+def unit_label(uom: str) -> str:
+ """Return a display unit label, disambiguating the bare '1,000' head unit."""
+ return UNIT_LABELS.get(uom.strip(), uom.strip())
+
+
+def food_group_column_label(commodity: str) -> str:
+ """Return the by-food-group column label for an aggregate commodity name."""
+ return AGG_COLUMN_LABELS.get(commodity, commodity)
+
+
+def parse_rows(text: str) -> list[dict]:
+ """Parse the summary CSV text into tidy long-format observation records.
+
+ Parameters
+ ----------
+ text : str
+ Decoded CSV text of the summary file.
+
+ Returns
+ -------
+ list[dict]
+ Records with commodity, country, uom, category, subcategory,
+ row_number, year, and value keys. Rows whose year token is a period
+ average ('means', 'means10years') or whose value is non-numeric are
+ skipped.
+ """
+ records: list[dict] = []
+ for row in csv.DictReader(StringIO(text)):
+ year_token = (row.get("YearNum") or "").strip()
+ if not year_token.isdigit():
+ continue
+ raw_value = (row.get("FoodValue") or "").strip()
+ try:
+ value = float(raw_value)
+ except ValueError:
+ continue
+ raw_row_number = (row.get("RowNumber") or "").strip()
+ try:
+ row_number = int(raw_row_number)
+ except ValueError:
+ continue
+ records.append(
+ {
+ "commodity": (row.get("Commodity") or "").strip(),
+ "country": (row.get("Country") or "").strip(),
+ "uom": (row.get("UOM") or "").strip(),
+ "category": (row.get("Category") or "").strip(),
+ "subcategory": (row.get("SubCategory") or "").strip(),
+ "row_number": row_number,
+ "year": int(year_token),
+ "value": value,
+ }
+ )
+ return records
+
+
+def product_lines(records: list[dict], food_group: str) -> list[str]:
+ """List a food group's product-line commodities in published rank order.
+
+ Parameters
+ ----------
+ records : list[dict]
+ Long-format records from parse_rows.
+ food_group : str
+ Detail food-group category code, e.g. 'Fruits'.
+
+ Returns
+ -------
+ list[str]
+ Distinct product-line commodity names, ordered by their first
+ RowNumber, e.g. 'Total fruit and preparations' first.
+ """
+ first_seen: dict[str, int] = {}
+ for record in records:
+ if record["category"] != food_group:
+ continue
+ name = record["commodity"]
+ if name not in first_seen or record["row_number"] < first_seen[name]:
+ first_seen[name] = record["row_number"]
+ return sorted(first_seen, key=lambda name: first_seen[name])
+
+
+async def afetch_records() -> list[dict]:
+ """Download and parse the summary CSV through the ERS disk cache.
+
+ Returns
+ -------
+ list[dict]
+ Long-format records from parse_rows.
+ """
+ from openbb_government_us.usda.utils.ers_client import afetch_ers_file
+
+ content = await afetch_ers_file(MEDIA_PATH, product=PRODUCT_PAGE)
+ return parse_rows(content.decode("utf-8-sig", errors="replace"))
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_vegetables_and_pulses.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_vegetables_and_pulses.py
new file mode 100644
index 00000000000..11adc5b2311
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_vegetables_and_pulses.py
@@ -0,0 +1,243 @@
+"""USDA ERS Vegetables and Pulses Yearbook file catalog and long-format parser."""
+
+import csv
+import zipfile
+from io import BytesIO, StringIO
+
+BASE_URL = "https://www.ers.usda.gov"
+PRODUCT_PAGE = (
+ "data-products/vegetables-and-pulses-data/vegetables-and-pulses-yearbook-tables"
+)
+DATA_ZIP = "/media/5018/vegetables-and-pulses-machine-readable-data.zip"
+CSV_MEMBER = "Vegetables_Pulses.csv"
+
+DEFAULT_TABLE = "Table33_Potatoes"
+
+TABLE_LABELS: dict[str, str] = {
+ "Table09_WorldVegProduction": "Selected vegetable production in leading"
+ " countries and the world",
+ "Table10_CashReceipts": "Vegetables and pulse crops: U.S. farm cash receipts",
+ "Table11_TotalFresh": "U.S. fresh market vegetables: supply, availability,"
+ " and farm weight",
+ "Table12_Artichokes": "U.S. artichokes, all uses: supply, availability,"
+ " price, and farm weight",
+ "Table13_Asparagus": "U.S. fresh asparagus: supply, availability, price,"
+ " and farm weight",
+ "Table14_Broccoli": "U.S. fresh broccoli: supply, availability, price,"
+ " and farm weight",
+ "Table15_BrusselsSprouts": "U.S. brussels sprouts, all uses: supply,"
+ " availability, price, and farm weight",
+ "Table16_Cabbage": "U.S. cabbage: supply, availability, price, and farm weight",
+ "Table17_Carrots": "U.S. fresh carrots: supply, availability, price,"
+ " and farm weight",
+ "Table18_Cauliflower": "U.S. fresh cauliflower: supply, availability,"
+ " price, and farm weight",
+ "Table19_Celery": "U.S. celery, all uses: supply, availability, price,"
+ " and farm weight",
+ "Table20_CollardGreens": "U.S. collard greens, all uses: supply,"
+ " availability, price, and farm weight",
+ "Table21_Cucumbers": "U.S. fresh cucumbers: supply, availability, price,"
+ " and farm weight",
+ "Table22_Eggplant": "U.S. eggplant, all uses: supply, availability, price,"
+ " and farm weight",
+ "Table23_EscaroleandEndive": "U.S. escarole and endive, all uses: supply,"
+ " availability, price, and farm weight",
+ "Table24_Garlic": "U.S. garlic, all uses: supply, availability, price,"
+ " and farm weight",
+ "Table25_HeadLettuce": "U.S. fresh head lettuce: supply, availability,"
+ " price, and farm weight",
+ "Table26_LeafandRomaine": "U.S. leaf and romaine lettuce: supply,"
+ " availability, price, and farm weight",
+ "Table27_Kale": "U.S. kale, all uses: supply, availability, price, and farm weight",
+ "Table28_Mushrooms": "U.S. fresh mushrooms: supply, availability, price,"
+ " and farm weight",
+ "Table29_MustardGreens": "U.S. mustard greens, all uses: supply,"
+ " availability, price, and farm weight",
+ "Table30_Okra": "U.S. okra, all uses: supply, availability, and farm weight",
+ "Table31_Onions": "U.S. fresh onions: supply, availability, price, and farm weight",
+ "Table32_PeppersBell": "U.S. bell peppers, all uses: supply, availability,"
+ " price, and farm weight",
+ "Table33_Potatoes": "U.S. fresh potatoes: supply, availability, price,"
+ " and farm weight",
+ "Table34_Pumpkins": "U.S. pumpkins, all uses: supply, availability,"
+ " and farm weight",
+ "Table35_Radishes": "U.S. radishes, all uses: supply, availability,"
+ " and farm weight",
+ "Table36_SnapBeans": "U.S. fresh snap beans: supply, availability, price,"
+ " and farm weight",
+ "Table37_Spinach": "U.S. fresh spinach: supply, availability, price,"
+ " and farm weight",
+ "Table38_Squash": "U.S. squash, all uses: supply, availability, price,"
+ " and farm weight",
+ "Table39_SweetCorn": "U.S. fresh sweet corn: supply, availability, price,"
+ " and farm weight",
+ "Table40_SweetPotatoes": "U.S. sweet potatoes, all uses: supply,"
+ " availability, price, and farm weight",
+ "Table41_Tomatoes": "U.S. fresh tomatoes: supply, availability, price,"
+ " and farm weight",
+ "Table42_TurnipGreens": "U.S. turnip greens, all: supply, availability,"
+ " price, and farm weight",
+ "Table43_TotalVeg_Pr": "U.S. vegetables for processing (excluding potatoes):"
+ " supply, availability, and farm weight",
+ "Table44a_Asparagus_Pr": "U.S. asparagus for processing: supply,"
+ " availability, price, and farm weight",
+ "Table44b_Asparagus_Cn": "U.S. asparagus for canning: supply, availability,"
+ " price, and farm weight",
+ "Table44c_Asparagus_Fz": "U.S. asparagus for freezing: supply,"
+ " availability, price, and farm weight",
+ "Table45_Broccoli_Pr": "U.S. broccoli for processing: supply, availability,"
+ " price, and farm weight",
+ "Table46_Cabbage_Pr": "U.S. cabbage for sauerkraut: supply, availability,"
+ " price, and farm weight",
+ "Table47a_Carrots_Pr": "U.S. carrots for processing: supply, availability,"
+ " price, and farm weight",
+ "Table47b_Carrots_Fz": "U.S. carrots for freezing: supply, availability,"
+ " price, and farm weight",
+ "Table47c_Carrots_Cn": "U.S. carrots for canning: supply, availability,"
+ " price, and farm weight",
+ "Table48_Cauliflower_Pr": "U.S. cauliflower for processing: supply,"
+ " availability, price, and farm weight",
+ "Table49_Cucumbers_Pr": "U.S. cucumbers for processing (largely pickling):"
+ " supply, availability, price, and farm weight",
+ "Table50a_GreenLimaBeans_Pr": "U.S. green lima beans for processing:"
+ " supply, availability, price, and farm weight",
+ "Table50b_GreenLimaBeans_Cn": "U.S. green lima beans for canning: supply,"
+ " availability, price, and farm weight",
+ "Table50c_GreenLimaBeans_Fz": "U.S. green lima beans for freezing: supply,"
+ " availability, price, and farm weight",
+ "Table51_Mushrooms_Pr": "U.S. mushrooms for processing: supply,"
+ " availability, price, and farm weight",
+ "Table52_Onions_Dehy": "U.S. onions for dehydrating: supply, availability,"
+ " price, and farm weight",
+ "Table53a_GreenPeas_Pr": "U.S. peas (green) for processing: supply,"
+ " availability, price, and farm weight",
+ "Table53b_GreenPeas_Cn": "U.S. peas (green) for canning: supply,"
+ " availability, price, and farm weight",
+ "Table53c_GreenPeas_Fz": "U.S. peas (green) for freezing: supply,"
+ " availability, price, and farm weight",
+ "Table54_ChiliPeppers_Pr": "U.S. chili peppers, all uses: supply,"
+ " availability, and price, farm weight",
+ "Table55a_Potatoes_Cn": "U.S. potatoes for canning: supply, availability,"
+ " price, and farm weight",
+ "Table55b_Potatoes_Chip": "U.S. potatoes for chips and shoestrings: supply,"
+ " availability, price, and farm weight",
+ "Table55c_Potatoes_Dehy": "U.S. potatoes for dehydration: supply,"
+ " availability, price, and farm weight",
+ "Table55d_Potatoes_Fz": "U.S. potatoes for freezing: Supply and availability",
+ "Table56a_SnapBeans_Pr": "U.S. snap beans for processing: supply,"
+ " availability, price, and farm weight",
+ "Table56b_SnapBeans_Cn": "U.S. snap beans for canning: supply,"
+ " availability, price, and farm weight",
+ "Table56c_SnapBeans_Fz": "U.S. snap beans for freezing: supply,"
+ " availability, price, and farm weight",
+ "Table57a_Spinach_Pr": "U.S. spinach for processing: supply, availability,"
+ " price, and farm weight",
+ "Table57b_Spinach_Cn": "U.S. spinach for canning: supply, availability,"
+ " price, and farm weight",
+ "Table57c_Spinach_Fz": "U.S. spinach for freezing: supply, availability,"
+ " price, and farm weight",
+ "Table58a_SweetCorn_Pr": "U.S. sweet corn for processing: supply,"
+ " availability, price, and farm weight",
+ "Table58b_SweetCorn_Cn": "U.S. sweet corn for canning: supply,"
+ " availability, price, and farm weight",
+ "Table58c_SweetCorn_Fz": "U.S. sweet corn for freezing: supply,"
+ " availability, price, and farm weight",
+ "Table59_Tomatoes_Pr": "U.S. tomatoes for processing: supply, availability,"
+ " price, and farm weight",
+ "Table60_AllDryBeans": "U.S. dry edible beans: supply, availability,"
+ " and price, farm weight",
+ "Table61_Pinto": "U.S. pinto beans, dry: supply, availability, and price,"
+ " farm weight",
+ "Table62_Navy": "U.S. navy beans, dry: supply, availability, and price,"
+ " farm weight",
+ "Table63_GreatNorthern": "U.S. great northern beans, dry: supply,"
+ " availability, and price, farm weight",
+ "Table64_Black": "U.S. black beans, dry: supply, availability, and price,"
+ " farm weight",
+ "Table65_Lima": "U.S. lima beans, dry: supply, availability, and price,"
+ " farm weight",
+ "Table66_RedKidney": "U.S. all red kidney beans, dry: supply, availability,"
+ " and price, farm weight",
+ "Table67_Blackeye": "U.S. blackeye beans, dry: supply, availability,"
+ " and price, farm weight",
+ "Table68_Garbanzo": "U.S. garbanzo beans, dry: supply, availability,"
+ " and price, farm weight",
+ "Table69_SmallWhite": "U.S. small white beans, dry: supply, availability,"
+ " and price, farm weight",
+ "Table70_SmallRed": "U.S. small red beans, dry: supply, availability,"
+ " and price, farm weight",
+ "Table71_Pink": "U.S. pink beans, dry: supply, availability, and price,"
+ " farm weight",
+ "Table72_Other": "U.S. cranberry and other beans, dry: supply,"
+ " availability, and price, farm weight",
+}
+
+
+def parse_rows(text: str, table: str) -> list[dict]:
+ """Parse the yearbook CSV text into long-format records for one table.
+
+ Parameters
+ ----------
+ text : str
+ Decoded CSV text of the Vegetables_Pulses.csv member.
+ table : str
+ Table key from TABLE_LABELS to keep; other tables are skipped.
+
+ Returns
+ -------
+ list[dict]
+ One record per kept observation, carrying the integer year, the
+ commodity, end use, location, geographical level, item (the series
+ that becomes a wide column), unit, category, and the numeric value.
+ Rows for other tables, or whose year or value is blank or
+ non-numeric, are skipped.
+ """
+ records: list[dict] = []
+ for row in csv.DictReader(StringIO(text)):
+ if (row.get("Table") or "").strip() != table:
+ continue
+ raw_value = (row.get("PublishValue") or "").strip()
+ try:
+ value = float(raw_value)
+ except ValueError:
+ continue
+ year_label = (row.get("Year") or "").strip()
+ if not year_label[:4].isdigit():
+ continue
+ records.append(
+ {
+ "table": table,
+ "year": int(year_label[:4]),
+ "commodity": (row.get("Commodity") or "").strip() or None,
+ "end_use": (row.get("EndUse") or "").strip() or None,
+ "location": (row.get("Location") or "").strip() or None,
+ "geographical_level": (row.get("GeographicalLevel") or "").strip()
+ or None,
+ "item": (row.get("Item") or "").strip(),
+ "unit": (row.get("Unit") or "").strip() or None,
+ "category": (row.get("Category") or "").strip() or None,
+ "value": value,
+ }
+ )
+ return records
+
+
+async def afetch_table(table: str, **kwargs) -> list[dict]:
+ """Download the yearbook zip through the ERS cache and parse one table.
+
+ Parameters
+ ----------
+ table : str
+ Table key from TABLE_LABELS.
+
+ Returns
+ -------
+ list[dict]
+ Long-format records from parse_rows for the selected table.
+ """
+ from openbb_government_us.usda.utils.ers_client import afetch_ers_file
+
+ content = await afetch_ers_file(DATA_ZIP, product=PRODUCT_PAGE)
+ with zipfile.ZipFile(BytesIO(content)) as archive:
+ raw = archive.read(CSV_MEMBER)
+ return parse_rows(raw.decode("utf-8-sig", errors="replace"), table)
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_wheat_data.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_wheat_data.py
new file mode 100644
index 00000000000..e9c3107f46d
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_wheat_data.py
@@ -0,0 +1,310 @@
+"""USDA ERS Wheat Data file catalog and long-format parsers."""
+
+import csv
+import zipfile
+from io import BytesIO, StringIO
+
+BASE_URL = "https://www.ers.usda.gov"
+PRODUCT_PAGE = "data-products/wheat-data"
+
+ALL_YEARS_ZIP = "/media/5709/wheat-data-all-years.zip"
+QUARTERLY_ZIP = "/media/5708/wheat-by-class-quarterly.zip"
+
+CLASS_CANON: dict[str, str] = {
+ "all wheat": "All wheat",
+ "durum": "Durum",
+ "hard red spring": "Hard red spring",
+ "hard red winter": "Hard red winter",
+ "hard white": "Hard white",
+ "soft red winter": "Soft red winter",
+ "soft white": "Soft white",
+ "white": "White",
+ "rye": "Rye",
+}
+
+WHEAT_DATA_FILES: dict[str, dict] = {
+ "wheat_acreage_production_yield_price": {
+ "label": "U.S. wheat: acreage, production, yield, and farm price",
+ "media": ALL_YEARS_ZIP,
+ "member": "01_Wheat_US_Acreage_Production_Yield_and_Farm_Price.csv",
+ "value_col": "Amount",
+ "year_col": "Marketing_Year",
+ "period_col": "Timeperiod_Desc",
+ "series_col": "Attribute_Desc2",
+ "value_name": None,
+ "row_dims": (("Commodity_Desc2", "wheat_class"),),
+ },
+ "rye_acreage_production_yield_price": {
+ "label": "U.S. rye: acreage, production, yield, and farm price",
+ "media": ALL_YEARS_ZIP,
+ "member": "02_Rye_US_Acreage_Production_Yield_and_Farm_Price.csv",
+ "value_col": "Amount",
+ "year_col": "Marketing_Year",
+ "period_col": "Timeperiod_Desc",
+ "series_col": "Attribute_Desc2",
+ "value_name": None,
+ "row_dims": (("Commodity_Desc2", "wheat_class"),),
+ },
+ "world_supply_and_disappearance": {
+ "label": "World and U.S. wheat supply and disappearance",
+ "media": ALL_YEARS_ZIP,
+ "member": "03_04_World_wheat_supply_and_disappearance.csv",
+ "value_col": "Amount",
+ "year_col": "Marketing_Year",
+ "period_col": None,
+ "series_col": "Attribute_Desc",
+ "value_name": None,
+ "row_dims": (),
+ },
+ "us_supply_and_disappearance": {
+ "label": "U.S. wheat supply and disappearance, by class",
+ "media": ALL_YEARS_ZIP,
+ "member": "05_11_US_Supply_And_Disappearance.csv",
+ "value_col": "Amount",
+ "year_col": "Marketing_Year",
+ "period_col": "Timeperiod_Desc",
+ "series_col": "Attribute_Desc",
+ "value_name": None,
+ "row_dims": (("Commodity_Desc2", "wheat_class"),),
+ },
+ "rye_supply_and_disappearance": {
+ "label": "U.S. rye supply and disappearance",
+ "media": ALL_YEARS_ZIP,
+ "member": "12_Rye_US_Supply_And_Disappearance.csv",
+ "value_col": "Amount",
+ "year_col": "Marketing_Year",
+ "period_col": "Timeperiod_Desc",
+ "series_col": "Attribute_Desc",
+ "value_name": None,
+ "row_dims": (),
+ },
+ "food_use": {
+ "label": "U.S. wheat food use",
+ "media": ALL_YEARS_ZIP,
+ "member": "13_16_Food_Use.csv",
+ "value_col": "Amount",
+ "year_col": "Marketing_Year",
+ "period_col": "Timeperiod_Desc",
+ "series_col": "Commodity_Desc2",
+ "value_name": None,
+ "row_dims": (("Commodity_Desc", "commodity_group"),),
+ },
+ "government_and_private_stocks": {
+ "label": "Government and private wheat stocks",
+ "media": ALL_YEARS_ZIP,
+ "member": "17_Government_and_private_stocks.csv",
+ "value_col": "Amount",
+ "year_col": "Marketing_Year",
+ "period_col": "Timeperiod_Desc",
+ "series_col": "Attribute_Desc",
+ "value_name": None,
+ "row_dims": (),
+ },
+ "domestic_and_international_prices": {
+ "label": "Domestic and international wheat prices",
+ "media": ALL_YEARS_ZIP,
+ "member": "18_20_Domestic_and_international_prices.csv",
+ "value_col": "Amount",
+ "year_col": "Marketing_Year",
+ "period_col": "Timeperiod_Desc",
+ "series_col": "Commodity_Desc",
+ "value_name": None,
+ "row_dims": (("Geography_Desc", "geography"), ("Unit_Desc", "unit")),
+ },
+ "export_import_trade_by_component": {
+ "label": "Wheat export and import trade, by component",
+ "media": ALL_YEARS_ZIP,
+ "member": "21_24_Export_and_import_trade_data_by_component.csv",
+ "value_col": "Amount",
+ "year_col": "Marketing_Year",
+ "period_col": "Timeperiod_Desc",
+ "series_col": "Attribute_Desc",
+ "value_name": None,
+ "row_dims": (
+ ("Commodity_Desc", "product"),
+ ("Commodity_Desc2", "commodity_group"),
+ ),
+ },
+ "all_wheat_destination_exports": {
+ "label": "All-wheat exports, by destination",
+ "media": ALL_YEARS_ZIP,
+ "member": "25_All_wheat_destination_export_trade_data.csv",
+ "value_col": "Amount",
+ "year_col": "Marketing_Year",
+ "period_col": "Timeperiod_Desc",
+ "series_col": None,
+ "value_name": "Exports",
+ "row_dims": (("Geography_Desc", "geography"),),
+ },
+ "by_class_inspections": {
+ "label": "Wheat inspections, by class and destination",
+ "media": ALL_YEARS_ZIP,
+ "member": "26_By_class_inspections _data.csv",
+ "value_col": "Amount",
+ "year_col": "Marketing_Year",
+ "period_col": "Timeperiod_Desc",
+ "series_col": "Commodity_Desc",
+ "value_name": None,
+ "row_dims": (("Geography_Desc", "geography"),),
+ },
+ "exports_by_government_program": {
+ "label": "Wheat exports, by government program",
+ "media": ALL_YEARS_ZIP,
+ "member": "27_Exports_by_government_program.csv",
+ "value_col": "Amount",
+ "year_col": "Fiscal_Year",
+ "period_col": "Timeperiod_Desc",
+ "series_col": "Attribute_Desc",
+ "value_name": None,
+ "row_dims": (),
+ },
+ "flour_production_and_disappearance": {
+ "label": "Wheat flour production and disappearance",
+ "media": ALL_YEARS_ZIP,
+ "member": "28_31_Flour_production_and_disappearance.csv",
+ "value_col": "Amount",
+ "year_col": "Calendar_Year",
+ "period_col": "Timeperiod_Desc",
+ "series_col": "Attribute_Desc",
+ "value_name": None,
+ "row_dims": (
+ ("Commodity_Desc2", "product"),
+ ("Commodity_Desc", "commodity_group"),
+ ),
+ },
+ "wheat_flour_price_relationships": {
+ "label": "Wheat and flour price relationships",
+ "media": ALL_YEARS_ZIP,
+ "member": "32_33_Wheat_and_flour_price_relationships.csv",
+ "value_col": "Amount",
+ "year_col": "Marketing_Year",
+ "period_col": "Timeperiod_Desc",
+ "series_col": "Commodity_Desc2",
+ "value_name": None,
+ "row_dims": (("Geography_Desc", "geography"),),
+ },
+ "by_class_trade_data": {
+ "label": "Wheat trade, by class",
+ "media": ALL_YEARS_ZIP,
+ "member": "34_35_By-class_trade_data.csv",
+ "value_col": "Amount",
+ "year_col": "Marketing_Year",
+ "period_col": "Timeperiod_Desc",
+ "series_col": "Attribute_Desc",
+ "value_name": None,
+ "row_dims": (("Commodity_Desc", "wheat_class"),),
+ },
+ "by_class_quarterly": {
+ "label": "By-class quarterly balance sheet",
+ "media": QUARTERLY_ZIP,
+ "member": "wheat-by-class-quarterly.csv",
+ "value_col": "Value",
+ "year_col": "MarketingYear",
+ "period_col": "TimePeriod",
+ "series_col": "Attribute",
+ "value_name": None,
+ "row_dims": (("Class", "wheat_class"), ("Unit", "unit")),
+ },
+}
+
+ROW_DIM_FIELDS = (
+ "wheat_class",
+ "commodity_group",
+ "product",
+ "geography",
+ "unit",
+)
+
+
+def normalize_class(value: str) -> str:
+ """Normalize a wheat-class label to its canonical casing.
+
+ Parameters
+ ----------
+ value : str
+ Class label as published, e.g. 'All Wheat'.
+
+ Returns
+ -------
+ str
+ Canonical label, e.g. 'All wheat'; the input unchanged when unknown.
+ """
+ return CLASS_CANON.get(value.strip().casefold(), value.strip())
+
+
+def parse_table(text: str, table: str) -> list[dict]:
+ """Parse one table's CSV text into long-format observation records.
+
+ Parameters
+ ----------
+ text : str
+ Decoded CSV text of the table member.
+ table : str
+ Table key from WHEAT_DATA_FILES.
+
+ Returns
+ -------
+ list[dict]
+ Records carrying the year label, integer year, period, the five
+ row-dimension fields, the series name, and the numeric amount.
+ Rows whose value is blank or non-numeric are skipped.
+ """
+ config = WHEAT_DATA_FILES[table]
+ series_col = config["series_col"]
+ period_col = config["period_col"]
+ records: list[dict] = []
+ for row in csv.DictReader(StringIO(text)):
+ raw_value = (row.get(config["value_col"]) or "").strip()
+ try:
+ amount = float(raw_value)
+ except ValueError:
+ continue
+ year_label = (row.get(config["year_col"]) or "").strip()
+ if not year_label[:4].isdigit():
+ continue
+ record: dict = {
+ "table": table,
+ "marketing_year": year_label,
+ "year": int(year_label[:4]),
+ "period": (
+ ((row.get(period_col) or "").strip() or None) if period_col else None
+ ),
+ "wheat_class": None,
+ "commodity_group": None,
+ "product": None,
+ "geography": None,
+ "unit": None,
+ }
+ for source_col, output_field in config["row_dims"]:
+ value = (row.get(source_col) or "").strip()
+ if output_field == "wheat_class":
+ value = normalize_class(value)
+ record[output_field] = value or None
+ record["series"] = (
+ (row.get(series_col) or "").strip() if series_col else config["value_name"]
+ )
+ record["amount"] = amount
+ records.append(record)
+ return records
+
+
+async def afetch_table(table: str, **kwargs) -> list[dict]:
+ """Download and parse one wheat-data table through the ERS disk cache.
+
+ Parameters
+ ----------
+ table : str
+ Table key from WHEAT_DATA_FILES.
+
+ Returns
+ -------
+ list[dict]
+ Long-format records from parse_table.
+ """
+ from openbb_government_us.usda.utils.ers_client import afetch_ers_file
+
+ config = WHEAT_DATA_FILES[table]
+ content = await afetch_ers_file(config["media"], product=PRODUCT_PAGE)
+ with zipfile.ZipFile(BytesIO(content)) as archive:
+ raw = archive.read(config["member"])
+ return parse_table(raw.decode("utf-8-sig", errors="replace"), table)
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/fas_bell_report.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/fas_bell_report.py
new file mode 100644
index 00000000000..639137e2c76
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/fas_bell_report.py
@@ -0,0 +1,337 @@
+"""USDA FAS Bell Report fetching."""
+
+import re
+from datetime import (
+ date as dateType,
+ datetime,
+ timedelta,
+)
+from typing import Any
+from urllib.parse import urlencode
+
+REPORT_HOST = "apps.fas.usda.gov"
+BASE_URL = f"https://{REPORT_HOST}/esrqs/"
+API_URL = f"{BASE_URL}api/"
+TOKEN_URL = f"{BASE_URL}token"
+PUBLIC_CLIENT_ID = "eAuth_Client"
+PUBLIC_CLIENT_SECRET = (
+ "00000000-0000-0000-0000-00000000000000000000-0000-0000-0000-000000000000" # noqa: S105
+)
+USER_AGENT = (
+ "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36"
+ " (KHTML, like Gecko) Chrome/126 Safari/537.36"
+)
+
+REPORT_NAMES = {"my1": "BR", "my2": "BRMY2", "my3": "BRMY3"}
+YEAR_AGO_DAYS = 364
+PUBLISHED_STATUS_ID = 2
+EMPTY_REPORT_MARKER = "Outstanding Export Sales"
+
+SESSION_PATTERN = re.compile(r"ReportSession=([A-Za-z0-9]+)")
+CONTROL_PATTERN = re.compile(r"ControlID=([A-Za-z0-9]+)")
+
+_TOKEN: dict[str, Any] = {}
+
+
+def year_ago_date(week_ending: dateType) -> dateType:
+ """Derive the year-ago period ending date, preserving the weekday.
+
+ Parameters
+ ----------
+ week_ending : dateType
+ The period ending date.
+
+ Returns
+ -------
+ dateType
+ The date 52 weeks earlier, which is what the source publishes as the
+ year-ago comparison.
+ """
+ return week_ending - timedelta(days=YEAR_AGO_DAYS)
+
+
+def format_date(value: dateType) -> str:
+ """Format a date as MM/DD/YYYY, the only format the report accepts."""
+ return value.strftime("%m/%d/%Y")
+
+
+async def get_token() -> str:
+ """Get an anonymous bearer token for the ESR API.
+
+ Returns
+ -------
+ str
+ The access token, cached until it expires.
+
+ Raises
+ ------
+ OpenBBError
+ If the token endpoint does not return an access token.
+ """
+ from openbb_core.app.model.abstract.error import OpenBBError
+ from openbb_core.provider.utils.helpers import amake_request
+
+ now = datetime.now().timestamp()
+ if _TOKEN and _TOKEN["expires_at"] > now:
+ return _TOKEN["access_token"]
+
+ payload = urlencode(
+ {
+ "client_id": PUBLIC_CLIENT_ID,
+ "client_secret": PUBLIC_CLIENT_SECRET,
+ "grant_type": "client_credentials",
+ }
+ )
+ response = await amake_request(
+ TOKEN_URL,
+ method="POST",
+ data=payload,
+ headers={
+ "Content-Type": "application/x-www-form-urlencoded",
+ "User-Agent": USER_AGENT,
+ },
+ )
+ if not isinstance(response, dict) or "access_token" not in response:
+ raise OpenBBError(f"FAS token request failed -> {response}")
+ _TOKEN.update(
+ {
+ "access_token": response["access_token"],
+ "expires_at": now + int(response.get("expires_in", 0)) - 60,
+ }
+ )
+ return _TOKEN["access_token"]
+
+
+async def get_lookup(path: str, authorize: bool = True, timeout: int = 120) -> Any:
+ """Fetch an ESR API lookup.
+
+ Parameters
+ ----------
+ path : str
+ Path under the API base, e.g. 'lookups/Commodities'.
+ authorize : bool
+ Whether to attach the bearer token. The commodity list is public.
+ timeout : int
+ Seconds to wait. GetWeekEndingDates returns several megabytes, so the
+ default is well above the platform's ten second default.
+
+ Returns
+ -------
+ Any
+ The decoded JSON response.
+ """
+ from openbb_core.provider.utils.helpers import amake_request
+
+ headers = {"User-Agent": USER_AGENT}
+ if authorize:
+ headers["Authorization"] = f"Bearer {await get_token()}"
+ return await amake_request(f"{API_URL}{path}", headers=headers, timeout=timeout)
+
+
+async def get_commodities() -> list[dict]:
+ """Get the commodity list, mapping the published code to the report's id.
+
+ Returns
+ -------
+ list[dict]
+ Records with id, commodity_code, and commodity_name keys, sorted by
+ commodity code.
+ """
+ rows = await get_lookup("lookups/Commodities", authorize=False)
+ return sorted(
+ (
+ {
+ "id": row["id"],
+ "commodity_code": row["commodityCode"],
+ "commodity_name": row["commodityName"].strip(),
+ }
+ for row in rows or []
+ ),
+ key=lambda row: row["commodity_code"],
+ )
+
+
+async def get_published_weeks() -> list[dict]:
+ """Get the published period ending dates and their release timestamps.
+
+ Returns
+ -------
+ list[dict]
+ Records with week_ending and released_at keys for weeks the source
+ has published, newest first. Unpublished weeks are excluded so an
+ embargoed report is never requested.
+ """
+ rows = await get_lookup("lookups/GetWeekEndingDates")
+ weeks = [
+ {
+ "week_ending": datetime.fromisoformat(row["weekEndingDate"]).date(),
+ "released_at": (
+ datetime.fromisoformat(row["publishedDatetime"])
+ if row.get("publishedDatetime")
+ else None
+ ),
+ }
+ for row in rows or []
+ if row.get("weekEndingDateStatusId") == PUBLISHED_STATUS_ID
+ and row.get("publishedDatetime")
+ ]
+ return sorted(weeks, key=lambda row: row["week_ending"], reverse=True)
+
+
+async def _get_session():
+ """Return an aiohttp session that keeps the report viewer's cookies."""
+ from openbb_core.provider.utils.helpers import get_async_requests_session
+
+ return await get_async_requests_session()
+
+
+def build_report_url(
+ report: str,
+ week_ending: dateType,
+ release_date: dateType,
+ commodity_ids: list[int],
+) -> str:
+ """Build the ReportsHome URL that opens a Bell Report session.
+
+ Parameters
+ ----------
+ report : str
+ One of my1, my2, or my3.
+ week_ending : dateType
+ The period ending date.
+ release_date : dateType
+ The date the report was released.
+ commodity_ids : list[int]
+ Commodity ids, which are not the published commodity codes.
+
+ Returns
+ -------
+ str
+ The ReportsHome URL.
+ """
+ params: dict[str, Any] = {
+ "RN": REPORT_NAMES[report],
+ "wed": format_date(week_ending),
+ }
+ if report == "my1":
+ params["YGO"] = format_date(year_ago_date(week_ending))
+ params["RD"] = format_date(release_date)
+ params["MY"] = "N"
+ params["CID"] = "".join(f"{i}," for i in commodity_ids)
+ params["AppUserId"] = 0
+ return f"{BASE_URL}ReportsHome.aspx?{urlencode(params)}"
+
+
+def report_from_url(url: str) -> str:
+ """Return the marketing year report a ReportsHome URL addresses.
+
+ Parameters
+ ----------
+ url : str
+ A ReportsHome URL.
+
+ Returns
+ -------
+ str
+ One of my1, my2, or my3.
+
+ Raises
+ ------
+ OpenBBError
+ If the URL does not address the FAS report viewer, or names an
+ unknown report.
+ """
+ from urllib.parse import parse_qs, urlparse
+
+ from openbb_core.app.model.abstract.error import OpenBBError
+
+ parsed = urlparse(url)
+ if parsed.netloc != REPORT_HOST or not parsed.path.endswith("ReportsHome.aspx"):
+ raise OpenBBError(
+ f"Invalid Bell Report URL -> {url}."
+ f" Expected a {REPORT_HOST} report viewer URL."
+ )
+ name = (parse_qs(parsed.query).get("RN") or [""])[0]
+ for report, report_name in REPORT_NAMES.items():
+ if report_name == name:
+ return report
+ raise OpenBBError(f"Unknown Bell Report -> '{name}' in {url}.")
+
+
+def report_file_name(url: str) -> str:
+ """Build a descriptive PDF file name for a ReportsHome URL."""
+ from urllib.parse import parse_qs, urlparse
+
+ params = parse_qs(urlparse(url).query)
+ report = report_from_url(url)
+ week = (params.get("wed") or [""])[0].replace("/", "")
+ codes = "_".join(i for i in (params.get("CID") or [""])[0].split(",") if i)
+ return f"bell_report_{report}_{codes}_{week}.pdf"
+
+
+def build_export_url(session_id: str, control_id: str, file_name: str) -> str:
+ """Build the report viewer export URL that returns the PDF."""
+ return f"{BASE_URL}Reserved.ReportViewerWebControl.axd?" + urlencode(
+ {
+ "ReportSession": session_id,
+ "Culture": 1033,
+ "CultureOverrides": "True",
+ "UICulture": 1033,
+ "UICultureOverrides": "True",
+ "ReportStack": 1,
+ "ControlID": control_id,
+ "OpType": "Export",
+ "FileName": file_name,
+ "ContentDisposition": "OnlyHtmlInline",
+ "Format": "PDF",
+ }
+ )
+
+
+async def afetch_report(url: str) -> bytes:
+ """Fetch a Bell Report as PDF bytes from its report viewer URL.
+
+ The report viewer binds its session to a cookie, so the session-opening
+ request and the export request share one session.
+
+ Parameters
+ ----------
+ url : str
+ A ReportsHome URL, as built by ``build_report_url``.
+
+ Returns
+ -------
+ bytes
+ The PDF content.
+
+ Raises
+ ------
+ OpenBBError
+ If the report session cannot be opened or the export is not a PDF.
+ """
+ from openbb_core.app.model.abstract.error import OpenBBError
+
+ report = report_from_url(url)
+ session = await _get_session()
+ try:
+ response = await session.get(url, headers={"User-Agent": USER_AGENT})
+ html = await response.text()
+ session_match = SESSION_PATTERN.search(html)
+ control_match = CONTROL_PATTERN.search(html)
+ if not (session_match and control_match):
+ raise OpenBBError(
+ f"The FAS report viewer did not open a session for {url}."
+ )
+ export_url = build_export_url(
+ session_match.group(1), control_match.group(1), f"Bell{report.upper()}"
+ )
+ response = await session.get(export_url, headers={"User-Agent": USER_AGENT})
+ content = await response.read()
+ finally:
+ await session.close()
+
+ if not content.startswith(b"%PDF"):
+ raise OpenBBError(
+ f"The FAS report viewer returned {len(content)} bytes that are not a PDF."
+ )
+ return content
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/fas_report_calendar.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/fas_report_calendar.py
new file mode 100644
index 00000000000..ea1947436b5
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/fas_report_calendar.py
@@ -0,0 +1,182 @@
+"""USDA FAS report release calendar scraper."""
+
+import re
+from datetime import datetime, timezone
+from urllib.parse import unquote
+
+BASE_URL = "https://www.fas.usda.gov"
+CALENDAR_URL = f"{BASE_URL}/data/scheduled-reports"
+SESSION_KEY = "fas.usda.gov"
+
+REPORT_TYPES = {
+ "export_sales": "10254",
+ "trade_forecast": "10257",
+ "world_markets_trade": "10259",
+}
+
+COMMODITIES = {
+ "coffee": "609",
+ "cotton_and_hemp": "13040",
+ "cotton": "6",
+ "dairy_livestock_and_poultry": "13005",
+ "beef": "13008",
+ "dairy_products": "13012",
+ "pork": "13009",
+ "poultry": "13010",
+ "fruits_and_vegetables": "8",
+ "fresh_fruit": "13013",
+ "grains_feeds_and_fodders": "13",
+ "corn": "14",
+ "rice": "16",
+ "wheat": "15",
+ "oilseeds": "26",
+ "oilseeds_excluding_soybean": "28",
+ "soybeans": "27",
+ "processed_food_products": "31",
+ "tree_nuts": "30",
+}
+
+ROW_PATTERN = re.compile(
+ r'(.*?)(?=|\Z)', re.S
+)
+MONTH_DAY_PATTERN = re.compile(r'class="fas-date__month-day">([^<]+)<')
+DOW_PATTERN = re.compile(r'class="fas-date__dow">([^<]+)<')
+TIME_PATTERN = re.compile(r'class="fas-date__time">([^<]+)<')
+TAG_PATTERN = re.compile(r'class="c-card__tags">\s*([^<]+)', re.S)
+TITLE_PATTERN = re.compile(r'class="c-card__title">([^<]+)<')
+URL_PATTERN = re.compile(r'href="([^"]+)"\s+class="c-card__url"')
+DTSTART_PATTERN = re.compile(r"DTSTART:(\d{8}T\d{6}Z)")
+COUNT_PATTERN = re.compile(r"([\d,]+) results found")
+
+
+def build_url(
+ report_type: str | None = None,
+ commodity: str | None = None,
+ keyword: str | None = None,
+) -> str:
+ """Build the calendar URL with optional facet filters.
+
+ Parameters
+ ----------
+ report_type : str | None
+ Report type slug from REPORT_TYPES.
+ commodity : str | None
+ Commodity slug from COMMODITIES.
+ keyword : str | None
+ Free-text filter; matches associated commodities, not only titles.
+
+ Returns
+ -------
+ str
+ The calendar URL with any facet query params applied.
+ """
+ params: list[str] = []
+ index = 0
+ if report_type is not None:
+ params.append(
+ f"report_releases[{index}]=report_type:{REPORT_TYPES[report_type]}"
+ )
+ index += 1
+ if commodity is not None:
+ params.append(
+ f"report_releases[{index}]=report_listing_commodities:{COMMODITIES[commodity]}"
+ )
+ if keyword is not None:
+ params.append(f"keyword={keyword}")
+ return f"{CALENDAR_URL}?{'&'.join(params)}" if params else CALENDAR_URL
+
+
+def _search(pattern: re.Pattern, text: str) -> str | None:
+ """Return the first capture group of a pattern, or None."""
+ match = pattern.search(text)
+ return match.group(1).strip() if match else None
+
+
+def parse_calendar(html: str) -> list[dict]:
+ """Parse the calendar listing HTML into release records.
+
+ The release timestamp comes from each card's embedded iCalendar DTSTART,
+ which is the only source carrying a year; the visible date shows just the
+ month and day. Cards for releases whose time has already passed omit the
+ calendar block, so the timestamp is optional.
+
+ Parameters
+ ----------
+ html : str
+ The listing page HTML.
+
+ Returns
+ -------
+ list[dict]
+ Records with release_date, release_time, day_of_week, report_type,
+ title, url, and released keys.
+ """
+ records: list[dict] = []
+ for match in ROW_PATTERN.finditer(html):
+ row = match.group(1)
+ title = _search(TITLE_PATTERN, row)
+ if title is None:
+ continue
+ url = _search(URL_PATTERN, row)
+ dtstart = _search(DTSTART_PATTERN, unquote(row))
+ released = dtstart is None
+ release_datetime = (
+ datetime.strptime(dtstart, "%Y%m%dT%H%M%SZ").replace(tzinfo=timezone.utc)
+ if dtstart
+ else None
+ )
+ records.append(
+ {
+ "release_datetime": release_datetime,
+ "month_day": _search(MONTH_DAY_PATTERN, row),
+ "day_of_week": _search(DOW_PATTERN, row),
+ "release_time": _search(TIME_PATTERN, row),
+ "report_type": _search(TAG_PATTERN, row),
+ "title": title,
+ "url": f"{BASE_URL}{url}" if url else None,
+ "released": released,
+ }
+ )
+ return records
+
+
+def parse_result_count(html: str) -> int | None:
+ """Parse the listing's stated result count, used to validate the parse."""
+ count = _search(COUNT_PATTERN, html)
+ return int(count.replace(",", "")) if count else None
+
+
+async def _get_session():
+ """Return the shared Chrome-impersonating session for fas.usda.gov."""
+ from openbb_government_us.utils.curl_session import get_session
+
+ return await get_session(SESSION_KEY)
+
+
+async def afetch_calendar(url: str) -> str:
+ """Fetch the calendar listing HTML.
+
+ Parameters
+ ----------
+ url : str
+ The calendar URL, optionally carrying facet filters.
+
+ Returns
+ -------
+ str
+ The response body.
+
+ Raises
+ ------
+ OpenBBError
+ If the request is rejected or returns a non-200 status.
+ """
+ from openbb_core.app.model.abstract.error import OpenBBError
+
+ session = await _get_session()
+ response = await session.get(url)
+ if response.status_code != 200:
+ raise OpenBBError(
+ f"FAS request failed with status {response.status_code} -> {url}"
+ )
+ return response.text
diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/psd_circulars.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/psd_circulars.py
new file mode 100644
index 00000000000..bb5634c0568
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/psd_circulars.py
@@ -0,0 +1,528 @@
+"""USDA FAS PSD commodity circular discovery and download."""
+
+import os
+import re
+from datetime import datetime, timezone
+from typing import Any
+
+CIRCULAR_HOST = "apps.fas.usda.gov"
+LISTING_URL = f"https://{CIRCULAR_HOST}/psdonline/CircularsHandler.ashx"
+CIRCULARS_BASE_URL = f"https://{CIRCULAR_HOST}/PSDOnline/Circulars/"
+NEXT_RELEASE_URL = (
+ f"https://{CIRCULAR_HOST}/PSDOnlineApi/api/downloadableData/GetNextRelease"
+)
+
+CACHE_ENV_VAR = "OPENBB_USDA_CACHE_DIR"
+PDF_HEADERS = {"Accept": "application/pdf"}
+RELEASE_FORMAT = "%m/%d/%Y %I:%M%p"
+DEFAULT_TTL = 86400
+MIN_TTL = 3600
+MAX_CARRY_FORWARD = 13
+
+COMMODITIES = {
+ "citrus": "Citrus",
+ "coffee": "Coffee",
+ "cotton": "Cotton",
+ "dairy": "Dairy",
+ "fruit": "Fruit",
+ "grain": "Grain",
+ "livestock": "Livestock_poultry",
+ "oilseeds": "Oilseeds",
+ "stone_fruit": "StoneFruit",
+ "sugar": "Sugar",
+ "tree_nuts": "TreeNuts",
+ "world_production": "production",
+}
+
+CIRCULAR_PATH_PATTERN = re.compile(
+ r"^/PSDOnline/Circulars/(\d{4})/(\d{2})/([A-Za-z_]+)\.pdf$",
+ re.IGNORECASE,
+)
+
+_MISSING = object()
+
+
+def cache_directory() -> str:
+ """Resolve the PSD cache directory, honoring the environment override."""
+ from openbb_core.app.utils import get_user_cache_directory
+
+ return os.environ.get(CACHE_ENV_VAR) or os.path.join(
+ get_user_cache_directory(), "usda", "psd"
+ )
+
+
+def get_cache() -> Any:
+ """Open the diskcache store at the resolved cache directory."""
+ from diskcache import Cache
+
+ return Cache(cache_directory())
+
+
+def compute_ttl(next_release: str | None, now: datetime | None = None) -> int:
+ """Compute a cache TTL in seconds, expiring at the next circular release.
+
+ Parameters
+ ----------
+ next_release : str | None
+ The next release timestamp as M/D/YYYY H:MMAM, when the source
+ publishes one.
+ now : datetime | None
+ Reference time, defaulting to the current UTC time.
+
+ Returns
+ -------
+ int
+ Seconds until the next release, clamped to at least one hour; the
+ one-day default when no future release timestamp is known.
+ """
+ if not next_release:
+ return DEFAULT_TTL
+ try:
+ expiry = datetime.strptime(next_release.strip(), RELEASE_FORMAT).replace(
+ tzinfo=timezone.utc
+ )
+ except ValueError:
+ return DEFAULT_TTL
+ remaining = int((expiry - (now or datetime.now(timezone.utc))).total_seconds())
+ if remaining <= 0:
+ return DEFAULT_TTL
+ return max(remaining, MIN_TTL)
+
+
+async def _get_session():
+ """Return an aiohttp session for the PSD Online host."""
+ from openbb_core.provider.utils.helpers import get_async_requests_session
+
+ return await get_async_requests_session()
+
+
+async def get_next_release() -> str | None:
+ """Get the next circular release timestamp, as the source reports it.
+
+ Returns
+ -------
+ str | None
+ The timestamp string, or None when the source does not report one.
+ The release schedule only sets a cache TTL, so a failure to read it
+ is not an error.
+ """
+ key = "next_release"
+ with get_cache() as cache:
+ cached = cache.get(key, default=_MISSING)
+ if cached is not _MISSING:
+ return cached
+ release: str | None = None
+ try:
+ session = await _get_session()
+ try:
+ response = await session.get(NEXT_RELEASE_URL)
+ if response.status == 200:
+ release = (await response.text()).strip().strip('"').strip() or None
+ finally:
+ await session.close()
+ except Exception: # noqa: BLE001
+ return None
+ with get_cache() as cache:
+ cache.set(key, release, expire=MIN_TTL)
+ return release
+
+
+async def cache_ttl() -> int:
+ """Get the cache TTL in seconds, expiring at the next circular release."""
+ return compute_ttl(await get_next_release())
+
+
+async def fetch_folders() -> list[tuple[int, int]]:
+ """Get every month folder the source publishes, newest first.
+
+ Returns
+ -------
+ list[tuple[int, int]]
+ (year, month) pairs, newest first. The source omits months it never
+ published, so this bounds the month universe.
+
+ Raises
+ ------
+ OpenBBError
+ If the listing cannot be read, or is empty.
+ """
+ import json
+
+ from openbb_core.app.model.abstract.error import OpenBBError
+
+ key = "folders"
+ with get_cache() as cache:
+ cached = cache.get(key)
+ if cached is not None:
+ return cached
+
+ session = await _get_session()
+ try:
+ response = await session.get(LISTING_URL)
+ status = response.status
+ body = await response.text()
+ finally:
+ await session.close()
+
+ if status != 200:
+ raise OpenBBError(
+ f"The PSD circular listing request failed with status {status}"
+ f" -> {LISTING_URL}"
+ )
+ payload = json.loads(body)
+ folders = sorted(
+ {
+ (int(year["CalendarYear"]), int(month["Item1"]))
+ for year in payload.get("CircularSets") or []
+ for month in year.get("Months") or []
+ },
+ reverse=True,
+ )
+ if not folders:
+ raise OpenBBError(f"The PSD circular listing is empty -> {LISTING_URL}")
+ with get_cache() as cache:
+ cache.set(key, folders, expire=await cache_ttl())
+ return folders
+
+
+def _commodity_key(commodity: str) -> str:
+ """Normalize a commodity to its key.
+
+ Parameters
+ ----------
+ commodity : str
+ A commodity key, in any case.
+
+ Returns
+ -------
+ str
+ The normalized key.
+
+ Raises
+ ------
+ OpenBBError
+ If the commodity is not one the source publishes.
+ """
+ from openbb_core.app.model.abstract.error import OpenBBError
+
+ key = commodity.lower().strip()
+ if key not in COMMODITIES:
+ raise OpenBBError(
+ f"Unsupported PSD commodity -> '{commodity}'."
+ f" Expected one of: {', '.join(COMMODITIES)}."
+ )
+ return key
+
+
+def circular_url(year: int, month: int, commodity: str) -> str:
+ """Build the static URL of a commodity's circular in a month folder.
+
+ Parameters
+ ----------
+ year : int
+ The folder's calendar year.
+ month : int
+ The folder's month.
+ commodity : str
+ A supported commodity key.
+
+ Returns
+ -------
+ str
+ The circular's URL.
+
+ Raises
+ ------
+ OpenBBError
+ If the commodity is not one the source publishes.
+ """
+ api_commodity = COMMODITIES[_commodity_key(commodity)]
+ return f"{CIRCULARS_BASE_URL}{year}/{month:02d}/{api_commodity}.pdf"
+
+
+def circular_from_url(url: str) -> dict:
+ """Return the circular a static PSD URL addresses.
+
+ Parameters
+ ----------
+ url : str
+ A circular URL, as built by ``circular_url``.
+
+ Returns
+ -------
+ dict
+ The commodity key, year, and month the URL addresses.
+
+ Raises
+ ------
+ OpenBBError
+ If the URL does not address a PSD circular, or names a commodity the
+ source does not publish.
+ """
+ from urllib.parse import urlparse
+
+ from openbb_core.app.model.abstract.error import OpenBBError
+
+ parsed = urlparse(url)
+ match = CIRCULAR_PATH_PATTERN.match(parsed.path)
+ if parsed.netloc != CIRCULAR_HOST or match is None:
+ raise OpenBBError(
+ f"Invalid PSD circular URL -> {url}. Expected a"
+ f" https://{CIRCULAR_HOST}/PSDOnline/Circulars/YYYY/MM/Commodity.pdf URL."
+ )
+ year, month, api_commodity = match.groups()
+ for key, name in COMMODITIES.items():
+ if name.lower() == api_commodity.lower():
+ return {"commodity": key, "year": int(year), "month": int(month)}
+ raise OpenBBError(f"Unknown PSD commodity -> '{api_commodity}' in {url}.")
+
+
+def circular_file_name(url: str) -> str:
+ """Build a descriptive PDF file name for a circular URL."""
+ circular = circular_from_url(url)
+ return (
+ f"psd_report_{circular['commodity']}"
+ f"_{circular['year']}_{circular['month']:02d}.pdf"
+ )
+
+
+async def head_circular(url: str) -> dict | None:
+ """Read a circular's headers without downloading it.
+
+ Parameters
+ ----------
+ url : str
+ A circular URL, as built by ``circular_url``.
+
+ Returns
+ -------
+ dict | None
+ The etag, last_modified, and length, or None when the source does not
+ hold the file.
+
+ Raises
+ ------
+ OpenBBError
+ If the source answers with anything but 200 or 404.
+ """
+ from openbb_core.app.model.abstract.error import OpenBBError
+
+ key = f"head:{url}"
+ with get_cache() as cache:
+ cached = cache.get(key, default=_MISSING)
+ if cached is not _MISSING:
+ return cached
+
+ session = await _get_session()
+ try:
+ response = await session.head(url, headers=PDF_HEADERS)
+ status = response.status
+ headers = response.headers
+ finally:
+ await session.close()
+
+ if status not in (200, 404):
+ raise OpenBBError(
+ f"The PSD circular request failed with status {status} -> {url}"
+ )
+ head: dict | None = None
+ if status == 200:
+ length = headers.get("Content-Length")
+ head = {
+ "etag": headers.get("ETag"),
+ "last_modified": headers.get("Last-Modified"),
+ "length": int(length) if length else None,
+ }
+ with get_cache() as cache:
+ cache.set(key, head, expire=await cache_ttl())
+ return head
+
+
+def _released_at(last_modified: str | None) -> datetime | None:
+ """Parse an HTTP Last-Modified header into a datetime."""
+ from email.utils import parsedate_to_datetime
+
+ if not last_modified:
+ return None
+ try:
+ return parsedate_to_datetime(last_modified)
+ except (TypeError, ValueError):
+ return None
+
+
+async def _resolve_latest(
+ commodity: str, folders: list[tuple[int, int]]
+) -> dict | None:
+ """Resolve a commodity's newest circular and the month that published it.
+
+ Parameters
+ ----------
+ commodity : str
+ A supported commodity key.
+ folders : list[tuple[int, int]]
+ The published month folders, newest first.
+
+ Returns
+ -------
+ dict | None
+ The resolved circular, or None when no folder holds the commodity.
+ """
+ window = folders[: MAX_CARRY_FORWARD + 1]
+ anchor = -1
+ head: dict | None = None
+ for index, (year, month) in enumerate(window):
+ head = await head_circular(circular_url(year, month, commodity))
+ if head is not None:
+ anchor = index
+ break
+ if head is None:
+ return None
+
+ etag = head["etag"]
+ low, high = anchor, len(window) - 1
+ while low < high:
+ mid = (low + high + 1) // 2
+ year, month = window[mid]
+ candidate = await head_circular(circular_url(year, month, commodity))
+ if candidate is not None and candidate["etag"] == etag:
+ low = mid
+ else:
+ high = mid - 1
+
+ year, month = window[low]
+ return {
+ "commodity": commodity,
+ "api_commodity": COMMODITIES[commodity],
+ "year": year,
+ "month": month,
+ "url": circular_url(year, month, commodity),
+ "released_at": _released_at(head["last_modified"]),
+ "size": head["length"],
+ }
+
+
+async def latest_circulars(commodity: str | None = None) -> list[dict]:
+ """Get the newest circular for each commodity, with its true report month.
+
+ Parameters
+ ----------
+ commodity : str | None
+ A supported commodity key, or None for every commodity.
+
+ Returns
+ -------
+ list[dict]
+ One record per commodity that resolves, carrying the commodity key,
+ the source's commodity name, the year and month that published the
+ circular, its URL, its release timestamp, and its size in bytes.
+
+ Raises
+ ------
+ OpenBBError
+ If the commodity is not one the source publishes, or the month
+ listing cannot be read.
+ """
+ import asyncio
+
+ keys = [_commodity_key(commodity)] if commodity else list(COMMODITIES)
+ folders = await fetch_folders()
+ resolved = await asyncio.gather(*(_resolve_latest(k, folders) for k in keys))
+ return [circular for circular in resolved if circular is not None]
+
+
+async def historical_circulars(commodity: str) -> list[dict]:
+ """Get every distinct historical circular for one commodity, newest first.
+
+ Parameters
+ ----------
+ commodity : str
+ A supported commodity key.
+
+ Returns
+ -------
+ list[dict]
+ One record per distinct published circular, carrying the commodity key,
+ the source's commodity name, the year and month that published it, its
+ URL, its release timestamp, and its size in bytes. The source carries a
+ circular forward into later month folders, so folders that share an etag
+ are collapsed to the earliest one, the month that actually published it.
+
+ Raises
+ ------
+ OpenBBError
+ If the commodity is not one the source publishes, or the month listing
+ cannot be read.
+ """
+ import asyncio
+
+ key = _commodity_key(commodity)
+ folders = await fetch_folders()
+ heads = await asyncio.gather(
+ *(head_circular(circular_url(year, month, key)) for year, month in folders)
+ )
+ by_etag: dict[str, dict] = {}
+ for (year, month), head in zip(folders, heads):
+ if head is None:
+ continue
+ by_etag[head["etag"]] = {
+ "commodity": key,
+ "api_commodity": COMMODITIES[key],
+ "year": year,
+ "month": month,
+ "url": circular_url(year, month, key),
+ "released_at": _released_at(head["last_modified"]),
+ "size": head["length"],
+ }
+ return sorted(
+ by_etag.values(), key=lambda item: (item["year"], item["month"]), reverse=True
+ )
+
+
+async def afetch_circular(url: str) -> bytes:
+ """Fetch a circular's PDF bytes from its static URL.
+
+ Parameters
+ ----------
+ url : str
+ A circular URL, as built by ``circular_url``.
+
+ Returns
+ -------
+ bytes
+ The PDF content.
+
+ Raises
+ ------
+ OpenBBError
+ If the URL does not address a PSD circular, the source does not serve
+ it, or the response is not a PDF.
+ """
+ from openbb_core.app.model.abstract.error import OpenBBError
+
+ circular_from_url(url)
+ key = f"file:{url}"
+ with get_cache() as cache:
+ cached = cache.get(key)
+ if cached is not None:
+ return cached
+
+ session = await _get_session()
+ try:
+ response = await session.get(url, headers=PDF_HEADERS)
+ status = response.status
+ content = await response.read()
+ finally:
+ await session.close()
+
+ if status != 200:
+ raise OpenBBError(
+ f"The PSD circular request failed with status {status} -> {url}"
+ )
+ if not content.startswith(b"%PDF"):
+ raise OpenBBError(
+ f"The PSD circular at {url} returned {len(content)} bytes"
+ " that are not a PDF."
+ )
+ with get_cache() as cache:
+ cache.set(key, content, expire=await cache_ttl())
+ return content
diff --git a/openbb_platform/providers/government_us/openbb_government_us/utils/psd_codes.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/psd_codes.py
similarity index 97%
rename from openbb_platform/providers/government_us/openbb_government_us/utils/psd_codes.py
rename to openbb_platform/providers/government_us/openbb_government_us/usda/utils/psd_codes.py
index a2e21e3c056..81234a4ae42 100644
--- a/openbb_platform/providers/government_us/openbb_government_us/utils/psd_codes.py
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/psd_codes.py
@@ -1,11 +1,8 @@
"""Report Codes and Metadata for USDA FAS Commodity Production Supply & Distribution Reports."""
-# pylint: disable=too-many-lines
-
-from typing import Literal
+from typing import Any, Literal
PsdReportNames = Literal[
- # === COFFEE ===
"coffee_summary",
"coffee_summary_2",
"coffee_summary_3",
@@ -21,7 +18,6 @@ PsdReportNames = Literal[
"coffee_imports_total",
"coffee_imports_green_bean",
"coffee_imports_soluble",
- # === COTTON ===
"cotton_monthly_changes",
"cotton_world_supply_use",
"cotton_world_supply_use_2",
@@ -33,7 +29,6 @@ PsdReportNames = Literal[
"cotton_world_supply",
"cotton_us_supply",
"cotton_foreign_supply",
- # === DAIRY ===
"butter_production_consumption",
"butter_trade",
"cheese_production_consumption",
@@ -44,7 +39,6 @@ PsdReportNames = Literal[
"nonfat_dry_milk_trade",
"whole_milk_powder_production_consumption",
"whole_milk_powder_trade",
- # === FIELD CROPS (fcr) ===
"world_crop_production_summary",
"wheat_area_yield_production",
"coarse_grains_area_yield_production",
@@ -62,7 +56,6 @@ PsdReportNames = Literal[
"rapeseed_area_yield_production",
"copra_palm_kernel_palm_oil_production",
"cotton_area_yield_production_fcr",
- # === FRUITS & VEGETABLES (htp) ===
"apples_supply_distribution",
"apples_selected_countries",
"cherries_supply_distribution",
@@ -80,7 +73,6 @@ PsdReportNames = Literal[
"raisins_supply_distribution",
"raisins_selected_countries",
"tangerines_mandarins_selected_countries",
- # === GRAINS (gra) ===
"grains_summary_comparison",
"barley_supply_disappearance",
"china_grain_supply_demand",
@@ -122,9 +114,7 @@ PsdReportNames = Literal[
"wheat_coarse_grains_world_supply_demand",
"wheat_world_production_consumption_stocks",
"wheat_flour_products_world_trade",
- # === JUICE ===
"orange_juice_supply_distribution",
- # === LIVESTOCK ===
"beef_veal_production",
"beef_veal_trade",
"cattle_stocks",
@@ -135,7 +125,6 @@ PsdReportNames = Literal[
"pork_trade",
"swine_stocks",
"swine_trade",
- # === OILSEEDS (oil) ===
"oilseeds_world_commodity_view",
"protein_meals_world_commodity_view",
"vegetable_oils_world_commodity_view",
@@ -164,11 +153,9 @@ PsdReportNames = Literal[
"oilseeds_eu",
"oilseeds_china",
"oilseeds_india",
- # === SUGAR ===
"sugar_ending_stocks",
"sugar_imports_exports",
"sugar_production_consumption",
- # === TREE NUTS ===
"almonds_supply_distribution",
"almonds_summary",
"pistachios_summary",
@@ -177,7 +164,7 @@ PsdReportNames = Literal[
"walnuts_summary",
]
-PSD_REPORTS_METADATA = {
+PSD_REPORTS_METADATA: dict[str, dict[str, Any]] = {
"cof": {
"name": "Coffee",
"reports": {
@@ -747,7 +734,6 @@ PSD_REPORTS_METADATA = {
},
}
-# Mapping of human-readable commodity names to group codes
PSD_COMMODITY_GROUPS = {
"coffee": "cof",
"cotton": "cot",
@@ -762,9 +748,7 @@ PSD_COMMODITY_GROUPS = {
"tree_nuts": "nut",
}
-# Each friendly name maps to the report_id
PSD_REPORT_NAMES = {
- # === COFFEE ===
"coffee_summary": 2109,
"coffee_summary_2": 2535,
"coffee_summary_3": 2619,
@@ -780,7 +764,6 @@ PSD_REPORT_NAMES = {
"coffee_imports_total": 2055,
"coffee_imports_green_bean": 1774,
"coffee_imports_soluble": 2056,
- # === COTTON ===
"cotton_monthly_changes": 837,
"cotton_world_supply_use": 849,
"cotton_world_supply_use_2": 850,
@@ -792,7 +775,6 @@ PSD_REPORT_NAMES = {
"cotton_world_supply": 856,
"cotton_us_supply": 857,
"cotton_foreign_supply": 858,
- # === DAIRY ===
"butter_production_consumption": 2538,
"butter_trade": 2539,
"cheese_production_consumption": 2540,
@@ -803,7 +785,6 @@ PSD_REPORT_NAMES = {
"nonfat_dry_milk_trade": 2545,
"whole_milk_powder_production_consumption": 2549,
"whole_milk_powder_trade": 2547,
- # === FIELD CROPS (fcr) ===
"world_crop_production_summary": 425,
"wheat_area_yield_production": 448,
"coarse_grains_area_yield_production": 453,
@@ -821,7 +802,6 @@ PSD_REPORT_NAMES = {
"rapeseed_area_yield_production": 922,
"copra_palm_kernel_palm_oil_production": 454,
"cotton_area_yield_production_fcr": 958,
- # === FRUITS & VEGETABLES (htp) ===
"apples_supply_distribution": 2418,
"apples_selected_countries": 2415,
"cherries_supply_distribution": 2560,
@@ -839,7 +819,6 @@ PSD_REPORT_NAMES = {
"raisins_supply_distribution": 2151,
"raisins_selected_countries": 2304,
"tangerines_mandarins_selected_countries": 2182,
- # === GRAINS (gra) ===
"grains_summary_comparison": 965,
"barley_supply_disappearance": 866,
"china_grain_supply_demand": 867,
@@ -881,9 +860,7 @@ PSD_REPORT_NAMES = {
"wheat_coarse_grains_world_supply_demand": 878,
"wheat_world_production_consumption_stocks": 750,
"wheat_flour_products_world_trade": 386,
- # === JUICE ===
"orange_juice_supply_distribution": 2181,
- # === LIVESTOCK ===
"beef_veal_production": 2693,
"beef_veal_trade": 2694,
"cattle_stocks": 2695,
@@ -894,7 +871,6 @@ PSD_REPORT_NAMES = {
"pork_trade": 2661,
"swine_stocks": 2658,
"swine_trade": 2659,
- # === OILSEEDS (oil) ===
"oilseeds_world_commodity_view": 531,
"protein_meals_world_commodity_view": 701,
"vegetable_oils_world_commodity_view": 533,
@@ -923,11 +899,9 @@ PSD_REPORT_NAMES = {
"oilseeds_eu": 725,
"oilseeds_china": 726,
"oilseeds_india": 727,
- # === SUGAR ===
"sugar_ending_stocks": 2423,
"sugar_imports_exports": 2421,
"sugar_production_consumption": 2422,
- # === TREE NUTS ===
"almonds_supply_distribution": 2077,
"almonds_summary": 2095,
"pistachios_summary": 2364,
@@ -1197,7 +1171,6 @@ COMMODITIES = {
"wheat": "0410000",
}
-# Attribute IDs
ATTRIBUTES = {
"area_planted": 1,
"area_harvested": 4,
@@ -1334,7 +1307,6 @@ ATTRIBUTES = {
}
CountryType = Literal[
- # Countries
"world",
"afghanistan",
"albania",
@@ -1529,7 +1501,6 @@ CountryType = Literal[
"yemen",
"zambia",
"zimbabwe",
- # Regions
"north_america",
"caribbean",
"central_america",
@@ -1761,11 +1732,9 @@ REGIONS = {
}
COUNTRY_TO_REGION = {
- # North America (R01)
"US": "R01",
"CA": "R01",
"MX": "R01",
- # Caribbean (R02)
"BF": "R02",
"BB": "R02",
"CU": "R02",
@@ -1775,7 +1744,6 @@ COUNTRY_TO_REGION = {
"JM": "R02",
"RQ": "R02",
"TD": "R02",
- # Central America (R03)
"BH": "R03",
"CS": "R03",
"ES": "R03",
@@ -1783,7 +1751,6 @@ COUNTRY_TO_REGION = {
"HO": "R03",
"NU": "R03",
"PN": "R03",
- # South America (R04)
"AR": "R04",
"BL": "R04",
"BR": "R04",
@@ -1796,7 +1763,6 @@ COUNTRY_TO_REGION = {
"NS": "R04",
"UY": "R04",
"VE": "R04",
- # European Union (R05)
"AU": "R05",
"S8": "R05",
"BU": "R05",
@@ -1825,7 +1791,6 @@ COUNTRY_TO_REGION = {
"SP": "R05",
"SW": "R05",
"E4": "R05",
- # Former Soviet Union (R07)
"AM": "R07",
"AJ": "R07",
"BO": "R07",
@@ -1838,7 +1803,6 @@ COUNTRY_TO_REGION = {
"TX": "R07",
"UP": "R07",
"UZ": "R07",
- # Middle East (R09)
"BA": "R09",
"IR": "R09",
"IZ": "R09",
@@ -1852,13 +1816,11 @@ COUNTRY_TO_REGION = {
"SY": "R09",
"TC": "R09",
"YM": "R09",
- # North Africa (R10)
"AG": "R10",
"EG": "R10",
"LY": "R10",
"MO": "R10",
"TS": "R10",
- # Sub-Saharan Africa (R11)
"AO": "R11",
"DM": "R11",
"BC": "R11",
@@ -1909,7 +1871,6 @@ COUNTRY_TO_REGION = {
"UG": "R11",
"ZA": "R11",
"RH": "R11",
- # South Asia (R12)
"AF": "R12",
"BG": "R12",
"BT": "R12",
@@ -1918,7 +1879,6 @@ COUNTRY_TO_REGION = {
"NP": "R12",
"PK": "R12",
"CE": "R12",
- # Oceania (R14)
"AS": "R14",
"FJ": "R14",
"NC": "R14",
@@ -1928,7 +1888,6 @@ COUNTRY_TO_REGION = {
"BP": "R14",
"TN": "R14",
"NH": "R14",
- # Other Europe (R16)
"AL": "R16",
"BK": "R16",
"IC": "R16",
@@ -1940,7 +1899,6 @@ COUNTRY_TO_REGION = {
"SZ": "R16",
"TU": "R16",
"UK": "R16",
- # Southeast Asia (R17)
"BX": "R17",
"BM": "R17",
"CB": "R17",
@@ -1951,7 +1909,6 @@ COUNTRY_TO_REGION = {
"SN": "R17",
"TH": "R17",
"VM": "R17",
- # East Asia (R18)
"CH": "R18",
"HK": "R18",
"JA": "R18",
diff --git a/openbb_platform/providers/government_us/openbb_government_us/utils/psd_data_downloader.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/psd_data_downloader.py
similarity index 76%
rename from openbb_platform/providers/government_us/openbb_government_us/utils/psd_data_downloader.py
rename to openbb_platform/providers/government_us/openbb_government_us/usda/utils/psd_data_downloader.py
index 7bc0e7ae5cb..cb5eb49a47e 100644
--- a/openbb_platform/providers/government_us/openbb_government_us/utils/psd_data_downloader.py
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/psd_data_downloader.py
@@ -2,7 +2,7 @@
from typing import Literal
-from openbb_government_us.utils.psd_codes import (
+from openbb_government_us.usda.utils.psd_codes import (
ATTRIBUTES,
COMMODITIES,
COUNTRIES,
@@ -19,14 +19,13 @@ def get_report_url(
report_id: int,
file_format: Literal["csv", "html"] = "csv",
) -> str:
- """
- Get the download URL for a PSD report.
+ """Get the download URL for a PSD report.
Parameters
----------
report_id : int
The report ID from list_reports()
- format : str
+ file_format : str
Output format: 'csv' or 'html' (default: 'csv')
Returns
@@ -36,18 +35,16 @@ def get_report_url(
Examples
--------
- >>> url = get_report_url(2109) # Coffee Summary
+ >>> url = get_report_url(2109)
>>> print(url)
'https://apps.fas.usda.gov/psdonline/reportHandler.ashx?reportId=2109&templateId=8&format=csv&fileName=Coffee_Summary'
"""
- # Find the report in metadata
for group_data in PSD_REPORTS_METADATA.values():
if report_id in group_data["reports"]:
report = group_data["reports"][report_id]
title = report["title"]
template_id = report["templateId"]
- # Create a clean filename from the title
filename = (
title.replace(" ", "_")
.replace(":", "")
@@ -69,8 +66,7 @@ def get_report_url(
async def get_psd_report_data(report_id: int) -> dict:
- """
- Download and parse a PSD report into structured data ready for pandas DataFrame.
+ """Download and parse a PSD report into structured data ready for pandas DataFrame.
Parameters
----------
@@ -80,37 +76,30 @@ async def get_psd_report_data(report_id: int) -> dict:
Returns
-------
dict
- Parsed report with flat structure for DataFrame compatibility:
- {
- 'report': str, # Report title
- 'template': int, # Template ID used for parsing
- 'template_description': str, # Human-readable template description
- 'units': dict, # Column name -> unit mapping (use for df.attrs)
- 'columns': list[str], # All column names in order
- 'row_count': int, # Number of data rows
- 'data': list[dict] # Flat row data ready for DataFrame()
- }
+ Parsed report with flat structure for DataFrame compatibility, with keys:
+ 'report' (str) report title, 'template' (int) template ID used for parsing,
+ 'template_description' (str) human-readable template description,
+ 'units' (dict) column name -> unit mapping, 'columns' (list[str]) all
+ column names in order, 'row_count' (int) number of data rows, and
+ 'data' (list[dict]) flat row data ready for DataFrame().
Examples
--------
>>> import pandas as pd
- >>> result = await get_psd_report_data(2109) # Coffee Summary
+ >>> result = await get_psd_report_data(2109)
>>> df = DataFrame(result['data'])
- >>> df.attrs['units'] = result['units'] # Store units as metadata
+ >>> df.attrs['units'] = result['units']
>>> print(df.head())
- >>> result = await get_psd_report_data(884) # Corn Area, Yield, Production
+ >>> result = await get_psd_report_data(884)
>>> df = DataFrame(result['data'])
- >>> # All numeric columns are float64, ready for analysis
>>> print(df.dtypes)
"""
- # pylint: disable=import-outside-toplevel
from aiohttp import ClientError # noqa
from openbb_core.app.model.abstract.error import OpenBBError
from openbb_core.provider.utils.helpers import get_async_requests_session
- from openbb_government_us.utils.psd_template_parser import parse_report
+ from openbb_government_us.usda.utils.psd_template_parser import parse_report
- # Get template ID for this report
template_id = None
for group_data in PSD_REPORTS_METADATA.values():
if report_id in group_data["reports"]:
@@ -120,13 +109,11 @@ async def get_psd_report_data(report_id: int) -> dict:
if template_id is None:
raise OpenBBError(f"Invalid report ID -> {report_id} was not found.")
- # Build URLs for both formats
html_url = get_report_url(report_id, "html")
csv_url = get_report_url(report_id, "csv")
session = await get_async_requests_session()
try:
- # Fetch both HTML (for units) and CSV (for data)
html_resp = await session.get(html_url)
html = await html_resp.text()
csv_resp = await session.get(csv_url)
@@ -136,7 +123,6 @@ async def get_psd_report_data(report_id: int) -> dict:
finally:
await session.close()
- # Check for server error
if "error" in csv_text.lower():
raise OpenBBError(f"Server error fetching report {report_id} -> {csv_text}")
@@ -147,7 +133,6 @@ async def get_psd_report_data(report_id: int) -> dict:
def _get_commodity_attributes(commodity_code: str) -> list[str]:
"""Fetch valid attribute names for a commodity using the metadata API."""
- # pylint: disable=import-outside-toplevel
from openbb_core.provider.utils.helpers import make_request
try:
@@ -165,9 +150,8 @@ def _get_commodity_attributes(commodity_code: str) -> list[str]:
valid_keys.add(id_to_key[attr_id])
if valid_keys:
return sorted(list(valid_keys))
- except Exception: # noqa # pylint: disable=broad-except
+ except Exception: # noqa
pass
- # Fallback: return all attributes
return list(ATTRIBUTES.keys())
@@ -184,7 +168,6 @@ def _get_commodity_countries(commodity_code: str) -> dict[str, str]:
dict[str, str]
Mapping of country name -> country code
"""
- # pylint: disable=import-outside-toplevel
from openbb_core.provider.utils.helpers import make_request
base_url = "https://apps.fas.usda.gov/PSDOnlineApi/api/CompositeVisualization/GetCountries?regionCode=R00&commodityCode="
@@ -199,17 +182,34 @@ def _get_commodity_countries(commodity_code: str) -> dict[str, str]:
for item in data:
code = item.get("value")
name = item.get("text", "").strip()
- if code and name and code != "00": # Skip "All Countries" (00)
+ if code and name and code != "00":
name_to_code[name] = code
if name_to_code:
return name_to_code
- except Exception: # noqa # pylint: disable=broad-except
+ except Exception: # noqa
pass
- # Fallback: return empty (will use global COUNTRIES list)
return {}
-def get_timeseries( # pylint: disable=R0912,R0914,R0915,R0917 # noqa: PLR0912
+def _year_columns(columns) -> list[str]:
+ """Return the year-value columns of a query result.
+
+ Parameters
+ ----------
+ columns : iterable of str
+ The result frame's column names.
+
+ Returns
+ -------
+ list[str]
+ Columns whose first four characters are digits, covering both the
+ marketing-year headers such as '2023/2024' and the calendar-year
+ headers such as '2023' that livestock and meat commodities publish.
+ """
+ return [c for c in columns if c[0:4].isdigit()]
+
+
+def get_timeseries( # noqa: PLR0912
commodity: str,
attribute: str | list[str] | None = None,
country: str | list[str] | None = None,
@@ -217,8 +217,7 @@ def get_timeseries( # pylint: disable=R0912,R0914,R0915,R0917 # noqa: PLR0912
end_year: int | None = None,
aggregate_region: bool = False,
) -> list:
- """
- Get time series for a commodity/attribute using ASYNC parallel requests.
+ """Get time series for a commodity/attribute using ASYNC parallel requests.
Parameters
----------
@@ -257,12 +256,11 @@ def get_timeseries( # pylint: disable=R0912,R0914,R0915,R0917 # noqa: PLR0912
>>> get_timeseries('wheat', 'production', country='United States')
>>> get_timeseries('wheat', 'production', country=['US', 'China', 'Brazil'])
>>> get_timeseries('wheat', 'production', country='US, China, Brazil')
- >>> get_timeseries('wheat', 'production, exports, ending_stocks') # Multiple attributes
- >>> get_timeseries('wheat', ['production', 'exports']) # List of attributes
- >>> get_timeseries('wheat', 'production', aggregate_region=True) # World + regions only
- >>> get_timeseries('wheat') # ALL attributes for wheat
+ >>> get_timeseries('wheat', 'production, exports, ending_stocks')
+ >>> get_timeseries('wheat', ['production', 'exports'])
+ >>> get_timeseries('wheat', 'production', aggregate_region=True)
+ >>> get_timeseries('wheat')
"""
- # pylint: disable=import-outside-toplevel
import asyncio # noqa
from datetime import datetime
from aiohttp import ClientError, ClientSession
@@ -280,17 +278,14 @@ def get_timeseries( # pylint: disable=R0912,R0914,R0915,R0917 # noqa: PLR0912
commodity_code = COMMODITIES[key]
valid_attrs = _get_commodity_attributes(commodity_code)
- # Resolve attribute - None means ALL, can be single, list, or comma-separated
if attribute is None:
attr_ids = [ATTRIBUTES[a] for a in valid_attrs]
else:
- # Normalize to list
if isinstance(attribute, str):
attr_list = [a.strip() for a in attribute.split(",") if a.strip()]
else:
attr_list = list(attribute)
- # Validate each attribute
attr_ids = []
for attr in attr_list:
attr_key = attr.lower().replace(" ", "_").replace("-", "_")
@@ -304,10 +299,8 @@ def get_timeseries( # pylint: disable=R0912,R0914,R0915,R0917 # noqa: PLR0912
)
attr_ids.append(ATTRIBUTES[attr_key])
- # Resolve country/region - None means ALL
- # Accepts: lower_snake_case ("united_states"), codes ("US", "R05"), list, comma-separated, or None for all
- selected_region_codes: list[str] = [] # Track selected regions
- selected_country_codes: list[str] = [] # Track selected countries
+ selected_region_codes: list[str] = []
+ selected_country_codes: list[str] = []
valid_countries_map = _get_commodity_countries(commodity_code)
valid_country_codes = set(valid_countries_map.values())
code_to_key = {}
@@ -341,23 +334,18 @@ def get_timeseries( # pylint: disable=R0912,R0914,R0915,R0917 # noqa: PLR0912
for c in country_list:
country_key = c.lower().replace(" ", "_").replace("-", "_")
- # Check if it's a region code (R00, R01, etc.)
if c.upper() in REGION_DISPLAY:
selected_region_codes.append(c.upper())
country_codes.append(c.upper())
- # Check if it's a region name (includes "european_union" -> R05)
elif country_key in REGIONS:
region_code = REGIONS[country_key]
selected_region_codes.append(region_code)
country_codes.append(region_code)
- # Check for "eu" shorthand -> treat as region R05
elif country_key == "eu":
selected_region_codes.append("R05")
country_codes.append("R05")
- # Check if it's a country code
elif c.upper() in COUNTRY_TO_REGION:
code = c.upper()
- # Validate against commodity-specific countries
if valid_country_codes and code not in valid_country_codes:
valid_keys = get_valid_country_keys()
raise ValueError(
@@ -365,10 +353,8 @@ def get_timeseries( # pylint: disable=R0912,R0914,R0915,R0917 # noqa: PLR0912
)
selected_country_codes.append(code)
country_codes.append(code)
- # Check if it's a country name (snake_case)
elif country_key in COUNTRIES:
code = COUNTRIES[country_key]
- # Validate against commodity-specific countries
if valid_country_codes and code not in valid_country_codes:
valid_keys = get_valid_country_keys()
raise ValueError(
@@ -377,7 +363,6 @@ def get_timeseries( # pylint: disable=R0912,R0914,R0915,R0917 # noqa: PLR0912
selected_country_codes.append(code)
country_codes.append(code)
else:
- # Unknown country/region
valid_keys = sorted(
[k.lower().replace(" ", "_") for k in valid_countries_map]
)
@@ -387,20 +372,14 @@ def get_timeseries( # pylint: disable=R0912,R0914,R0915,R0917 # noqa: PLR0912
+ f"Valid countries: {valid_keys}. Valid regions: {valid_regions}"
)
- # If aggregate_region is True, expand appropriately
if aggregate_region:
if "R00" in selected_region_codes:
- # "world" selected - fetch all regional aggregates + any specific countries
- country_codes = (
- list(REGIONS.values()) + selected_country_codes
- ) # R00, R01, ... R18 + specific countries
- country_codes = list(set(country_codes)) # Dedupe
+ country_codes = list(REGIONS.values()) + selected_country_codes
+ country_codes = list(set(country_codes))
elif selected_region_codes:
- # Specific regions selected - add World + those regions + any countries
country_codes = ["R00"] + selected_region_codes + selected_country_codes
- country_codes = list(set(country_codes)) # Dedupe
+ country_codes = list(set(country_codes))
elif selected_country_codes:
- # Only countries selected - add World + their regions
regions_for_countries = set()
for cc in selected_country_codes:
if cc in COUNTRY_TO_REGION:
@@ -408,21 +387,18 @@ def get_timeseries( # pylint: disable=R0912,R0914,R0915,R0917 # noqa: PLR0912
country_codes = (
["R00"] + list(regions_for_countries) + selected_country_codes
)
- country_codes = list(set(country_codes)) # Dedupe
+ country_codes = list(set(country_codes))
elif country is None:
region_codes = set(REGIONS.values())
- # Exclude E4 (EU as country) since we're requesting R05 (EU as region)
country_only_codes = [
c for c in valid_country_codes if c not in region_codes and c != "E4"
]
country_codes = list(REGIONS.values()) + country_only_codes
country_codes = list(set(country_codes))
- # Determine year range
current_year = datetime.now().year
years = list(range(start_year or 1960, (end_year or current_year + 1) + 1))
- # Split attrs into batches for parallel fetching (20 attrs per request is optimal)
BATCH_SIZE = 20
attr_batches = [
attr_ids[i : i + BATCH_SIZE] for i in range(0, len(attr_ids), BATCH_SIZE)
@@ -471,13 +447,11 @@ def get_timeseries( # pylint: disable=R0912,R0914,R0915,R0917 # noqa: PLR0912
async with await get_async_requests_session() as session:
tasks = [fetch_batch(session, batch) for batch in attr_batches]
results = await asyncio.gather(*tasks)
- # Flatten results
all_results = []
for r in results:
all_results.extend(r)
return all_results
- # Run async fetch
result = run_async(fetch_all_batches)
if not result:
@@ -488,25 +462,18 @@ def get_timeseries( # pylint: disable=R0912,R0914,R0915,R0917 # noqa: PLR0912
df = DataFrame(result)
- # API only fills commodity/attribute on first row of each group - forward fill
df["commodity"] = df["commodity"].ffill()
df["attribute"] = df["attribute"].ffill()
- # Find year columns (format: 2024/2025)
- year_cols = [c for c in df.columns if "/" in c and c[0:4].isdigit()]
+ year_cols = _year_columns(df.columns)
name_to_code = {name.strip(): code for name, code in valid_countries_map.items()}
- # Also add region display names for lookup
for region_code, region_name in REGION_DISPLAY.items():
name_to_code[region_name] = region_code
- # Build region name lookup (for detecting when "country" is actually a region)
- region_names = {v: k for k, v in REGION_DISPLAY.items()} # "North America" -> "R01"
+ region_names = {v: k for k, v in REGION_DISPLAY.items()}
def get_region(country_name: str) -> str:
- """Check if this is actually a region (excluding EU)"""
- if country_name in region_names and country_name != "European Union":
- return country_name # It's a region name, return as-is
- # European Union is treated as a country in the EU region
+ """Resolve the region a country belongs to."""
if country_name == "European Union":
return "European Union"
code = name_to_code.get(country_name)
@@ -516,21 +483,17 @@ def get_timeseries( # pylint: disable=R0912,R0914,R0915,R0917 # noqa: PLR0912
def is_region(country_name: str) -> bool:
"""Check if the country name is actually a region aggregate."""
- # When aggregate_region=True, "European Union" is always a region aggregate
if aggregate_region and country_name == "European Union":
return True
if country_name == "European Union":
- return False # EU is a country otherwise
- # "Other" is a region aggregate, not a country
+ return False
if country_name == "Other":
return True
return country_name in region_names
- # Melt to long format - include attribute per row from API response
rows = []
for _, row in df.iterrows():
country_name = row["country"]
- # Skip rows with no country name
if not notna(country_name) or not country_name:
continue
commodity_display = row["commodity"] if row["commodity"] else commodity.title()
@@ -539,7 +502,6 @@ def get_timeseries( # pylint: disable=R0912,R0914,R0915,R0917 # noqa: PLR0912
for yr_col in year_cols:
val = row[yr_col]
if notna(val):
- # If country_name is actually a region, set region=name and country=""
if is_region(country_name):
region_val = country_name
country_val = "--"
@@ -566,7 +528,6 @@ def get_timeseries( # pylint: disable=R0912,R0914,R0915,R0917 # noqa: PLR0912
+ f"{commodity} | {attribute} | {country} | {start_year}-{end_year}"
)
- # Filter by year range (marketing_year is full format like "2024/2025")
if start_year is not None or end_year is not None:
df_long["_year"] = df_long["marketing_year"].str.split("/").str[0].astype(int)
@@ -577,7 +538,6 @@ def get_timeseries( # pylint: disable=R0912,R0914,R0915,R0917 # noqa: PLR0912
df_long = df_long.drop(columns=["_year"])
- # Reorder columns
df_long = df_long[
[
"region",
@@ -590,9 +550,7 @@ def get_timeseries( # pylint: disable=R0912,R0914,R0915,R0917 # noqa: PLR0912
]
]
- # Hierarchical sorting: World first, then regions by value, then countries by value
if not df_long[df_long["country"] == "--"].empty:
- # Get region totals for sorting regions by value (per attribute + year)
region_totals = (
df_long[df_long["country"] == "--"]
.groupby(["region", "attribute", "marketing_year"])["value"]
@@ -600,17 +558,12 @@ def get_timeseries( # pylint: disable=R0912,R0914,R0915,R0917 # noqa: PLR0912
.reset_index()
)
region_totals = region_totals.rename(columns={"value": "_region_value"})
- # Merge region totals for sorting
df_long = df_long.merge(
region_totals, on=["region", "attribute", "marketing_year"], how="left"
)
- # World always sorts first
df_long.loc[df_long["region"] == "World", "_region_value"] = float("inf")
- # Sort: region by value desc, then empty country first (aggregate), then countries by value desc
- df_long["_is_aggregate"] = (df_long["country"] == "--").astype(
- int
- ) # 1 for aggregate, 0 for country
+ df_long["_is_aggregate"] = (df_long["country"] == "--").astype(int)
df_long = (
df_long.sort_values(
by=[
@@ -627,8 +580,7 @@ def get_timeseries( # pylint: disable=R0912,R0914,R0915,R0917 # noqa: PLR0912
.reset_index(drop=True)
)
else:
- # No regional aggregates - just sort by commodity, attribute, year, value
- df_long = df_long.sort_values( # type: ignore
+ df_long = df_long.sort_values(
by=["commodity", "attribute", "marketing_year", "value"],
ascending=[True, True, True, False],
).reset_index(drop=True)
diff --git a/openbb_platform/providers/government_us/openbb_government_us/utils/psd_template_parser.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/psd_template_parser.py
similarity index 81%
rename from openbb_platform/providers/government_us/openbb_government_us/utils/psd_template_parser.py
rename to openbb_platform/providers/government_us/openbb_government_us/usda/utils/psd_template_parser.py
index 2295775ddba..ca20a80f561 100644
--- a/openbb_platform/providers/government_us/openbb_government_us/utils/psd_template_parser.py
+++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/psd_template_parser.py
@@ -1,22 +1,13 @@
-"""Utilities for handling PSD report templates by ID.
+"""Utilities for handling PSD report templates by ID."""
-Ensures all table formats are consistent long/tidy format:
+from openbb_government_us.usda.utils.psd_codes import REGION_DISPLAY
- region | country | commodity | attribute | marketing_year | value | unit
-"""
-
-# pylint: disable=unused-argument,C0302
-
-from openbb_government_us.utils.psd_codes import REGION_DISPLAY
-
-# Build KNOWN_REGIONS from REGION_DISPLAY values plus special cases
KNOWN_REGIONS = set(REGION_DISPLAY.values()) | {
"World Ex-US",
"Others",
"Asia",
}
-# Aliases for region names that appear differently in CSV reports
REGION_ALIASES = {
"Total Foreign": "World Ex-US",
"FSU-12": "Former Soviet Union - 12",
@@ -28,28 +19,25 @@ REGION_ALIASES = {
def set_region_country(
entity_name: str, parent_region: str | None = None
) -> tuple[str | None, str]:
- """
- Determine correct region/country values for an entity name.
+ """Determine correct region/country values for an entity name.
- Returns (region, country) tuple:
- - If entity_name is a known region -> (normalized_region_name, "--")
- - If entity_name is "European Union" -> ("European Union", "European Union")
- - Otherwise -> (parent_region, entity_name)
+ Returns
+ -------
+ tuple[str | None, str]
+ (normalized_region_name, "--") if entity_name is a known region,
+ ("European Union", "European Union") if entity_name is "European Union",
+ (parent_region, entity_name) otherwise.
"""
entity_name = entity_name.strip()
- # Apply alias normalization
normalized = REGION_ALIASES.get(entity_name, entity_name)
- # European Union is both a region and a country (special case per time series)
if normalized == "European Union":
return ("European Union", "European Union")
- # Check if it's a known region
if normalized in KNOWN_REGIONS:
return (normalized, "--")
- # Regular country - use parent_region if available
return (parent_region, entity_name)
@@ -73,9 +61,7 @@ COMMODITY_GROUP_MAP = {
"liv": "Livestock",
}
-# Known commodities to extract from titles
KNOWN_COMMODITIES = [
- # Grains
"Cotton",
"Corn",
"Wheat",
@@ -88,7 +74,6 @@ KNOWN_COMMODITIES = [
"Millet",
"Rye",
"Coarse Grains",
- # Oilseeds
"Palm Oil",
"Rapeseed",
"Sunflower",
@@ -99,13 +84,10 @@ KNOWN_COMMODITIES = [
"Oilseed",
"Protein Meals",
"Vegetable Oils",
- # Beverages
"Coffee",
"Cocoa",
"Tea",
- # Sugar
"Sugar",
- # Dairy
"Butter",
"Cheese",
"Milk",
@@ -114,7 +96,6 @@ KNOWN_COMMODITIES = [
"Whey",
"Nonfat Dry Milk",
"Fluid Milk",
- # Fruits (Horticultural)
"Apples",
"Apple",
"Oranges",
@@ -140,9 +121,7 @@ KNOWN_COMMODITIES = [
"Pear",
"Raisins",
"Raisin",
- # Juice
"Orange Juice",
- # Nuts
"Almond",
"Almonds",
"Walnut",
@@ -153,7 +132,6 @@ KNOWN_COMMODITIES = [
"Cashews",
"Hazelnut",
"Hazelnuts",
- # Livestock
"Beef",
"Beef and Veal",
"Veal",
@@ -179,12 +157,7 @@ def parse_value(val: str) -> float | None:
def extract_unit_from_html(html_text: str) -> str | None:
- """Extract unit from HTML rptSubTitle class.
-
- The subtitle format is often: "Commodity Group Unit" (double-space separated)
- We want just the unit part.
- """
- # pylint: disable=import-outside-toplevel
+ """Extract unit from HTML rptSubTitle class."""
import re
if not html_text:
@@ -195,10 +168,8 @@ def extract_unit_from_html(html_text: str) -> str | None:
subtitle = match.group(1).strip()
- # Check for double-space separation (commodity group unit)
if " " in subtitle:
parts = subtitle.split(" ")
- # Unit is typically the last part containing "Million", "Thousand", "Metric", etc.
for part in reversed(parts):
stripped_part = part.strip()
if any(
@@ -229,7 +200,6 @@ def extract_commodity_from_title(
if c.endswith("s") and c[:-1] in KNOWN_COMMODITIES:
return c[:-1]
return c
- # Fall back to commodity group if provided
if commodity_group:
return COMMODITY_GROUP_MAP.get(commodity_group, commodity_group.title())
@@ -239,24 +209,15 @@ def extract_commodity_from_title(
def parse_template_1(
lines: list, html_text: str | None = None, commodity_group: str | None = None
) -> dict:
- """
- Template 1, 20: Area, Yield, and Production tables.
- Used for: Corn/Cotton/Soybean/Wheat Area Yield Production
-
- These are single-commodity tables. The commodity is in the title.
- Countries are rows, attributes (area/yield/production) are column groups.
- """
- # pylint: disable=import-outside-toplevel
+ """Parse templates 1 and 20, area, yield, and production tables."""
import re
report_title = lines[0].strip()
commodity = extract_commodity_from_title(report_title, commodity_group) or "Unknown"
- # For Oilseed reports (Template 20), check if it's aggregated
if "Oilseed" in report_title or "Total" in report_title:
commodity = "Total Oilseeds"
- # Extract units from header line
header_line = lines[3] if len(lines) > 3 else ""
unit_matches = re.findall(r"\(([^)]+)\)", header_line)
units = {
@@ -265,7 +226,6 @@ def parse_template_1(
"production": unit_matches[2] if len(unit_matches) > 2 else "Unknown",
}
- # Extract periods from lines 4 and 5
proj_line = lines[4].split(",") if len(lines) > 4 else []
period_line = lines[5].split(",") if len(lines) > 5 else []
@@ -331,11 +291,9 @@ def parse_template_1(
current_region = country
continue
- # Determine region/country using helper
region_val, country_val = set_region_country(country, current_region)
for i, period in enumerate(periods[:4]):
- # Area
val = parse_value(values[i]) if i < len(values) else None
if val is not None:
data.append(
@@ -350,7 +308,6 @@ def parse_template_1(
}
)
- # Yield
val = parse_value(values[4 + i]) if 4 + i < len(values) else None
if val is not None:
data.append(
@@ -365,7 +322,6 @@ def parse_template_1(
}
)
- # Production
val = parse_value(values[8 + i]) if 8 + i < len(values) else None
if val is not None:
data.append(
@@ -380,7 +336,6 @@ def parse_template_1(
}
)
- # Change from last month/year (production change for latest projection period)
latest_period = periods[-1] if periods else "Unknown"
if len(values) > 12 and parse_value(values[12]) is not None:
data.append(
@@ -437,17 +392,10 @@ def parse_template_1(
def parse_template_3(
lines: list, html_text: str | None = None, commodity_group: str | None = None
) -> dict:
- """
- Template 3, 17: Supply and Distribution by country/year.
- Used for: Cotton Supply, Corn Supply Disappearance
-
- Single-commodity tables. Countries as section headers, years as rows.
- Attributes are columns (Area Harvested, Production, Imports, etc.)
- """
+ """Parse templates 3 and 17, supply and distribution by country/year."""
report_title = lines[0].strip()
commodity = extract_commodity_from_title(report_title, commodity_group) or "Unknown"
- # Get column headers from line 2
header_line = lines[2] if len(lines) > 2 else ""
columns = [c.strip() for c in header_line.split(",")[1:] if c.strip()]
@@ -476,7 +424,6 @@ def parse_template_3(
year = first_col
- # Determine region/country using helper
if current_country:
region_val, country_val = set_region_country(current_country, None)
else:
@@ -504,18 +451,9 @@ def parse_template_3(
def parse_template_5(
lines: list, html_text: str | None = None, commodity_group: str | None = None
) -> dict:
- """
- Template 5: World Trade/Production tables - COMMODITY VIEW (Oilseeds).
-
- Structure (from raw CSV):
- - Attribute (Production, Imports, Exports) is section header
- - Commodity (Oilseed Copra, Oilseed Soybean) is the row identifier
- - Marketing years are columns
- - NO country - this is world-level aggregate data
- """
+ """Parse template 5, world trade/production tables in commodity view."""
report_title = lines[0].strip()
- # Get periods from line 2
period_line = lines[2] if len(lines) > 2 else ""
periods = [p.strip() for p in period_line.split(",")[1:] if p.strip()]
@@ -534,7 +472,6 @@ def parse_template_5(
has_data = any(parse_value(v) is not None for v in values)
- # Section header = attribute (Production, Imports, etc)
if first_col and not has_data:
current_attribute = first_col
continue
@@ -542,7 +479,6 @@ def parse_template_5(
if not first_col:
continue
- # first_col = commodity name (Oilseed Copra, etc)
commodity = first_col
for i, period in enumerate(periods):
@@ -552,7 +488,7 @@ def parse_template_5(
data.append(
{
"region": "World",
- "country": "--", # World aggregates
+ "country": "--",
"commodity": commodity,
"attribute": current_attribute,
"marketing_year": period,
@@ -567,19 +503,9 @@ def parse_template_5(
def parse_template_7(
lines: list, html_text: str | None = None, commodity_group: str | None = None
) -> dict:
- """
- Template 7: Production/Consumption summary - COUNTRY VIEW (Dairy).
-
- Structure (from raw CSV):
- - Attribute (Production, Domestic Consumption) is section header
- - Country (India, EU, US, etc.) is the row identifier
- - Commodity (Butter, Cheese, etc.) is in the TITLE
- - Marketing years are columns
- """
+ """Parse template 7, production/consumption summary in country view."""
report_title = lines[0].strip()
- # Extract commodity from title - e.g. "Butter Production and Consumption"
- # Try to find commodity name at the start of title
commodity = None
dairy_commodities = [
"Butter",
@@ -601,7 +527,6 @@ def parse_template_7(
extract_commodity_from_title(report_title, commodity_group) or "Unknown"
)
- # Get periods from line 2
period_line = lines[2] if len(lines) > 2 else ""
periods = [p.strip() for p in period_line.split(",")[1:] if p.strip()]
@@ -620,7 +545,6 @@ def parse_template_7(
has_data = any(parse_value(v) is not None for v in values)
- # Section header = attribute (Production, Domestic Consumption, etc)
if first_col and not has_data:
current_attribute = first_col
continue
@@ -628,10 +552,8 @@ def parse_template_7(
if not first_col:
continue
- # first_col = country name (India, EU, etc)
country = first_col
- # Determine region/country using helper
region_val, country_val = set_region_country(country, None)
for i, period in enumerate(periods):
@@ -656,14 +578,10 @@ def parse_template_7(
def parse_template_8(
lines: list, html_text: str | None = None, commodity_group: str | None = None
) -> dict:
- """
- Template 8: Summary tables (like Coffee, Cotton World Supply).
- Attribute = section header, Country = row, periods = columns
- """
+ """Parse template 8, summary tables like Coffee and Cotton World Supply."""
report_title = lines[0].strip()
commodity = extract_commodity_from_title(report_title) or "Unknown"
- # Get periods from line 3
period_line = lines[3] if len(lines) > 3 else ""
periods = [p.strip() for p in period_line.split(",")[1:] if p.strip()]
@@ -696,7 +614,6 @@ def parse_template_8(
country = first_col
- # Determine region/country using helper
region_val, country_val = set_region_country(country, None)
for i, period in enumerate(periods):
@@ -721,25 +638,13 @@ def parse_template_8(
def parse_template_13(
lines: list, html_text: str | None = None, commodity_group: str | None = None
) -> dict:
- """
- Template 13: Multi-commodity supply/demand tables.
- Used for: China Grain Supply, EU Grain Supply
-
- Structure (from raw CSV):
- - Row 0: Title (e.g., "China: Grain Supply and Demand")
- - Row 3: Attribute headers (Area Harvested, Yield, Production, Imports, etc.)
- - Row 5+: Commodity sections (Wheat, Coarse Grains, etc.) with year rows
-
- Country is in the title (China, EU, etc.)
- """
+ """Parse template 13, multi-commodity supply/demand tables."""
report_title = lines[0].strip()
- # Extract country from title (e.g., "China: Grain Supply" -> "China")
country = None
if ":" in report_title:
country = report_title.split(":")[0].strip()
else:
- # Try common patterns
for c in ["China", "EU", "India", "Brazil", "Russia"]:
if c in report_title:
country = c
@@ -748,10 +653,8 @@ def parse_template_13(
if not country:
country = "Unknown"
- # Determine region/country using helper
region_val, country_val = set_region_country(country, None)
- # Get column headers from line 3
header_line = lines[3] if len(lines) > 3 else ""
columns = [c.strip() for c in header_line.split(",")[1:] if c.strip()]
@@ -770,7 +673,6 @@ def parse_template_13(
has_data = any(parse_value(v) is not None for v in values)
- # Section header = commodity (Wheat, Coarse Grains, etc.)
if first_col and not has_data:
current_commodity = first_col
continue
@@ -778,7 +680,6 @@ def parse_template_13(
if not first_col:
continue
- # first_col = marketing year
year = first_col
for i, col in enumerate(columns):
@@ -808,19 +709,9 @@ def parse_template_13(
def parse_template_2(
lines: list, html_text: str | None = None, commodity_group: str | None = None
) -> dict:
- """
- Template 2: World Crop Production Summary.
-
- Structure:
- - Row 3: Regions (World, Total Foreign, North America, FSU-12, Asia (WAP), ...)
- - Row 4: Countries under each region
- - Commodity sections (Wheat, Coarse Grains, Rice, etc.) with marketing year rows
-
- Attribute is ALWAYS "Production" for this template.
- """
+ """Parse template 2, World Crop Production Summary."""
report_title = lines[0].strip()
- # Get region/country headers from lines 3-4
region_line = lines[3].split(",") if len(lines) > 3 else []
country_line = lines[4].split(",") if len(lines) > 4 else []
@@ -838,18 +729,13 @@ def parse_template_2(
if i == 0:
continue
- # Use set_region_country for proper region/country assignment
if current_region and not country:
- # This is a region-level aggregate (World, Total Foreign, North America, etc.)
region_val, country_val = set_region_country(current_region, None)
columns.append((region_val, country_val))
elif country and current_region:
- # This is a country under a region
- # Apply alias normalization to region name
normalized_region = REGION_ALIASES.get(current_region, current_region)
columns.append((normalized_region, country))
elif country:
- # Country without a region
columns.append((None, country))
unit_line = lines[5] if len(lines) > 5 else ""
@@ -960,40 +846,27 @@ def parse_template_2(
def parse_template_9(
lines: list, html_text: str | None = None, commodity_group: str | None = None
) -> dict:
- """
- Template 9: Copra, Palm Kernel, Palm Oil Production.
-
- Multi-commodity table with country rows.
- Commodity is section header, Country is row, periods are columns.
- """
- # pylint: disable=import-outside-toplevel
+ """Parse template 9, Copra, Palm Kernel, and Palm Oil Production."""
import re
report_title = lines[0].strip()
unit = extract_unit_from_html(html_text) or "Million metric tons" # type: ignore
- # Extract periods from line 5 (e.g., "2023/24444" -> "2023/24")
def clean_period(p):
"""Remove trailing garbage digits from period strings like '2023/24444' -> '2023/24'"""
p = p.strip()
if not p:
return p
- # Match patterns like 2023/24, 2024/25, Nov, Dec, Prel., Proj.
- # Strip any trailing digits that don't belong
- # Handle "2023/24444" -> "2023/24"
m = re.match(r"^(\d{4}/\d{2})\d*$", p)
if m:
return m.group(1)
- # Handle "Prel. 2024/25222" -> "Prel. 2024/25"
m = re.match(r"^(Prel\.\s*\d{4}/\d{2})\d*$", p)
if m:
return m.group(1)
- # Handle "2025/26Proj.111" -> "2025/26 Proj."
m = re.match(r"^(\d{4}/\d{2})(Proj\.?)\d*$", p)
if m:
return f"{m.group(1)} {m.group(2)}"
- # Handle "Nov333" -> "Nov"
m = re.match(r"^(Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)\d*$", p)
if m:
return m.group(1)
@@ -1002,12 +875,10 @@ def parse_template_9(
period_line = lines[5].split(",") if len(lines) > 5 else []
sub_period_line = lines[6].split(",") if len(lines) > 6 else []
- # Build periods list
periods = []
for i, p in enumerate(period_line[1:5]):
_p = clean_period(p)
if _p:
- # Check if there's a sub-period (Nov, Dec)
sub = (
clean_period(sub_period_line[i + 1])
if i + 1 < len(sub_period_line)
@@ -1040,7 +911,6 @@ def parse_template_9(
has_data = any(parse_value(v) is not None for v in values)
- # Commodity header
if first_col and not has_data:
current_commodity = first_col
continue
@@ -1050,7 +920,6 @@ def parse_template_9(
country = first_col
- # Determine region/country using helper
region_val, country_val = set_region_country(country, None)
for i, period in enumerate(periods[:4]):
@@ -1069,7 +938,6 @@ def parse_template_9(
}
)
- # Change values
latest_period = periods[-1] if periods else "Unknown"
if len(values) > 4 and parse_value(values[4]) is not None:
data.append(
@@ -1102,25 +970,15 @@ def parse_template_9(
def parse_template_11(
lines: list, html_text: str | None = None, commodity_group: str | None = None
) -> dict:
- """
- Template 11: All Grain Summary Comparison.
-
- Structure (from raw CSV):
- - Row 3: Commodities (Wheat, Rice Milled, Corn, ...)
- - Row 4: Marketing years repeated for each commodity
- - Attribute (Production, Domestic Consumption, etc.) is section header
- - Country (United States, Other, World Total) is row identifier
- """
+ """Parse template 11, All Grain Summary Comparison."""
report_title = lines[0].strip()
- # Get commodity headers from line 3
commodity_line = lines[3].split(",") if len(lines) > 3 else []
commodities = [c.strip() for c in commodity_line if c.strip()]
- # Get periods from line 4 - they repeat for each commodity
period_line = lines[4].split(",") if len(lines) > 4 else []
periods = []
- for p in period_line[2:5]: # First 3 periods for first commodity
+ for p in period_line[2:5]:
_p = p.strip()
if _p:
periods.append(_p)
@@ -1140,7 +998,6 @@ def parse_template_11(
has_data = any(parse_value(v) is not None for v in values)
- # Section header = attribute
if first_col and not has_data:
current_attribute = first_col
continue
@@ -1150,11 +1007,8 @@ def parse_template_11(
country = first_col
- # Determine region/country using helper
region_val, country_val = set_region_country(country, None)
- # Values are: [note], [c1_y1, c1_y2, c1_y3], [c2_y1, c2_y2, c2_y3], ...
- # Skip the note column (values[0])
val_idx = 1
for commodity in commodities:
for period in periods:
@@ -1185,19 +1039,10 @@ def parse_template_11(
def parse_template_20(
lines: list, html_text: str | None = None, commodity_group: str | None = None
) -> dict:
- """
- Template 20: Total Oilseed Area, Yield, and Production.
-
- Complex structure with country/commodity sections.
- - Aggregate rows (World Total, Total Foreign, Major OilSeeds, Foreign Oilseeds)
- - Regional subtotals (South America, South Asia, etc. with production value on same row)
- - Individual country rows under regions
- """
- # pylint: disable=import-outside-toplevel
+ """Parse template 20, Total Oilseed Area, Yield, and Production."""
import re
report_title = lines[0].strip()
- # Extract units from header line
header_line = lines[3] if len(lines) > 3 else ""
unit_matches = re.findall(r"\(([^)]+)\)", header_line)
units = {
@@ -1210,7 +1055,6 @@ def parse_template_20(
),
}
- # Extract periods from lines 4 and 5
proj_line = lines[4].split(",") if len(lines) > 4 else []
period_line = lines[5].split(",") if len(lines) > 5 else []
@@ -1252,9 +1096,8 @@ def parse_template_20(
data = []
current_region = None
- current_commodity = "Total Oilseeds" # Default commodity for this report
+ current_commodity = "Total Oilseeds"
- # Aggregate/commodity names to watch for
aggregates = ["World Total", "Total Foreign", "Major OilSeeds", "Foreign Oilseeds"]
special_commodities = ["Oilseed Copra", "Oilseed Palm Kernel"]
@@ -1275,39 +1118,31 @@ def parse_template_20(
current_commodity = first_col
continue
- # Check if this is a region subtotal line (region name + values on same line)
region_match = re.match(r"^([A-Za-z ]+)\s+(\d+\.?\d*)$", first_col)
if not has_data:
current_region = first_col
continue
- # Determine entity type and set appropriate values
is_aggregate = any(agg.lower() in first_col.lower() for agg in aggregates)
is_special_commodity = first_col in special_commodities
if region_match:
- # Region subtotal - extract region name
entity_name = region_match.group(1).strip()
current_region = entity_name
commodity = current_commodity
- # Use helper for region/country assignment
region_val, country_val = set_region_country(entity_name, None)
elif is_aggregate:
- # Aggregates like "World Total", "Total Foreign"
commodity = current_commodity
region_val, country_val = set_region_country(first_col, None)
elif is_special_commodity:
commodity = first_col
region_val, country_val = ("World", "--")
else:
- # Regular country row
commodity = current_commodity
region_val, country_val = set_region_country(first_col, current_region)
- # Emit data for each period and attribute
for i, period in enumerate(periods[:4]):
- # Area
val = parse_value(values[i]) if i < len(values) else None
if val is not None:
data.append(
@@ -1322,7 +1157,6 @@ def parse_template_20(
}
)
- # Yield
val = parse_value(values[4 + i]) if 4 + i < len(values) else None
if val is not None:
data.append(
@@ -1337,7 +1171,6 @@ def parse_template_20(
}
)
- # Production
val = parse_value(values[8 + i]) if 8 + i < len(values) else None
if val is not None:
data.append(
@@ -1352,7 +1185,6 @@ def parse_template_20(
}
)
- # Change from last month/year
latest_period = periods[-1] if periods else "Unknown"
if len(values) > 12 and parse_value(values[12]) is not None:
@@ -1412,7 +1244,6 @@ def parse_template_20(
}
-# Template router
PARSERS = {
1: parse_template_1,
2: parse_template_2,
@@ -1436,11 +1267,16 @@ def parse_report(
) -> dict:
"""Route to appropriate parser based on template ID.
- Args:
- template_id: The template ID for the report
- lines: CSV lines from the report
- html_text: HTML text from the report (for unit extraction)
- commodity_group: The commodity group code (e.g., 'cot', 'crn', 'wht')
+ Parameters
+ ----------
+ template_id : int
+ The template ID for the report.
+ lines : list
+ CSV lines from the report.
+ html_text : str | None
+ HTML text from the report (for unit extraction).
+ commodity_group : str | None
+ The commodity group code (e.g., 'cot', 'crn', 'wht').
"""
parser = PARSERS.get(template_id)
diff --git a/openbb_platform/providers/government_us/openbb_government_us/utils/__init__.py b/openbb_platform/providers/government_us/openbb_government_us/utils/__init__.py
index 7267bae0ade..090141c1292 100644
--- a/openbb_platform/providers/government_us/openbb_government_us/utils/__init__.py
+++ b/openbb_platform/providers/government_us/openbb_government_us/utils/__init__.py
@@ -1 +1 @@
-"""OpenBB Government US utils."""
+"""Shared utilities for the US Government provider extensions."""
diff --git a/openbb_platform/providers/government_us/openbb_government_us/utils/curl_session.py b/openbb_platform/providers/government_us/openbb_government_us/utils/curl_session.py
new file mode 100644
index 00000000000..4f3c75f6b24
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/utils/curl_session.py
@@ -0,0 +1,43 @@
+"""Shared curl_cffi session helper for hosts behind a TLS-fingerprinting WAF."""
+
+from typing import Any
+
+_SESSIONS: dict[str, Any] = {}
+
+
+async def get_session(key: str, warmup: str | None = None) -> Any:
+ """Get or create a Chrome-impersonating session for a WAF-protected host.
+
+ Hosts such as www.fas.usda.gov sit behind an Akamai WAF that inspects the
+ TLS fingerprint, so aiohttp and plain curl are rejected with HTTP 403
+ regardless of headers.
+
+ Parameters
+ ----------
+ key : str
+ Identifier for the cached session, conventionally the host name.
+ warmup : str | None
+ URL to request once when the session is created, priming any cookies
+ the edge sets before the first real request.
+
+ Returns
+ -------
+ AsyncSession
+ A curl_cffi session impersonating Chrome, cached under the key.
+ """
+ from curl_cffi.requests import AsyncSession
+
+ session = _SESSIONS.get(key)
+ if session is None:
+ session = AsyncSession(impersonate="chrome")
+ if warmup:
+ await session.get(warmup)
+ _SESSIONS[key] = session
+ return session
+
+
+async def close_sessions() -> None:
+ """Close every cached session and clear the cache."""
+ while _SESSIONS:
+ _, session = _SESSIONS.popitem()
+ await session.close()
diff --git a/openbb_platform/providers/government_us/openbb_government_us/utils/serializers.py b/openbb_platform/providers/government_us/openbb_government_us/utils/serializers.py
new file mode 100644
index 00000000000..a40269c42ad
--- /dev/null
+++ b/openbb_platform/providers/government_us/openbb_government_us/utils/serializers.py
@@ -0,0 +1,53 @@
+"""Shared serialization helpers."""
+
+from typing import Any
+
+from openbb_core.provider.abstract.data import Data
+from pydantic import model_serializer, model_validator
+
+NULL_TOKENS = frozenset(
+ {"", "-", "--", "---", "----", "n/a", "na", "null", "none", "nan"}
+)
+
+
+def is_null_token(value: Any) -> bool:
+ """Return whether a value is a source placeholder standing in for null."""
+ return isinstance(value, str) and value.strip().casefold() in NULL_TOKENS
+
+
+class NullTokenMixin(Data):
+ """Mixin that coerces source placeholder tokens to None before validation.
+
+ Source files use placeholder tokens such as '--' or blank cells where a
+ value does not apply; these are coerced to None so they never surface as
+ row values. The coercion runs in a before-validator, which leaves the
+ model's field schema intact so column definitions still generate.
+ """
+
+ @model_validator(mode="before")
+ @classmethod
+ def _null_placeholder_tokens(cls, data: Any) -> Any:
+ """Coerce placeholder tokens to None before validation."""
+ if isinstance(data, dict):
+ return {
+ key: (None if is_null_token(value) else value)
+ for key, value in data.items()
+ }
+ return data
+
+
+class OmitNoneMixin(NullTokenMixin):
+ """Mixin that also drops None fields from the serialized record.
+
+ Models covering many source tables in one schema leave most fields
+ unpopulated for any single table; dropping the None-valued fields keeps
+ them out of the response entirely. This replaces the serialized schema
+ with a free-form object, so a model using it cannot also carry column
+ definitions; use it only where per-table column pruning matters more than
+ column presentation.
+ """
+
+ @model_serializer(mode="wrap")
+ def _serialize(self, handler) -> dict[str, Any]:
+ """Drop keys whose value is None."""
+ return {k: v for k, v in handler(self).items() if v is not None}
diff --git a/openbb_platform/providers/government_us/poetry.lock b/openbb_platform/providers/government_us/poetry.lock
deleted file mode 100644
index f082ade0288..00000000000
--- a/openbb_platform/providers/government_us/poetry.lock
+++ /dev/null
@@ -1,1971 +0,0 @@
-# This file is automatically @generated by Poetry 2.4.1 and should not be changed by hand.
-
-[[package]]
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-]
-
-[[package]]
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-multidict = ">=4.0"
-propcache = ">=0.2.1"
-
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-type = ["pytest-mypy (>=1.0.1) ; platform_python_implementation != \"PyPy\""]
-
-[metadata]
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diff --git a/openbb_platform/providers/government_us/pyproject.toml b/openbb_platform/providers/government_us/pyproject.toml
index 8f9e8ef3459..1093407c134 100644
--- a/openbb_platform/providers/government_us/pyproject.toml
+++ b/openbb_platform/providers/government_us/pyproject.toml
@@ -1,20 +1,128 @@
-[tool.poetry]
+[project]
name = "openbb-government-us"
-version = "1.6.1"
-description = "US Government Data Extension for OpenBB"
-authors = ["OpenBB "]
-license = "AGPL-3.0-only"
+version = "2.0.0"
+description = "US Government data extensions for OpenBB Platform, one provider per agency."
+authors = [{ name = "OpenBB Team", email = "hello@openbb.co" }]
+license = { text = "AGPL-3.0-only" }
readme = "README.md"
-packages = [{ include = "openbb_government_us" }]
+requires-python = ">=3.10,<4"
+dependencies = [
+ "openbb-core[pandas]>=2.0.0",
+ "curl-cffi>=0.7",
+ "defusedxml>=0.7",
+ "diskcache>=5",
+ "httpx>=0.27",
+ "random-user-agent>=1.0",
+ "openpyxl>=3.1.5,<4.0.0",
+ "xlrd>=2.0.2",
-[tool.poetry.dependencies]
-python = ">=3.10,<4"
-openbb-core = "^1.6.10"
-random-user-agent = "^1.0.1"
+]
+
+[project.entry-points."openbb_provider_extension"]
+congress_gov = "openbb_government_us.congress:congress_gov_provider"
+us_treasury = "openbb_government_us.treasury:us_treasury_provider"
+usda = "openbb_government_us.usda:usda_provider"
+
+[project.entry-points."openbb_core_extension"]
+uscongress = "openbb_government_us.congress.congress_router:router"
+usda = "openbb_government_us.usda.usda_router:router"
+ustreasury = "openbb_government_us.treasury.treasury_router:router"
+
+[project.scripts]
+generate-usda-ers-catalog = "openbb_government_us.usda.utils.ers_catalog:main"
+
+[project.urls]
+Homepage = "https://openbb.co"
+Repository = "https://github.com/OpenBB-finance/OpenBB"
+
+[dependency-groups]
+dev = ["openbb-devtools>=2.0.0", "openbb-platform-api>=2.0.0"]
[build-system]
-requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"
+requires = ["hatchling>=1.25"]
+build-backend = "hatchling.build"
-[tool.poetry.plugins."openbb_provider_extension"]
-government_us = "openbb_government_us:government_us_provider"
+[tool.hatch.build.targets.wheel]
+packages = ["openbb_government_us"]
+include = [
+ "openbb_government_us/congress/assets/apps.json",
+ "openbb_government_us/treasury/assets/apps.json",
+ "openbb_government_us/treasury/assets/award_info.html",
+ "openbb_government_us/treasury/assets/recipient_info.html",
+ "openbb_government_us/usda/assets/apps.json",
+ "openbb_government_us/usda/assets/ers_chart.html",
+ "openbb_government_us/usda/assets/ers_viz_catalog.json",
+]
+
+[tool.hatch.build.targets.sdist]
+include = [
+ "openbb_government_us",
+ "openbb_government_us/congress/assets/apps.json",
+ "openbb_government_us/treasury/assets/apps.json",
+ "openbb_government_us/usda/assets/apps.json",
+ "openbb_government_us/usda/assets/ers_chart.html",
+ "openbb_government_us/usda/assets/ers_viz_catalog.json",
+ "README.md",
+ "pyproject.toml",
+]
+
+[tool.uv.sources]
+openbb-core = { path = "../../core", editable = true }
+openbb-devtools = { path = "../../extensions/devtools", editable = true }
+openbb-platform-api = { path = "../../extensions/platform_api", editable = true }
+
+[tool.ruff]
+line-length = 88
+target-version = "py310"
+fix = true
+
+[tool.ruff.lint]
+select = ["D", "E", "W", "F", "S", "UP", "I", "PLC", "PLE", "PLR", "PLW", "SIM", "T20"]
+ignore = [
+ "PLR2004", "PLR0913", "PLR0915", "PLC0415",
+ "E402", "E501",
+ "D202", "D203", "D205", "D206", "D210", "D212", "D213", "D214",
+ "D215", "D300", "D400", "D404", "D405", "D406", "D407", "D408",
+ "D409", "D410", "D411", "D413", "D415", "D416", "D417",
+]
+
+[tool.ruff.lint.per-file-ignores]
+"openbb_government_us/congress/congress_router.py" = ["ARG001", "PLC0415", "PLR0913", "PLR0912", "PLR0915", "SLF001"]
+"openbb_government_us/congress/utils/helpers.py" = ["PLR0911", "PLR0912", "PLR0915", "SIM102", "SIM114", "BLE001"]
+"openbb_government_us/congress/utils/committees.py" = ["PLR0912", "PLR0915"]
+"openbb_government_us/congress/utils/congress_search.py" = ["PLR0912", "BLE001"]
+"openbb_government_us/*/models/*.py" = ["ARG002", "PLC0415", "PLR0912"]
+"openbb_government_us/usda/utils/psd_data_downloader.py" = ["PLR0911", "PLR0912", "PLW2901"]
+"openbb_government_us/usda/utils/psd_template_parser.py" = ["PLR0911", "PLR0912", "PLW2901", "SIM102"]
+"tests/**" = ["S101", "S108", "D", "SIM105", "SIM117"]
+"integration/**" = ["S101", "S108", "D", "SIM105", "SIM117", "PLC0415", "ARG001"]
+"**/__init__.py" = ["F401"]
+
+[tool.ruff.lint.isort]
+combine-as-imports = true
+force-wrap-aliases = true
+
+[tool.ruff.lint.pydocstyle]
+convention = "numpy"
+
+[tool.ty]
+[tool.ty.environment]
+python-version = "3.10"
+
+[tool.ty.src]
+include = ["openbb_government_us"]
+exclude = ["tests", "integration"]
+
+[tool.ty.rules]
+unresolved-import = "warn"
+
+[tool.coverage.report]
+exclude_lines = ["pragma: no cover", "if TYPE_CHECKING:", "raise NotImplementedError", "@overload", "if __name__ == .__main__.:"]
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+addopts = "-q --strict-markers"
+markers = [
+ "record_http: VCR-backed HTTP cassette tests",
+ "integration: end-to-end tests that hit live services (excluded from the coverage gate)",
+]
diff --git a/openbb_platform/providers/government_us/tests/conftest.py b/openbb_platform/providers/government_us/tests/conftest.py
new file mode 100644
index 00000000000..b79588542eb
--- /dev/null
+++ b/openbb_platform/providers/government_us/tests/conftest.py
@@ -0,0 +1,61 @@
+"""Shared fixtures for the openbb_government_us test suite."""
+
+import pytest
+
+
+def _redact_binary_response(response: dict) -> dict:
+ headers = response.get("headers", {})
+ content_type_values = (
+ headers.get("Content-Type") or headers.get("content-type") or []
+ )
+ content_type = " ".join(content_type_values).lower()
+ body = response.get("body", {})
+ payload = body.get("string")
+ if isinstance(payload, str):
+ payload_bytes = payload.encode("utf-8", errors="ignore")
+ else:
+ payload_bytes = payload
+ is_pdf = (
+ "application/pdf" in content_type or "application/octet-stream" in content_type
+ )
+ if is_pdf and isinstance(payload_bytes, (bytes, bytearray)):
+ body["string"] = b"%PDF-1.4\n%OpenBB VCR redacted binary body\n%%EOF\n"
+ response["body"] = body
+ return response
+
+
+@pytest.fixture(scope="module")
+def vcr_config():
+ """VCR configuration: strip auth headers and api_key from recorded cassettes."""
+ return {
+ "filter_headers": [
+ ("User-Agent", None),
+ ("api_key", "MOCK_API_KEY"),
+ ],
+ "filter_query_parameters": [
+ ("api_key", "MOCK_API_KEY"),
+ ],
+ "before_record_response": _redact_binary_response,
+ }
+
+
+@pytest.fixture(autouse=True)
+def _clear_state_caches():
+ """Reset module-level caches so state never leaks between tests."""
+ from openbb_government_us.congress.utils import bulk, committees
+ from openbb_government_us.congress.utils.helpers import BillsState
+
+ def _reset():
+ BillsState().bulk.clear()
+ committees._GOVTRACK_DATA_CACHE.clear()
+ bulk._LOAD_LOCKS.clear()
+
+ _reset()
+ yield
+ _reset()
+
+
+@pytest.fixture(autouse=True)
+def _isolate_ers_cache(tmp_path, monkeypatch):
+ """Point the ERS disk cache at a per-test directory so HTTP always fires."""
+ monkeypatch.setenv("OPENBB_USDA_CACHE_DIR", str(tmp_path / "ers_cache"))
diff --git a/openbb_platform/providers/congress_gov/tests/record/http/test_congress_gov_fetchers/test_congress_amendment_text_fetcher_urllib3_v2.yaml b/openbb_platform/providers/government_us/tests/record/http/test_congress_gov_fetchers/test_congress_amendment_text_fetcher_urllib3_v2.yaml
similarity index 100%
rename from openbb_platform/providers/congress_gov/tests/record/http/test_congress_gov_fetchers/test_congress_amendment_text_fetcher_urllib3_v2.yaml
rename to openbb_platform/providers/government_us/tests/record/http/test_congress_gov_fetchers/test_congress_amendment_text_fetcher_urllib3_v2.yaml
diff --git a/openbb_platform/providers/congress_gov/tests/record/http/test_congress_gov_fetchers/test_congress_bill_text_fetcher_urllib3_v2.yaml b/openbb_platform/providers/government_us/tests/record/http/test_congress_gov_fetchers/test_congress_bill_text_fetcher_urllib3_v2.yaml
similarity index 100%
rename from openbb_platform/providers/congress_gov/tests/record/http/test_congress_gov_fetchers/test_congress_bill_text_fetcher_urllib3_v2.yaml
rename to openbb_platform/providers/government_us/tests/record/http/test_congress_gov_fetchers/test_congress_bill_text_fetcher_urllib3_v2.yaml
diff --git a/openbb_platform/providers/government_us/tests/record/http/test_government_us_fetchers/test_government_us_commodity_psd_report_fetcher_urllib3_v2.yaml b/openbb_platform/providers/government_us/tests/record/http/test_government_us_fetchers/test_government_us_commodity_psd_report_fetcher_urllib3_v2.yaml
deleted file mode 100644
index eb16b7e734f..00000000000
--- a/openbb_platform/providers/government_us/tests/record/http/test_government_us_fetchers/test_government_us_commodity_psd_report_fetcher_urllib3_v2.yaml
+++ /dev/null
@@ -1,7715 +0,0 @@
-interactions:
-- request:
- body: null
- headers:
- Accept:
- - '*/*'
- Accept-Encoding:
- - gzip, deflate
- Connection:
- - keep-alive
- method: GET
- uri: https://apps.fas.usda.gov/PSDOnline/CircularDownloader.ashx?commodity=Sugar&month=05&year=2025
- response:
- body:
- string: !!binary |
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- Content-Length:
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- Content-Type:
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- Date:
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- Strict-Transport-Security:
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- X-Content-Type-Options:
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- content-disposition:
- - attachment; filename="052025Sugar.pdf"
- status:
- code: 200
- message: OK
-version: 1
diff --git a/openbb_platform/providers/government_us/tests/record/http/test_government_us_fetchers/test_government_us_weather_bulletin_download_fetcher_urllib3_v2.yaml b/openbb_platform/providers/government_us/tests/record/http/test_government_us_fetchers/test_government_us_weather_bulletin_download_fetcher_urllib3_v2.yaml
deleted file mode 100644
index 6618939c469..00000000000
--- a/openbb_platform/providers/government_us/tests/record/http/test_government_us_fetchers/test_government_us_weather_bulletin_download_fetcher_urllib3_v2.yaml
+++ /dev/null
@@ -1,52942 +0,0 @@
-interactions:
-- request:
- body: null
- headers:
- Accept:
- - application/json
- Accept-Encoding:
- - gzip, deflate
- Connection:
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- method: GET
- uri: https://esmis.nal.usda.gov/sites/default/release-files/cj82k728n/9w033w568/x059f4232/wwcb0125.pdf
- response:
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+ headers:
+ Access-Control-Allow-Origin:
+ - '*'
+ Connection:
+ - keep-alive
+ Content-Encoding:
+ - gzip
+ Content-Length:
+ - '2903'
+ Content-Type:
+ - application/json
+ Date:
+ - Thu, 16 Jul 2026 18:32:49 GMT
+ Server:
+ - nginx
+ Set-Cookie:
+ - cookiesession1=678A3E0F047CDDB8CFF9EC78C9376D69;Expires=Fri, 16 Jul 2027 18:32:48
+ GMT;Path=/;HttpOnly
+ - BIGipServerddsfMj0s2EaKsi9WsSRnAw=!BYOAIY/odU5ai83fcKTyKInrG7ukTjyPenwPNFjeyvmHIxaPokOywncDm423KWpMSoBy/tu/xdVb7g==;
+ path=/; Httponly; Secure
+ Strict-Transport-Security:
+ - max-age=31536000; includeSubDomains
+ Vary:
+ - Accept-Encoding
+ X-Frame-Options:
+ - SAMEORIGIN
+ X-XSS-Protection:
+ - 1; mode=block
+ status:
+ code: 200
+ message: OK
+version: 1
diff --git a/openbb_platform/providers/government_us/tests/record/http/test_us_treasury_fetchers/test_treasury_auction_results_fetcher_urllib3_v2.yaml b/openbb_platform/providers/government_us/tests/record/http/test_us_treasury_fetchers/test_treasury_auction_results_fetcher_urllib3_v2.yaml
new file mode 100644
index 00000000000..bef99877df8
--- /dev/null
+++ b/openbb_platform/providers/government_us/tests/record/http/test_us_treasury_fetchers/test_treasury_auction_results_fetcher_urllib3_v2.yaml
@@ -0,0 +1,215 @@
+interactions:
+- request:
+ body: null
+ headers:
+ Accept:
+ - application/json
+ Accept-Encoding:
+ - gzip, deflate
+ Connection:
+ - keep-alive
+ method: GET
+ uri: https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v1/accounting/od/auctions_query?filter=auction_date%3Agte%3A2026-01-01%2Cauction_date%3Alte%3A2026-01-31%2Csecurity_type%3Aeq%3ABill&page%5Bnumber%5D=1&page%5Bsize%5D=10000&sort=auction_date
+ response:
+ body:
+ string: !!binary |
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+ headers:
+ Access-Control-Allow-Origin:
+ - '*'
+ Connection:
+ - keep-alive
+ Content-Encoding:
+ - gzip
+ Content-Length:
+ - '9522'
+ Content-Type:
+ - application/json
+ Date:
+ - Thu, 16 Jul 2026 18:32:52 GMT
+ Server:
+ - nginx
+ Set-Cookie:
+ - cookiesession1=678A3E0E2E3C3A76D04A6B361DF6B176;Expires=Fri, 16 Jul 2027 18:32:52
+ GMT;Path=/;HttpOnly
+ - BIGipServerddsfMj0s2EaKsi9WsSRnAw=!Y8JGOrh/IIrqCX7fcKTyKInrG7ukTrTy5njmsbQi9qvZNE5Y0EPNoTa2TZVKes0nvok/qOisaJdqnQ==;
+ path=/; Httponly; Secure
+ Strict-Transport-Security:
+ - max-age=31536000; includeSubDomains
+ Vary:
+ - Accept-Encoding
+ X-Frame-Options:
+ - SAMEORIGIN
+ X-XSS-Protection:
+ - 1; mode=block
+ status:
+ code: 200
+ message: OK
+version: 1
diff --git a/openbb_platform/providers/government_us/tests/record/http/test_government_us_fetchers/test_government_us_treasury_auctions_fetcher_urllib3_v2.yaml b/openbb_platform/providers/government_us/tests/record/http/test_us_treasury_fetchers/test_treasury_auctions_fetcher_urllib3_v2.yaml
similarity index 100%
rename from openbb_platform/providers/government_us/tests/record/http/test_government_us_fetchers/test_government_us_treasury_auctions_fetcher_urllib3_v2.yaml
rename to openbb_platform/providers/government_us/tests/record/http/test_us_treasury_fetchers/test_treasury_auctions_fetcher_urllib3_v2.yaml
diff --git a/openbb_platform/providers/government_us/tests/record/http/test_us_treasury_fetchers/test_treasury_bill_offerings_fetcher_urllib3_v2.yaml b/openbb_platform/providers/government_us/tests/record/http/test_us_treasury_fetchers/test_treasury_bill_offerings_fetcher_urllib3_v2.yaml
new file mode 100644
index 00000000000..f61606f5991
--- /dev/null
+++ b/openbb_platform/providers/government_us/tests/record/http/test_us_treasury_fetchers/test_treasury_bill_offerings_fetcher_urllib3_v2.yaml
@@ -0,0 +1,84 @@
+interactions:
+- request:
+ body: null
+ headers:
+ Accept:
+ - application/json
+ Accept-Encoding:
+ - gzip, deflate
+ Connection:
+ - keep-alive
+ method: GET
+ uri: https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v1/accounting/tb/pdo1_offerings_regular_weekly_treasury_bills?filter=issue_date%3Agte%3A2026-03-01%2Cissue_date%3Alte%3A2026-03-31&page%5Bnumber%5D=1&page%5Bsize%5D=10000&sort=issue_date%2Csrc_line_nbr
+ response:
+ body:
+ string: !!binary |
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+ Access-Control-Allow-Origin:
+ - '*'
+ Connection:
+ - keep-alive
+ Content-Encoding:
+ - gzip
+ Content-Length:
+ - '2075'
+ Content-Type:
+ - application/json
+ Date:
+ - Thu, 16 Jul 2026 18:32:53 GMT
+ Server:
+ - nginx
+ Set-Cookie:
+ - cookiesession1=678A3E0FA86BB49B8A5F90C35D28BC6A;Expires=Fri, 16 Jul 2027 18:32:53
+ GMT;Path=/;HttpOnly
+ - BIGipServerddsfMj0s2EaKsi9WsSRnAw=!GEjQsmSrGDVrDcPfcKTyKInrG7ukTqf3T/0cHhI4hwNmXJKlKJknCq3v562ty2VUAv+ETBTt6xH2Ig==;
+ path=/; Httponly; Secure
+ Strict-Transport-Security:
+ - max-age=31536000; includeSubDomains
+ Vary:
+ - Accept-Encoding
+ X-Frame-Options:
+ - SAMEORIGIN
+ X-XSS-Protection:
+ - 1; mode=block
+ status:
+ code: 200
+ message: OK
+version: 1
diff --git a/openbb_platform/providers/government_us/tests/record/http/test_us_treasury_fetchers/test_treasury_bulletin_fetcher_urllib3_v2.yaml b/openbb_platform/providers/government_us/tests/record/http/test_us_treasury_fetchers/test_treasury_bulletin_fetcher_urllib3_v2.yaml
new file mode 100644
index 00000000000..fd6a08c5d20
--- /dev/null
+++ b/openbb_platform/providers/government_us/tests/record/http/test_us_treasury_fetchers/test_treasury_bulletin_fetcher_urllib3_v2.yaml
@@ -0,0 +1,142 @@
+interactions:
+- request:
+ body: null
+ headers:
+ Accept:
+ - application/json
+ Accept-Encoding:
+ - gzip, deflate
+ Connection:
+ - keep-alive
+ method: GET
+ uri: https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v1/accounting/tb/ofs2_estimated_ownership_treasury_securities?filter=record_date%3Agte%3A2026-01-01&page%5Bnumber%5D=1&page%5Bsize%5D=10000&sort=record_date
+ response:
+ body:
+ string: !!binary |
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+ headers:
+ Access-Control-Allow-Origin:
+ - '*'
+ Connection:
+ - keep-alive
+ Content-Encoding:
+ - gzip
+ Content-Length:
+ - '5377'
+ Content-Type:
+ - application/json
+ Date:
+ - Thu, 16 Jul 2026 20:18:33 GMT
+ Server:
+ - nginx
+ Set-Cookie:
+ - cookiesession1=678A3E0F261AA78519DFB3879FD7257C;Expires=Fri, 16 Jul 2027 20:18:33
+ GMT;Path=/;HttpOnly
+ - BIGipServerddsfMj0s2EaKsi9WsSRnAw=!P/fso6f8G2sOKMffcKTyKInrG7ukTtl+gAIpdk3Skibj6gCBBlySpoBkhOx5hY4adbogtBlf804kQg==;
+ path=/; Httponly; Secure
+ Strict-Transport-Security:
+ - max-age=31536000; includeSubDomains
+ Vary:
+ - Accept-Encoding
+ X-Frame-Options:
+ - SAMEORIGIN
+ X-XSS-Protection:
+ - 1; mode=block
+ status:
+ code: 200
+ message: OK
+version: 1
diff --git a/openbb_platform/providers/government_us/tests/record/http/test_government_us_fetchers/test_government_us_treasury_prices_fetcher_urllib3_v2.yaml b/openbb_platform/providers/government_us/tests/record/http/test_us_treasury_fetchers/test_treasury_prices_fetcher_urllib3_v2.yaml
similarity index 100%
rename from openbb_platform/providers/government_us/tests/record/http/test_government_us_fetchers/test_government_us_treasury_prices_fetcher_urllib3_v2.yaml
rename to openbb_platform/providers/government_us/tests/record/http/test_us_treasury_fetchers/test_treasury_prices_fetcher_urllib3_v2.yaml
diff --git a/openbb_platform/providers/government_us/tests/record/http/test_us_treasury_fetchers/test_upcoming_treasury_auctions_fetcher_urllib3_v2.yaml b/openbb_platform/providers/government_us/tests/record/http/test_us_treasury_fetchers/test_upcoming_treasury_auctions_fetcher_urllib3_v2.yaml
new file mode 100644
index 00000000000..6b54f21c412
--- /dev/null
+++ b/openbb_platform/providers/government_us/tests/record/http/test_us_treasury_fetchers/test_upcoming_treasury_auctions_fetcher_urllib3_v2.yaml
@@ -0,0 +1,84 @@
+interactions:
+- request:
+ body: null
+ headers:
+ Accept:
+ - application/json
+ Accept-Encoding:
+ - gzip, deflate
+ Connection:
+ - keep-alive
+ method: GET
+ uri: https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v1/accounting/od/upcoming_auctions?page%5Bnumber%5D=1&page%5Bsize%5D=10000
+ response:
+ body:
+ string: !!binary |
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+ headers:
+ Access-Control-Allow-Origin:
+ - '*'
+ Connection:
+ - keep-alive
+ Content-Encoding:
+ - gzip
+ Content-Length:
+ - '2094'
+ Content-Type:
+ - application/json
+ Date:
+ - Thu, 16 Jul 2026 18:32:50 GMT
+ Server:
+ - nginx
+ Set-Cookie:
+ - cookiesession1=678A3E0FD7BE18730E612910B29C46AF;Expires=Fri, 16 Jul 2027 18:32:50
+ GMT;Path=/;HttpOnly
+ - BIGipServerddsfMj0s2EaKsi9WsSRnAw=!LZ8wQ8xxXm0VDfvfcKTyKInrG7ukTuHqlMANnxcMwyLrPZxILzGMQVjwJxKAOWc5j8UoK9NwmvIKqg==;
+ path=/; Httponly; Secure
+ Strict-Transport-Security:
+ - max-age=31536000; includeSubDomains
+ Vary:
+ - Accept-Encoding
+ X-Frame-Options:
+ - SAMEORIGIN
+ X-XSS-Protection:
+ - 1; mode=block
+ status:
+ code: 200
+ message: OK
+version: 1
diff --git a/openbb_platform/providers/government_us/tests/record/http/test_government_us_fetchers/test_government_us_commodity_psd_data_fetcher_urllib3_v2.yaml b/openbb_platform/providers/government_us/tests/record/http/test_usda_fetchers/test_commodity_psd_data_fetcher_urllib3_v2.yaml
similarity index 100%
rename from openbb_platform/providers/government_us/tests/record/http/test_government_us_fetchers/test_government_us_commodity_psd_data_fetcher_urllib3_v2.yaml
rename to openbb_platform/providers/government_us/tests/record/http/test_usda_fetchers/test_commodity_psd_data_fetcher_urllib3_v2.yaml
diff --git a/openbb_platform/providers/government_us/tests/record/http/test_usda_fetchers/test_dairy_data_fetcher_urllib3_v2.yaml b/openbb_platform/providers/government_us/tests/record/http/test_usda_fetchers/test_dairy_data_fetcher_urllib3_v2.yaml
new file mode 100644
index 00000000000..6a133e398d9
--- /dev/null
+++ b/openbb_platform/providers/government_us/tests/record/http/test_usda_fetchers/test_dairy_data_fetcher_urllib3_v2.yaml
@@ -0,0 +1,989 @@
+interactions:
+- request:
+ body: null
+ headers:
+ Accept:
+ - application/json
+ Accept-Encoding:
+ - gzip, deflate
+ Connection:
+ - keep-alive
+ method: GET
+ uri: https://www.ers.usda.gov/data-products/dairy-data
+ response:
+ body:
+ string: !!binary |
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+ products (milk equivalent skim-solids basis)\",48869.506,\"Million pounds\"\n2024,\"Annual\",17,\"Annual\",\"Imports\",\"All
+ products (milk equivalent milk-fat basis)\",9130.855,\"Million pounds\"\n2024,\"Annual\",17,\"Annual\",\"Imports\",\"All
+ products (milk equivalent skim-solids basis)\",6800.928,\"Million pounds\"\n2024,\"Annual\",17,\"Annual\",\"Milk
+ prices\",\"All milk (at average milk-fat test)\",22.55,\"Dollars per hundredweight\"\n2024,\"Annual\",17,\"Annual\",\"Milk
+ prices\",\"ClassIIWhole\",21.342,\"Dollars per hundredweight\"\n2024,\"Annual\",17,\"Annual\",\"Milk
+ prices\",\"ClassIWhole\",23.166,\"Dollars per hundredweight\"\n2024,\"Annual\",17,\"Annual\",\"Milk
+ production\",\"Production\",225889,\"Million pounds\"\n2024,\"Annual\",17,\"Annual\",\"Milk
+ production\",\"Milk cows\",9345,\"Thousands\"\n2024,\"Annual\",17,\"Annual\",\"Milk
+ production\",\"Milk per cow\",24172,\"Pounds\"\n2024,\"Annual\",17,\"Annual\",\"Oceania
+ export prices\",\"Skim milk powder\",2701,\"Dollars per metric ton\"\n2024,\"Annual\",17,\"Annual\",\"Oceania
+ export prices\",\"Butter\",6746,\"Dollars per metric ton\"\n2024,\"Annual\",17,\"Annual\",\"Wholesale
+ dairy product prices\",\"Butter\",2.887,\"Dollars per pound\"\n2024,\"Annual\",17,\"Annual\",\"Wholesale
+ dairy product prices\",\"Cheddar cheese, 40-pound blocks\",1.8247,\"Dollars
+ per pound\"\n2024,\"Annual\",17,\"Annual\",\"Wholesale dairy product prices\",\"Cheddar
+ cheese, 500-pound barrels\",1.8658,\"Dollars per pound\"\n2024,\"Annual\",17,\"Annual\",\"Wholesale
+ dairy product prices\",\"Dry whey\",0.4913,\"Dollars per pound\"\n2024,\"Annual\",17,\"Annual\",\"Wholesale
+ dairy product prices\",\"Nonfat dry milk\",1.242,\"Dollars per pound\"\n2025,\"Annual\",17,\"Annual\",\"Consumer
+ Price Indexes\",\"All food\",2.869,\"Year-over-year percent change\"\n2025,\"Annual\",17,\"Annual\",\"Consumer
+ Price Indexes\",\"All products\",2.69,\"Year-over-year percent change\"\n2025,\"Annual\",17,\"Annual\",\"Consumer
+ Price Indexes\",\"Butter\",0.093,\"Year-over-year percent change\"\n2025,\"Annual\",17,\"Annual\",\"Consumer
+ Price Indexes\",\"Cheese\",1.124,\"Year-over-year percent change\"\n2025,\"Annual\",17,\"Annual\",\"Consumer
+ Price Indexes\",\"Dairy products\",0.854,\"Year-over-year percent change\"\n2025,\"Annual\",17,\"Annual\",\"Consumer
+ Price Indexes\",\"Fluid milk\",1.825,\"Year-over-year percent change\"\n2025,\"Annual\",17,\"Annual\",\"Dairy
+ product output\",\"American cheese\",5832.351,\"Million pounds\"\n2025,\"Annual\",17,\"Annual\",\"Dairy
+ product output\",\"Butter\",2386.405,\"Million pounds\"\n2025,\"Annual\",17,\"Annual\",\"Dairy
+ product output\",\"Other-than-American cheese\",8967.713,\"Million pounds\"\n2025,\"Annual\",17,\"Annual\",\"Dairy
+ product output\",\"Dry skim milk products\",2170.583,\"Million pounds\"\n2025,\"Annual\",17,\"Annual\",\"Dairy
+ product output\",\"Frozen products\",1156.29,\"Million gallons\"\n2025,\"Annual\",17,\"Annual\",\"Domestic
+ disappearance\",\"American cheese\",5527.096,\"Million pounds\"\n2025,\"Annual\",17,\"Annual\",\"Domestic
+ disappearance\",\"Butter\",2326.639,\"Million pounds\"\n2025,\"Annual\",17,\"Annual\",\"Domestic
+ disappearance\",\"Other-than-American cheese\",8205.213,\"Million pounds\"\n2025,\"Annual\",17,\"Annual\",\"Domestic
+ disappearance\",\"Dry skim milk products\",650.241,\"Million pounds\"\n2025,\"Annual\",17,\"Annual\",\"Domestic
+ disappearance\",\"All products (milk equivalent milk-fat basis)\",221722.576,\"Million
+ pounds\"\n2025,\"Annual\",17,\"Annual\",\"Domestic disappearance\",\"All products
+ (milk equivalent skim-solids basis)\",189249.799,\"Million pounds\"\n2025,\"Annual\",17,\"Annual\",\"Ending
+ stocks\",\"All products (milk equivalent milk-fat basis)\",12644.99,\"Million
+ pounds\"\n2025,\"Annual\",17,\"Annual\",\"Ending stocks\",\"All products (milk
+ equivalent skim-solids basis)\",9216.664,\"Million pounds\"\n2025,\"Annual\",17,\"Annual\",\"Exports\",\"All
+ products (milk equivalent milk-fat basis)\",16736.789,\"Million pounds\"\n2025,\"Annual\",17,\"Annual\",\"Exports\",\"All
+ products (milk equivalent skim-solids basis)\",48162.233,\"Million pounds\"\n2025,\"Annual\",17,\"Annual\",\"Imports\",\"All
+ products (milk equivalent milk-fat basis)\",7364.25,\"Million pounds\"\n2025,\"Annual\",17,\"Annual\",\"Imports\",\"All
+ products (milk equivalent skim-solids basis)\",6876.606,\"Million pounds\"\n2025,\"Annual\",17,\"Annual\",\"Milk
+ prices\",\"All milk (at average milk-fat test)\",21.175,\"Dollars per hundredweight\"\n2025,\"Annual\",17,\"Annual\",\"Milk
+ prices\",\"ClassIIWhole\",18.333,\"Dollars per hundredweight\"\n2025,\"Annual\",17,\"Annual\",\"Milk
+ prices\",\"ClassIWhole\",22.522,\"Dollars per hundredweight\"\n2025,\"Annual\",17,\"Annual\",\"Milk
+ production\",\"Production\",231658,\"Million pounds\"\n2025,\"Annual\",17,\"Annual\",\"Milk
+ production\",\"Milk cows\",9498,\"Thousands\"\n2025,\"Annual\",17,\"Annual\",\"Milk
+ production\",\"Milk per cow\",24390,\"Pounds\"\n2025,\"Annual\",17,\"Annual\",\"Oceania
+ export prices\",\"Skim milk powder\",2784,\"Dollars per metric ton\"\n2025,\"Annual\",17,\"Annual\",\"Oceania
+ export prices\",\"Butter\",7081,\"Dollars per metric ton\"\n2025,\"Annual\",17,\"Annual\",\"Wholesale
+ dairy product prices\",\"Butter\",2.2202,\"Dollars per pound\"\n2025,\"Annual\",17,\"Annual\",\"Wholesale
+ dairy product prices\",\"Cheddar cheese, 40-pound blocks\",1.7871,\"Dollars
+ per pound\"\n2025,\"Annual\",17,\"Annual\",\"Wholesale dairy product prices\",\"Cheddar
+ cheese, 500-pound barrels\",1.8089,\"Dollars per pound\"\n2025,\"Annual\",17,\"Annual\",\"Wholesale
+ dairy product prices\",\"Dry whey\",0.5956,\"Dollars per pound\"\n2025,\"Annual\",17,\"Annual\",\"Wholesale
+ dairy product prices\",\"Nonfat dry milk\",1.2348,\"Dollars per pound\"\n2025,\"August\",8,\"Monthly\",\"Consumer
+ Price Indexes\",\"All products\",2.916,\"Year-over-year percent change\"\n2025,\"August\",8,\"Monthly\",\"Consumer
+ Price Indexes\",\"Dairy products\",1.251,\"Year-over-year percent change\"\n2025,\"August\",8,\"Monthly\",\"Consumer
+ Price Indexes\",\"Butter\",0.068,\"Year-over-year percent change\"\n2025,\"August\",8,\"Monthly\",\"Consumer
+ Price Indexes\",\"Cheese\",2.708,\"Year-over-year percent change\"\n2025,\"August\",8,\"Monthly\",\"Consumer
+ Price Indexes\",\"All food\",3.188,\"Year-over-year percent change\"\n2025,\"August\",8,\"Monthly\",\"Consumer
+ Price Indexes\",\"Fluid milk\",1.672,\"Year-over-year percent change\"\n2025,\"August\",8,\"Monthly\",\"Dairy
+ product output\",\"American cheese\",481.321,\"Million pounds\"\n2025,\"August\",8,\"Monthly\",\"Dairy
+ product output\",\"Butter\",176.312,\"Million pounds\"\n2025,\"August\",8,\"Monthly\",\"Dairy
+ product output\",\"Other-than-American cheese\",746.861,\"Million pounds\"\n2025,\"August\",8,\"Monthly\",\"Dairy
+ product output\",\"Dry skim milk products\",164.486,\"Million pounds\"\n2025,\"August\",8,\"Monthly\",\"Dairy
+ product output\",\"Frozen products\",104.389,\"Million gallons\"\n2025,\"August\",8,\"Monthly\",\"Domestic
+ disappearance\",\"American cheese\",455.403,\"Million pounds\"\n2025,\"August\",8,\"Monthly\",\"Domestic
+ disappearance\",\"Butter\",208.641,\"Million pounds\"\n2025,\"August\",8,\"Monthly\",\"Domestic
+ disappearance\",\"Other-than-American cheese\",693.92,\"Million pounds\"\n2025,\"August\",8,\"Monthly\",\"Domestic
+ disappearance\",\"Dry skim milk products\",60.799,\"Million pounds\"\n2025,\"August\",8,\"Monthly\",\"Domestic
+ disappearance\",\"All products (milk equivalent milk-fat basis)\",19581.164,\"Million
+ pounds\"\n2025,\"August\",8,\"Monthly\",\"Domestic disappearance\",\"All products
+ (milk equivalent skim-solids basis)\",16221.281,\"Million pounds\"\n2025,\"August\",8,\"Monthly\",\"Ending
+ stocks\",\"All products (milk equivalent milk-fat basis)\",15873.184,\"Million
+ pounds\"\n2025,\"August\",8,\"Monthly\",\"Ending stocks\",\"All products (milk
+ equivalent skim-solids basis)\",9771.552,\"Million pounds\"\n2025,\"August\",8,\"Monthly\",\"Exports\",\"All
+ products (milk equivalent milk-fat basis)\",1517.624,\"Million pounds\"\n2025,\"August\",8,\"Monthly\",\"Exports\",\"All
+ products (milk equivalent skim-solids basis)\",4115.116,\"Million pounds\"\n2025,\"August\",8,\"Monthly\",\"Imports\",\"All
+ products (milk equivalent milk-fat basis)\",511.486,\"Million pounds\"\n2025,\"August\",8,\"Monthly\",\"Imports\",\"All
+ products (milk equivalent skim-solids basis)\",512.225,\"Million pounds\"\n2025,\"August\",8,\"Monthly\",\"Milk
+ prices\",\"All milk (at average milk-fat test)\",20.9,\"Dollars per hundredweight\"\n2025,\"August\",8,\"Monthly\",\"Milk
+ prices\",\"ClassIWhole\",23.05,\"Dollars per hundredweight\"\n2025,\"August\",8,\"Monthly\",\"Milk
+ prices\",\"ClassIIWhole\",19.18,\"Dollars per hundredweight\"\n2025,\"August\",8,\"Monthly\",\"Milk
+ production\",\"Production\",19560,\"Million pounds\"\n2025,\"August\",8,\"Monthly\",\"Milk
+ production\",\"Milk cows\",9543,\"Thousands\"\n2025,\"August\",8,\"Monthly\",\"Milk
+ production\",\"Milk per cow\",2050,\"Pounds\"\n2025,\"August\",8,\"Monthly\",\"Oceania
+ export prices\",\"Skim milk powder\",2955,\"Dollars per metric ton\"\n2025,\"August\",8,\"Monthly\",\"Oceania
+ export prices\",\"Butter\",7277,\"Dollars per metric ton\"\n2025,\"August\",8,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Butter\",2.4778,\"Dollars per pound\"\n2025,\"August\",8,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Nonfat dry milk\",1.282,\"Dollars per pound\"\n2025,\"August\",8,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Cheddar cheese, 40-pound blocks\",1.7529,\"Dollars
+ per pound\"\n2025,\"August\",8,\"Monthly\",\"Wholesale dairy product prices\",\"Cheddar
+ cheese, 500-pound barrels\",NA,\"Dollars per pound\"\n2025,\"August\",8,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Dry whey\",0.5779,\"Dollars per pound\"\n2025,\"December\",12,\"Monthly\",\"Consumer
+ Price Indexes\",\"All products\",2.677,\"Year-over-year percent change\"\n2025,\"December\",12,\"Monthly\",\"Consumer
+ Price Indexes\",\"Dairy products\",-0.887,\"Year-over-year percent change\"\n2025,\"December\",12,\"Monthly\",\"Consumer
+ Price Indexes\",\"Butter\",-3.423,\"Year-over-year percent change\"\n2025,\"December\",12,\"Monthly\",\"Consumer
+ Price Indexes\",\"Cheese\",-1.566,\"Year-over-year percent change\"\n2025,\"December\",12,\"Monthly\",\"Consumer
+ Price Indexes\",\"All food\",3.067,\"Year-over-year percent change\"\n2025,\"December\",12,\"Monthly\",\"Consumer
+ Price Indexes\",\"Fluid milk\",-0.968,\"Year-over-year percent change\"\n2025,\"December\",12,\"Monthly\",\"Dairy
+ product output\",\"American cheese\",503.875,\"Million pounds\"\n2025,\"December\",12,\"Monthly\",\"Dairy
+ product output\",\"Butter\",207.53,\"Million pounds\"\n2025,\"December\",12,\"Monthly\",\"Dairy
+ product output\",\"Other-than-American cheese\",784.308,\"Million pounds\"\n2025,\"December\",12,\"Monthly\",\"Dairy
+ product output\",\"Dry skim milk products\",176.631,\"Million pounds\"\n2025,\"December\",12,\"Monthly\",\"Dairy
+ product output\",\"Frozen products\",75.385,\"Million gallons\"\n2025,\"December\",12,\"Monthly\",\"Domestic
+ disappearance\",\"American cheese\",475.471,\"Million pounds\"\n2025,\"December\",12,\"Monthly\",\"Domestic
+ disappearance\",\"Butter\",215.749,\"Million pounds\"\n2025,\"December\",12,\"Monthly\",\"Domestic
+ disappearance\",\"Other-than-American cheese\",702.428,\"Million pounds\"\n2025,\"December\",12,\"Monthly\",\"Domestic
+ disappearance\",\"Dry skim milk products\",32.67,\"Million pounds\"\n2025,\"December\",12,\"Monthly\",\"Domestic
+ disappearance\",\"All products (milk equivalent milk-fat basis)\",18882.493,\"Million
+ pounds\"\n2025,\"December\",12,\"Monthly\",\"Domestic disappearance\",\"All
+ products (milk equivalent skim-solids basis)\",15965.08,\"Million pounds\"\n2025,\"December\",12,\"Monthly\",\"Ending
+ stocks\",\"All products (milk equivalent milk-fat basis)\",12644.99,\"Million
+ pounds\"\n2025,\"December\",12,\"Monthly\",\"Ending stocks\",\"All products
+ (milk equivalent skim-solids basis)\",9216.664,\"Million pounds\"\n2025,\"December\",12,\"Monthly\",\"Exports\",\"All
+ products (milk equivalent milk-fat basis)\",1606.107,\"Million pounds\"\n2025,\"December\",12,\"Monthly\",\"Exports\",\"All
+ products (milk equivalent skim-solids basis)\",3970.269,\"Million pounds\"\n2025,\"December\",12,\"Monthly\",\"Imports\",\"All
+ products (milk equivalent milk-fat basis)\",608.349,\"Million pounds\"\n2025,\"December\",12,\"Monthly\",\"Imports\",\"All
+ products (milk equivalent skim-solids basis)\",649.28,\"Million pounds\"\n2025,\"December\",12,\"Monthly\",\"Milk
+ prices\",\"All milk (at average milk-fat test)\",19,\"Dollars per hundredweight\"\n2025,\"December\",12,\"Monthly\",\"Milk
+ prices\",\"ClassIWhole\",22.33,\"Dollars per hundredweight\"\n2025,\"December\",12,\"Monthly\",\"Milk
+ prices\",\"ClassIIWhole\",14.41,\"Dollars per hundredweight\"\n2025,\"December\",12,\"Monthly\",\"Milk
+ production\",\"Production\",19529,\"Million pounds\"\n2025,\"December\",12,\"Monthly\",\"Milk
+ production\",\"Milk cows\",9566,\"Thousands\"\n2025,\"December\",12,\"Monthly\",\"Milk
+ production\",\"Milk per cow\",2042,\"Pounds\"\n2025,\"December\",12,\"Monthly\",\"Oceania
+ export prices\",\"Butter\",5288,\"Dollars per metric ton\"\n2025,\"December\",12,\"Monthly\",\"Oceania
+ export prices\",\"Skim milk powder\",2508,\"Dollars per metric ton\"\n2025,\"December\",12,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Butter\",1.5287,\"Dollars per pound\"\n2025,\"December\",12,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Nonfat dry milk\",1.1541,\"Dollars per pound\"\n2025,\"December\",12,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Cheddar cheese, 40-pound blocks\",1.5271,\"Dollars
+ per pound\"\n2025,\"December\",12,\"Monthly\",\"Wholesale dairy product prices\",\"Cheddar
+ cheese, 500-pound barrels\",NA,\"Dollars per pound\"\n2025,\"December\",12,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Dry whey\",0.6893,\"Dollars per pound\"\n2025,\"July\",7,\"Monthly\",\"Consumer
+ Price Indexes\",\"All products\",2.705,\"Year-over-year percent change\"\n2025,\"July\",7,\"Monthly\",\"Consumer
+ Price Indexes\",\"Dairy products\",1.464,\"Year-over-year percent change\"\n2025,\"July\",7,\"Monthly\",\"Consumer
+ Price Indexes\",\"Butter\",-1.613,\"Year-over-year percent change\"\n2025,\"July\",7,\"Monthly\",\"Consumer
+ Price Indexes\",\"Cheese\",2.306,\"Year-over-year percent change\"\n2025,\"July\",7,\"Monthly\",\"Consumer
+ Price Indexes\",\"All food\",2.866,\"Year-over-year percent change\"\n2025,\"July\",7,\"Monthly\",\"Consumer
+ Price Indexes\",\"Fluid milk\",2.612,\"Year-over-year percent change\"\n2025,\"July\",7,\"Monthly\",\"Dairy
+ product output\",\"American cheese\",485.117,\"Million pounds\"\n2025,\"July\",7,\"Monthly\",\"Dairy
+ product output\",\"Butter\",179.391,\"Million pounds\"\n2025,\"July\",7,\"Monthly\",\"Dairy
+ product output\",\"Other-than-American cheese\",752.513,\"Million pounds\"\n2025,\"July\",7,\"Monthly\",\"Dairy
+ product output\",\"Dry skim milk products\",175.588,\"Million pounds\"\n2025,\"July\",7,\"Monthly\",\"Dairy
+ product output\",\"Frozen products\",112.447,\"Million gallons\"\n2025,\"July\",7,\"Monthly\",\"Domestic
+ disappearance\",\"American cheese\",482.282,\"Million pounds\"\n2025,\"July\",7,\"Monthly\",\"Domestic
+ disappearance\",\"Butter\",193.261,\"Million pounds\"\n2025,\"July\",7,\"Monthly\",\"Domestic
+ disappearance\",\"Other-than-American cheese\",679.825,\"Million pounds\"\n2025,\"July\",7,\"Monthly\",\"Domestic
+ disappearance\",\"Dry skim milk products\",42.546,\"Million pounds\"\n2025,\"July\",7,\"Monthly\",\"Domestic
+ disappearance\",\"All products (milk equivalent milk-fat basis)\",19006.403,\"Million
+ pounds\"\n2025,\"July\",7,\"Monthly\",\"Domestic disappearance\",\"All products
+ (milk equivalent skim-solids basis)\",16008.863,\"Million pounds\"\n2025,\"July\",7,\"Monthly\",\"Ending
+ stocks\",\"All products (milk equivalent milk-fat basis)\",16984.569,\"Million
+ pounds\"\n2025,\"July\",7,\"Monthly\",\"Ending stocks\",\"All products (milk
+ equivalent skim-solids basis)\",10119.806,\"Million pounds\"\n2025,\"July\",7,\"Monthly\",\"Exports\",\"All
+ products (milk equivalent milk-fat basis)\",1613.977,\"Million pounds\"\n2025,\"July\",7,\"Monthly\",\"Exports\",\"All
+ products (milk equivalent skim-solids basis)\",4364.495,\"Million pounds\"\n2025,\"July\",7,\"Monthly\",\"Imports\",\"All
+ products (milk equivalent milk-fat basis)\",608.534,\"Million pounds\"\n2025,\"July\",7,\"Monthly\",\"Imports\",\"All
+ products (milk equivalent skim-solids basis)\",575.255,\"Million pounds\"\n2025,\"July\",7,\"Monthly\",\"Milk
+ prices\",\"All milk (at average milk-fat test)\",20.8,\"Dollars per hundredweight\"\n2025,\"July\",7,\"Monthly\",\"Milk
+ prices\",\"ClassIWhole\",22.94,\"Dollars per hundredweight\"\n2025,\"July\",7,\"Monthly\",\"Milk
+ prices\",\"ClassIIWhole\",19.31,\"Dollars per hundredweight\"\n2025,\"July\",7,\"Monthly\",\"Milk
+ production\",\"Production\",19706,\"Million pounds\"\n2025,\"July\",7,\"Monthly\",\"Milk
+ production\",\"Milk cows\",9511,\"Thousands\"\n2025,\"July\",7,\"Monthly\",\"Milk
+ production\",\"Milk per cow\",2072,\"Pounds\"\n2025,\"July\",7,\"Monthly\",\"Oceania
+ export prices\",\"Skim milk powder\",2788,\"Dollars per metric ton\"\n2025,\"July\",7,\"Monthly\",\"Oceania
+ export prices\",\"Butter\",7569,\"Dollars per metric ton\"\n2025,\"July\",7,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Butter\",2.5753,\"Dollars per pound\"\n2025,\"July\",7,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Nonfat dry milk\",1.2783,\"Dollars per pound\"\n2025,\"July\",7,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Cheddar cheese, 40-pound blocks\",1.7634,\"Dollars
+ per pound\"\n2025,\"July\",7,\"Monthly\",\"Wholesale dairy product prices\",\"Cheddar
+ cheese, 500-pound barrels\",NA,\"Dollars per pound\"\n2025,\"July\",7,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Dry whey\",0.567,\"Dollars per pound\"\n2025,\"June\",6,\"Monthly\",\"Consumer
+ Price Indexes\",\"All products\",2.669,\"Year-over-year percent change\"\n2025,\"June\",6,\"Monthly\",\"Consumer
+ Price Indexes\",\"Dairy products\",0.943,\"Year-over-year percent change\"\n2025,\"June\",6,\"Monthly\",\"Consumer
+ Price Indexes\",\"Butter\",-0.435,\"Year-over-year percent change\"\n2025,\"June\",6,\"Monthly\",\"Consumer
+ Price Indexes\",\"Cheese\",2.944,\"Year-over-year percent change\"\n2025,\"June\",6,\"Monthly\",\"Consumer
+ Price Indexes\",\"All food\",2.969,\"Year-over-year percent change\"\n2025,\"June\",6,\"Monthly\",\"Consumer
+ Price Indexes\",\"Fluid milk\",2.084,\"Year-over-year percent change\"\n2025,\"June\",6,\"Monthly\",\"Dairy
+ product output\",\"American cheese\",479.371,\"Million pounds\"\n2025,\"June\",6,\"Monthly\",\"Dairy
+ product output\",\"Butter\",191.789,\"Million pounds\"\n2025,\"June\",6,\"Monthly\",\"Dairy
+ product output\",\"Other-than-American cheese\",738.723,\"Million pounds\"\n2025,\"June\",6,\"Monthly\",\"Dairy
+ product output\",\"Dry skim milk products\",198.952,\"Million pounds\"\n2025,\"June\",6,\"Monthly\",\"Dairy
+ product output\",\"Frozen products\",106.669,\"Million gallons\"\n2025,\"June\",6,\"Monthly\",\"Domestic
+ disappearance\",\"American cheese\",455.054,\"Million pounds\"\n2025,\"June\",6,\"Monthly\",\"Domestic
+ disappearance\",\"Butter\",192.894,\"Million pounds\"\n2025,\"June\",6,\"Monthly\",\"Domestic
+ disappearance\",\"Other-than-American cheese\",663.347,\"Million pounds\"\n2025,\"June\",6,\"Monthly\",\"Domestic
+ disappearance\",\"Dry skim milk products\",81,\"Million pounds\"\n2025,\"June\",6,\"Monthly\",\"Domestic
+ disappearance\",\"All products (milk equivalent milk-fat basis)\",18269.482,\"Million
+ pounds\"\n2025,\"June\",6,\"Monthly\",\"Domestic disappearance\",\"All products
+ (milk equivalent skim-solids basis)\",15427.285,\"Million pounds\"\n2025,\"June\",6,\"Monthly\",\"Ending
+ stocks\",\"All products (milk equivalent milk-fat basis)\",17374.496,\"Million
+ pounds\"\n2025,\"June\",6,\"Monthly\",\"Ending stocks\",\"All products (milk
+ equivalent skim-solids basis)\",10295.991,\"Million pounds\"\n2025,\"June\",6,\"Monthly\",\"Exports\",\"All
+ products (milk equivalent milk-fat basis)\",1437.532,\"Million pounds\"\n2025,\"June\",6,\"Monthly\",\"Exports\",\"All
+ products (milk equivalent skim-solids basis)\",4575.6,\"Million pounds\"\n2025,\"June\",6,\"Monthly\",\"Imports\",\"All
+ products (milk equivalent milk-fat basis)\",570.369,\"Million pounds\"\n2025,\"June\",6,\"Monthly\",\"Imports\",\"All
+ products (milk equivalent skim-solids basis)\",588.278,\"Million pounds\"\n2025,\"June\",6,\"Monthly\",\"Milk
+ prices\",\"All milk (at average milk-fat test)\",21.3,\"Dollars per hundredweight\"\n2025,\"June\",6,\"Monthly\",\"Milk
+ prices\",\"ClassIWhole\",21.38,\"Dollars per hundredweight\"\n2025,\"June\",6,\"Monthly\",\"Milk
+ prices\",\"ClassIIWhole\",18.43,\"Dollars per hundredweight\"\n2025,\"June\",6,\"Monthly\",\"Milk
+ production\",\"Production\",19254,\"Million pounds\"\n2025,\"June\",6,\"Monthly\",\"Milk
+ production\",\"Milk cows\",9485,\"Thousands\"\n2025,\"June\",6,\"Monthly\",\"Milk
+ production\",\"Milk per cow\",2030,\"Pounds\"\n2025,\"June\",6,\"Monthly\",\"Oceania
+ export prices\",\"Skim milk powder\",2813,\"Dollars per metric ton\"\n2025,\"June\",6,\"Monthly\",\"Oceania
+ export prices\",\"Butter\",7913,\"Dollars per metric ton\"\n2025,\"June\",6,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Butter\",2.488,\"Dollars per pound\"\n2025,\"June\",6,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Nonfat dry milk\",1.2531,\"Dollars per pound\"\n2025,\"June\",6,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Cheddar cheese, 40-pound blocks\",1.9322,\"Dollars
+ per pound\"\n2025,\"June\",6,\"Monthly\",\"Wholesale dairy product prices\",\"Cheddar
+ cheese, 500-pound barrels\",NA,\"Dollars per pound\"\n2025,\"June\",6,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Dry whey\",0.5497,\"Dollars per pound\"\n2025,\"May\",5,\"Monthly\",\"Consumer
+ Price Indexes\",\"All products\",2.355,\"Year-over-year percent change\"\n2025,\"May\",5,\"Monthly\",\"Consumer
+ Price Indexes\",\"Dairy products\",1.724,\"Year-over-year percent change\"\n2025,\"May\",5,\"Monthly\",\"Consumer
+ Price Indexes\",\"Butter\",1.949,\"Year-over-year percent change\"\n2025,\"May\",5,\"Monthly\",\"Consumer
+ Price Indexes\",\"Cheese\",2.815,\"Year-over-year percent change\"\n2025,\"May\",5,\"Monthly\",\"Consumer
+ Price Indexes\",\"All food\",2.88,\"Year-over-year percent change\"\n2025,\"May\",5,\"Monthly\",\"Consumer
+ Price Indexes\",\"Fluid milk\",3.26,\"Year-over-year percent change\"\n2025,\"May\",5,\"Monthly\",\"Dairy
+ product output\",\"American cheese\",512.263,\"Million pounds\"\n2025,\"May\",5,\"Monthly\",\"Dairy
+ product output\",\"Butter\",216.713,\"Million pounds\"\n2025,\"May\",5,\"Monthly\",\"Dairy
+ product output\",\"Other-than-American cheese\",745.265,\"Million pounds\"\n2025,\"May\",5,\"Monthly\",\"Dairy
+ product output\",\"Dry skim milk products\",206.691,\"Million pounds\"\n2025,\"May\",5,\"Monthly\",\"Dairy
+ product output\",\"Frozen products\",105.295,\"Million gallons\"\n2025,\"May\",5,\"Monthly\",\"Domestic
+ disappearance\",\"American cheese\",480.238,\"Million pounds\"\n2025,\"May\",5,\"Monthly\",\"Domestic
+ disappearance\",\"Butter\",183.323,\"Million pounds\"\n2025,\"May\",5,\"Monthly\",\"Domestic
+ disappearance\",\"Other-than-American cheese\",677.743,\"Million pounds\"\n2025,\"May\",5,\"Monthly\",\"Domestic
+ disappearance\",\"Dry skim milk products\",86.365,\"Million pounds\"\n2025,\"May\",5,\"Monthly\",\"Domestic
+ disappearance\",\"All products (milk equivalent milk-fat basis)\",18478.774,\"Million
+ pounds\"\n2025,\"May\",5,\"Monthly\",\"Domestic disappearance\",\"All products
+ (milk equivalent skim-solids basis)\",16671.036,\"Million pounds\"\n2025,\"May\",5,\"Monthly\",\"Ending
+ stocks\",\"All products (milk equivalent milk-fat basis)\",17338.511,\"Million
+ pounds\"\n2025,\"May\",5,\"Monthly\",\"Ending stocks\",\"All products (milk
+ equivalent skim-solids basis)\",10537.969,\"Million pounds\"\n2025,\"May\",5,\"Monthly\",\"Exports\",\"All
+ products (milk equivalent milk-fat basis)\",1345.828,\"Million pounds\"\n2025,\"May\",5,\"Monthly\",\"Exports\",\"All
+ products (milk equivalent skim-solids basis)\",3935.549,\"Million pounds\"\n2025,\"May\",5,\"Monthly\",\"Imports\",\"All
+ products (milk equivalent milk-fat basis)\",708.99,\"Million pounds\"\n2025,\"May\",5,\"Monthly\",\"Imports\",\"All
+ products (milk equivalent skim-solids basis)\",574.56,\"Million pounds\"\n2025,\"May\",5,\"Monthly\",\"Milk
+ prices\",\"All milk (at average milk-fat test)\",21.4,\"Dollars per hundredweight\"\n2025,\"May\",5,\"Monthly\",\"Milk
+ prices\",\"ClassIWhole\",21.19,\"Dollars per hundredweight\"\n2025,\"May\",5,\"Monthly\",\"Milk
+ prices\",\"ClassIIWhole\",18.72,\"Dollars per hundredweight\"\n2025,\"May\",5,\"Monthly\",\"Milk
+ production\",\"Production\",20097,\"Million pounds\"\n2025,\"May\",5,\"Monthly\",\"Milk
+ production\",\"Milk cows\",9481,\"Thousands\"\n2025,\"May\",5,\"Monthly\",\"Milk
+ production\",\"Milk per cow\",2120,\"Pounds\"\n2025,\"May\",5,\"Monthly\",\"Oceania
+ export prices\",\"Skim milk powder\",2935,\"Dollars per metric ton\"\n2025,\"May\",5,\"Monthly\",\"Oceania
+ export prices\",\"Butter\",7826,\"Dollars per metric ton\"\n2025,\"May\",5,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Butter\",2.3703,\"Dollars per pound\"\n2025,\"May\",5,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Nonfat dry milk\",1.1925,\"Dollars per pound\"\n2025,\"May\",5,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Cheddar cheese, 40-pound blocks\",1.8177,\"Dollars
+ per pound\"\n2025,\"May\",5,\"Monthly\",\"Wholesale dairy product prices\",\"Cheddar
+ cheese, 500-pound barrels\",1.8284,\"Dollars per pound\"\n2025,\"May\",5,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Dry whey\",0.5119,\"Dollars per pound\"\n2025,\"November\",11,\"Monthly\",\"Consumer
+ Price Indexes\",\"All products\",2.735,\"Year-over-year percent change\"\n2025,\"November\",11,\"Monthly\",\"Consumer
+ Price Indexes\",\"Dairy products\",-1.598,\"Year-over-year percent change\"\n2025,\"November\",11,\"Monthly\",\"Consumer
+ Price Indexes\",\"Butter\",-4.449,\"Year-over-year percent change\"\n2025,\"November\",11,\"Monthly\",\"Consumer
+ Price Indexes\",\"Cheese\",-2.367,\"Year-over-year percent change\"\n2025,\"November\",11,\"Monthly\",\"Consumer
+ Price Indexes\",\"All food\",2.649,\"Year-over-year percent change\"\n2025,\"November\",11,\"Monthly\",\"Consumer
+ Price Indexes\",\"Fluid milk\",-0.801,\"Year-over-year percent change\"\n2025,\"November\",11,\"Monthly\",\"Dairy
+ product output\",\"American cheese\",483.837,\"Million pounds\"\n2025,\"November\",11,\"Monthly\",\"Dairy
+ product output\",\"Butter\",178.831,\"Million pounds\"\n2025,\"November\",11,\"Monthly\",\"Dairy
+ product output\",\"Other-than-American cheese\",757.186,\"Million pounds\"\n2025,\"November\",11,\"Monthly\",\"Dairy
+ product output\",\"Dry skim milk products\",154.597,\"Million pounds\"\n2025,\"November\",11,\"Monthly\",\"Dairy
+ product output\",\"Frozen products\",77.54,\"Million gallons\"\n2025,\"November\",11,\"Monthly\",\"Domestic
+ disappearance\",\"American cheese\",474.981,\"Million pounds\"\n2025,\"November\",11,\"Monthly\",\"Domestic
+ disappearance\",\"Butter\",180.347,\"Million pounds\"\n2025,\"November\",11,\"Monthly\",\"Domestic
+ disappearance\",\"Other-than-American cheese\",703.108,\"Million pounds\"\n2025,\"November\",11,\"Monthly\",\"Domestic
+ disappearance\",\"Dry skim milk products\",41.821,\"Million pounds\"\n2025,\"November\",11,\"Monthly\",\"Domestic
+ disappearance\",\"All products (milk equivalent milk-fat basis)\",18577.869,\"Million
+ pounds\"\n2025,\"November\",11,\"Monthly\",\"Domestic disappearance\",\"All
+ products (milk equivalent skim-solids basis)\",15481.291,\"Million pounds\"\n2025,\"November\",11,\"Monthly\",\"Ending
+ stocks\",\"All products (milk equivalent milk-fat basis)\",13080.324,\"Million
+ pounds\"\n2025,\"November\",11,\"Monthly\",\"Ending stocks\",\"All products
+ (milk equivalent skim-solids basis)\",9057.816,\"Million pounds\"\n2025,\"November\",11,\"Monthly\",\"Exports\",\"All
+ products (milk equivalent milk-fat basis)\",1550.997,\"Million pounds\"\n2025,\"November\",11,\"Monthly\",\"Exports\",\"All
+ products (milk equivalent skim-solids basis)\",3908.454,\"Million pounds\"\n2025,\"November\",11,\"Monthly\",\"Imports\",\"All
+ products (milk equivalent milk-fat basis)\",576.484,\"Million pounds\"\n2025,\"November\",11,\"Monthly\",\"Imports\",\"All
+ products (milk equivalent skim-solids basis)\",557.511,\"Million pounds\"\n2025,\"November\",11,\"Monthly\",\"Milk
+ prices\",\"All milk (at average milk-fat test)\",19.6,\"Dollars per hundredweight\"\n2025,\"November\",11,\"Monthly\",\"Milk
+ prices\",\"ClassIWhole\",20.87,\"Dollars per hundredweight\"\n2025,\"November\",11,\"Monthly\",\"Milk
+ prices\",\"ClassIIWhole\",14.54,\"Dollars per hundredweight\"\n2025,\"November\",11,\"Monthly\",\"Milk
+ production\",\"Production\",18804,\"Million pounds\"\n2025,\"November\",11,\"Monthly\",\"Milk
+ production\",\"Milk cows\",9559,\"Thousands\"\n2025,\"November\",11,\"Monthly\",\"Milk
+ production\",\"Milk per cow\",1967,\"Pounds\"\n2025,\"November\",11,\"Monthly\",\"Oceania
+ export prices\",\"Butter\",6150,\"Dollars per metric ton\"\n2025,\"November\",11,\"Monthly\",\"Oceania
+ export prices\",\"Skim milk powder\",2566,\"Dollars per metric ton\"\n2025,\"November\",11,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Butter\",1.636,\"Dollars per pound\"\n2025,\"November\",11,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Nonfat dry milk\",1.1609,\"Dollars per pound\"\n2025,\"November\",11,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Cheddar cheese, 40-pound blocks\",1.7419,\"Dollars
+ per pound\"\n2025,\"November\",11,\"Monthly\",\"Wholesale dairy product prices\",\"Cheddar
+ cheese, 500-pound barrels\",NA,\"Dollars per pound\"\n2025,\"November\",11,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Dry whey\",0.6415,\"Dollars per pound\"\n2025,\"October\",10,\"Monthly\",\"Consumer
+ Price Indexes\",\"All products\",NA,\"Year-over-year percent change\"\n2025,\"October\",10,\"Monthly\",\"Consumer
+ Price Indexes\",\"Dairy products\",NA,\"Year-over-year percent change\"\n2025,\"October\",10,\"Monthly\",\"Consumer
+ Price Indexes\",\"Butter\",NA,\"Year-over-year percent change\"\n2025,\"October\",10,\"Monthly\",\"Consumer
+ Price Indexes\",\"Cheese\",NA,\"Year-over-year percent change\"\n2025,\"October\",10,\"Monthly\",\"Consumer
+ Price Indexes\",\"All food\",NA,\"Year-over-year percent change\"\n2025,\"October\",10,\"Monthly\",\"Consumer
+ Price Indexes\",\"Fluid milk\",NA,\"Year-over-year percent change\"\n2025,\"October\",10,\"Monthly\",\"Dairy
+ product output\",\"American cheese\",491.479,\"Million pounds\"\n2025,\"October\",10,\"Monthly\",\"Dairy
+ product output\",\"Butter\",188.338,\"Million pounds\"\n2025,\"October\",10,\"Monthly\",\"Dairy
+ product output\",\"Other-than-American cheese\",781.405,\"Million pounds\"\n2025,\"October\",10,\"Monthly\",\"Dairy
+ product output\",\"Dry skim milk products\",165.871,\"Million pounds\"\n2025,\"October\",10,\"Monthly\",\"Dairy
+ product output\",\"Frozen products\",96.515,\"Million gallons\"\n2025,\"October\",10,\"Monthly\",\"Domestic
+ disappearance\",\"American cheese\",469.944,\"Million pounds\"\n2025,\"October\",10,\"Monthly\",\"Domestic
+ disappearance\",\"Butter\",200.401,\"Million pounds\"\n2025,\"October\",10,\"Monthly\",\"Domestic
+ disappearance\",\"Other-than-American cheese\",719.767,\"Million pounds\"\n2025,\"October\",10,\"Monthly\",\"Domestic
+ disappearance\",\"Dry skim milk products\",39.722,\"Million pounds\"\n2025,\"October\",10,\"Monthly\",\"Domestic
+ disappearance\",\"All products (milk equivalent milk-fat basis)\",19368.922,\"Million
+ pounds\"\n2025,\"October\",10,\"Monthly\",\"Domestic disappearance\",\"All
+ products (milk equivalent skim-solids basis)\",16120.724,\"Million pounds\"\n2025,\"October\",10,\"Monthly\",\"Ending
+ stocks\",\"All products (milk equivalent milk-fat basis)\",13910.076,\"Million
+ pounds\"\n2025,\"October\",10,\"Monthly\",\"Ending stocks\",\"All products
+ (milk equivalent skim-solids basis)\",9167.42,\"Million pounds\"\n2025,\"October\",10,\"Monthly\",\"Exports\",\"All
+ products (milk equivalent milk-fat basis)\",1440.688,\"Million pounds\"\n2025,\"October\",10,\"Monthly\",\"Exports\",\"All
+ products (milk equivalent skim-solids basis)\",4092.823,\"Million pounds\"\n2025,\"October\",10,\"Monthly\",\"Imports\",\"All
+ products (milk equivalent milk-fat basis)\",496.837,\"Million pounds\"\n2025,\"October\",10,\"Monthly\",\"Imports\",\"All
+ products (milk equivalent skim-solids basis)\",469.205,\"Million pounds\"\n2025,\"October\",10,\"Monthly\",\"Milk
+ prices\",\"All milk (at average milk-fat test)\",19.9,\"Dollars per hundredweight\"\n2025,\"October\",10,\"Monthly\",\"Milk
+ prices\",\"ClassIWhole\",22.16,\"Dollars per hundredweight\"\n2025,\"October\",10,\"Monthly\",\"Milk
+ prices\",\"ClassIIWhole\",16.02,\"Dollars per hundredweight\"\n2025,\"October\",10,\"Monthly\",\"Milk
+ production\",\"Production\",19469,\"Million pounds\"\n2025,\"October\",10,\"Monthly\",\"Milk
+ production\",\"Milk cows\",9566,\"Thousands\"\n2025,\"October\",10,\"Monthly\",\"Milk
+ production\",\"Milk per cow\",2035,\"Pounds\"\n2025,\"October\",10,\"Monthly\",\"Oceania
+ export prices\",\"Skim milk powder\",2599,\"Dollars per metric ton\"\n2025,\"October\",10,\"Monthly\",\"Oceania
+ export prices\",\"Butter\",6744,\"Dollars per metric ton\"\n2025,\"October\",10,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Butter\",1.7344,\"Dollars per pound\"\n2025,\"October\",10,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Nonfat dry milk\",1.1601,\"Dollars per pound\"\n2025,\"October\",10,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Cheddar cheese, 40-pound blocks\",1.7384,\"Dollars
+ per pound\"\n2025,\"October\",10,\"Monthly\",\"Wholesale dairy product prices\",\"Cheddar
+ cheese, 500-pound barrels\",NA,\"Dollars per pound\"\n2025,\"October\",10,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Dry whey\",0.593,\"Dollars per pound\"\n2025,\"September\",9,\"Monthly\",\"Consumer
+ Price Indexes\",\"All products\",3.013,\"Year-over-year percent change\"\n2025,\"September\",9,\"Monthly\",\"Consumer
+ Price Indexes\",\"Dairy products\",0.719,\"Year-over-year percent change\"\n2025,\"September\",9,\"Monthly\",\"Consumer
+ Price Indexes\",\"Butter\",-1.784,\"Year-over-year percent change\"\n2025,\"September\",9,\"Monthly\",\"Consumer
+ Price Indexes\",\"Cheese\",1.123,\"Year-over-year percent change\"\n2025,\"September\",9,\"Monthly\",\"Consumer
+ Price Indexes\",\"All food\",3.108,\"Year-over-year percent change\"\n2025,\"September\",9,\"Monthly\",\"Consumer
+ Price Indexes\",\"Fluid milk\",2.102,\"Year-over-year percent change\"\n2025,\"September\",9,\"Monthly\",\"Dairy
+ product output\",\"American cheese\",475.989,\"Million pounds\"\n2025,\"September\",9,\"Monthly\",\"Dairy
+ product output\",\"Butter\",183.898,\"Million pounds\"\n2025,\"September\",9,\"Monthly\",\"Dairy
+ product output\",\"Other-than-American cheese\",748.523,\"Million pounds\"\n2025,\"September\",9,\"Monthly\",\"Dairy
+ product output\",\"Dry skim milk products\",172.09,\"Million pounds\"\n2025,\"September\",9,\"Monthly\",\"Dairy
+ product output\",\"Frozen products\",94.151,\"Million gallons\"\n2025,\"September\",9,\"Monthly\",\"Domestic
+ disappearance\",\"American cheese\",459.91,\"Million pounds\"\n2025,\"September\",9,\"Monthly\",\"Domestic
+ disappearance\",\"Butter\",207.255,\"Million pounds\"\n2025,\"September\",9,\"Monthly\",\"Domestic
+ disappearance\",\"Other-than-American cheese\",689.57,\"Million pounds\"\n2025,\"September\",9,\"Monthly\",\"Domestic
+ disappearance\",\"Dry skim milk products\",68.8,\"Million pounds\"\n2025,\"September\",9,\"Monthly\",\"Domestic
+ disappearance\",\"All products (milk equivalent milk-fat basis)\",19069.177,\"Million
+ pounds\"\n2025,\"September\",9,\"Monthly\",\"Domestic disappearance\",\"All
+ products (milk equivalent skim-solids basis)\",15720.83,\"Million pounds\"\n2025,\"September\",9,\"Monthly\",\"Ending
+ stocks\",\"All products (milk equivalent milk-fat basis)\",14837.932,\"Million
+ pounds\"\n2025,\"September\",9,\"Monthly\",\"Ending stocks\",\"All products
+ (milk equivalent skim-solids basis)\",9526.845,\"Million pounds\"\n2025,\"September\",9,\"Monthly\",\"Exports\",\"All
+ products (milk equivalent milk-fat basis)\",1358.823,\"Million pounds\"\n2025,\"September\",9,\"Monthly\",\"Exports\",\"All
+ products (milk equivalent skim-solids basis)\",3943.828,\"Million pounds\"\n2025,\"September\",9,\"Monthly\",\"Imports\",\"All
+ products (milk equivalent milk-fat basis)\",532.117,\"Million pounds\"\n2025,\"September\",9,\"Monthly\",\"Imports\",\"All
+ products (milk equivalent skim-solids basis)\",559.32,\"Million pounds\"\n2025,\"September\",9,\"Monthly\",\"Milk
+ prices\",\"All milk (at average milk-fat test)\",20.4,\"Dollars per hundredweight\"\n2025,\"September\",9,\"Monthly\",\"Milk
+ prices\",\"ClassIWhole\",22.82,\"Dollars per hundredweight\"\n2025,\"September\",9,\"Monthly\",\"Milk
+ prices\",\"ClassIIWhole\",17.39,\"Dollars per hundredweight\"\n2025,\"September\",9,\"Monthly\",\"Milk
+ production\",\"Production\",18942,\"Million pounds\"\n2025,\"September\",9,\"Monthly\",\"Milk
+ production\",\"Milk cows\",9574,\"Thousands\"\n2025,\"September\",9,\"Monthly\",\"Milk
+ production\",\"Milk per cow\",1978,\"Pounds\"\n2025,\"September\",9,\"Monthly\",\"Oceania
+ export prices\",\"Skim milk powder\",2641,\"Dollars per metric ton\"\n2025,\"September\",9,\"Monthly\",\"Oceania
+ export prices\",\"Butter\",6992,\"Dollars per metric ton\"\n2025,\"September\",9,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Butter\",2.0377,\"Dollars per pound\"\n2025,\"September\",9,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Nonfat dry milk\",1.2265,\"Dollars per pound\"\n2025,\"September\",9,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Cheddar cheese, 40-pound blocks\",1.8066,\"Dollars
+ per pound\"\n2025,\"September\",9,\"Monthly\",\"Wholesale dairy product prices\",\"Cheddar
+ cheese, 500-pound barrels\",NA,\"Dollars per pound\"\n2025,\"September\",9,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Dry whey\",0.5782,\"Dollars per pound\"\n2026,\"April\",4,\"Monthly\",\"Consumer
+ Price Indexes\",\"All products\",3.811,\"Year-over-year percent change\"\n2026,\"April\",4,\"Monthly\",\"Consumer
+ Price Indexes\",\"Dairy products\",-0.584,\"Year-over-year percent change\"\n2026,\"April\",4,\"Monthly\",\"Consumer
+ Price Indexes\",\"Butter\",-5.837,\"Year-over-year percent change\"\n2026,\"April\",4,\"Monthly\",\"Consumer
+ Price Indexes\",\"Cheese\",-3.111,\"Year-over-year percent change\"\n2026,\"April\",4,\"Monthly\",\"Consumer
+ Price Indexes\",\"All food\",3.183,\"Year-over-year percent change\"\n2026,\"April\",4,\"Monthly\",\"Consumer
+ Price Indexes\",\"Fluid milk\",0.463,\"Year-over-year percent change\"\n2026,\"April\",4,\"Monthly\",\"Dairy
+ product output\",\"American cheese\",491.594,\"Million pounds\"\n2026,\"April\",4,\"Monthly\",\"Dairy
+ product output\",\"Butter\",224.282,\"Million pounds\"\n2026,\"April\",4,\"Monthly\",\"Dairy
+ product output\",\"Other-than-American cheese\",776.727,\"Million pounds\"\n2026,\"April\",4,\"Monthly\",\"Dairy
+ product output\",\"Dry skim milk products\",216.583,\"Million pounds\"\n2026,\"April\",4,\"Monthly\",\"Dairy
+ product output\",\"Frozen products\",109.029,\"Million gallons\"\n2026,\"April\",4,\"Monthly\",\"Domestic
+ disappearance\",\"American cheese\",457.075,\"Million pounds\"\n2026,\"April\",4,\"Monthly\",\"Domestic
+ disappearance\",\"Butter\",206.707,\"Million pounds\"\n2026,\"April\",4,\"Monthly\",\"Domestic
+ disappearance\",\"Other-than-American cheese\",693.55,\"Million pounds\"\n2026,\"April\",4,\"Monthly\",\"Domestic
+ disappearance\",\"Dry skim milk products\",75.763,\"Million pounds\"\n2026,\"April\",4,\"Monthly\",\"Domestic
+ disappearance\",\"All products (milk equivalent milk-fat basis)\",18376.244,\"Million
+ pounds\"\n2026,\"April\",4,\"Monthly\",\"Domestic disappearance\",\"All products
+ (milk equivalent skim-solids basis)\",15961.984,\"Million pounds\"\n2026,\"April\",4,\"Monthly\",\"Ending
+ stocks\",\"All products (milk equivalent milk-fat basis)\",15642.773,\"Million
+ pounds\"\n2026,\"April\",4,\"Monthly\",\"Ending stocks\",\"All products (milk
+ equivalent skim-solids basis)\",9999.774,\"Million pounds\"\n2026,\"April\",4,\"Monthly\",\"Exports\",\"All
+ products (milk equivalent milk-fat basis)\",1780.904,\"Million pounds\"\n2026,\"April\",4,\"Monthly\",\"Exports\",\"All
+ products (milk equivalent skim-solids basis)\",4209.294,\"Million pounds\"\n2026,\"April\",4,\"Monthly\",\"Imports\",\"All
+ products (milk equivalent milk-fat basis)\",847.475,\"Million pounds\"\n2026,\"April\",4,\"Monthly\",\"Imports\",\"All
+ products (milk equivalent skim-solids basis)\",576.32,\"Million pounds\"\n2026,\"April\",4,\"Monthly\",\"Milk
+ prices\",\"All milk (at average milk-fat test)\",20.8,\"Dollars per hundredweight\"\n2026,\"April\",4,\"Monthly\",\"Milk
+ prices\",\"ClassIWhole\",22.78,\"Dollars per hundredweight\"\n2026,\"April\",4,\"Monthly\",\"Milk
+ prices\",\"ClassIIWhole\",18.82,\"Dollars per hundredweight\"\n2026,\"April\",4,\"Monthly\",\"Milk
+ production\",\"Production\",19955,\"Million pounds\"\n2026,\"April\",4,\"Monthly\",\"Milk
+ production\",\"Milk cows\",9655,\"Thousands\"\n2026,\"April\",4,\"Monthly\",\"Milk
+ production\",\"Milk per cow\",2067,\"Pounds\"\n2026,\"April\",4,\"Monthly\",\"Oceania
+ export prices\",\"Butter\",6323,\"Dollars per metric ton\"\n2026,\"April\",4,\"Monthly\",\"Oceania
+ export prices\",\"Skim milk powder\",3577,\"Dollars per metric ton\"\n2026,\"April\",4,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Butter\",1.7707,\"Dollars per pound\"\n2026,\"April\",4,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Nonfat dry milk\",1.7788,\"Dollars per pound\"\n2026,\"April\",4,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Cheddar cheese, 40-pound blocks\",1.6427,\"Dollars
+ per pound\"\n2026,\"April\",4,\"Monthly\",\"Wholesale dairy product prices\",\"Cheddar
+ cheese, 500-pound barrels\",NA,\"Dollars per pound\"\n2026,\"April\",4,\"Monthly\",\"Wholesale
+ dairy product prices\",\"Dry whey\",0.6443,\"Dollars per pound\"\n2026,\"February\",2,\"Monthly\",\"Consumer
+ Price Indexes\",\"All products\",2.414,\"Year-over-year percent change\"\n2026,\"February\",2,\"Monthly\",\"Consumer
+ Price Indexes\",\"Dairy products\",0.062,\"Year-over-year percent change\"\n2026,\"February\",2,\"Monthly\",\"Consumer
+ Price Indexes\",\"Butter\",-7.615,\"Year-over-year percent change\"\n2026,\"February\",2,\"Monthly\",\"Consumer
+ Price Indexes\",\"Cheese\",-1.102,\"Year-over-year percent change\"\n2026,\"February\",2,\"Monthly\",\"Consumer
+ Price Indexes\",\"All food\",3.06,\"Year-over-year percent change\"\n2026,\"February\",2,\"Monthly\",\"Consumer
+ Price Indexes\",\"Fluid milk\",0.512,\"Year-over-year percent change\"\n2026,\"February\",2,\"Monthly\",\"Dairy
+ product output\",\"American cheese\",454.687,\"Million pounds\"\n2026,\"February\",2,\"Monthly\",\"Dairy
+ product output\",\"Butter\",222.399,\"Million pounds\"\n2026,\"February\",2,\"Monthly\",\"Dairy
+ product output\",\"Other-than-American cheese\",709.434,\"Million pounds\"\n2026,\"February\",2,\"Monthly\",\"Dairy
+ product output\",\"Dry skim milk products\",180.185,\"Million pounds\"\n2026,\"February\",2,\"Monthly\",\"Dairy
+ product output\",\"Frozen products\",91.019,\"Million gallons\"\n2026,\"February\",2,\"Monthly\",\"Domestic
+ disappearance\",\"American cheese\",431.806,\"Million pounds\"\n2026,\"February\",2,\"Monthly\",\"Domestic
+ disappearance\",\"Butter\",179.592,\"Million pounds\"\n2026,\"February\",2,\"Monthly\",\"Domestic
+ disappearance\",\"Other-than-American cheese\",620.794,\"Million pounds\"\n2026,\"February\",2,\"Monthly\",\"Domestic
+ disappearance\",\"Dry skim milk products\",58.21,\"Million pounds\"\n2026,\"February\",2,\"Monthly\",\"Domestic
+ disappearance\",\"All products (milk equivalent milk-fat basis)\",16385.997,\"Million
+ pounds\"\n2026,\"February\",2,\"Monthly\",\"Domestic disappearance\",\"All
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diff --git a/openbb_platform/providers/government_us/tests/record/http/test_usda_fetchers/test_major_land_uses_fetcher_urllib3_v2.yaml b/openbb_platform/providers/government_us/tests/record/http/test_usda_fetchers/test_major_land_uses_fetcher_urllib3_v2.yaml
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+ 7997,48 States,48 States,2012,Grazed forest-use land grazed,129783,"1,000
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+ 8060,48 States,48 States,2017,Grazed forest-use land grazed,131636,"1,000
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+ 13, 2024.","USDA, Economic Research Service, Major Land Uses."
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+ 9068,48 States,48 States,2017,Ungrazed forest-use land,396969,"1,000 acres","September
+ 13, 2024.","USDA, Economic Research Service, Major Land Uses."
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+ 9071,U.S. total,U.S. total,2017,Ungrazed forest-use land,490153,"1,000 acres","September
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+ 10013,48 States,48 States,2012,All special uses of land,168679,"1,000 acres","September
+ 13, 2024.","USDA, Economic Research Service, Major Land Uses."
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+ 10016,U.S. total,U.S. total,2012,All special uses of land,315872,"1,000 acres","September
+ 13, 2024.","USDA, Economic Research Service, Major Land Uses."
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+ 10076,48 States,48 States,2017,All special uses of land,169334,"1,000 acres","September
+ 13, 2024.","USDA, Economic Research Service, Major Land Uses."
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+ 10079,U.S. total,U.S. total,2017,All special uses of land,317661,"1,000 acres","September
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+ 11021,48 States,48 States,2012,Land in rural transportation facilities,26409,"1,000
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+ 11024,U.S. total,U.S. total,2012,Land in rural transportation facilities,26941,"1,000
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+
+ 11084,48 States,48 States,2017,Land in rural transportation facilities,25233,"1,000
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+ 11087,U.S. total,U.S. total,2017,Land in rural transportation facilities,25611,"1,000
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+
+ 12029,48 States,48 States,2012,Land in rural parks and wildlife areas,108980,"1,000
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+
+ 12032,U.S. total,U.S. total,2012,Land in rural parks and wildlife areas,253613,"1,000
+ acres","September 13, 2024.","USDA, Economic Research Service, Major Land
+ Uses."
+
+ 12092,48 States,48 States,2017,Land in rural parks and wildlife areas,112396,"1,000
+ acres","September 13, 2024.","USDA, Economic Research Service, Major Land
+ Uses."
+
+ 12095,U.S. total,U.S. total,2017,Land in rural parks and wildlife areas,258038,"1,000
+ acres","September 13, 2024.","USDA, Economic Research Service, Major Land
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+
+ 13037,48 States,48 States,2012,Land in defense and industrial areas,24988,"1,000
+ acres","September 13, 2024.","USDA, Economic Research Service, Major Land
+ Uses."
+
+ 13040,U.S. total,U.S. total,2012,Land in defense and industrial areas,26995,"1,000
+ acres","September 13, 2024.","USDA, Economic Research Service, Major Land
+ Uses."
+
+ 13100,48 States,48 States,2017,Land in defense and industrial areas,25739,"1,000
+ acres","September 13, 2024.","USDA, Economic Research Service, Major Land
+ Uses."
+
+ 13103,U.S. total,U.S. total,2017,Land in defense and industrial areas,28025,"1,000
+ acres","September 13, 2024.","USDA, Economic Research Service, Major Land
+ Uses."
+
+ 14045,48 States,48 States,2012,"Farmsteads, roads, and miscellaneous farmland",8301,"1,000
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+ Uses."
+
+ 14048,U.S. total,U.S. total,2012,"Farmsteads, roads, and miscellaneous farmland",8323,"1,000
+ acres","September 13, 2024.","USDA, Economic Research Service, Major Land
+ Uses."
+
+ 14108,48 States,48 States,2017,"Farmsteads, roads, and miscellaneous farmland",5967,"1,000
+ acres","September 13, 2024.","USDA, Economic Research Service, Major Land
+ Uses."
+
+ 14111,U.S. total,U.S. total,2017,"Farmsteads, roads, and miscellaneous farmland",5985,"1,000
+ acres","September 13, 2024.","USDA, Economic Research Service, Major Land
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+
+ 15053,48 States,48 States,2012,Land in urban areas,69441,"1,000 acres","September
+ 13, 2024.","USDA, Economic Research Service, Major Land Uses."
+
+ 15056,U.S. total,U.S. total,2012,Land in urban areas,69864,"1,000 acres","September
+ 13, 2024.","USDA, Economic Research Service, Major Land Uses."
+
+ 15116,48 States,48 States,2017,Land in urban areas,73870,"1,000 acres","September
+ 13, 2024.","USDA, Economic Research Service, Major Land Uses."
+
+ 15119,U.S. total,U.S. total,2017,Land in urban areas,74305,"1,000 acres","September
+ 13, 2024.","USDA, Economic Research Service, Major Land Uses."
+
+ 16061,48 States,48 States,2012,Miscellaneous other land,68870,"1,000 acres","September
+ 13, 2024.","USDA, Economic Research Service, Major Land Uses."
+
+ 16064,U.S. total,U.S. total,2012,Miscellaneous other land,195542,"1,000 acres","September
+ 13, 2024.","USDA, Economic Research Service, Major Land Uses."
+
+ 16124,48 States,48 States,2017,Miscellaneous other land,71390,"1,000 acres","September
+ 13, 2024.","USDA, Economic Research Service, Major Land Uses."
+
+ 16127,U.S. total,U.S. total,2017,Miscellaneous other land,196994,"1,000 acres","September
+ 13, 2024.","USDA, Economic Research Service, Major Land Uses."
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diff --git a/openbb_platform/providers/government_us/tests/record/http/test_usda_fetchers/test_milk_cost_of_production_fetcher_urllib3_v2.yaml b/openbb_platform/providers/government_us/tests/record/http/test_usda_fetchers/test_milk_cost_of_production_fetcher_urllib3_v2.yaml
new file mode 100644
index 00000000000..770f0a10c5a
--- /dev/null
+++ b/openbb_platform/providers/government_us/tests/record/http/test_usda_fetchers/test_milk_cost_of_production_fetcher_urllib3_v2.yaml
@@ -0,0 +1,3638 @@
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+ States\",2025,0.77,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United
+ States\",2025,0.08,\"Base survey of 2021\"\r\n\"Milk\",\"Supporting information\",\"Output
+ per cow\",\"pounds\",\"No specific size\",\"Kentucky\",\"United States\",2025,17662,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Capital recovery of
+ machinery and equipment \",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Wisconsin\",\"United States\",2025,4.66,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Opportunity cost of land\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"Iowa\",\"United States\",2025,0.09,\"Base survey of 2021\"\r\n\"Milk\",\"Supporting
+ information\",\"Output per cow\",\"pounds\",\"No specific size\",\"California\",\"United
+ States\",2025,24768,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Indiana\",\"United
+ States\",2025,0.06,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total,
+ gross value of production\",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Kentucky\",\"United States\",2025,23.68,\"Base survey of 2021\"\r\n\"Milk\",\"Operating
+ costs\",\"Marketing\",\"dollars per hundredweight sold\",\"No specific size\",\"Michigan\",\"United
+ States\",2025,0.28,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2025,1,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2025,0.99,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2025,0.67,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total, gross
+ value of production\",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2025,23.61,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom
+ services\",\"dollars per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United
+ States\",2025,0.74,\"Base survey of 2021\"\r\n\"Milk\",\"Supporting information\",\"Milk
+ cows\",\"head per farm\",\"No specific size\",\"Michigan\",\"United States\",2025,211,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"New York\",\"United States\",2025,22218,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"Ohio\",\"United States\",2025,21773,\"Base survey of 2021\"\r\n\"Milk\",\"Supporting
+ information\",\"Milk cows\",\"head per farm\",\"No specific size\",\"Minnesota\",\"United
+ States\",2025,155,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United
+ States\",2025,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United
+ States\",2025,3.08,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total,
+ feed costs\",\"dollars per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United
+ States\",2025,11,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total,
+ gross value of production\",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Pennsylvania\",\"United States\",2025,24.92,\"Base survey of 2021\"\r\n\"Milk\",\"Operating
+ costs\",\"Interest on operating capital\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"New York\",\"United States\",2025,0.27,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2025,21.38,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2025,22.34,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2025,0.89,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2025,20.98,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2025,16.95,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2025,0.03,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2025,4.39,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity cost of
+ unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Iowa\",\"United
+ States\",2025,2.64,\"Base survey of 2021\"\r\n\"Milk\",\"Supporting information\",\"Output
+ per cow\",\"pounds\",\"No specific size\",\"Iowa\",\"United States\",2025,23325,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2025,25.55,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less operating
+ costs\",\"dollars per hundredweight sold\",\"No specific size\",\"Michigan\",\"United
+ States\",2025,7.19,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United
+ States\",2025,0.59,\"Base survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value
+ of production less total costs listed\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"Minnesota\",\"United States\",2025,-0.9,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2025,2.51,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2025,2.35,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2025,21.01,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other income
+ \ \",\"dollars per hundredweight sold\",\"No specific size\",\"New York\",\"United
+ States\",2025,0.64,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest
+ on operating capital\",\"dollars per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United
+ States\",2025,0.31,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total,
+ operating costs\",\"dollars per hundredweight sold\",\"No specific size\",\"Michigan\",\"United
+ States\",2025,17.33,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2025,2.86,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2025,0.08,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2025,4.74,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2025,14.21,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2025,0.36,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other income
+ \ \",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2025,0.65,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest
+ on operating capital\",\"dollars per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United
+ States\",2025,0.26,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed
+ feed\",\"dollars per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United
+ States\",2025,0.02,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2025,0.07,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other income
+ \ \",\"dollars per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United
+ States\",2025,0.73,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel,
+ lube, and electricity\",\"dollars per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United
+ States\",2025,0.99,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total,
+ allocated overhead\",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2025,6.15,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel,
+ lube, and electricity\",\"dollars per hundredweight sold\",\"No specific size\",\"Michigan\",\"United
+ States\",2025,1.15,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Michigan\",\"United
+ States\",2025,3.55,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2025,0.2,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2025,4.64,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2025,0.02,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity cost of
+ unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United
+ States\",2025,4.06,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed
+ feed\",\"dollars per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United
+ States\",2025,0.15,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2025,0.33,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed
+ feed\",\"dollars per hundredweight sold\",\"No specific size\",\"Iowa\",\"United
+ States\",2025,0.02,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes
+ and insurance\",\"dollars per hundredweight sold\",\"No specific size\",\"Michigan\",\"United
+ States\",2025,0.25,\"Base survey of 2021\"\r\n\"Milk\",\"Supporting information\",\"Milk
+ cows\",\"head per farm\",\"No specific size\",\"Kentucky\",\"United States\",2025,128,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2025,0.66,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2025,9.8,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Capital recovery of
+ machinery and equipment \",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Ohio\",\"United States\",2025,5.41,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Opportunity cost of land\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"New York\",\"United States\",2025,0.04,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2025,0.46,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2025,0.32,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2025,14.69,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2025,16.15,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2025,8.88,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2025,0.4,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2025,3.81,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"Minnesota\",\"United States\",2025,21559,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2025,4.58,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2025,2.6,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2025,1.05,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2025,13.42,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2025,0.24,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2025,0.21,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"Indiana\",\"United States\",2025,19121,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2025,-0.55,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Capital recovery of
+ machinery and equipment \",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Kentucky\",\"United States\",2025,8.89,\"Base survey of 2021\"\r\n\"Milk\",\"Costs
+ listed\",\"Total, costs listed\",\"dollars per hundredweight sold\",\"No specific
+ size\",\"California\",\"United States\",2025,18.85,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Capital recovery of machinery and equipment \",\"dollars per
+ hundredweight sold\",\"No specific size\",\"California\",\"United States\",2025,2.76,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2025,0.7,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2025,0.86,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2025,27.08,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2025,1.16,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2025,3.58,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2025,1.21,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2025,0.67,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2025,0.33,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2025,0.24,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total
+ costs listed\",\"dollars per hundredweight sold\",\"No specific size\",\"Michigan\",\"United
+ States\",2025,-7.65,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total,
+ gross value of production\",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Wisconsin\",\"United States\",2025,23.7,\"Base survey of 2021\"\r\n\"Milk\",\"Operating
+ costs\",\"Total, feed costs\",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Kentucky\",\"United States\",2025,8.97,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Opportunity cost of unpaid labor\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"Wisconsin\",\"United States\",2025,2.37,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2025,24.02,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Supporting information\",\"Milk cows\",\"head
+ per farm\",\"No specific size\",\"Wisconsin\",\"United States\",2025,225,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2025,28.31,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Capital recovery of
+ machinery and equipment \",\"dollars per hundredweight sold\",\"No specific
+ size\",\"New York\",\"United States\",2025,8.54,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Total, allocated overhead\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"Ohio\",\"United States\",2025,11.64,\"Base survey of 2021\"\r\n\"Milk\",\"Operating
+ costs\",\"Purchased feed\",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Michigan\",\"United States\",2025,4.55,\"Base survey of 2021\"\r\n\"Milk\",\"Net
+ value\",\"Value of production less operating costs\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"Wisconsin\",\"United States\",2025,10.07,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2025,0.84,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2025,4.62,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2025,9.8,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2025,23.92,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2025,0.92,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2025,0.43,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other income
+ \ \",\"dollars per hundredweight sold\",\"No specific size\",\"Iowa\",\"United
+ States\",2025,0.59,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk
+ sold\",\"dollars per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United
+ States\",2025,21.36,\"Base survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value
+ of production less total costs listed\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"Indiana\",\"United States\",2025,-4.96,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"Indiana\",\"United States\",2025,5.21,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less operating
+ costs\",\"dollars per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United
+ States\",2025,11.11,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United
+ States\",2025,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2025,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired
+ labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United
+ States\",2025,2.3,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Ohio\",\"United
+ States\",2025,0.04,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General
+ farm overhead\",\"dollars per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United
+ States\",2025,0.58,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2025,2.8,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2025,25.43,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2025,1.98,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total
+ costs listed\",\"dollars per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United
+ States\",2025,-8.18,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total,
+ gross value of production\",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Ohio\",\"United States\",2025,25,\"Base survey of 2021\"\r\n\"Milk\",\"Operating
+ costs\",\"Interest on operating capital\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"Indiana\",\"United States\",2025,0.32,\"Base survey of 2021\"\r\n\"Milk\",\"Operating
+ costs\",\"Total, operating costs\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"New York\",\"United States\",2025,14.1,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2025,9.4,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2025,11.66,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2025,12.39,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2025,12.7,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2025,13.63,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less operating
+ costs\",\"dollars per hundredweight sold\",\"No specific size\",\"Ohio\",\"United
+ States\",2025,11.09,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2025,1.95,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2025,2.39,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total, gross
+ value of production\",\"dollars per hundredweight sold\",\"No specific size\",\"New
+ York\",\"United States\",2025,25.58,\"Base survey of 2021\"\r\n\"Milk\",\"Operating
+ costs\",\"Grazed feed\",\"dollars per hundredweight sold\",\"No specific size\",\"Ohio\",\"United
+ States\",2025,0.08,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased
+ feed\",\"dollars per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United
+ States\",2025,4.43,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk
+ sold\",\"dollars per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United
+ States\",2025,20.56,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United
+ States\",2025,0.64,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk
+ sold\",\"dollars per hundredweight sold\",\"No specific size\",\"Ohio\",\"United
+ States\",2025,22.34,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes
+ and insurance\",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2025,0.19,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed
+ feed\",\"dollars per hundredweight sold\",\"No specific size\",\"Indiana\",\"United
+ States\",2025,0.1,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased
+ feed\",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2025,7.43,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased
+ feed\",\"dollars per hundredweight sold\",\"No specific size\",\"Iowa\",\"United
+ States\",2025,4.69,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes
+ and insurance\",\"dollars per hundredweight sold\",\"No specific size\",\"Iowa\",\"United
+ States\",2025,0.29,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United
+ States\",2025,0.06,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General
+ farm overhead\",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2025,0.58,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General
+ farm overhead\",\"dollars per hundredweight sold\",\"No specific size\",\"Indiana\",\"United
+ States\",2025,0.81,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Michigan\",\"United
+ States\",2025,0.02,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes
+ and insurance\",\"dollars per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United
+ States\",2025,0.14,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown
+ harvested feed\",\"dollars per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United
+ States\",2025,4.19,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2025,0.2,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2025,0.35,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2025,0.99,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2025,1.01,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2025,0.81,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2025,1.17,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2025,1.1,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2025,1.3,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Supporting information\",\"Milk cows\",\"head
+ per farm\",\"No specific size\",\"California\",\"United States\",2025,972,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity cost of
+ unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Ohio\",\"United
+ States\",2025,3.41,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Michigan\",\"United
+ States\",2025,0,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"New
+ York\",\"United States\",2025,1.85,\"Base survey of 2021\"\r\n\"Milk\",\"Net
+ value\",\"Value of production less operating costs\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2025,8.77,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2025,1.26,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity cost of
+ land\",\"dollars per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United
+ States\",2025,0.06,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General
+ farm overhead\",\"dollars per hundredweight sold\",\"No specific size\",\"New
+ York\",\"United States\",2025,0.67,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Taxes and insurance\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"Ohio\",\"United States\",2025,0.18,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Total, allocated overhead\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"Minnesota\",\"United States\",2025,12.01,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2025,14.84,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2025,1.41,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2025,9.45,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2025,3.22,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2025,0.7,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2025,1.21,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2025,0.79,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2025,0.84,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other income
+ \ \",\"dollars per hundredweight sold\",\"No specific size\",\"Michigan\",\"United
+ States\",2025,0.65,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel,
+ lube, and electricity\",\"dollars per hundredweight sold\",\"No specific size\",\"New
+ York\",\"United States\",2025,0.96,\"Base survey of 2021\"\r\n\"Milk\",\"Supporting
+ information\",\"Milk cows\",\"head per farm\",\"No specific size\",\"Indiana\",\"United
+ States\",2025,71,\"Base survey of 2021\"\r\n\"Milk\",\"Supporting information\",\"Output
+ per cow\",\"pounds\",\"No specific size\",\"Michigan\",\"United States\",2025,21995,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2025,1.9,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Supporting information\",\"Milk cows\",\"head
+ per farm\",\"No specific size\",\"Pennsylvania\",\"United States\",2025,100,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity cost of
+ land\",\"dollars per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United
+ States\",2025,0.04,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Iowa\",\"United
+ States\",2025,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Supporting information\",\"Output
+ per cow\",\"pounds\",\"No specific size\",\"Wisconsin\",\"United States\",2025,24902,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Supporting information\",\"Milk cows\",\"head
+ per farm\",\"No specific size\",\"Iowa\",\"United States\",2025,178,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other, operating costs
+ \ \",\"dollars per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United
+ States\",2025,0,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired
+ labor\",\"dollars per hundredweight sold\",\"No specific size\",\"New York\",\"United
+ States\",2025,2.87,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Capital
+ recovery of machinery and equipment \",\"dollars per hundredweight sold\",\"No
+ specific size\",\"Minnesota\",\"United States\",2025,5.28,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2025,11.29,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2025,16.6,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2025,0.05,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2025,2.01,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2025,0.27,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2025,21.16,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Supporting information\",\"Milk cows\",\"head
+ per farm\",\"No specific size\",\"New York\",\"United States\",2025,192,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less operating
+ costs\",\"dollars per hundredweight sold\",\"No specific size\",\"Iowa\",\"United
+ States\",2025,10.49,\"Base survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value
+ of production less total costs listed\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"Kentucky\",\"United States\",2025,-3.4,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2025,12,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other, operating costs
+ \ \",\"dollars per hundredweight sold\",\"No specific size\",\"Ohio\",\"United
+ States\",2025,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Supporting information\",\"Output
+ per cow\",\"pounds\",\"No specific size\",\"Pennsylvania\",\"United States\",2025,20445,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less operating
+ costs\",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2025,10.91,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General
+ farm overhead\",\"dollars per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United
+ States\",2025,0.53,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total,
+ allocated overhead\",\"dollars per hundredweight sold\",\"No specific size\",\"Indiana\",\"United
+ States\",2025,16.61,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2025,0,\"Base survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value
+ of production less total costs listed\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"California\",\"United States\",2025,4.76,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2025,6.12,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2025,11.43,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2025,0.72,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2025,0.35,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2025,0.5,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other income
+ \ \",\"dollars per hundredweight sold\",\"No specific size\",\"Ohio\",\"United
+ States\",2025,0.65,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total,
+ gross value of production\",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Michigan\",\"United States\",2025,24.52,\"Base survey of 2021\"\r\n\"Milk\",\"Operating
+ costs\",\"Repairs\",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2025,0.52,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Capital
+ recovery of machinery and equipment \",\"dollars per hundredweight sold\",\"No
+ specific size\",\"Iowa\",\"United States\",2025,4.95,\"Base survey of 2021\"\r\n\"Milk\",\"Operating
+ costs\",\"Grazed feed\",\"dollars per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United
+ States\",2025,0.05,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2025,0.11,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2025,0.85,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2025,3.88,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total
+ costs listed\",\"dollars per hundredweight sold\",\"No specific size\",\"New
+ York\",\"United States\",2025,-2.73,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Opportunity cost of unpaid labor\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2025,3.5,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less operating
+ costs\",\"dollars per hundredweight sold\",\"No specific size\",\"New York\",\"United
+ States\",2025,11.48,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Indiana\",\"United
+ States\",2025,0.12,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes
+ and insurance\",\"dollars per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United
+ States\",2025,0.34,\"Base survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total,
+ costs listed\",\"dollars per hundredweight sold\",\"No specific size\",\"Iowa\",\"United
+ States\",2025,21.46,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Indiana\",\"United
+ States\",2025,7.78,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown
+ harvested feed\",\"dollars per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United
+ States\",2025,7.09,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed
+ feed\",\"dollars per hundredweight sold\",\"No specific size\",\"Michigan\",\"United
+ States\",2025,0.02,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total,
+ operating costs\",\"dollars per hundredweight sold\",\"No specific size\",\"Ohio\",\"United
+ States\",2025,13.91,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United
+ States\",2025,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown
+ harvested feed\",\"dollars per hundredweight sold\",\"No specific size\",\"Michigan\",\"United
+ States\",2025,7.43,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total,
+ feed costs\",\"dollars per hundredweight sold\",\"No specific size\",\"Iowa\",\"United
+ States\",2025,7.93,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired
+ labor\",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2025,2.28,\"Base survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total,
+ costs listed\",\"dollars per hundredweight sold\",\"No specific size\",\"Indiana\",\"United
+ States\",2025,33.21,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"New
+ York\",\"United States\",2025,0.02,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Hired labor\",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Kentucky\",\"United States\",2025,0.99,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Capital recovery of machinery and equipment \",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2025,5.82,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2025,0.29,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other income
+ \ \",\"dollars per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United
+ States\",2025,0.76,\"Base survey of 2021\"\r\n\"Milk\",\"Supporting information\",\"Milk
+ cows\",\"head per farm\",\"No specific size\",\"Ohio\",\"United States\",2025,144,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2025,5.33,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2025,0.79,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2025,0.45,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2025,1.96,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2025,3.74,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2025,0.92,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2025,1.15,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2025,0.22,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2025,0.26,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total, gross
+ value of production\",\"dollars per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United
+ States\",2025,24.53,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2025,0.22,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2025,19.6,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2025,1.12,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2025,0.66,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2025,0.97,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2025,0.35,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total
+ costs listed\",\"dollars per hundredweight sold\",\"No specific size\",\"Iowa\",\"United
+ States\",2025,0.69,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased
+ feed\",\"dollars per hundredweight sold\",\"No specific size\",\"New York\",\"United
+ States\",2025,4.75,\"Base survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value
+ of production less operating costs\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"Indiana\",\"United States\",2025,11.65,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2025,32.17,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2025,1.79,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2025,33.1,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total
+ costs listed\",\"dollars per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United
+ States\",2025,-0.32,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total,
+ allocated overhead\",\"dollars per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United
+ States\",2025,10.39,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Capital
+ recovery of machinery and equipment \",\"dollars per hundredweight sold\",\"No
+ specific size\",\"Michigan\",\"United States\",2025,6.75,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Capital recovery of machinery
+ and equipment \",\"dollars per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United
+ States\",2025,10.93,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General
+ farm overhead\",\"dollars per hundredweight sold\",\"No specific size\",\"Iowa\",\"United
+ States\",2025,0.53,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes
+ and insurance\",\"dollars per hundredweight sold\",\"No specific size\",\"New
+ York\",\"United States\",2025,0.24,\"Base survey of 2021\"\r\n\"Milk\",\"Gross
+ value of production\",\"Milk sold\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"U.S. total\",\"United States\",2021,18.44,\"Base survey
+ of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2022,25.18,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2023,20.24,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2024,22.38,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2021,18.52,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2022,25.42,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2023,20.31,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2024,21.97,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2021,19.85,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2022,27.45,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2023,22.87,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2024,25.32,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2021,17.81,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2022,23.22,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2023,17.92,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2024,20.81,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2021,18.74,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2022,25.63,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2023,20.97,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2024,22.23,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2021,17.24,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2022,24.15,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2023,19.65,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2024,22.21,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2021,19.07,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2022,25.2,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2023,19.4,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2024,22.45,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2021,19.32,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2022,26.76,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2023,22.39,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2024,24.16,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2021,18.76,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2022,25.03,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2023,21.1,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2024,23.05,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2021,19.01,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2022,26.36,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2023,21.33,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2024,23.27,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2021,19.07,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2022,24.93,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2023,19.67,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Milk sold\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2024,22.02,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2021,1.06,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2022,1.26,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2023,1.54,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2024,1.94,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2021,0.93,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2022,1.11,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2023,1.36,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2024,1.71,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2021,1.69,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2022,2.01,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2023,2.45,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2024,3.1,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2021,0.92,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2022,1.1,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2023,1.34,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2024,1.7,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2021,1.13,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2022,1.34,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2023,1.64,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2024,2.07,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2021,1.35,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2022,1.61,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2023,1.96,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2024,2.48,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2021,1.18,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2022,1.41,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2023,1.72,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2024,2.17,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2021,1.23,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2022,1.46,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2023,1.78,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2024,2.25,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2021,0.95,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2022,1.13,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2023,1.38,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2024,1.74,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2021,1.32,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2022,1.57,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2023,1.91,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2024,2.42,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2021,0.92,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2022,1.09,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2023,1.33,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2024,1.69,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other income
+ \ \",\"dollars per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United
+ States\",2021,0.55,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"U.S.
+ total\",\"United States\",2022,0.9,\"Base survey of 2021\"\r\n\"Milk\",\"Gross
+ value of production\",\"Other income \",\"dollars per hundredweight sold\",\"No
+ specific size\",\"U.S. total\",\"United States\",2023,0.73,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other income \",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2024,0.62,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other income
+ \ \",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2021,0.54,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2022,0.87,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2023,0.7,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2024,0.6,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Indiana\",\"United
+ States\",2021,0.62,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Indiana\",\"United
+ States\",2022,1.02,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Indiana\",\"United
+ States\",2023,0.82,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Indiana\",\"United
+ States\",2024,0.7,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Iowa\",\"United
+ States\",2021,0.49,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Iowa\",\"United
+ States\",2022,0.8,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Iowa\",\"United
+ States\",2023,0.64,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Iowa\",\"United
+ States\",2024,0.54,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United
+ States\",2021,0.61,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United
+ States\",2022,0.99,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United
+ States\",2023,0.8,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United
+ States\",2024,0.68,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Michigan\",\"United
+ States\",2021,0.54,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Michigan\",\"United
+ States\",2022,0.88,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Michigan\",\"United
+ States\",2023,0.71,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Michigan\",\"United
+ States\",2024,0.6,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United
+ States\",2021,0.54,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United
+ States\",2022,0.87,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United
+ States\",2023,0.7,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United
+ States\",2024,0.6,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"New York\",\"United
+ States\",2021,0.53,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"New York\",\"United
+ States\",2022,0.87,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"New York\",\"United
+ States\",2023,0.7,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"New York\",\"United
+ States\",2024,0.59,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Ohio\",\"United
+ States\",2021,0.54,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Ohio\",\"United
+ States\",2022,0.88,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Ohio\",\"United
+ States\",2023,0.71,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Ohio\",\"United
+ States\",2024,0.6,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United
+ States\",2021,0.63,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United
+ States\",2022,1.03,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United
+ States\",2023,0.83,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United
+ States\",2024,0.7,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United
+ States\",2021,0.49,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United
+ States\",2022,0.79,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United
+ States\",2023,0.64,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other
+ income \",\"dollars per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United
+ States\",2024,0.54,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total,
+ gross value of production\",\"dollars per hundredweight sold\",\"No specific
+ size\",\"U.S. total\",\"United States\",2021,20.05,\"Base survey of 2021\"\r\n\"Milk\",\"Gross
+ value of production\",\"Total, gross value of production\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"U.S. total\",\"United States\",2022,27.34,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total, gross
+ value of production\",\"dollars per hundredweight sold\",\"No specific size\",\"U.S.
+ total\",\"United States\",2023,22.51,\"Base survey of 2021\"\r\n\"Milk\",\"Gross
+ value of production\",\"Total, gross value of production\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"U.S. total\",\"United States\",2024,24.94,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total, gross
+ value of production\",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2021,19.99,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total,
+ gross value of production\",\"dollars per hundredweight sold\",\"No specific
+ size\",\"California\",\"United States\",2022,27.4,\"Base survey of 2021\"\r\n\"Milk\",\"Gross
+ value of production\",\"Total, gross value of production\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"California\",\"United States\",2023,22.37,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total, gross
+ value of production\",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2024,24.28,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total,
+ gross value of production\",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Indiana\",\"United States\",2021,22.16,\"Base survey of 2021\"\r\n\"Milk\",\"Gross
+ value of production\",\"Total, gross value of production\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"Indiana\",\"United States\",2022,30.48,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total, gross
+ value of production\",\"dollars per hundredweight sold\",\"No specific size\",\"Indiana\",\"United
+ States\",2023,26.14,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total,
+ gross value of production\",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Indiana\",\"United States\",2024,29.12,\"Base survey of 2021\"\r\n\"Milk\",\"Gross
+ value of production\",\"Total, gross value of production\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"Iowa\",\"United States\",2021,19.22,\"Base survey
+ of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total, gross value of
+ production\",\"dollars per hundredweight sold\",\"No specific size\",\"Iowa\",\"United
+ States\",2022,25.12,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total,
+ gross value of production\",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Iowa\",\"United States\",2023,19.9,\"Base survey of 2021\"\r\n\"Milk\",\"Gross
+ value of production\",\"Total, gross value of production\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"Iowa\",\"United States\",2024,23.05,\"Base survey
+ of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total, gross value of
+ production\",\"dollars per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United
+ States\",2021,20.48,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total,
+ gross value of production\",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Kentucky\",\"United States\",2022,27.96,\"Base survey of 2021\"\r\n\"Milk\",\"Gross
+ value of production\",\"Total, gross value of production\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"Kentucky\",\"United States\",2023,23.41,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total, gross
+ value of production\",\"dollars per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United
+ States\",2024,24.98,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total,
+ gross value of production\",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Michigan\",\"United States\",2021,19.13,\"Base survey of 2021\"\r\n\"Milk\",\"Gross
+ value of production\",\"Total, gross value of production\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"Michigan\",\"United States\",2022,26.64,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total, gross
+ value of production\",\"dollars per hundredweight sold\",\"No specific size\",\"Michigan\",\"United
+ States\",2023,22.32,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total,
+ gross value of production\",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Michigan\",\"United States\",2024,25.29,\"Base survey of 2021\"\r\n\"Milk\",\"Gross
+ value of production\",\"Total, gross value of production\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"Minnesota\",\"United States\",2021,20.79,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total, gross
+ value of production\",\"dollars per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United
+ States\",2022,27.48,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total,
+ gross value of production\",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Minnesota\",\"United States\",2023,21.82,\"Base survey of 2021\"\r\n\"Milk\",\"Gross
+ value of production\",\"Total, gross value of production\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"Minnesota\",\"United States\",2024,25.22,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total, gross
+ value of production\",\"dollars per hundredweight sold\",\"No specific size\",\"New
+ York\",\"United States\",2021,21.08,\"Base survey of 2021\"\r\n\"Milk\",\"Gross
+ value of production\",\"Total, gross value of production\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"New York\",\"United States\",2022,29.09,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total, gross
+ value of production\",\"dollars per hundredweight sold\",\"No specific size\",\"New
+ York\",\"United States\",2023,24.87,\"Base survey of 2021\"\r\n\"Milk\",\"Gross
+ value of production\",\"Total, gross value of production\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"New York\",\"United States\",2024,27,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total, gross
+ value of production\",\"dollars per hundredweight sold\",\"No specific size\",\"Ohio\",\"United
+ States\",2021,20.25,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total,
+ gross value of production\",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Ohio\",\"United States\",2022,27.04,\"Base survey of 2021\"\r\n\"Milk\",\"Gross
+ value of production\",\"Total, gross value of production\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"Ohio\",\"United States\",2023,23.19,\"Base survey
+ of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total, gross value of
+ production\",\"dollars per hundredweight sold\",\"No specific size\",\"Ohio\",\"United
+ States\",2024,25.39,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total,
+ gross value of production\",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Pennsylvania\",\"United States\",2021,20.96,\"Base survey of 2021\"\r\n\"Milk\",\"Gross
+ value of production\",\"Total, gross value of production\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2022,28.96,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total, gross
+ value of production\",\"dollars per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United
+ States\",2023,24.07,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total,
+ gross value of production\",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Pennsylvania\",\"United States\",2024,26.39,\"Base survey of 2021\"\r\n\"Milk\",\"Gross
+ value of production\",\"Total, gross value of production\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"Wisconsin\",\"United States\",2021,20.48,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total, gross
+ value of production\",\"dollars per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United
+ States\",2022,26.81,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total,
+ gross value of production\",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Wisconsin\",\"United States\",2023,21.64,\"Base survey of 2021\"\r\n\"Milk\",\"Gross
+ value of production\",\"Total, gross value of production\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"Wisconsin\",\"United States\",2024,24.25,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2021,7.99,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2022,9.92,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2023,8.79,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2024,6.48,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2021,9.42,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2022,11.69,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2023,10.35,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2024,7.63,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2021,6.6,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2022,8.19,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2023,7.26,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2024,5.35,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2021,5.94,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2022,7.37,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2023,6.53,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2024,4.82,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2021,5.62,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2022,6.97,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2023,6.17,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2024,4.55,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2021,5.77,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2022,7.16,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2023,6.34,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2024,4.68,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2021,5.89,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2022,7.31,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2023,6.47,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2024,4.77,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2021,6.03,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2022,7.48,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2023,6.63,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2024,4.89,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2021,6.01,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2022,7.46,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2023,6.61,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2024,4.87,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2021,4.92,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2022,6.1,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2023,5.41,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2024,3.99,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2021,6.76,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2022,8.38,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2023,7.43,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2024,5.48,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2021,3.91,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2022,4.82,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2023,4.74,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2024,3.8,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2021,2.65,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2022,3.26,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2023,3.21,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2024,2.57,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2021,6.88,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2022,8.48,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2023,8.34,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2024,6.69,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2021,3.63,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2022,4.47,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2023,4.4,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2024,3.53,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2021,4.93,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2022,6.08,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2023,5.98,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2024,4.8,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2021,8.36,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2022,10.3,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2023,10.13,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2024,8.13,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2021,4.72,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2022,5.81,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2023,5.72,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2024,4.59,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2021,5.2,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2022,6.41,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2023,6.3,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2024,5.06,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2021,5.15,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2022,6.35,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2023,6.24,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2024,5.01,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2021,7.98,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2022,9.83,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2023,9.67,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2024,7.76,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2021,4.2,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2022,5.18,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2023,5.09,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2024,4.09,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2021,0.03,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2022,0.03,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2023,0.03,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2024,0.03,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2021,0.01,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2022,0.01,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2023,0.01,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2024,0.02,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2021,0.08,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2022,0.08,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2023,0.09,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2024,0.09,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2021,0.02,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2022,0.02,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2023,0.02,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2024,0.02,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2021,0.12,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2022,0.12,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2023,0.13,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2024,0.13,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2021,0.02,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2022,0.02,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2023,0.02,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2024,0.02,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2021,0.04,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2022,0.04,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2023,0.04,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2024,0.05,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2021,0.06,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2022,0.06,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2023,0.06,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2024,0.07,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2021,0.07,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2022,0.06,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2023,0.07,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2024,0.07,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2021,0.03,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2022,0.03,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2023,0.03,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2024,0.03,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2021,0.02,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2022,0.02,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2023,0.02,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2024,0.02,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2021,11.93,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2022,14.77,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2023,13.56,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2024,10.31,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2021,12.08,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2022,14.96,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2023,13.57,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2024,10.22,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2021,13.56,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2022,16.75,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2023,15.69,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2024,12.13,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2021,9.59,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2022,11.86,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2023,10.95,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2024,8.37,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2021,10.67,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2022,13.17,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2023,12.28,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2024,9.48,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2021,14.15,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2022,17.48,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2023,16.49,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2024,12.83,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2021,10.65,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2022,13.16,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2023,12.23,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2024,9.41,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2021,11.29,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2022,13.95,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2023,12.99,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2024,10.02,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2021,11.23,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2022,13.87,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2023,12.92,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2024,9.95,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2021,12.93,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2022,15.96,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2023,15.11,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2024,11.78,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2021,10.98,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2022,13.58,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2023,12.54,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2024,9.59,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2021,0.62,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2022,0.7,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2023,0.74,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2024,0.76,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2021,0.58,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2022,0.65,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2023,0.69,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2024,0.71,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2021,0.87,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2022,0.98,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2023,1.03,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2024,1.07,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2021,0.58,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2022,0.65,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2023,0.68,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2024,0.7,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2021,0.59,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2022,0.66,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2023,0.7,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2024,0.72,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2021,0.67,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2022,0.75,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2023,0.8,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2024,0.82,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2021,0.56,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2022,0.63,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2023,0.66,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2024,0.68,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2021,0.61,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2022,0.68,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2023,0.72,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2024,0.75,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2021,0.67,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2022,0.75,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2023,0.79,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2024,0.81,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2021,1.12,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2022,1.26,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2023,1.32,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2024,1.37,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2021,0.93,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2022,1.04,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2023,1.1,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2024,1.13,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2021,0.63,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2022,0.91,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2023,0.79,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2024,0.73,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2021,0.44,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2022,0.64,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2023,0.55,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2024,0.52,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2021,1,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2022,1.44,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2023,1.24,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2024,1.16,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2021,0.71,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2022,1.02,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2023,0.88,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2024,0.82,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2021,0.58,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2022,0.84,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2023,0.73,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2024,0.68,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2021,1.02,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2022,1.48,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2023,1.27,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2024,1.19,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2021,0.88,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2022,1.27,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2023,1.09,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2024,1.02,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2021,0.85,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2022,1.23,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2023,1.06,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2024,0.99,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2021,0.88,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2022,1.28,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2023,1.1,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2024,1.03,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2021,0.9,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2022,1.3,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2023,1.12,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2024,1.04,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2021,0.64,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2022,0.93,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2023,0.8,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2024,0.75,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2021,0.7,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2022,0.77,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2023,0.8,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2024,0.82,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"California\",\"United States\",2021,0.44,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"California\",\"United States\",2022,0.48,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"California\",\"United States\",2023,0.5,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"California\",\"United States\",2024,0.51,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2021,1.02,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2022,1.12,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2023,1.17,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2024,1.2,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2021,0.77,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2022,0.86,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2023,0.89,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2024,0.91,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2021,0.56,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2022,0.62,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2023,0.65,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2024,0.66,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2021,1.01,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2022,1.12,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2023,1.16,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2024,1.19,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2021,0.96,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2022,1.06,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2023,1.1,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2024,1.13,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2021,0.84,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2022,0.93,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2023,0.96,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2024,0.99,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2021,0.88,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2022,0.97,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2023,1,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2024,1.03,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2021,0.68,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2022,0.75,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2023,0.78,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2024,0.8,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2021,0.77,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2022,0.85,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2023,0.89,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2024,0.91,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other, operating costs
+ \ \",\"dollars per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United
+ States\",2021,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"U.S.
+ total\",\"United States\",2022,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Operating
+ costs\",\"Other, operating costs \",\"dollars per hundredweight sold\",\"No
+ specific size\",\"U.S. total\",\"United States\",2023,0.01,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Operating costs\",\"Other, operating costs \",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2024,0.01,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other, operating costs
+ \ \",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2021,0,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2022,0,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2023,0,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2024,0,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Indiana\",\"United
+ States\",2021,0.05,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Indiana\",\"United
+ States\",2022,0.06,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Indiana\",\"United
+ States\",2023,0.06,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Indiana\",\"United
+ States\",2024,0.06,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Iowa\",\"United
+ States\",2021,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Iowa\",\"United
+ States\",2022,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Iowa\",\"United
+ States\",2023,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Iowa\",\"United
+ States\",2024,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United
+ States\",2021,0,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United
+ States\",2022,0,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United
+ States\",2023,0,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United
+ States\",2024,0,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Michigan\",\"United
+ States\",2021,0,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Michigan\",\"United
+ States\",2022,0,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Michigan\",\"United
+ States\",2023,0,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Michigan\",\"United
+ States\",2024,0,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United
+ States\",2021,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United
+ States\",2022,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United
+ States\",2023,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United
+ States\",2024,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"New
+ York\",\"United States\",2021,0.02,\"Base survey of 2021\"\r\n\"Milk\",\"Operating
+ costs\",\"Other, operating costs \",\"dollars per hundredweight sold\",\"No
+ specific size\",\"New York\",\"United States\",2022,0.02,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Operating costs\",\"Other, operating costs \",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2023,0.02,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other, operating costs
+ \ \",\"dollars per hundredweight sold\",\"No specific size\",\"New York\",\"United
+ States\",2024,0.02,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Ohio\",\"United
+ States\",2021,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Ohio\",\"United
+ States\",2022,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Ohio\",\"United
+ States\",2023,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Ohio\",\"United
+ States\",2024,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United
+ States\",2021,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United
+ States\",2022,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United
+ States\",2023,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United
+ States\",2024,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United
+ States\",2021,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United
+ States\",2022,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United
+ States\",2023,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Other,
+ operating costs \",\"dollars per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United
+ States\",2024,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest
+ on operating capital\",\"dollars per hundredweight sold\",\"No specific size\",\"U.S.
+ total\",\"United States\",2021,0,\"Base survey of 2021\"\r\n\"Milk\",\"Operating
+ costs\",\"Interest on operating capital\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"U.S. total\",\"United States\",2022,0.22,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2023,0.43,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2024,0.33,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2021,0,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2022,0.21,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2023,0.41,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2024,0.31,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2021,0.01,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2022,0.26,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2023,0.51,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2024,0.4,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2021,0,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2022,0.19,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2023,0.36,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2024,0.28,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2021,0,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2022,0.2,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2023,0.39,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2024,0.3,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2021,0.01,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2022,0.27,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2023,0.54,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2024,0.42,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2021,0,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2022,0.21,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2023,0.41,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2024,0.32,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2021,0,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2022,0.22,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2023,0.43,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2024,0.34,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2021,0,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2022,0.22,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2023,0.43,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2024,0.34,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2021,0.01,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2022,0.25,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2023,0.5,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2024,0.39,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2021,0,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2022,0.21,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2023,0.42,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2024,0.33,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2021,14.94,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2022,18.56,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2023,17.57,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2024,14.24,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2021,14.26,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2022,17.75,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2023,16.57,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2024,13.16,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2021,17.6,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2022,21.83,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2023,20.98,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2024,17.35,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2021,12.5,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2022,15.54,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2023,14.76,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2024,12.12,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2021,13.28,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2022,16.48,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2023,15.8,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2024,12.92,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2021,18.28,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2022,22.69,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2023,21.94,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2024,18.18,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2021,14.2,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2022,17.61,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2023,16.85,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2024,13.96,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2021,14.9,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2022,18.47,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2023,17.7,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2024,14.69,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2021,14.74,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2022,18.3,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2023,17.51,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2024,14.48,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2021,16.91,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2022,20.94,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2023,20.33,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2024,16.94,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2021,14.49,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2022,17.93,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2023,17.14,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2024,14.14,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2021,1.94,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2022,2.11,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2023,2.23,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2024,2.27,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2021,1.96,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2022,2.09,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2023,2.22,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2024,2.23,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2021,1.42,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2022,1.53,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2023,1.61,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2024,1.75,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2021,1.1,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2022,1.2,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2023,1.21,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2024,1.27,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2021,0.85,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2022,0.88,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2023,0.93,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2024,0.97,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2021,3.14,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2022,3.54,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2023,3.8,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2024,3.73,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2021,2.21,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2022,2.5,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2023,2.68,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2024,2.62,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2021,2.35,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2022,2.53,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2023,2.64,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2024,2.81,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2021,1.51,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2022,1.62,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2023,1.72,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2024,1.86,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2021,1.06,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2022,1.14,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2023,1.18,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2024,1.24,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2021,1.89,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2022,2.14,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2023,2.29,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2024,2.25,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity cost of
+ unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"U.S.
+ total\",\"United States\",2021,1.34,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Opportunity cost of unpaid labor\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"U.S. total\",\"United States\",2022,1.43,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity cost of
+ unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"U.S.
+ total\",\"United States\",2023,1.5,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Opportunity cost of unpaid labor\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"U.S. total\",\"United States\",2024,1.58,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity cost of
+ unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2021,0.26,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2022,0.27,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2023,0.29,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2024,0.31,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Indiana\",\"United
+ States\",2021,6.48,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Indiana\",\"United
+ States\",2022,7.06,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Indiana\",\"United
+ States\",2023,7.44,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Indiana\",\"United
+ States\",2024,7.84,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Iowa\",\"United
+ States\",2021,2.32,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Iowa\",\"United
+ States\",2022,2.49,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Iowa\",\"United
+ States\",2023,2.57,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Iowa\",\"United
+ States\",2024,2.58,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United
+ States\",2021,3.57,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United
+ States\",2022,3.64,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United
+ States\",2023,3.67,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United
+ States\",2024,3.92,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Michigan\",\"United
+ States\",2021,2.87,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Michigan\",\"United
+ States\",2022,3.07,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Michigan\",\"United
+ States\",2023,3.22,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Michigan\",\"United
+ States\",2024,3.42,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United
+ States\",2021,2.49,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United
+ States\",2022,2.62,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United
+ States\",2023,2.76,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United
+ States\",2024,2.86,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"New
+ York\",\"United States\",2021,1.56,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Opportunity cost of unpaid labor\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"New York\",\"United States\",2022,1.61,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity cost of
+ unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"New
+ York\",\"United States\",2023,1.64,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Opportunity cost of unpaid labor\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"New York\",\"United States\",2024,1.78,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity cost of
+ unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Ohio\",\"United
+ States\",2021,2.81,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Ohio\",\"United
+ States\",2022,3.01,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Ohio\",\"United
+ States\",2023,3.17,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Ohio\",\"United
+ States\",2024,3.3,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United
+ States\",2021,3.02,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United
+ States\",2022,3.19,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United
+ States\",2023,3.33,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United
+ States\",2024,3.42,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United
+ States\",2021,1.92,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United
+ States\",2022,2.05,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United
+ States\",2023,2.16,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of unpaid labor\",\"dollars per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United
+ States\",2024,2.28,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Capital
+ recovery of machinery and equipment \",\"dollars per hundredweight sold\",\"No
+ specific size\",\"U.S. total\",\"United States\",2021,4,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Capital recovery of machinery and equipment \",\"dollars per
+ hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2022,4.43,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Capital recovery of
+ machinery and equipment \",\"dollars per hundredweight sold\",\"No specific
+ size\",\"U.S. total\",\"United States\",2023,4.59,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Capital recovery of machinery and equipment \",\"dollars per
+ hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2024,4.71,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Capital recovery of
+ machinery and equipment \",\"dollars per hundredweight sold\",\"No specific
+ size\",\"California\",\"United States\",2021,2.31,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Capital recovery of machinery and equipment \",\"dollars per
+ hundredweight sold\",\"No specific size\",\"California\",\"United States\",2022,2.55,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Capital recovery of
+ machinery and equipment \",\"dollars per hundredweight sold\",\"No specific
+ size\",\"California\",\"United States\",2023,2.65,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Capital recovery of machinery and equipment \",\"dollars per
+ hundredweight sold\",\"No specific size\",\"California\",\"United States\",2024,2.72,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Capital recovery of
+ machinery and equipment \",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Indiana\",\"United States\",2021,4.87,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Capital recovery of machinery and equipment \",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2022,5.39,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Capital recovery of
+ machinery and equipment \",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Indiana\",\"United States\",2023,5.58,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Capital recovery of machinery and equipment \",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2024,5.73,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Capital recovery of
+ machinery and equipment \",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Iowa\",\"United States\",2021,4.14,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Capital recovery of machinery and equipment \",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2022,4.58,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Capital recovery of
+ machinery and equipment \",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Iowa\",\"United States\",2023,4.75,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Capital recovery of machinery and equipment \",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2024,4.88,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Capital recovery of
+ machinery and equipment \",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Kentucky\",\"United States\",2021,7.44,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Capital recovery of machinery and equipment \",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2022,8.24,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Capital recovery of
+ machinery and equipment \",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Kentucky\",\"United States\",2023,8.54,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Capital recovery of machinery and equipment \",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2024,8.76,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Capital recovery of
+ machinery and equipment \",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Michigan\",\"United States\",2021,5.65,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Capital recovery of machinery and equipment \",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2022,6.25,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Capital recovery of
+ machinery and equipment \",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Michigan\",\"United States\",2023,6.48,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Capital recovery of machinery and equipment \",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2024,6.65,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Capital recovery of
+ machinery and equipment \",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Minnesota\",\"United States\",2021,4.42,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Capital recovery of machinery and equipment \",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2022,4.89,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Capital recovery of
+ machinery and equipment \",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Minnesota\",\"United States\",2023,5.07,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Capital recovery of machinery and equipment \",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2024,5.21,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Capital recovery of
+ machinery and equipment \",\"dollars per hundredweight sold\",\"No specific
+ size\",\"New York\",\"United States\",2021,7.15,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Capital recovery of machinery and equipment \",\"dollars per
+ hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2022,7.9,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Capital recovery of
+ machinery and equipment \",\"dollars per hundredweight sold\",\"No specific
+ size\",\"New York\",\"United States\",2023,8.19,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Capital recovery of machinery and equipment \",\"dollars per
+ hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2024,8.41,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Capital recovery of
+ machinery and equipment \",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Ohio\",\"United States\",2021,4.53,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Capital recovery of machinery and equipment \",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2022,5.01,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Capital recovery of
+ machinery and equipment \",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Ohio\",\"United States\",2023,5.19,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Capital recovery of machinery and equipment \",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2024,5.33,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Capital recovery of
+ machinery and equipment \",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Pennsylvania\",\"United States\",2021,9.15,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Capital recovery of machinery and equipment \",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2022,10.12,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Capital recovery of
+ machinery and equipment \",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Pennsylvania\",\"United States\",2023,10.49,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Capital recovery of machinery and equipment \",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2024,10.77,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Capital recovery of
+ machinery and equipment \",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Wisconsin\",\"United States\",2021,3.9,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Capital recovery of machinery and equipment \",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2022,4.32,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Capital recovery of
+ machinery and equipment \",\"dollars per hundredweight sold\",\"No specific
+ size\",\"Wisconsin\",\"United States\",2023,4.47,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Capital recovery of machinery and equipment \",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2024,4.59,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity cost of
+ land\",\"dollars per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United
+ States\",2021,0.02,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"U.S.
+ total\",\"United States\",2022,0.02,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Opportunity cost of land\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"U.S. total\",\"United States\",2023,0.03,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity cost of land\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2024,0.03,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity cost of
+ land\",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2021,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2022,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2023,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2024,0.01,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Indiana\",\"United
+ States\",2021,0.09,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Indiana\",\"United
+ States\",2022,0.09,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Indiana\",\"United
+ States\",2023,0.1,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Indiana\",\"United
+ States\",2024,0.1,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Iowa\",\"United
+ States\",2021,0.08,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Iowa\",\"United
+ States\",2022,0.08,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Iowa\",\"United
+ States\",2023,0.08,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Iowa\",\"United
+ States\",2024,0.09,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United
+ States\",2021,0.06,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United
+ States\",2022,0.07,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United
+ States\",2023,0.07,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United
+ States\",2024,0.08,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Michigan\",\"United
+ States\",2021,0.02,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Michigan\",\"United
+ States\",2022,0.02,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Michigan\",\"United
+ States\",2023,0.02,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Michigan\",\"United
+ States\",2024,0.02,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United
+ States\",2021,0.04,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United
+ States\",2022,0.05,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United
+ States\",2023,0.05,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United
+ States\",2024,0.05,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"New
+ York\",\"United States\",2021,0.03,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Opportunity cost of land\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"New York\",\"United States\",2022,0.03,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity cost of land\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2023,0.03,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity cost of
+ land\",\"dollars per hundredweight sold\",\"No specific size\",\"New York\",\"United
+ States\",2024,0.04,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Ohio\",\"United
+ States\",2021,0.03,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Ohio\",\"United
+ States\",2022,0.03,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Ohio\",\"United
+ States\",2023,0.03,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Ohio\",\"United
+ States\",2024,0.04,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United
+ States\",2021,0.05,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United
+ States\",2022,0.05,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United
+ States\",2023,0.06,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United
+ States\",2024,0.06,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United
+ States\",2021,0.03,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United
+ States\",2022,0.03,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United
+ States\",2023,0.03,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity
+ cost of land\",\"dollars per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United
+ States\",2024,0.03,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes
+ and insurance\",\"dollars per hundredweight sold\",\"No specific size\",\"U.S.
+ total\",\"United States\",2021,0.2,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Taxes and insurance\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"U.S. total\",\"United States\",2022,0.22,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2023,0.24,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2024,0.24,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2021,0.15,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2022,0.17,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2023,0.18,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2024,0.19,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2021,0.23,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2022,0.26,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2023,0.27,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2024,0.28,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2021,0.23,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2022,0.26,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2023,0.27,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2024,0.28,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2021,0.11,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2022,0.12,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2023,0.13,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2024,0.13,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2021,0.19,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2022,0.22,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2023,0.23,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2024,0.24,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2021,0.26,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2022,0.29,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2023,0.3,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2024,0.31,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2021,0.19,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2022,0.21,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2023,0.23,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2024,0.23,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2021,0.14,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2022,0.16,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2023,0.17,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2024,0.17,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2021,0.27,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2022,0.3,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2023,0.32,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2024,0.33,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2021,0.29,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2022,0.32,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2023,0.34,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2024,0.35,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2021,0.5,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2022,0.56,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2023,0.58,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2024,0.58,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2021,0.49,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2022,0.54,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2023,0.56,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2024,0.57,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2021,0.68,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2022,0.76,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2023,0.78,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2024,0.79,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2021,0.44,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2022,0.5,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2023,0.51,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2024,0.52,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2021,0.45,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2022,0.5,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2023,0.52,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2024,0.52,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2021,0.39,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2022,0.43,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2023,0.45,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2024,0.45,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2021,0.49,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2022,0.54,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2023,0.56,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2024,0.57,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2021,0.56,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2022,0.63,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2023,0.65,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2024,0.66,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2021,0.59,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2022,0.66,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2023,0.68,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2024,0.69,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2021,0.72,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2022,0.81,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2023,0.84,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2024,0.84,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2021,0.56,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2022,0.62,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2023,0.64,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2024,0.65,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2021,8,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2022,8.77,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2023,9.17,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2024,9.41,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2021,5.18,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2022,5.63,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2023,5.91,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2024,6.03,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2021,13.77,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2022,15.09,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2023,15.78,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2024,16.49,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2021,8.31,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2022,9.11,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2023,9.39,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2024,9.62,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2021,12.48,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2022,13.45,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2023,13.86,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2024,14.38,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2021,12.26,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2022,13.53,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2023,14.2,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2024,14.51,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2021,9.91,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2022,10.89,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2023,11.42,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2024,11.62,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2021,11.84,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2022,12.91,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2023,13.38,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2024,13.93,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2021,9.61,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2022,10.49,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2023,10.96,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2024,11.39,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2021,14.27,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2022,15.61,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2023,16.22,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2024,16.66,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2021,8.59,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2022,9.48,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2023,9.93,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Total, allocated overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2024,10.15,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2021,22.94,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2022,27.33,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2023,26.74,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2024,23.65,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2021,19.44,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2022,23.38,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2023,22.48,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2024,19.19,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2021,31.37,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2022,36.92,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2023,36.76,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2024,33.84,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2021,20.81,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2022,24.65,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2023,24.15,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2024,21.74,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2021,25.76,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2022,29.93,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2023,29.66,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2024,27.3,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"U.S. total\",\"United States\",2021,22653,\"Base survey
+ of 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"U.S. total\",\"United States\",2022,22653,\"Base survey
+ of 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"U.S. total\",\"United States\",2023,22653,\"Base survey
+ of 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"U.S. total\",\"United States\",2024,22653,\"Base survey
+ of 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"California\",\"United States\",2021,24768,\"Base survey
+ of 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"California\",\"United States\",2022,24768,\"Base survey
+ of 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"California\",\"United States\",2023,24768,\"Base survey
+ of 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"California\",\"United States\",2024,24768,\"Base survey
+ of 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"Indiana\",\"United States\",2021,19121,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"Indiana\",\"United States\",2022,19121,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"Indiana\",\"United States\",2023,19121,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"Indiana\",\"United States\",2024,19121,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"Iowa\",\"United States\",2021,23325,\"Base survey of 2021\"\r\n\"Milk\",\"Supporting
+ information\",\"Output per cow\",\"pounds\",\"No specific size\",\"Iowa\",\"United
+ States\",2022,23325,\"Base survey of 2021\"\r\n\"Milk\",\"Supporting information\",\"Output
+ per cow\",\"pounds\",\"No specific size\",\"Iowa\",\"United States\",2023,23325,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"Iowa\",\"United States\",2024,23325,\"Base survey of 2021\"\r\n\"Milk\",\"Supporting
+ information\",\"Output per cow\",\"pounds\",\"No specific size\",\"Kentucky\",\"United
+ States\",2021,17662,\"Base survey of 2021\"\r\n\"Milk\",\"Supporting information\",\"Output
+ per cow\",\"pounds\",\"No specific size\",\"Kentucky\",\"United States\",2022,17662,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"Kentucky\",\"United States\",2023,17662,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"Kentucky\",\"United States\",2024,17662,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"Michigan\",\"United States\",2021,21995,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"Michigan\",\"United States\",2022,21995,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"Michigan\",\"United States\",2023,21995,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"Michigan\",\"United States\",2024,21995,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"Minnesota\",\"United States\",2021,21559,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"Minnesota\",\"United States\",2022,21559,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"Minnesota\",\"United States\",2023,21559,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"Minnesota\",\"United States\",2024,21559,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"New York\",\"United States\",2021,22218,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"New York\",\"United States\",2022,22218,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"New York\",\"United States\",2023,22218,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"New York\",\"United States\",2024,22218,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"Ohio\",\"United States\",2021,21773,\"Base survey of 2021\"\r\n\"Milk\",\"Supporting
+ information\",\"Output per cow\",\"pounds\",\"No specific size\",\"Ohio\",\"United
+ States\",2022,21773,\"Base survey of 2021\"\r\n\"Milk\",\"Supporting information\",\"Output
+ per cow\",\"pounds\",\"No specific size\",\"Ohio\",\"United States\",2023,21773,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"Ohio\",\"United States\",2024,21773,\"Base survey of 2021\"\r\n\"Milk\",\"Supporting
+ information\",\"Output per cow\",\"pounds\",\"No specific size\",\"Pennsylvania\",\"United
+ States\",2021,20445,\"Base survey of 2021\"\r\n\"Milk\",\"Supporting information\",\"Output
+ per cow\",\"pounds\",\"No specific size\",\"Pennsylvania\",\"United States\",2022,20445,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"Pennsylvania\",\"United States\",2023,20445,\"Base survey
+ of 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"Pennsylvania\",\"United States\",2024,20445,\"Base survey
+ of 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"Wisconsin\",\"United States\",2021,24902,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"Wisconsin\",\"United States\",2022,24902,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"Wisconsin\",\"United States\",2023,24902,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Supporting information\",\"Output per cow\",\"pounds\",\"No
+ specific size\",\"Wisconsin\",\"United States\",2024,24902,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Gross value of production\",\"Cattle\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2025,2.25,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Purchased feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2025,6.3,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Fuel, lube, and electricity\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2025,0.71,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Hired labor\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2025,2.32,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, feed costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2025,9.82,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Taxes and insurance\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2025,0.25,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total
+ costs listed\",\"dollars per hundredweight sold\",\"No specific size\",\"U.S.
+ total\",\"United States\",2025,0.55,\"Base survey of 2021\"\r\n\"Milk\",\"Net
+ value\",\"Value of production less operating costs\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"U.S. total\",\"United States\",2025,10.17,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Total, operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2025,13.75,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Opportunity cost of
+ land\",\"dollars per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United
+ States\",2025,0.03,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"Capital
+ recovery of machinery and equipment \",\"dollars per hundredweight sold\",\"No
+ specific size\",\"U.S. total\",\"United States\",2025,4.78,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"U.S. total\",\"United States\",2025,0.9,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Allocated overhead\",\"General farm overhead\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2025,0.6,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Homegrown harvested feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2025,3.48,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2025,0.22,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2025,0.21,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Grazed feed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2025,0.04,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Other income
+ \ \",\"dollars per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United
+ States\",2025,0.67,\"Base survey of 2021\"\r\n\"Milk\",\"Gross value of production\",\"Total,
+ gross value of production\",\"dollars per hundredweight sold\",\"No specific
+ size\",\"U.S. total\",\"United States\",2025,23.92,\"Base survey of 2021\"\r\n\"Milk\",\"Costs
+ listed\",\"Total, costs listed\",\"dollars per hundredweight sold\",\"No specific
+ size\",\"U.S. total\",\"United States\",2025,23.37,\"Base survey of 2021\"\r\n\"Milk\",\"Supporting
+ information\",\"Output per cow\",\"pounds\",\"No specific size\",\"U.S. total\",\"United
+ States\",2025,22653,\"Base survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary
+ and medicine\",\"dollars per hundredweight sold\",\"No specific size\",\"U.S.
+ total\",\"United States\",2025,0.79,\"Base survey of 2021\"\r\n\"Milk\",\"Allocated
+ overhead\",\"Total, allocated overhead\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"U.S. total\",\"United States\",2025,9.62,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Operating costs\",\"Interest on operating capital\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2025,0.26,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Repairs\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2025,0.83,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2021,0.53,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2022,0.6,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2023,0.63,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2024,0.65,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2021,0.67,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2022,0.75,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2023,0.79,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2024,0.82,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2021,0.63,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2022,0.71,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2023,0.74,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2024,0.77,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2021,0.64,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2022,0.72,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2023,0.76,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2024,0.79,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2021,0.92,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2022,1.03,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2023,1.09,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2024,1.12,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2021,0.77,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2022,0.86,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2023,0.91,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2024,0.94,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2021,0.79,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2022,0.89,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2023,0.94,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2024,0.97,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2021,0.73,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2022,0.82,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2023,0.86,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2024,0.89,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2021,0.78,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2022,0.88,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2023,0.93,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2024,0.96,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2021,0.71,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2022,0.79,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2023,0.84,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Veterinary and medicine\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2024,0.87,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2021,0.18,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2022,0.2,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2023,0.21,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2024,0.21,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2021,0.03,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2022,0.03,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2023,0.03,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2024,0.04,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2021,0.36,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2022,0.4,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2023,0.42,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2024,0.44,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2021,0.17,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2022,0.19,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2023,0.2,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2024,0.21,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2021,0.18,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2022,0.21,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2023,0.22,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2024,0.22,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2021,0.28,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2022,0.31,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2023,0.33,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2024,0.34,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2021,0.28,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2022,0.31,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2023,0.33,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2024,0.34,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2021,0.34,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2022,0.38,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2023,0.4,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2024,0.42,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2021,0.28,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2022,0.31,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2023,0.33,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2024,0.34,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2021,0.32,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2022,0.35,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2023,0.37,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2024,0.39,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2021,0.28,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2022,0.32,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2023,0.33,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Bedding and litter\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2024,0.34,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2021,0.16,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2022,0.18,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2023,0.19,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2024,0.2,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"California\",\"United States\",2021,0.16,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"California\",\"United States\",2022,0.18,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"California\",\"United States\",2023,0.19,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"California\",\"United States\",2024,0.2,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2021,0.06,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2022,0.07,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2023,0.07,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2024,0.07,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2021,0.04,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2022,0.05,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2023,0.05,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2024,0.05,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2021,0.06,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2022,0.06,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2023,0.07,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2024,0.07,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2021,0.22,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2022,0.25,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2023,0.26,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2024,0.27,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2021,0.09,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2022,0.1,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2023,0.11,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2024,0.11,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2021,0.16,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2022,0.17,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2023,0.18,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2024,0.19,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2021,0.06,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2022,0.07,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2023,0.07,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2024,0.08,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2021,0.16,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2022,0.18,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2023,0.19,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2024,0.2,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2021,0.17,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2022,0.2,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2023,0.21,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Marketing\",\"dollars per
+ hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2024,0.21,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2021,0.71,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2022,0.8,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2023,0.84,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Operating costs\",\"Custom services\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2024,0.87,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2021,30.54,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2022,36.22,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2023,36.14,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2024,32.69,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2021,24.11,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2022,28.5,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2023,28.27,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2024,25.58,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2021,26.74,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2022,31.38,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2023,31.08,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"New York\",\"United States\",2024,28.62,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2021,24.35,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2022,28.79,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2023,28.47,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Ohio\",\"United States\",2024,25.87,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2021,31.18,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2022,36.55,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2023,36.55,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United States\",2024,33.6,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2021,23.08,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2022,27.41,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2023,27.07,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Costs listed\",\"Total, costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2024,24.29,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total
+ costs listed\",\"dollars per hundredweight sold\",\"No specific size\",\"U.S.
+ total\",\"United States\",2021,-2.89,\"Base survey of 2021\"\r\n\"Milk\",\"Net
+ value\",\"Value of production less total costs listed\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"U.S. total\",\"United States\",2022,0.01,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total
+ costs listed\",\"dollars per hundredweight sold\",\"No specific size\",\"U.S.
+ total\",\"United States\",2023,-4.23,\"Base survey of 2021\"\r\n\"Milk\",\"Net
+ value\",\"Value of production less total costs listed\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"U.S. total\",\"United States\",2024,1.29,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total
+ costs listed\",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2021,0.55,\"Base survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value
+ of production less total costs listed\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"California\",\"United States\",2022,4.02,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2023,-0.11,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total
+ costs listed\",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2024,5.09,\"Base survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value
+ of production less total costs listed\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"Indiana\",\"United States\",2021,-9.21,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Indiana\",\"United States\",2022,-6.44,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total
+ costs listed\",\"dollars per hundredweight sold\",\"No specific size\",\"Indiana\",\"United
+ States\",2023,-10.62,\"Base survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value
+ of production less total costs listed\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"Indiana\",\"United States\",2024,-4.72,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2021,-1.59,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total
+ costs listed\",\"dollars per hundredweight sold\",\"No specific size\",\"Iowa\",\"United
+ States\",2022,0.47,\"Base survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value
+ of production less total costs listed\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"Iowa\",\"United States\",2023,-4.25,\"Base survey of 2021\"\r\n\"Milk\",\"Net
+ value\",\"Value of production less total costs listed\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"Iowa\",\"United States\",2024,1.31,\"Base survey
+ of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total costs
+ listed\",\"dollars per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United
+ States\",2021,-5.28,\"Base survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value
+ of production less total costs listed\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"Kentucky\",\"United States\",2022,-1.97,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2023,-6.25,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total
+ costs listed\",\"dollars per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United
+ States\",2024,-2.32,\"Base survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value
+ of production less total costs listed\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"Michigan\",\"United States\",2021,-11.41,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Michigan\",\"United States\",2022,-9.58,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total
+ costs listed\",\"dollars per hundredweight sold\",\"No specific size\",\"Michigan\",\"United
+ States\",2023,-13.82,\"Base survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value
+ of production less total costs listed\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"Michigan\",\"United States\",2024,-7.4,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2021,-3.32,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total
+ costs listed\",\"dollars per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United
+ States\",2022,-1.02,\"Base survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value
+ of production less total costs listed\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"Minnesota\",\"United States\",2023,-6.45,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Minnesota\",\"United States\",2024,-0.36,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total
+ costs listed\",\"dollars per hundredweight sold\",\"No specific size\",\"New
+ York\",\"United States\",2021,-5.66,\"Base survey of 2021\"\r\n\"Milk\",\"Net
+ value\",\"Value of production less total costs listed\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"New York\",\"United States\",2022,-2.29,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total
+ costs listed\",\"dollars per hundredweight sold\",\"No specific size\",\"New
+ York\",\"United States\",2023,-6.21,\"Base survey of 2021\"\r\n\"Milk\",\"Net
+ value\",\"Value of production less total costs listed\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"New York\",\"United States\",2024,-1.62,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total
+ costs listed\",\"dollars per hundredweight sold\",\"No specific size\",\"Ohio\",\"United
+ States\",2021,-4.1,\"Base survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value
+ of production less total costs listed\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"Ohio\",\"United States\",2022,-1.75,\"Base survey of 2021\"\r\n\"Milk\",\"Net
+ value\",\"Value of production less total costs listed\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"Ohio\",\"United States\",2023,-5.28,\"Base survey
+ of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total costs
+ listed\",\"dollars per hundredweight sold\",\"No specific size\",\"Ohio\",\"United
+ States\",2024,-0.48,\"Base survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value
+ of production less total costs listed\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"Pennsylvania\",\"United States\",2021,-10.22,\"Base survey
+ of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total costs
+ listed\",\"dollars per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United
+ States\",2022,-7.59,\"Base survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value
+ of production less total costs listed\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"Pennsylvania\",\"United States\",2023,-12.48,\"Base survey
+ of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total costs
+ listed\",\"dollars per hundredweight sold\",\"No specific size\",\"Pennsylvania\",\"United
+ States\",2024,-7.21,\"Base survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value
+ of production less total costs listed\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"Wisconsin\",\"United States\",2021,-2.6,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total costs listed\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United States\",2022,-0.6,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less total
+ costs listed\",\"dollars per hundredweight sold\",\"No specific size\",\"Wisconsin\",\"United
+ States\",2023,-5.43,\"Base survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value
+ of production less total costs listed\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"Wisconsin\",\"United States\",2024,-0.04,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2021,5.11,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less operating
+ costs\",\"dollars per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United
+ States\",2022,8.78,\"Base survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value
+ of production less operating costs\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"U.S. total\",\"United States\",2023,4.94,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"U.S. total\",\"United States\",2024,10.7,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less operating
+ costs\",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2021,5.73,\"Base survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value
+ of production less operating costs\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"California\",\"United States\",2022,9.65,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"California\",\"United States\",2023,5.8,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less operating
+ costs\",\"dollars per hundredweight sold\",\"No specific size\",\"California\",\"United
+ States\",2024,11.12,\"Base survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value
+ of production less operating costs\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"Indiana\",\"United States\",2021,4.56,\"Base survey of 2021\"\r\n\"Milk\",\"Net
+ value\",\"Value of production less operating costs\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"Indiana\",\"United States\",2022,8.65,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less operating
+ costs\",\"dollars per hundredweight sold\",\"No specific size\",\"Indiana\",\"United
+ States\",2023,5.16,\"Base survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value
+ of production less operating costs\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"Indiana\",\"United States\",2024,11.77,\"Base survey of
+ 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Iowa\",\"United States\",2021,6.72,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less operating
+ costs\",\"dollars per hundredweight sold\",\"No specific size\",\"Iowa\",\"United
+ States\",2022,9.58,\"Base survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value
+ of production less operating costs\",\"dollars per hundredweight sold\",\"No
+ specific size\",\"Iowa\",\"United States\",2023,5.14,\"Base survey of 2021\"\r\n\"Milk\",\"Net
+ value\",\"Value of production less operating costs\",\"dollars per hundredweight
+ sold\",\"No specific size\",\"Iowa\",\"United States\",2024,10.93,\"Base survey
+ of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less operating costs\",\"dollars
+ per hundredweight sold\",\"No specific size\",\"Kentucky\",\"United States\",2021,7.2,\"Base
+ survey of 2021\"\r\n\"Milk\",\"Net value\",\"Value of production less operating
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diff --git a/openbb_platform/providers/government_us/tests/record/http/test_usda_fetchers/test_price_spreads_fetcher_urllib3_v2.yaml b/openbb_platform/providers/government_us/tests/record/http/test_usda_fetchers/test_price_spreads_fetcher_urllib3_v2.yaml
new file mode 100644
index 00000000000..86b6fccbfe4
--- /dev/null
+++ b/openbb_platform/providers/government_us/tests/record/http/test_usda_fetchers/test_price_spreads_fetcher_urllib3_v2.yaml
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diff --git a/openbb_platform/providers/government_us/tests/record/http/test_usda_fetchers/test_us_agricultural_trade_fetcher_urllib3_v2.yaml b/openbb_platform/providers/government_us/tests/record/http/test_usda_fetchers/test_us_agricultural_trade_fetcher_urllib3_v2.yaml
new file mode 100644
index 00000000000..728a5eb997e
--- /dev/null
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+ Access-Control-Allow-Methods:
+ - GET, POST, OPTIONS
+ Cache-Control:
+ - must-revalidate, no-cache, private
+ Connection:
+ - keep-alive
+ Content-Encoding:
+ - gzip
+ Content-Language:
+ - en
+ Content-Security-Policy:
+ - 'connect-src ''self'' https://*.google-analytics.com https://api.data.gov
+ https://dap.digitalgov.gov; frame-src ''self'' https://*.ers.usda.gov https://*.tableau.com
+ https://www.google.com; img-src ''self'' https://ers.usda.gov https://ewapsdev.ers.usda.gov
+ https://ewapsbeta.ers.usda.gov http://usda.sc.omtrdc.net https://www.googletagmanager.com
+ data: w3.org/svg/2000 https://latex.codecogs.com https://www-tx.ers.usda.gov;
+ script-src ''self'' ''unsafe-inline'' https://www.googletagmanager.com https://*.google-analytics.com
+ https://*.tableau.com https://api.data.gov https://www.gstatic.com/ https://cdnjs.cloudflare.com
+ https://dap.digitalgov.gov https://www.google.com; script-src-attr ''self''
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+ https://*.google-analytics.com https://*.tableau.com https://api.data.gov
+ https://www.gstatic.com https://cdnjs.cloudflare.com https://dap.digitalgov.gov
+ https://www.google.com; style-src ''self'' ''unsafe-inline'' https://*.google-analytics.com
+ https://googletagmanager.com https://cdnjs.cloudflare.com www.ers.usda.gov;
+ style-src-attr ''self'' ''unsafe-inline''; style-src-elem ''self'' ''unsafe-inline''
+ https://*.google-analytics.com https://googletagmanager.com https://api.data.gov
+ https://cdnjs.cloudflare.com www.ers.usda.gov; frame-ancestors ''self''; upgrade-insecure-requests'
+ Content-Type:
+ - text/html; charset=UTF-8
+ Date:
+ - Wed, 22 Jul 2026 19:34:10 GMT
+ Expires:
+ - Sun, 19 Nov 1978 05:00:00 GMT
+ Permissions-Policy:
+ - accelerometer=(), camera=(), geolocation=(), gyroscope=(), magnetometer=(),
+ microphone=(), payment=(), usb=()
+ Referrer-Policy:
+ - strict-origin-when-cross-origin
+ Server:
+ - Apache
+ Set-Cookie:
+ - ARRAffinity=a10f44edd3f055fdca0a01e05751d7a39f07008667893c1271b222fec234413d;Path=/;HttpOnly;Secure;Domain=www.ers.usda.gov
+ - ARRAffinitySameSite=a10f44edd3f055fdca0a01e05751d7a39f07008667893c1271b222fec234413d;Path=/;HttpOnly;SameSite=None;Secure;Domain=www.ers.usda.gov
+ Strict-Transport-Security:
+ - max-age=31536000;
+ Transfer-Encoding:
+ - chunked
+ Vary:
+ - Accept-Encoding,Origin
+ X-Content-Type-Options:
+ - nosniff
+ X-Drupal-Cache:
+ - HIT
+ X-Drupal-Cache-Contexts:
+ - cookies:big_pipe_nojs languages layout_builder_is_active:node route session.exists
+ theme timezone url user.permissions user.roles
+ X-Drupal-Cache-Max-Age:
+ - -1 (Permanent)
+ X-Drupal-Cache-Tags:
+ - block_content:42 block_content:43 block_content:44 block_content:45 block_content:46
+ block_content:47 block_content:48 block_content:49 block_content:50 block_content:51
+ block_content:52 block_content:53 block_content:54 block_content:55 block_view
+ config:block.block.erslinks config:block.block.footersignup config:block.block.footersociallinks
+ config:block.block.navanalysiscolumn1 config:block.block.navanalysiscolumn2
+ config:block.block.navanalysiscolumn3 config:block.block.navdatacolumn1 config:block.block.navdatacolumn2
+ config:block.block.navdatacolumn3 config:block.block.navnewsroomcolumn1 config:block.block.navnewsroomcolumn2
+ config:block.block.navnewsroomcolumn3 config:block.block.navsecondarylinks
+ config:block.block.usdalinks config:block.block.uswds_ers_about config:block.block.uswds_ers_account_menu
+ config:block.block.uswds_ers_adobeanalytics config:block.block.uswds_ers_branding
+ config:block.block.uswds_ers_breadcrumbs config:block.block.uswds_ers_contactus
+ config:block.block.uswds_ers_content config:block.block.uswds_ers_covid19
+ config:block.block.uswds_ers_footer config:block.block.uswds_ers_googleanalyticsdap
+ config:block.block.uswds_ers_googleanalyticsers config:block.block.uswds_ers_googlesiteverification
+ config:block.block.uswds_ers_help config:block.block.uswds_ers_help_2 config:block.block.uswds_ers_local_actions
+ config:block.block.uswds_ers_local_tasks config:block.block.uswds_ers_loginloginlogoutmyaccount
+ config:block.block.uswds_ers_main_menu config:block.block.uswds_ers_messages
+ config:block.block.uswds_ers_newsroom config:block.block.uswds_ers_page_title
+ config:block.block.uswds_ers_pagestatusarchived config:block.block.uswds_ers_pagestatusdraft
+ config:block.block.uswds_ers_pagestatusunpublished config:block.block.uswds_ers_search
+ config:block.block.uswds_ers_sidenav_data_products config:block.block.uswds_ers_sidenav_developer
+ config:block.block.uswds_ers_sidenav_privacy config:block.block.uswds_ers_sidenav_topics
+ config:block.block.uswds_ers_sitewidealert config:block_list config:context.context.data_products_data_files
+ config:context.context.data_products_sidenav config:context.context.ers_csat_survey_invite
+ config:context.context.hero_default config:core.entity_view_display.node.data_products.default
+ config:easy_breadcrumb.settings config:filter.format.full_html config:system.site
+ config:user.role.anonymous config:workflows.workflow.cms_posting context_block_page
+ http_response local_task node:3710 node:3823 node:7112 node_view rendered
+ scheduled_transitions_for:node:3823 user:0
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+ - gzip, deflate
+ Connection:
+ - keep-alive
+ method: GET
+ uri: https://www.ers.usda.gov/media/5029/csv-comma-separated-values-format-of-all-data.csv
+ response:
+ body:
+ string: 'Commodity,Country,Time_period,Year,US_Trade,Value_type,Units,Value,Update_version,Source_table,Data_source,Notes
+
+ Corn,World total,January-October,2025,Exports,Volume,Metric tons,67520159,January
+ update of October data,"Top 10 U.S. export markets for soybeans, corn, wheat,
+ and cotton, by volume","USDA, Economic Research Service based on data from
+ U.S. Department of Commerce, Bureau of the Census.",NA
+
+ Corn,World total,October,2025,Exports,Volume,Metric tons,6564115,January update
+ of October data,"Top 10 U.S. export markets for soybeans, corn, wheat, and
+ cotton, by volume","USDA, Economic Research Service based on data from U.S.
+ Department of Commerce, Bureau of the Census.",NA
+
+ Corn,World total,January-November,2025,Exports,Volume,Metric tons,74825576,January
+ update of November data,"Top 10 U.S. export markets for soybeans, corn, wheat,
+ and cotton, by volume","USDA, Economic Research Service based on data from
+ U.S. Department of Commerce, Bureau of the Census.",NA
+
+ Corn,World total,November,2025,Exports,Volume,Metric tons,7305417,January
+ update of November data,"Top 10 U.S. export markets for soybeans, corn, wheat,
+ and cotton, by volume","USDA, Economic Research Service based on data from
+ U.S. Department of Commerce, Bureau of the Census.",NA
+
+ Corn,World total,January-December,2025,Exports,Volume,Metric tons,81924188,February
+ update of December data,"Top 10 U.S. export markets for soybeans, corn, wheat,
+ and cotton, by volume","USDA, Economic Research Service based on data from
+ U.S. Department of Commerce, Bureau of the Census.",NA
+
+ Corn,World total,December,2025,Exports,Volume,Metric tons,7098612,February
+ update of December data,"Top 10 U.S. export markets for soybeans, corn, wheat,
+ and cotton, by volume","USDA, Economic Research Service based on data from
+ U.S. Department of Commerce, Bureau of the Census.",NA
+
+ Corn,World total,October-January,2025,Exports,Volume,Metric tons,20377393,March
+ update of January data,"Top 10 U.S. export markets for soybeans, corn, wheat,
+ and cotton, by volume","USDA, Economic Research Service based on data from
+ U.S. Department of Commerce, Bureau of the Census.",NA
+
+ Corn,World total,October-January,2026,Exports,Volume,Metric tons,27575870,March
+ update of January data,"Top 10 U.S. export markets for soybeans, corn, wheat,
+ and cotton, by volume","USDA, Economic Research Service based on data from
+ U.S. Department of Commerce, Bureau of the Census.",NA
+
+ Corn,World total,January,2025,Exports,Volume,Metric tons,6162009,March update
+ of January data,"Top 10 U.S. export markets for soybeans, corn, wheat, and
+ cotton, by volume","USDA, Economic Research Service based on data from U.S.
+ Department of Commerce, Bureau of the Census.",NA
+
+ Corn,World total,January,2026,Exports,Volume,Metric tons,6607726,March update
+ of January data,"Top 10 U.S. export markets for soybeans, corn, wheat, and
+ cotton, by volume","USDA, Economic Research Service based on data from U.S.
+ Department of Commerce, Bureau of the Census.",NA
+
+ Corn,World total,October-February,2025,Exports,Volume,Metric tons,26409412,April
+ update of February data,"Top 10 U.S. export markets for soybeans, corn, wheat,
+ and cotton, by volume","USDA, Economic Research Service based on data from
+ U.S. Department of Commerce, Bureau of the Census.",NA
+
+ Corn,World total,October-February,2026,Exports,Volume,Metric tons,34344015,April
+ update of February data,"Top 10 U.S. export markets for soybeans, corn, wheat,
+ and cotton, by volume","USDA, Economic Research Service based on data from
+ U.S. Department of Commerce, Bureau of the Census.",NA
+
+ Corn,World total,February,2025,Exports,Volume,Metric tons,6032019,April update
+ of February data,"Top 10 U.S. export markets for soybeans, corn, wheat, and
+ cotton, by volume","USDA, Economic Research Service based on data from U.S.
+ Department of Commerce, Bureau of the Census.",NA
+
+ Corn,World total,February,2026,Exports,Volume,Metric tons,6768145,April update
+ of February data,"Top 10 U.S. export markets for soybeans, corn, wheat, and
+ cotton, by volume","USDA, Economic Research Service based on data from U.S.
+ Department of Commerce, Bureau of the Census.",NA
+
+ Corn,World total,October-March,2025,Exports,Volume,Metric tons,33750264,May
+ update of March data,"Top 10 U.S. export markets for soybeans, corn, wheat,
+ and cotton, by volume","USDA, Economic Research Service based on data from
+ U.S. Department of Commerce, Bureau of the Census.",NA
+
+ Corn,World total,October-March,2026,Exports,Volume,Metric tons,42375075,May
+ update of March data,"Top 10 U.S. export markets for soybeans, corn, wheat,
+ and cotton, by volume","USDA, Economic Research Service based on data from
+ U.S. Department of Commerce, Bureau of the Census.",NA
+
+ Corn,World total,March,2025,Exports,Volume,Metric tons,7340852,May update
+ of March data,"Top 10 U.S. export markets for soybeans, corn, wheat, and cotton,
+ by volume","USDA, Economic Research Service based on data from U.S. Department
+ of Commerce, Bureau of the Census.",NA
+
+ Corn,World total,March,2026,Exports,Volume,Metric tons,8031060,May update
+ of March data,"Top 10 U.S. export markets for soybeans, corn, wheat, and cotton,
+ by volume","USDA, Economic Research Service based on data from U.S. Department
+ of Commerce, Bureau of the Census.",NA
+
+ Corn,World total,October-April,2025,Exports,Volume,Metric tons,41537153,June
+ update of April data,"Top 10 U.S. export markets for soybeans, corn, wheat,
+ and cotton, by volume","USDA, Economic Research Service based on data from
+ U.S. Department of Commerce, Bureau of the Census.",NA
+
+ Corn,World total,October-April,2026,Exports,Volume,Metric tons,49899479,June
+ update of April data,"Top 10 U.S. export markets for soybeans, corn, wheat,
+ and cotton, by volume","USDA, Economic Research Service based on data from
+ U.S. Department of Commerce, Bureau of the Census.",NA
+
+ Corn,World total,April,2025,Exports,Volume,Metric tons,7786889,June update
+ of April data,"Top 10 U.S. export markets for soybeans, corn, wheat, and cotton,
+ by volume","USDA, Economic Research Service based on data from U.S. Department
+ of Commerce, Bureau of the Census.",NA
+
+ Corn,World total,April,2026,Exports,Volume,Metric tons,7524404,June update
+ of April data,"Top 10 U.S. export markets for soybeans, corn, wheat, and cotton,
+ by volume","USDA, Economic Research Service based on data from U.S. Department
+ of Commerce, Bureau of the Census.",NA
+
+ Corn,World total,October-May,2025,Exports,Volume,Metric tons,49081636,July
+ update of May data,"Top 10 U.S. export markets for soybeans, corn, wheat,
+ and cotton, by volume","USDA, Economic Research Service based on data from
+ U.S. Department of Commerce, Bureau of the Census.",NA
+
+ Corn,World total,October-May,2026,Exports,Volume,Metric tons,57145952,July
+ update of May data,"Top 10 U.S. export markets for soybeans, corn, wheat,
+ and cotton, by volume","USDA, Economic Research Service based on data from
+ U.S. Department of Commerce, Bureau of the Census.",NA
+
+ Corn,World total,May,2025,Exports,Volume,Metric tons,7238409,July update of
+ May data,"Top 10 U.S. export markets for soybeans, corn, wheat, and cotton,
+ by volume","USDA, Economic Research Service based on data from U.S. Department
+ of Commerce, Bureau of the Census.",NA
+
+ Corn,World total,May,2026,Exports,Volume,Metric tons,7251661,July update of
+ May data,"Top 10 U.S. export markets for soybeans, corn, wheat, and cotton,
+ by volume","USDA, Economic Research Service based on data from U.S. Department
+ of Commerce, Bureau of the Census.",NA
+
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+ Strict-Transport-Security:
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diff --git a/openbb_platform/providers/government_us/tests/record/http/test_usda_fetchers/test_weather_bulletin_download_fetcher_urllib3_v2.yaml b/openbb_platform/providers/government_us/tests/record/http/test_usda_fetchers/test_weather_bulletin_download_fetcher_urllib3_v2.yaml
new file mode 100644
index 00000000000..35d8c22d76d
--- /dev/null
+++ b/openbb_platform/providers/government_us/tests/record/http/test_usda_fetchers/test_weather_bulletin_download_fetcher_urllib3_v2.yaml
@@ -0,0 +1,73 @@
+interactions:
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+ response:
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+
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diff --git a/openbb_platform/providers/government_us/tests/record/http/test_government_us_fetchers/test_government_us_weather_bulletin_fetcher_urllib3_v2.yaml b/openbb_platform/providers/government_us/tests/record/http/test_usda_fetchers/test_weather_bulletin_fetcher_urllib3_v2.yaml
similarity index 100%
rename from openbb_platform/providers/government_us/tests/record/http/test_government_us_fetchers/test_government_us_weather_bulletin_fetcher_urllib3_v2.yaml
rename to openbb_platform/providers/government_us/tests/record/http/test_usda_fetchers/test_weather_bulletin_fetcher_urllib3_v2.yaml
diff --git a/openbb_platform/providers/government_us/tests/test_apps_json.py b/openbb_platform/providers/government_us/tests/test_apps_json.py
new file mode 100644
index 00000000000..d17e5ab0113
--- /dev/null
+++ b/openbb_platform/providers/government_us/tests/test_apps_json.py
@@ -0,0 +1,206 @@
+"""Tests for the packaged Workspace app definitions."""
+
+import asyncio
+import json
+from pathlib import Path
+
+import pytest
+
+import openbb_government_us
+from openbb_government_us.congress.congress_router import get_congress_gov_apps_json
+from openbb_government_us.treasury.treasury_router import get_us_treasury_apps_json
+from openbb_government_us.usda.usda_router import get_usda_apps_json
+
+PACKAGE_ROOT = Path(openbb_government_us.__file__).parent
+
+APPS = {
+ "congress": (
+ get_congress_gov_apps_json,
+ PACKAGE_ROOT / "congress/assets/apps.json",
+ ),
+ "treasury": (
+ get_us_treasury_apps_json,
+ PACKAGE_ROOT / "treasury/assets/apps.json",
+ ),
+ "usda": (
+ get_usda_apps_json,
+ PACKAGE_ROOT / "usda/assets/apps.json",
+ ),
+}
+
+
+class TestAppsJsonAssets:
+ """Tests for the packaged app definition files."""
+
+ @pytest.mark.parametrize("agency", sorted(APPS))
+ def test_asset_exists_and_parses(self, agency):
+ """Each agency ships a parseable app definition."""
+ _, path = APPS[agency]
+ assert path.exists(), f"{agency} is missing its apps.json asset"
+ apps = json.loads(path.read_text(encoding="utf-8"))
+ assert isinstance(apps, list)
+ assert len(apps) == 1
+
+ @pytest.mark.parametrize("agency", sorted(APPS))
+ def test_required_keys(self, agency):
+ """Each app carries the keys Workspace requires."""
+ _, path = APPS[agency]
+ app = json.loads(path.read_text(encoding="utf-8"))[0]
+ for key in ("name", "img", "img_dark", "img_light", "description", "tabs"):
+ assert key in app, f"{agency} app is missing {key}"
+ assert app["tabs"], f"{agency} app has no tabs"
+
+ @pytest.mark.parametrize("agency", sorted(APPS))
+ def test_tab_ids_match_their_keys(self, agency):
+ """Each tab's id matches the key it is stored under."""
+ _, path = APPS[agency]
+ app = json.loads(path.read_text(encoding="utf-8"))[0]
+ for key, tab in app["tabs"].items():
+ assert tab["id"] == key
+ assert tab["name"]
+ assert tab["layout"]
+
+ @pytest.mark.parametrize("agency", sorted(APPS))
+ def test_layout_entries_are_well_formed(self, agency):
+ """Every widget in a layout carries an id and a grid position."""
+ _, path = APPS[agency]
+ app = json.loads(path.read_text(encoding="utf-8"))[0]
+ for tab in app["tabs"].values():
+ for widget in tab["layout"]:
+ for key in ("i", "x", "y", "w", "h"):
+ assert key in widget, f"{agency} widget missing {key}"
+ assert widget["w"] > 0
+ assert widget["h"] > 0
+
+ @pytest.mark.parametrize("agency", sorted(APPS))
+ def test_widget_ids_are_namespaced(self, agency):
+ """Widget ids follow the router's generated naming."""
+ prefix = {
+ "congress": "uscongress_",
+ "treasury": "ustreasury_",
+ "usda": "usda_",
+ }[agency]
+ _, path = APPS[agency]
+ app = json.loads(path.read_text(encoding="utf-8"))[0]
+ for tab in app["tabs"].values():
+ for widget in tab["layout"]:
+ assert widget["i"].startswith(prefix)
+ assert widget["i"].endswith("_obb")
+
+
+class TestWidgetConfigs:
+ """Tests for the generated Workspace widget configs."""
+
+ @staticmethod
+ def _widgets():
+ """Build the widget configs the Workspace consumes."""
+ import os
+
+ os.environ["OPENBB_API_AUTH"] = "false"
+ from openbb_core.api.rest_api import app
+ from openbb_platform_api.utils.widgets import build_json
+
+ return build_json(app.openapi(), [])
+
+ def test_multiple_is_only_set_on_text_params(self):
+ """Workspace rejects 'multiple' on any param that is not text.
+
+ The platform sets multiple only after forcing a param's type to
+ text, so a widget_config that overrides the type must not leave
+ the flag behind.
+ """
+ offenders = [
+ f"{widget_id}[{param.get('paramName')}] type={param.get('type')!r}"
+ for widget_id, config in self._widgets().items()
+ for param in config.get("params") or []
+ if param.get("multiple") is True and param.get("type") != "text"
+ ]
+ assert not offenders
+
+ def test_bell_report_commodity_is_an_endpoint_multiselect(self):
+ """The Bell Report commodity picker is a multi-select endpoint param."""
+ params = {
+ p.get("paramName"): p
+ for p in self._widgets()["usda_bell_report_custom_obb"]["params"]
+ }
+ commodity = params["commodity_code"]
+ assert commodity["type"] == "endpoint"
+ assert commodity["multiSelect"] is True
+ assert "multiple" not in commodity
+ assert commodity["optionsEndpoint"].endswith("/usda/bell_report_commodities")
+ assert params["week_ending"]["optionsEndpoint"].endswith(
+ "/usda/bell_report_weeks"
+ )
+
+ @pytest.mark.parametrize("agency", sorted(APPS))
+ def test_saved_state_params_exist_on_the_widget(self, agency):
+ """A tab's saved params must all exist on the widget it configures."""
+ widgets = self._widgets()
+ _, path = APPS[agency]
+ app = json.loads(path.read_text(encoding="utf-8"))[0]
+ offenders = [
+ f"{tab['id']}: {item['i']}[{name}]"
+ for tab in app["tabs"].values()
+ for item in tab["layout"]
+ for name in ((item.get("state") or {}).get("params") or {})
+ if name
+ not in {
+ param.get("paramName")
+ for param in (widgets.get(item["i"]) or {}).get("params") or []
+ }
+ ]
+ assert not offenders
+
+ def test_multi_file_viewers_declare_a_file_selector(self):
+ """Workspace requires an endpoint param with the fileSelector role."""
+ offenders = [
+ widget_id
+ for widget_id, config in self._widgets().items()
+ if config.get("type") == "multi_file_viewer"
+ and not any(
+ param.get("type") == "endpoint"
+ and "fileSelector" in (param.get("roles") or [])
+ for param in config.get("params") or []
+ )
+ ]
+ assert not offenders
+
+ def test_bell_report_file_selector_cascades_from_the_pickers(self):
+ """The file selector's options follow the commodity and week pickers."""
+ params = {
+ p.get("paramName"): p
+ for p in self._widgets()["usda_bell_report_custom_obb"]["params"]
+ }
+ urls = params["urls"]
+ assert urls["roles"] == ["fileSelector"]
+ assert urls["show"] is False
+ assert urls["multiSelect"] is True
+ assert urls["optionsEndpoint"].endswith("/usda/bell_report_urls")
+ assert urls["optionsParams"] == {
+ "commodity_code": "$commodity_code",
+ "week_ending": "$week_ending",
+ }
+
+
+class TestAppsJsonEndpoints:
+ """Tests for the endpoints that serve the app definitions."""
+
+ @pytest.mark.parametrize("agency", sorted(APPS))
+ def test_endpoint_returns_the_asset(self, agency):
+ """The endpoint serves the packaged file verbatim."""
+ endpoint, path = APPS[agency]
+ served = asyncio.run(endpoint())
+ assert served == json.loads(path.read_text(encoding="utf-8"))
+
+ @pytest.mark.parametrize("agency", sorted(APPS))
+ def test_endpoint_returns_empty_when_the_asset_is_unreadable(
+ self, agency, monkeypatch
+ ):
+ """An unreadable asset yields an empty list rather than an error."""
+ endpoint, _ = APPS[agency]
+
+ def _raise(*args, **kwargs):
+ raise OSError("unreadable")
+
+ monkeypatch.setattr(Path, "open", _raise)
+ assert asyncio.run(endpoint()) == []
diff --git a/openbb_platform/providers/government_us/tests/test_congress_api.py b/openbb_platform/providers/government_us/tests/test_congress_api.py
new file mode 100644
index 00000000000..6e471bd9a18
--- /dev/null
+++ b/openbb_platform/providers/government_us/tests/test_congress_api.py
@@ -0,0 +1,628 @@
+"""Tests for the Congress.gov API client (pre-108th Congress fallback)."""
+
+import asyncio
+from datetime import date
+
+import pytest
+from openbb_core.app.model.abstract.error import OpenBBError
+
+from openbb_government_us.congress.utils import congress_api as api
+
+
+@pytest.fixture(autouse=True)
+def _no_ambient_key(monkeypatch):
+ """Keep a real key in user settings from leaking into these tests."""
+ monkeypatch.setattr(api, "user_credentials", dict)
+
+
+CREDS = {"congress_gov_api_key": "test-key"}
+
+# Captured before the autouse fixture stubs the module attribute, so the tests
+# below can exercise the real settings reader.
+_REAL_USER_CREDENTIALS = api.user_credentials
+
+
+def _responder(routes: dict, calls: list | None = None):
+ """Build a fake ``_get`` that serves ``routes`` and records every call."""
+
+ async def _get(path, key, **params):
+ if calls is not None:
+ calls.append((path, params))
+ value = routes.get(path)
+ if value is None:
+ raise OpenBBError(f"no route for {path}")
+ if callable(value):
+ return value(params)
+ return value
+
+ return _get
+
+
+def _bill_page(numbers, *, next_url=None, count=None):
+ """Build one page of a bill-list response."""
+ return {
+ "bills": [
+ {
+ "congress": 95,
+ "type": "HR",
+ "number": str(n),
+ "title": f"Bill {n}",
+ "originChamber": "House",
+ "originChamberCode": "H",
+ "updateDate": "1978-06-22T00:00:00Z",
+ "updateDateIncludingText": "1978-06-23T00:00:00Z",
+ "latestAction": {"actionDate": f"1978-06-{n:02d}", "text": "Passed."},
+ }
+ for n in numbers
+ ],
+ "pagination": {"next": next_url, "count": count},
+ }
+
+
+# --------------------------------------------------------------------------
+# credentials
+# --------------------------------------------------------------------------
+
+
+def test_api_key_prefers_explicit_over_settings(monkeypatch):
+ """An injected credential wins over whatever user settings hold."""
+ monkeypatch.setattr(
+ api, "user_credentials", lambda: {"congress_gov_api_key": "settings"}
+ )
+ assert api.api_key({"congress_gov_api_key": "explicit"}) == "explicit"
+ assert api.api_key(None) == "settings"
+
+
+def test_api_key_missing_names_the_credential():
+ """The error tells the user exactly which credential to set and where to get it."""
+ with pytest.raises(OpenBBError, match="congress_gov_api_key"):
+ api.api_key({})
+
+
+def _patch_user_service(monkeypatch, credentials):
+ """Point the lazily-imported UserService at a stand-in settings object."""
+ from openbb_core.app.service import user_service
+
+ settings = type("Settings", (), {"credentials": credentials})()
+ service = type("Service", (), {"default_user_settings": settings})
+
+ monkeypatch.setattr(user_service, "UserService", service)
+
+
+def test_user_credentials_reads_a_secret_str(monkeypatch):
+ """A SecretStr credential is unwrapped to its plain value."""
+
+ class _Secret:
+ def get_secret_value(self):
+ return "unwrapped"
+
+ _patch_user_service(
+ monkeypatch, type("Creds", (), {"congress_gov_api_key": _Secret()})()
+ )
+
+ assert _REAL_USER_CREDENTIALS() == {"congress_gov_api_key": "unwrapped"}
+
+
+def test_user_credentials_reads_a_plain_string(monkeypatch):
+ """A plain-string credential is passed through unchanged."""
+ _patch_user_service(
+ monkeypatch, type("Creds", (), {"congress_gov_api_key": "plain"})()
+ )
+
+ assert _REAL_USER_CREDENTIALS() == {"congress_gov_api_key": "plain"}
+
+
+def test_user_credentials_absent_key(monkeypatch):
+ """No configured key yields an empty mapping rather than raising."""
+ _patch_user_service(
+ monkeypatch, type("Creds", (), {"congress_gov_api_key": None})()
+ )
+
+ assert _REAL_USER_CREDENTIALS() == {}
+
+
+def test_user_credentials_survives_a_broken_settings_file(monkeypatch, caplog):
+ """An unreadable settings file degrades to no key instead of failing a request."""
+ from openbb_core.app.service import user_service
+
+ def _boom():
+ raise RuntimeError("settings file is corrupt")
+
+ monkeypatch.setattr(user_service, "UserService", _boom)
+
+ with caplog.at_level("WARNING"):
+ assert _REAL_USER_CREDENTIALS() == {}
+
+ assert "could not read user credentials" in caplog.text
+
+
+# --------------------------------------------------------------------------
+# coverage floor
+# --------------------------------------------------------------------------
+
+
+def test_require_coverage_rejects_pre_93rd():
+ """Congresses with no structured records explain both source floors."""
+ with pytest.raises(OpenBBError, match="predates structured bill data"):
+ api._require_coverage(92)
+
+ api._require_coverage(api.API_MIN_CONGRESS)
+
+
+def test_bill_record_and_list_enforce_the_floor(monkeypatch):
+ """Both entry points refuse a Congress the API cannot serve."""
+ with pytest.raises(OpenBBError, match="predates structured bill data"):
+ asyncio.run(api.bill_record(50, "hr", 1, CREDS))
+
+ with pytest.raises(OpenBBError, match="predates structured bill data"):
+ asyncio.run(api.list_bills(50, ["hr"], CREDS))
+
+
+# --------------------------------------------------------------------------
+# request plumbing
+# --------------------------------------------------------------------------
+
+
+def test_get_sends_key_and_format(monkeypatch):
+ """Every request carries the key and asks for JSON."""
+ seen: dict = {}
+
+ async def _amake_request(url, **kwargs):
+ seen["url"] = url
+ seen["params"] = kwargs.get("params")
+ return {"ok": True}
+
+ import openbb_core.provider.utils.helpers as core_helpers
+
+ monkeypatch.setattr(core_helpers, "amake_request", _amake_request)
+
+ out = asyncio.run(api._get("bill/95/hr", "k", offset=0, limit=250))
+
+ assert out == {"ok": True}
+ assert seen["url"] == f"{api.CONGRESS_API_BASE}/bill/95/hr"
+ assert seen["params"]["api_key"] == "k"
+ assert seen["params"]["format"] == "json"
+ assert seen["params"]["limit"] == 250
+
+
+def test_get_wraps_a_transport_failure(monkeypatch):
+ """A network error is reported against the path that failed."""
+
+ async def _amake_request(url, **kwargs):
+ raise TimeoutError("connection reset")
+
+ import openbb_core.provider.utils.helpers as core_helpers
+
+ monkeypatch.setattr(core_helpers, "amake_request", _amake_request)
+
+ with pytest.raises(OpenBBError, match="request failed -> bill/95/hr"):
+ asyncio.run(api._get("bill/95/hr", "k"))
+
+
+def test_get_rejects_a_non_dict_body(monkeypatch):
+ """An unexpected body shape is surfaced rather than silently treated as empty."""
+
+ async def _amake_request(url, **kwargs):
+ return ["not", "a", "dict"]
+
+ import openbb_core.provider.utils.helpers as core_helpers
+
+ monkeypatch.setattr(core_helpers, "amake_request", _amake_request)
+
+ with pytest.raises(OpenBBError, match="Unexpected Congress.gov API response"):
+ asyncio.run(api._get("bill/95/hr", "k"))
+
+
+def test_get_surfaces_an_api_error_body(monkeypatch):
+ """An API-level error (bad key, throttling) is raised with its message."""
+
+ async def _amake_request(url, **kwargs):
+ return {"error": {"code": "API_KEY_INVALID", "message": "bad key"}}
+
+ import openbb_core.provider.utils.helpers as core_helpers
+
+ monkeypatch.setattr(core_helpers, "amake_request", _amake_request)
+
+ with pytest.raises(OpenBBError, match="API_KEY_INVALID"):
+ asyncio.run(api._get("bill/95/hr", "k"))
+
+
+# --------------------------------------------------------------------------
+# pagination
+# --------------------------------------------------------------------------
+
+
+def test_paged_filtered_walks_to_the_reported_count(monkeypatch):
+ """Paging continues until the collection's own count is satisfied.
+
+ A fixed page budget silently truncated large Congresses: the 95th House
+ returned exactly 10,000 of its 14,414 bills.
+ """
+ pages = [
+ _bill_page(range(1, 251), next_url="more", count=600),
+ _bill_page(range(251, 501), next_url="more", count=600),
+ _bill_page(range(501, 601), next_url=None, count=600),
+ ]
+ calls: list = []
+
+ async def _get(path, key, **params):
+ calls.append(params["offset"])
+ return pages[len(calls) - 1]
+
+ monkeypatch.setattr(api, "_get", _get)
+
+ out = asyncio.run(api._paged_filtered("bill/95/hr", "k", {}))
+
+ assert len(out) == 600
+ assert calls == [0, 250, 500]
+
+
+def test_paged_filtered_stops_when_count_is_reached(monkeypatch):
+ """A full final page that completes the count ends the walk."""
+ pages = [
+ _bill_page(range(1, 251), next_url="more", count=500),
+ _bill_page(range(251, 501), next_url="more", count=500),
+ ]
+ calls: list = []
+
+ async def _get(path, key, **params):
+ calls.append(params["offset"])
+ return pages[len(calls) - 1]
+
+ monkeypatch.setattr(api, "_get", _get)
+
+ assert len(asyncio.run(api._paged_filtered("bill/95/hr", "k", {}))) == 500
+ assert calls == [0, 250]
+
+
+def test_paged_filtered_warns_when_the_budget_runs_out(monkeypatch, caplog):
+ """Exhausting the page budget is logged rather than passing off partial data."""
+ monkeypatch.setattr(api, "_MAX_PAGES", 2)
+
+ async def _get(path, key, **params):
+ return _bill_page(range(1, 251), next_url="more", count=10_000)
+
+ monkeypatch.setattr(api, "_get", _get)
+
+ with caplog.at_level("WARNING"):
+ out = asyncio.run(api._paged_filtered("bill/95/hr", "k", {}))
+
+ assert len(out) == 500
+ assert "page budget" in caplog.text
+
+
+def test_paged_handles_an_empty_first_page(monkeypatch):
+ """A collection with no entries returns empty without a second request."""
+ calls: list = []
+
+ async def _get(path, key, **params):
+ calls.append(params)
+ return {"textVersions": [], "pagination": {"count": 0}}
+
+ monkeypatch.setattr(api, "_get", _get)
+
+ assert asyncio.run(api._paged("bill/95/hr/1/text", "k", "textVersions")) == []
+ assert len(calls) == 1
+
+
+def test_paged_returns_a_dict_field_as_one_item(monkeypatch):
+ """``subjects`` nests its lists under the field instead of listing them."""
+
+ async def _get(path, key, **params):
+ return {
+ "subjects": {
+ "legislativeSubjects": [{"name": "Labor"}],
+ "policyArea": {"name": "Labor and Employment"},
+ }
+ }
+
+ monkeypatch.setattr(api, "_get", _get)
+
+ out = asyncio.run(api._paged("bill/95/hr/1/subjects", "k", "subjects"))
+
+ assert out[0]["policyArea"] == {"name": "Labor and Employment"}
+
+
+def test_paged_respects_an_explicit_limit(monkeypatch):
+ """A caller-supplied limit caps the page size and stops the walk."""
+ calls: list = []
+
+ async def _get(path, key, **params):
+ calls.append(params["limit"])
+ return {
+ "actions": [{"text": "x"}] * params["limit"],
+ "pagination": {"next": "more", "count": 999},
+ }
+
+ monkeypatch.setattr(api, "_get", _get)
+
+ out = asyncio.run(api._paged("bill/95/hr/1/actions", "k", "actions", limit=10))
+
+ assert len(out) == 10
+ assert calls == [10]
+
+
+# --------------------------------------------------------------------------
+# shaping
+# --------------------------------------------------------------------------
+
+
+def test_slim_record_shape():
+ """A list entry becomes a BILLSTATUS-shaped record with empty collections."""
+ record = api.slim_record(
+ {
+ "congress": 95,
+ "type": "HR",
+ "number": "8410",
+ "title": "Labor Reform Act",
+ "originChamber": "House",
+ "originChamberCode": "H",
+ "updateDate": "1978-06-22T00:00:00Z",
+ "latestAction": {"actionDate": "1978-06-22", "text": "Passed."},
+ }
+ )
+
+ assert record["bill_id"] == "95-hr-8410"
+ assert record["number"] == 8410
+ assert record["_detailed"] is False
+ for field in ("sponsors", "actions", "summaries", "textVersions", "titles"):
+ assert record[field] == []
+
+
+def test_slim_record_tolerates_a_non_numeric_number():
+ """A malformed number does not crash the projection."""
+ record = api.slim_record({"congress": 95, "type": "HR", "number": "E1"})
+ assert record["number"] == 0
+ assert record["bill_id"] == "95-hr-E1"
+
+
+def test_list_item_normalizes_empty_fields():
+ """Blank dates and actions come back as None, matching the store's rows."""
+ item = api._list_item({"congress": 95, "type": "HR", "number": "1"})
+
+ assert item["updateDate"] is None
+ assert item["updateDateIncludingText"] is None
+ assert item["latestAction"] == {"actionDate": None, "text": None}
+
+
+def test_text_versions_drops_urlless_formats():
+ """Formats with no URL are discarded, and a null date becomes empty."""
+ out = api._text_versions(
+ [
+ {
+ "type": "Enrolled Bill",
+ "date": None,
+ "formats": [{"url": None}, {"url": "https://x/BILLS-95hr8410enr.pdf"}],
+ }
+ ]
+ )
+
+ assert out[0]["date"] == ""
+ assert out[0]["formats"] == [{"url": "https://x/BILLS-95hr8410enr.pdf"}]
+
+
+def test_committees_keeps_activities_and_subcommittees():
+ """Committee nesting survives the projection."""
+ out = api._committees(
+ [
+ {
+ "name": "Judiciary",
+ "chamber": "House",
+ "activities": [{"name": "Referred to"}],
+ "subcommittees": [{"name": "Crime"}],
+ }
+ ]
+ )
+
+ assert out[0]["activities"] == [{"name": "Referred to"}]
+ assert out[0]["subcommittees"] == [{"name": "Crime"}]
+ assert out[0]["systemCode"] == ""
+
+
+def test_cosponsors_coerce_the_original_flag():
+ """The original-cosponsor flag becomes a real bool for the markdown renderer."""
+ out = api._cosponsors(
+ [{"fullName": "A", "isOriginalCosponsor": True}, {"fullName": "B"}]
+ )
+
+ assert out[0]["isOriginalCosponsor"] is True
+ assert out[1]["isOriginalCosponsor"] is False
+
+
+def test_bill_id_lowercases_the_type():
+ """Bill ids are canonical regardless of the casing the API returns."""
+ assert api.bill_id(95, "HR", 8410) == "95-hr-8410"
+
+
+# --------------------------------------------------------------------------
+# list_bills
+# --------------------------------------------------------------------------
+
+
+def test_list_bills_sorts_paginates_and_windows(monkeypatch):
+ """Merged types are sorted by latest action, then offset and limit applied."""
+ calls: list = []
+
+ async def _paged_filtered(path, key, params):
+ calls.append((path, dict(params)))
+ return _bill_page([1, 2, 3])["bills"]
+
+ monkeypatch.setattr(api, "_paged_filtered", _paged_filtered)
+
+ out = asyncio.run(
+ api.list_bills(
+ 95,
+ ["hr"],
+ CREDS,
+ start_date=date(1978, 1, 1),
+ end_date=date(1978, 12, 31),
+ limit=2,
+ offset=1,
+ sort_by="desc",
+ )
+ )
+
+ assert [b["number"] for b in out] == [2, 1]
+ assert calls[0][1] == {
+ "fromDateTime": "1978-01-01T00:00:00Z",
+ "toDateTime": "1978-12-31T23:59:59Z",
+ }
+
+
+def test_list_bills_ascending_and_unlimited(monkeypatch):
+ """``sort_by='asc'`` reverses the order and ``limit=0`` returns everything."""
+
+ async def _paged_filtered(path, key, params):
+ return _bill_page([1, 2, 3])["bills"]
+
+ monkeypatch.setattr(api, "_paged_filtered", _paged_filtered)
+
+ out = asyncio.run(api.list_bills(95, ["hr"], CREDS, limit=0, sort_by="asc"))
+
+ assert [b["number"] for b in out] == [1, 2, 3]
+
+
+def test_list_bills_degrades_when_one_type_fails(monkeypatch, caplog):
+ """One failing bill type is logged and skipped, not fatal to the whole list."""
+
+ async def _paged_filtered(path, key, params):
+ if path.endswith("/s"):
+ raise OpenBBError("upstream 500")
+ return _bill_page([1])["bills"]
+
+ monkeypatch.setattr(api, "_paged_filtered", _paged_filtered)
+
+ with caplog.at_level("ERROR"):
+ out = asyncio.run(api.list_bills(95, ["hr", "s"], CREDS))
+
+ assert [b["bill_id"] for b in out] == ["95-hr-1"]
+ assert "API list failed for 95-s" in caplog.text
+
+
+# --------------------------------------------------------------------------
+# bill_record
+# --------------------------------------------------------------------------
+
+
+def _detail_routes():
+ """Routes for a full bill detail fan-out."""
+ base = "bill/95/hr/8410"
+ return {
+ base: {
+ "bill": {
+ "congress": 95,
+ "number": 8410,
+ "type": "HR",
+ "title": "Labor Reform Act",
+ "originChamber": "House",
+ "originChamberCode": "H",
+ "introducedDate": "1977-07-19",
+ "updateDate": "1978-06-22T00:00:00Z",
+ "latestAction": {"actionDate": "1978-06-22", "text": "Recommitted."},
+ "sponsors": [{"fullName": "Rep. Thompson", "bioguideId": "T000001"}],
+ }
+ },
+ f"{base}/actions": {"actions": [{"actionDate": "1977-07-19", "text": "Intro"}]},
+ f"{base}/amendments": {"amendments": []},
+ f"{base}/committees": {"committees": [{"name": "Education and Labor"}]},
+ f"{base}/cosponsors": {"cosponsors": [{"fullName": "Rep. Perkins"}]},
+ f"{base}/relatedbills": {"relatedBills": [{"number": 77}]},
+ f"{base}/subjects": {
+ "subjects": {
+ "legislativeSubjects": [{"name": "Labor"}],
+ "policyArea": {"name": "Industrial relations"},
+ }
+ },
+ f"{base}/summaries": {"summaries": [{"text": "
29 119 HR
@@ -202,6 +204,94 @@ def test_derive_text_formats():
assert out["xml"].endswith("/content/pkg/BILLS-119hr29ih/xml/BILLS-119hr29ih.xml")
+def test_derive_text_formats_congress_gov_urls():
+ """Congress.gov API text versions resolve to the same GovInfo package.
+
+ The API links at congress.gov (no ``/content/pkg/`` segment), carrying the
+ package id only in the filename, so the viewer produced no choices at all
+ for any Congress served by the API.
+ """
+ version = {
+ "type": "Enrolled Bill",
+ "date": None,
+ "formats": [
+ {
+ "type": "Formatted Text",
+ "url": "https://www.congress.gov/107/bills/hr3162/BILLS-107hr3162enr.htm",
+ },
+ {
+ "type": "PDF",
+ "url": "https://www.congress.gov/107/bills/hr3162/BILLS-107hr3162enr.pdf",
+ },
+ ],
+ }
+ out = bulk.derive_text_formats(version)
+
+ assert out["version_type"] == "Enrolled Bill"
+ assert out["version_date"] == ""
+ assert out["pdf"].endswith(
+ "/content/pkg/BILLS-107hr3162enr/pdf/BILLS-107hr3162enr.pdf"
+ )
+ assert out["htm"].endswith(
+ "/content/pkg/BILLS-107hr3162enr/html/BILLS-107hr3162enr.htm"
+ )
+ assert out["xml"].endswith(
+ "/content/pkg/BILLS-107hr3162enr/xml/BILLS-107hr3162enr.xml"
+ )
+
+
+def test_derive_text_formats_scans_every_format():
+ """A package id is found even when it is not on the first format listed."""
+ version = {
+ "type": "Introduced in House",
+ "date": "2001-10-23T04:00:00Z",
+ "formats": [
+ {"type": "TXT", "url": "https://example.com/not-a-package.txt"},
+ {
+ "type": "PDF",
+ "url": "https://www.congress.gov/107/bills/hr3162/BILLS-107hr3162ih.pdf",
+ },
+ ],
+ }
+ out = bulk.derive_text_formats(version)
+
+ assert out["version_date"] == "2001-10-23"
+ assert out["pdf"].endswith("/BILLS-107hr3162ih.pdf")
+
+
+def test_load_bill_record_does_not_warm_the_whole_congress(monkeypatch, tmp_path):
+ """Opening one API bill fetches that bill only, not its Congress's full list."""
+ from openbb_government_us.congress.utils import congress_api, store
+
+ monkeypatch.setattr(bulk, "_cache_dir", lambda: str(tmp_path))
+ store.reset()
+ BillsState().bulk.clear()
+
+ async def _never_list(*_args, **_kwargs):
+ raise AssertionError("a single bill must not warm the Congress's bill list")
+
+ async def _detail(congress, bill_type, number, credentials):
+ return {
+ "bill_id": f"{congress}-{bill_type.lower()}-{number}",
+ "congress": congress,
+ "number": number,
+ "type": bill_type.upper(),
+ "title": "USA PATRIOT Act",
+ "latestAction": {"actionDate": "2001-10-26", "text": "Became law."},
+ "_detailed": True,
+ }
+
+ monkeypatch.setattr(congress_api, "fetch_bill_list", _never_list)
+ monkeypatch.setattr(congress_api, "bill_record", _detail)
+
+ record = asyncio.run(
+ bulk.load_bill_record("107-hr-3162", {"congress_gov_api_key": "k"})
+ )
+
+ assert record["title"] == "USA PATRIOT Act"
+ assert store.get_bill("107-hr-3162")["_detailed"] is True
+
+
def test_derive_text_formats_no_package():
"""derive_text_formats returns None when no package id can be found."""
assert bulk.derive_text_formats({"formats": []}) is None
@@ -314,14 +404,32 @@ def test_list_bills_empty_types(monkeypatch, tmp_path):
assert store.list_bills(119, [], None, None, None, None, "desc") == []
+class _FakeContent:
+ """Stand-in for an aiohttp response's streaming body."""
+
+ def __init__(self, body: bytes, chunk_exc=None):
+ self._body = body
+ self._chunk_exc = chunk_exc
+
+ async def iter_chunked(self, size):
+ """Yield the canned body in chunks, or raise mid-stream."""
+ for start in range(0, len(self._body), size):
+ yield self._body[start : start + size]
+ if self._chunk_exc is not None:
+ raise self._chunk_exc
+
+
class _FakeResponse:
"""Settable stand-in for an aiohttp response."""
- def __init__(self, status=200, headers=None, body=b"", raise_exc=None):
+ def __init__(
+ self, status=200, headers=None, body=b"", raise_exc=None, chunk_exc=None
+ ):
self.status = status
self.headers = headers or {}
self._body = body
self._raise_exc = raise_exc
+ self.content = _FakeContent(body, chunk_exc)
async def __aenter__(self):
return self
@@ -385,12 +493,105 @@ def _cache(monkeypatch, tmp_path):
return str(tmp_path)
-def test_download_zip_returns_body(monkeypatch):
- """_download_zip fetches the archive bytes (no on-disk caching)."""
+def _as_zip_file(body: bytes) -> str:
+ """Write archive bytes to a temp file, mirroring what _download_zip returns.
+
+ The production caller owns the file and removes it, so nothing leaks.
+ """
+ handle, path = tempfile.mkstemp(suffix=".zip")
+ with os.fdopen(handle, "wb") as file:
+ file.write(body)
+ return path
+
+
+def test_download_zip_streams_to_a_file(monkeypatch, _cache):
+ """_download_zip streams the archive to a file rather than into memory."""
body = _billstatus_zip()
_patch_session(monkeypatch, response=_FakeResponse(status=200, body=body))
- content = asyncio.run(bulk._download_zip("BILLSTATUS", 119, "HR"))
- assert content == body
+ path = asyncio.run(bulk._download_zip("BILLSTATUS", 119, "HR"))
+ try:
+ assert path.startswith(_cache)
+ assert Path(path).read_bytes() == body
+ finally:
+ bulk._remove_file(path)
+
+
+def test_download_zip_chunks_the_body(monkeypatch, _cache):
+ """The body is consumed in chunks, never read whole."""
+ body = b"x" * ((1 << 20) * 2 + 17)
+ response = _FakeResponse(status=200, body=body)
+ _patch_session(monkeypatch, response=response)
+ chunks: list = []
+ original = response.content.iter_chunked
+
+ async def _counting(size):
+ async for chunk in original(size):
+ chunks.append(len(chunk))
+ yield chunk
+
+ response.content.iter_chunked = _counting
+ path = asyncio.run(bulk._download_zip("BILLSTATUS", 119, "HR"))
+ try:
+ assert Path(path).stat().st_size == len(body)
+ assert chunks == [1 << 20, 1 << 20, 17]
+ finally:
+ bulk._remove_file(path)
+
+
+def test_download_zip_removes_the_file_when_the_stream_fails(monkeypatch, _cache):
+ """A truncated download leaves no partial file behind."""
+ _patch_session(
+ monkeypatch,
+ response=_FakeResponse(
+ status=200, body=b"x" * 32, chunk_exc=RuntimeError("truncated")
+ ),
+ )
+ with pytest.raises(OpenBBError, match="Failed to download data"):
+ asyncio.run(bulk._download_zip("BILLSTATUS", 119, "HR"))
+ assert list(Path(_cache).glob("*.zip")) == []
+
+
+def test_stream_download_returns_bytes_written(monkeypatch, tmp_path):
+ """The stream reports how many bytes it wrote."""
+ body = b"abcdef" * 100
+ _patch_session(monkeypatch, response=_FakeResponse(status=200, body=body))
+ target = tmp_path / "out.zip"
+ written = asyncio.run(bulk._stream_download("https://x/data.zip", str(target)))
+ assert written == len(body)
+ assert target.read_bytes() == body
+
+
+def test_remove_file_tolerates_a_missing_file(tmp_path):
+ """Removing an absent file is a no-op."""
+ bulk._remove_file(str(tmp_path / "gone.zip"))
+
+
+def test_file_size_of_a_missing_file_is_zero(tmp_path):
+ """An unmeasurable file reports zero bytes."""
+ assert bulk._file_size(str(tmp_path / "gone.zip")) == 0
+
+
+def test_parse_billstatus_accepts_a_path(tmp_path):
+ """The parser reads an archive from disk as well as from bytes."""
+ path = tmp_path / "b.zip"
+ path.write_bytes(_billstatus_zip())
+ from_path = bulk.parse_billstatus(str(path))
+ from_bytes = bulk.parse_billstatus(_billstatus_zip())
+ assert from_path == from_bytes
+ assert {r["number"] for r in from_path} == {29, 5}
+
+
+def test_parse_plaw_accepts_a_path(tmp_path):
+ """The law parser reads an archive from disk as well as from bytes."""
+ path = tmp_path / "p.zip"
+ path.write_bytes(_plaw_zip())
+ assert bulk.parse_plaw(str(path)) == bulk.parse_plaw(_plaw_zip())
+
+
+def test_download_returns_the_body(monkeypatch):
+ """_download reads a small body whole, for sitemaps and metadata."""
+ _patch_session(monkeypatch, response=_FakeResponse(status=200, body=b"
+
+ Public Law 115-285: Jack H. Brown Post Office Building
+ 285
+ Public Law 115-285
+ 132 Stat. 4369
+ 115
+ public
+
+ """
+
+
+def test_parse_plaw_without_an_approved_date():
+ """A law whose meta carries no approvedDate or date enacts as None, not ''."""
+ records = bulk.parse_plaw(_zip_bytes({"PLAW-115publ285.xml": _PLAW_NO_DATE}))
+ assert len(records) == 1
+ assert records[0]["law_id"] == "115-285"
+ assert records[0]["enacted_date"] is None
+
+
def test_filter_laws():
"""filter_laws sorts by law number and paginates."""
records = bulk.parse_plaw(_plaw_zip())
@@ -578,7 +799,7 @@ def test_load_plaw_ingests_then_serves_from_store(monkeypatch, tmp_path):
async def _download(collection, congress, law_type):
calls["n"] += 1
- return body
+ return _as_zip_file(body)
monkeypatch.setattr(bulk, "_download_zip", _download)
records = asyncio.run(bulk.load_plaw(119, "public"))
@@ -830,7 +1051,7 @@ def test_chamber_from_package():
assert bulk._chamber_from_package("CRPT-119hrpt637") == "House"
assert bulk._chamber_from_package("CRPT-119srpt1") == "Senate"
assert bulk._chamber_from_package("CHRG-119jhrg1") == "Joint"
- assert bulk._chamber_from_package("BADID") == ""
+ assert bulk._chamber_from_package("BADID") is None
def test_wssearch_record_full():
@@ -853,7 +1074,7 @@ def test_wssearch_record_no_date_no_citation_no_package():
assert record["citation"] is None
assert record["date"] is None
assert record["title"] == "PlainTitleNoSeparator"
- assert record["doc_url"] == ""
+ assert record["doc_url"] is None
def test_wssearch_record_bad_date():
@@ -1128,7 +1349,7 @@ def test_memoized_dedupes_concurrent_loads(monkeypatch, tmp_path):
async def _slow_download(collection, congress, bill_type):
calls["n"] += 1
await asyncio.sleep(0.05)
- return _billstatus_zip()
+ return _as_zip_file(_billstatus_zip())
monkeypatch.setattr(bulk, "_download_zip", _slow_download)
@@ -1211,10 +1432,11 @@ def _patch_congress_billstatus(monkeypatch, tmp_path):
real_parse = bulk.parse_billstatus
async def _download(collection, congress, bill_type):
- return body if bill_type == "hr" else b"empty"
+ return _as_zip_file(body if bill_type == "hr" else b"empty")
- def _parse(zip_bytes):
- return real_parse(zip_bytes) if zip_bytes == body else []
+ def _parse(source):
+ data = Path(source).read_bytes() if isinstance(source, str) else source
+ return real_parse(data) if data == body else []
monkeypatch.setattr(bulk, "_download_zip", _download)
monkeypatch.setattr(bulk, "parse_billstatus", _parse)
@@ -1559,9 +1781,28 @@ def test_member_committees(monkeypatch):
assert any(c["title"] == "Chair" for c in out)
+async def _fake_vv_members(congress, chamber):
+ """One member mapped to an icpsr id, for the passage-index tests."""
+ return {"C000127": "39310"}
+
+
+async def _fake_vv_rollcalls(congress, chamber):
+ """Two final-passage questions and one procedural question."""
+ return {
+ "1": {"question": "On Passage of the Bill"},
+ "2": {"question": "On the Motion to Recommit"},
+ "3": {"question": "On the Joint Resolution"},
+ }
+
+
+async def _fake_vv_votes(congress, chamber):
+ """A yea on rollcall 1 and a nay on rollcall 3, plus an unknown member."""
+ return {"39310": [("1", "1"), ("2", "6"), ("3", "4")], "99999": [("1", "1")]}
+
+
def _point_store_at(monkeypatch, tmp_path):
"""Route the cache/store at an isolated temp dir and start it empty."""
- from openbb_congress_gov.utils import store
+ from openbb_government_us.congress.utils import store
monkeypatch.setattr(bulk, "_cache_dir", lambda: str(tmp_path))
store.reset()
@@ -1604,10 +1845,11 @@ def test_ingest_billstatus_and_query(monkeypatch, tmp_path):
async def _download(collection, congress, bill_type):
calls.append(bill_type)
- return body if bill_type == "hr" else b"empty"
+ return _as_zip_file(body if bill_type == "hr" else b"empty")
- def _parse(zip_bytes):
- return real_parse(zip_bytes) if zip_bytes == body else []
+ def _parse(source):
+ data = Path(source).read_bytes() if isinstance(source, str) else source
+ return real_parse(data) if data == body else []
monkeypatch.setattr(bulk, "_download_zip", _download)
monkeypatch.setattr(bulk, "parse_billstatus", _parse)
@@ -1830,12 +2072,145 @@ def test_member_votes_short_circuits_old_congresses(monkeypatch):
def test_member_passage_record_absent(monkeypatch, tmp_path):
- """A member with no ingested votes yields a zeroed record."""
+ """A member with no ingested votes and no buildable units yields a zeroed record."""
_point_store_at(monkeypatch, tmp_path)
+
+ async def _no_units(bioguide):
+ return None
+
+ monkeypatch.setattr(bulk, "ensure_member_passage", _no_units)
+
rec = asyncio.run(bulk.member_passage_record("C000127"))
assert rec == {"yea": 0, "nay": 0, "total": 0, "yea_pct": None}
+def test_member_passage_record_builds_on_demand(monkeypatch, tmp_path):
+ """An unbuilt index is built for the member being viewed.
+
+ Only the startup warmup built this, behind minutes of bill archives, so any
+ bio opened first - or after a restart - reported no voting record at all.
+ """
+ _point_store_at(monkeypatch, tmp_path)
+ BillsState().bulk.clear()
+
+ async def _members():
+ return [
+ {
+ "id": {"bioguide": "C000127"},
+ "terms": [{"start": "2021-01-03", "type": "sen"}],
+ }
+ ]
+
+ monkeypatch.setattr(bulk, "load_members", _members)
+ monkeypatch.setattr(bulk, "load_voteview_members", _fake_vv_members)
+ monkeypatch.setattr(bulk, "load_voteview_rollcalls", _fake_vv_rollcalls)
+ monkeypatch.setattr(bulk, "load_voteview_votes", _fake_vv_votes)
+
+ rec = asyncio.run(bulk.member_passage_record("C000127"))
+
+ assert rec == {"yea": 1, "nay": 1, "total": 2, "yea_pct": 50.0}
+
+
+def test_member_passage_record_survives_a_failed_build(monkeypatch, tmp_path, caplog):
+ """A build failure logs and still renders the bio with an empty tally."""
+ _point_store_at(monkeypatch, tmp_path)
+
+ async def _boom(bioguide):
+ raise OpenBBError("voteview unreachable")
+
+ monkeypatch.setattr(bulk, "ensure_member_passage", _boom)
+
+ with caplog.at_level("ERROR"):
+ rec = asyncio.run(bulk.member_passage_record("C000127"))
+
+ assert rec["total"] == 0
+ assert "could not build the passage index" in caplog.text
+
+
+def test_ensure_member_passage_skips_already_loaded_units(monkeypatch, tmp_path):
+ """Units already in the store are not re-downloaded."""
+ store = _point_store_at(monkeypatch, tmp_path)
+ BillsState().bulk.clear()
+
+ async def _members():
+ return [
+ {
+ "id": {"bioguide": "C000127"},
+ "terms": [{"start": "2021-01-03", "type": "sen"}],
+ }
+ ]
+
+ calls: list = []
+
+ async def _vv_members(congress, chamber):
+ calls.append((congress, chamber))
+ return {"C000127": "39310"}
+
+ monkeypatch.setattr(bulk, "load_members", _members)
+ monkeypatch.setattr(bulk, "load_voteview_members", _vv_members)
+ monkeypatch.setattr(bulk, "load_voteview_rollcalls", _fake_vv_rollcalls)
+ monkeypatch.setattr(bulk, "load_voteview_votes", _fake_vv_votes)
+
+ asyncio.run(bulk.ensure_member_passage("C000127"))
+ assert len(calls) == 1
+
+ # Already stored, and the in-process memo is cleared, so the store guard alone
+ # has to prevent a second download.
+ BillsState().bulk.clear()
+ asyncio.run(bulk.ensure_member_passage("C000127"))
+ assert len(calls) == 1
+ assert store.loaded_keys("passage") == {"117-S"}
+
+
+def test_ensure_member_passage_tolerates_a_bad_unit(monkeypatch, tmp_path, caplog):
+ """One unavailable Voteview file does not sink the rest of the member's units."""
+ _point_store_at(monkeypatch, tmp_path)
+ BillsState().bulk.clear()
+
+ async def _members():
+ return [
+ {
+ "id": {"bioguide": "C000127"},
+ "terms": [
+ {"start": "2021-01-03", "type": "sen"},
+ {"start": "2023-01-03", "type": "sen"},
+ ],
+ }
+ ]
+
+ async def _vv_members(congress, chamber):
+ if congress == 118:
+ raise OpenBBError("404 from voteview")
+ return {"C000127": "39310"}
+
+ monkeypatch.setattr(bulk, "load_members", _members)
+ monkeypatch.setattr(bulk, "load_voteview_members", _vv_members)
+ monkeypatch.setattr(bulk, "load_voteview_rollcalls", _fake_vv_rollcalls)
+ monkeypatch.setattr(bulk, "load_voteview_votes", _fake_vv_votes)
+
+ with caplog.at_level("WARNING"):
+ asyncio.run(bulk.ensure_member_passage("C000127"))
+
+ assert "passage votes for S118 unavailable" in caplog.text
+ assert asyncio.run(bulk.member_passage_record("C000127"))["total"] == 2
+
+
+def test_ensure_member_passage_no_terms(monkeypatch, tmp_path):
+ """A record with no datable terms asks for nothing."""
+ _point_store_at(monkeypatch, tmp_path)
+
+ async def _members():
+ return [{"id": {"bioguide": "C000127"}, "terms": []}]
+
+ async def _never(*_args, **_kwargs):
+ raise AssertionError("no units should be ingested")
+
+ monkeypatch.setattr(bulk, "load_members", _members)
+ monkeypatch.setattr(bulk, "load_voteview_members", _never)
+
+ asyncio.run(bulk.ensure_member_passage("C000127"))
+
+
def test_is_passage_question():
"""Final-passage questions match across both chambers; procedural ones do not."""
passage = [
@@ -1892,6 +2267,11 @@ def test_build_passage_index(monkeypatch, tmp_path):
monkeypatch.setattr(bulk, "load_voteview_rollcalls", _rollcalls)
monkeypatch.setattr(bulk, "load_voteview_votes", _votes)
+ async def _already_built(bioguide):
+ return None
+
+ monkeypatch.setattr(bulk, "ensure_member_passage", _already_built)
+
asyncio.run(bulk.build_passage_index([119, 118, 117]))
rec = asyncio.run(bulk.member_passage_record("C000127"))
assert rec == {"yea": 6, "nay": 6, "total": 12, "yea_pct": 50.0}
@@ -2083,7 +2463,7 @@ def test_refresh_passage_reingests_when_changed(monkeypatch, tmp_path):
store = _point_store_at(monkeypatch, tmp_path)
from datetime import datetime
- from openbb_congress_gov.utils.helpers import year_to_congress
+ from openbb_government_us.congress.utils.helpers import year_to_congress
congress = year_to_congress(datetime.now().year)
store.put_parsed(f"lm:VV_H{congress}", "old")
@@ -2114,7 +2494,7 @@ def test_refresh_passage_handles_ingest_error(monkeypatch, tmp_path):
store = _point_store_at(monkeypatch, tmp_path)
from datetime import datetime
- from openbb_congress_gov.utils.helpers import year_to_congress
+ from openbb_government_us.congress.utils.helpers import year_to_congress
congress = year_to_congress(datetime.now().year)
@@ -2129,3 +2509,318 @@ def test_refresh_passage_handles_ingest_error(monkeypatch, tmp_path):
monkeypatch.setattr(bulk, "_invalidate_voteview", lambda c, ch: None)
asyncio.run(bulk.refresh_passage())
assert store.get_parsed(f"lm:VV_H{congress}") is None
+
+
+_BILLSTATUS_LEGACY = """
+ 9 117 HR
+ House H
+ Reserved for the Speaker.
+ 2021-01-03 2021-08-05T14:33:24Z
+ 2021-01-03 Introduced in House hsju00 Judiciary 00 2021-01-03 Legacy summary. Congress House rules Official Title as Introduced Reserved for the Speaker. 2021-01-03 Introduced in House IntroReferral 117
{escape(title)}
| Name | Obligations | ' + '% of Total |
|---|
Top Categories
' + f'{cards}
' + f'{name}' + f'{state}{amount_html}' + "
" + f'- {_rows_html(CHILD_FIELDS, child)}
Showing the first {len(children)} of {total}.
' + if total > len(children) + else "" + ) + + return ( + f'Child Recipients ({total})
' + + "".join(_child_card(child) for child in children) + + note + + "No recipient detail was returned.
' + tokens = { + "__TITLE__": escape(str(profile.get("name") or "Recipient")), + "__SUBTITLE__": escape(str(profile.get("recipient_id") or "")), + "__BADGES__": _badges_html(profile), + "__KPIS__": _kpis_html(profile), + "__BODY__": body, + "__TOPS__": _tops_html(tops or {}, denominator), + "__CHILDREN__": _children_html(children or [], child_total), + } + + return re.sub("|".join(tokens), lambda match: tokens[match.group(0)], template) + + +def recipient_rows( + profile: dict, + tops: dict[str, list[dict]] | None = None, + children: list[dict] | None = None, + denominator: float | None = None, +) -> list[dict]: + """Flatten a recipient profile into tabular records. + + Parameters + ---------- + profile : dict + The recipient profile payload. + tops : dict[str, list[dict]] | None + Top-category rows keyed by category name. + children : list[dict] | None + Child recipient rows. + denominator : float | None + Total obligations, used to derive each category row's share. + + Returns + ------- + list[dict] + One record per fact, sharing a single column set so the raw view + renders as a table. + """ + from openbb_government_us.treasury.utils.recipient import TOP_CATEGORIES + + location = profile.get("location") or {} + rows: list[dict] = [] + + def add(section: str, name: str, **values) -> None: + """Append one record with the shared column set.""" + rows.append( + { + "section": section, + "name": name, + "value": values.get("value"), + "amount": values.get("amount"), + "percent_of_total": values.get("percent_of_total"), + "code": values.get("code"), + "uei": values.get("uei"), + "state": values.get("state"), + "recipient_id": values.get("recipient_id"), + } + ) + + for field in IDENTITY_FIELDS: + if profile.get(field) not in (None, ""): + add("Identity", LABELS.get(field, field), value=str(profile[field])) + + for field in LOCATION_FIELDS: + if location.get(field) not in (None, ""): + add("Location", LABELS.get(field, field), value=str(location[field])) + + for name in profile.get("alternate_names") or []: + add("Alternate Names", str(name)) + + for slug in profile.get("business_types") or []: + add("Business Types", str(slug).replace("_", " ")) + + for field in KPI_FIELDS: + if profile.get(field) is not None: + add("Totals", LABELS[field], amount=profile[field]) + + for category, entries in (tops or {}).items(): + label = TOP_CATEGORIES.get(category, category) + + for entry in entries: + amount = entry.get("amount") + add( + label, + str(entry.get("name") or ""), + amount=amount, + percent_of_total=( + amount / denominator * 100 + if amount is not None and denominator + else None + ), + code=entry.get("code"), + ) + + for child in children or []: + add( + "Child Recipients", + str(child.get("name") or ""), + amount=child.get("amount"), + uei=child.get("uei"), + state=child.get("state_province"), + recipient_id=child.get("recipient_id"), + ) + + return rows diff --git a/openbb_platform/providers/government_us/openbb_government_us/treasury/utils/spending.py b/openbb_platform/providers/government_us/openbb_government_us/treasury/utils/spending.py new file mode 100644 index 00000000000..595471ac16f --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/treasury/utils/spending.py @@ -0,0 +1,244 @@ +"""USAspending Spending Explorer helpers.""" + +EARLIEST_FISCAL_YEAR = 2017 + +PERIOD_LABELS: dict[str, str] = { + "2": "Through November", + "3": "Through December (Q1)", + "4": "Through January", + "5": "Through February", + "6": "Through March (Q2)", + "7": "Through April", + "8": "Through May", + "9": "Through June (Q3)", + "10": "Through July", + "11": "Through August", + "12": "Through September (Q4)", +} + +STANDALONE_TYPES = ( + "object_class", + "budget_function", + "budget_subfunction", + "agency", + "federal_account", +) + +SCOPE_ONLY_TYPES = ("program_activity", "recipient", "award", "award_category") + +TYPE_LABELS: dict[str, str] = { + "object_class": "Object Class", + "budget_function": "Budget Function", + "budget_subfunction": "Budget Subfunction", + "agency": "Agency", + "federal_account": "Federal Account", + "program_activity": "Program Activity", + "recipient": "Recipient", + "award": "Award", + "award_category": "Award Category", +} + +SCOPE_DIMENSIONS = ( + "budget_function", + "budget_subfunction", + "agency", + "federal_account", + "program_activity", + "object_class", + "recipient", +) + + +def period_options() -> list[dict[str, str]]: + """Build the reporting-period options, latest first.""" + return [ + {"label": "Latest available", "value": "latest"}, + *({"label": label, "value": value} for value, label in PERIOD_LABELS.items()), + ] + + +async def latest_submission(fiscal_year: int | None = None) -> tuple[int, str]: + """Resolve the most recent published fiscal year and period. + + Parameters + ---------- + fiscal_year : int | None + Fiscal year to resolve within. When None, the latest published year + is used. + + Returns + ------- + tuple[int, str] + The fiscal year and the period, as the source expects it. + + Raises + ------ + OpenBBError + If the source publishes no submission period for the fiscal year. + """ + from openbb_core.app.model.abstract.error import OpenBBError + + from openbb_government_us.treasury.utils.recipient import REQUEST_TIMEOUT + from openbb_government_us.treasury.utils.usaspending import get_usaspending + + response = await get_usaspending( + "references/submission_periods/", timeout=REQUEST_TIMEOUT + ) + periods = [ + row + for row in response.get("available_periods") or [] + if row.get("submission_fiscal_month") + and (fiscal_year is None or row.get("submission_fiscal_year") == fiscal_year) + ] + + if not periods: + raise OpenBBError( + f"No published submission period was found for FY{fiscal_year}." + f" Data begins in FY{EARLIEST_FISCAL_YEAR} Q2 and the most recent" + " year is only published as its periods close." + ) + + latest = max( + periods, + key=lambda row: ( + row["submission_fiscal_year"], + row["submission_fiscal_month"], + ), + ) + + return latest["submission_fiscal_year"], str(latest["submission_fiscal_month"]) + + +def parse_scope(scope: str | None) -> tuple[str, str] | None: + """Split a '([^<]+)
', +) +LINK_PATTERN = re.compile( + r'href="(/media/\d+/[^"?]+)(?:\?v=\d+)?"', +) + + +class _CollectionParser(HTMLParser): + """Collect usa-collection items, keeping each item's own metadata. + + An item nests a usa-collection__meta list holding its Last Updated and + Next Update times, so item boundaries are tracked by tag depth rather + than by matching markup, which cannot bound nesting correctly. + """ + + def __init__(self) -> None: + super().__init__(convert_charrefs=True) + self.items: list[dict] = [] + self._item: dict | None = None + self._depth = 0 + self._heading = False + self._time: str | None = None + + @staticmethod + def _classes(attrs: list[tuple[str, str | None]]) -> str: + """Return an attribute list's class value.""" + return dict(attrs).get("class") or "" + + def handle_starttag(self, tag: str, attrs: list[tuple[str, str | None]]) -> None: + """Open an item, heading, link, or time.""" + values = dict(attrs) + if tag == "li" and ITEM_CLASS in self._classes(attrs): + if self._item is not None: + self.items.append(self._item) + self._item = {"title": None, "paths": [], "dates": {}} + self._depth = 1 + return + if self._item is None: + return + if tag == "li": + self._depth += 1 + elif tag == "h3" and HEADING_CLASS in self._classes(attrs): + self._heading = True + elif tag == "a": + match = MEDIA_PATTERN.match(values.get("href") or "") + if match: + self._item["paths"].append(match.group(1)) + elif tag == "time": + self._time = values.get("datetime") + + def handle_endtag(self, tag: str) -> None: + """Close an item, heading, or time.""" + if self._item is None: + return + if tag == "h3": + self._heading = False + elif tag == "time": + self._time = None + elif tag == "li": + self._depth -= 1 + if self._depth == 0: + self.items.append(self._item) + self._item = None + + def handle_data(self, data: str) -> None: + """Capture heading text and time labels.""" + if self._item is None: + return + if self._heading: + self._item["title"] = (self._item["title"] or "") + data + elif self._time: + kind = TIME_KIND_PATTERN.search(data) + if kind: + self._item["dates"][kind.group(1)] = self._time + + def close(self) -> None: + """Flush an item left open by unbalanced markup.""" + super().close() + if self._item is not None: + self.items.append(self._item) + self._item = None + + +def cache_directory() -> str: + """Resolve the ERS cache directory, honoring the environment override.""" + from openbb_core.app.utils import get_user_cache_directory + + return os.environ.get(CACHE_ENV_VAR) or os.path.join( + get_user_cache_directory(), "usda", "ers" + ) + + +def get_cache() -> Any: + """Open the diskcache store at the resolved cache directory.""" + from diskcache import Cache + + return Cache(cache_directory()) + + +def compute_ttl(next_update: str | None, now: datetime | None = None) -> int: + """Compute a cache TTL in seconds, expiring at the item's next update date. + + Parameters + ---------- + next_update : str | None + The 'Next Update' date as YYYY-MM-DD, when the product page shows one. + now : datetime | None + Reference time, defaulting to the current UTC time. + + Returns + ------- + int + Seconds until the end of the next-update day, clamped to at least one + hour; the one-day default when no future next-update date is known. + """ + if not next_update: + return DEFAULT_FILE_TTL + current = now or datetime.now(timezone.utc) + expiry = datetime.fromisoformat(next_update).replace( + hour=23, minute=59, second=59, tzinfo=timezone.utc + ) + remaining = int((expiry - current).total_seconds()) + if remaining <= 0: + return DEFAULT_FILE_TTL + return max(remaining, MIN_FILE_TTL) + + +def parse_product_page(html: str) -> dict[str, dict]: + """Parse an ERS data-product page into per-file metadata. + + Returns + ------- + dict[str, dict] + Keyed by the media path without the cache-busting query (e.g. + '/media/4962/corn.csv'), each entry holding title, url, + last_updated, and next_update. + """ + parser = _CollectionParser() + parser.feed(html) + parser.close() + files: dict[str, dict] = {} + for item in parser.items: + title = (item["title"] or "").strip() + for path in item["paths"]: + files[path] = { + "title": title or None, + "url": f"{BASE_URL}{path}", + "last_updated": item["dates"].get("Last Updated"), + "next_update": item["dates"].get("Next Update"), + } + return files + + +async def _download(url: str) -> bytes: + """Download a URL as raw bytes.""" + from openbb_core.app.model.abstract.error import OpenBBError + from openbb_core.provider.utils.helpers import amake_request + + async def read_bytes(response, _session) -> bytes: + if response.status != 200: + raise OpenBBError( + f"ERS request failed with status {response.status} -> {url}" + ) + return await response.read() + + result = await amake_request( + url, + response_callback=read_bytes, # ty: ignore[invalid-argument-type] + ) + return result # ty: ignore[invalid-return-type] + + +async def get_product_files(product: str) -> dict[str, dict]: + """Get the file inventory of an ERS data-product page, cached for a day. + + Parameters + ---------- + product : str + The page path after https://www.ers.usda.gov/, e.g. + 'data-products/commodity-costs-and-returns'. + """ + key = f"page:{product}" + with get_cache() as cache: + cached = cache.get(key) + if cached is not None: + return cached + url = f"{BASE_URL}/{product.strip('/')}" + html = (await _download(url)).decode("utf-8", errors="replace") + files = parse_product_page(html) + with get_cache() as cache: + cache.set(key, files, expire=PAGE_TTL) + return files + + +async def afetch_ers_file( + media_path: str, + product: str | None = None, + ttl: int | None = None, +) -> bytes: + """Fetch an ERS media file through the disk cache. + + Parameters + ---------- + media_path : str + The media path, e.g. '/media/4962/corn.csv'. + product : str | None + Product page path used to resolve the file's next-update date for + the TTL. Ignored when ttl is given. + ttl : int | None + Explicit TTL override in seconds. + + Returns + ------- + bytes + The raw file content. + """ + key = f"file:{media_path}" + with get_cache() as cache: + cached = cache.get(key) + if cached is not None: + return cached + if ttl is None and product is not None: + files = await get_product_files(product) + meta = files.get(media_path) + ttl = compute_ttl(meta.get("next_update") if meta else None) + content = await _download(f"{BASE_URL}{media_path}") + with get_cache() as cache: + cache.set(key, content, expire=ttl or DEFAULT_FILE_TTL) + return content diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_commodity_costs_and_returns.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_commodity_costs_and_returns.py new file mode 100644 index 00000000000..f3df768c00a --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_commodity_costs_and_returns.py @@ -0,0 +1,342 @@ +"""USDA ERS Commodity Costs and Returns file catalog and parsers.""" + +import csv +import re +from io import StringIO + +BASE_URL = "https://www.ers.usda.gov" +PRODUCT_PAGE = "data-products/commodity-costs-and-returns" + +COMMODITY_FILES: dict[str, dict] = { + "corn": {"media_id": 4962, "slug": "corn", "label": "Corn"}, + "cotton": {"media_id": 4964, "slug": "cotton", "label": "Cotton"}, + "barley": {"media_id": 4966, "slug": "barley", "label": "Barley"}, + "peanuts": {"media_id": 4968, "slug": "peanuts", "label": "Peanuts"}, + "rice": {"media_id": 4970, "slug": "rice", "label": "Rice"}, + "sorghum": {"media_id": 4972, "slug": "sorghum", "label": "Sorghum"}, + "oats": {"media_id": 4974, "slug": "oats", "label": "Oats"}, + "soybeans": {"media_id": 4976, "slug": "soybeans", "label": "Soybeans"}, + "wheat": {"media_id": 4978, "slug": "wheat", "label": "Wheat"}, + "milk": {"media_id": 4980, "slug": "milk", "label": "Milk"}, + "cow_calf": {"media_id": 4982, "slug": "cow-calf", "label": "Cow-calf"}, + "hogs_all": { + "media_id": 4984, + "slug": "hogs-all", + "label": "Hogs (all enterprises)", + }, + "hogs_farrow_finish": { + "media_id": 4986, + "slug": "hogs-farrow-finish", + "label": "Hogs, farrow-to-finish", + }, + "hogs_farrow_feeder": { + "media_id": 4988, + "slug": "hogs-farrow-feeder", + "label": "Hogs, farrow-to-feeder", + }, + "hogs_feeder_finish": { + "media_id": 4990, + "slug": "hogs-feeder-finish", + "label": "Hogs, feeder-to-finish", + }, + "hogs_farrow_weanling": { + "media_id": 4992, + "slug": "hogs-farrow-weanling", + "label": "Hogs, farrow-to-weanling", + }, + "hogs_weanling_feeder": { + "media_id": 4994, + "slug": "hogs-weanling-feeder", + "label": "Hogs, weanling-to-feeder", + }, +} + +US_TOTAL = "U.S. total" + +COMMODITY_REGIONS: dict[str, list[str]] = { + "corn": [ + US_TOTAL, + "Eastern Uplands", + "Heartland", + "Northern Crescent", + "Northern Great Plains", + "Prairie Gateway", + "Southern Seaboard", + ], + "cotton": [ + US_TOTAL, + "Eastern Uplands", + "Fruitful Rim", + "Heartland", + "Mississippi Portal", + "Prairie Gateway", + "Southern Seaboard", + ], + "barley": [ + US_TOTAL, + "Basin and Range", + "Fruitful Rim", + "Heartland", + "Northern Crescent", + "Northern Great Plains", + ], + "peanuts": [ + US_TOTAL, + "Fruitful Rim", + "Prairie Gateway", + "Southern Seaboard (AL, GA)", + "Southern Seaboard (VA, NC, SC)", + ], + "rice": [ + US_TOTAL, + "Arkansas Non-Delta", + "California", + "Gulf Coast", + "Mississippi River Delta", + ], + "sorghum": [ + US_TOTAL, + "Eastern Uplands", + "Fruitful Rim", + "Heartland", + "Mississippi Portal", + "Northern Great Plains", + "Prairie Gateway", + ], + "oats": [ + US_TOTAL, + "Eastern Uplands", + "Heartland", + "Northern Crescent", + "Northern Great Plains", + "Prairie Gateway", + ], + "soybeans": [ + US_TOTAL, + "Eastern Uplands", + "Heartland", + "Mississippi Portal", + "Northern Crescent", + "Northern Great Plains", + "Prairie Gateway", + "Southern Seaboard", + ], + "wheat": [ + US_TOTAL, + "Basin and Range", + "Fruitful Rim", + "Heartland", + "Mississippi Portal", + "Northern Crescent", + "Northern Great Plains", + "Prairie Gateway", + "Southern Seaboard", + ], + "milk": [ + US_TOTAL, + "Basin and Range", + "Eastern Uplands", + "Fruitful Rim", + "Heartland", + "Northern Crescent", + "Prairie Gateway", + "Southern Seaboard", + ], + "cow_calf": [ + US_TOTAL, + "Basin and Range", + "Eastern Uplands", + "Fruitful Rim", + "Heartland", + "Mississippi Portal", + "Northern Great Plains", + "Prairie Gateway", + "Southern Seaboard", + ], + "hogs_all": [ + US_TOTAL, + "Eastern Uplands", + "Heartland", + "Mississippi Portal", + "Northern Crescent", + "Northern Great Plains", + "Prairie Gateway", + "Southern Seaboard", + ], + "hogs_farrow_finish": [ + US_TOTAL, + "Eastern Uplands", + "Heartland", + "Northern Crescent", + "Prairie Gateway", + "Southern Seaboard", + ], + "hogs_farrow_feeder": [ + US_TOTAL, + "Eastern Uplands", + "Southern Seaboard", + ], + "hogs_feeder_finish": [ + US_TOTAL, + "Eastern Uplands", + "Heartland", + "Northern Crescent", + "Southern Seaboard", + ], + "hogs_farrow_weanling": [US_TOTAL], + "hogs_weanling_feeder": [ + US_TOTAL, + "Heartland", + "Southern Seaboard", + ], +} + +CATEGORY_CHOICES: dict[str, str] = { + "gross_value_of_production": "Gross value of production", + "operating_costs": "Operating costs", + "net_value": "Net value", + "allocated_overhead": "Allocated overhead", + "costs_listed": "Costs listed", + "supporting_information": "Supporting information", + "production_practices": "Production practices", + "size_of_operation": "Size of operation", + "production_arrangement": "Production arrangement", +} + +CATEGORY_ORDER: dict[str, int] = { + "Gross value of production": 0, + "Operating costs": 1, + "Net value": 2, + "Allocated overhead": 3, + "Costs listed": 4, + "Size of operation": 5, + "Production arrangement": 6, + "Supporting information": 7, + "Production practices": 8, +} + +SUPERSCRIPT_PATTERN = re.compile(r"[¹²³⁴⁵⁶⁷⁸⁹⁰]") +WHITESPACE_PATTERN = re.compile(r"\s+") + + +def media_path(commodity: str) -> str: + """Build the media path for one commodity's tidy CSV. + + Parameters + ---------- + commodity : str + Commodity key from COMMODITY_FILES. + + Returns + ------- + str + The '/media/{id}/{slug}.csv' path of the commodity's CSV file. + """ + entry = COMMODITY_FILES[commodity] + return f"/media/{entry['media_id']}/{entry['slug']}.csv" + + +def build_url(commodity: str) -> str: + """Build the download URL for one commodity's tidy CSV. + + Parameters + ---------- + commodity : str + Commodity key from COMMODITY_FILES. + + Returns + ------- + str + Full URL of the commodity's CSV file. + """ + return f"{BASE_URL}{media_path(commodity)}" + + +def clean_item(item: str) -> str: + """Strip footnote superscripts and normalize whitespace in an item label. + + Parameters + ---------- + item : str + Item label as published, e.g. 'Fertilizer ' or 'Custom services '. + + Returns + ------- + str + Cleaned label, e.g. 'Fertilizer'. + """ + return WHITESPACE_PATTERN.sub(" ", SUPERSCRIPT_PATTERN.sub("", item)).strip() + + +def category_order(category: str) -> int: + """Return the budget-section sort rank of a published category label. + + Parameters + ---------- + category : str + Published Category value, e.g. 'Operating costs'. + + Returns + ------- + int + The section's rank; a large sentinel for any unmapped category. + """ + return CATEGORY_ORDER.get(category, 99) + + +def parse_rows(text: str, commodity: str) -> list[dict]: + """Parse one commodity's tidy CSV text into row records. + + Parameters + ---------- + text : str + Decoded CSV text with columns Commodity, Category, Item, Units, + Size, Region, Country, Year, Value, and Survey base year. + commodity : str + Commodity key from COMMODITY_FILES tagged on each record. + + Returns + ------- + list[dict] + Records with commodity, commodity_key, category, item, units, + region, year, value, and survey_base_year keys, skipping rows with + an empty Value. + """ + rows: list[dict] = [] + for row in csv.DictReader(StringIO(text)): + value = (row.get("Value") or "").strip() + if not value: + continue + rows.append( + { + "commodity_key": commodity, + "commodity": (row.get("Commodity") or "").strip(), + "category": (row.get("Category") or "").strip(), + "item": clean_item(row.get("Item") or ""), + "units": (row.get("Units") or "").strip(), + "region": (row.get("Region") or "").strip(), + "year": int((row.get("Year") or "").strip()), + "value": float(value), + "survey_base_year": (row.get("Survey base year") or "").strip(), + } + ) + return rows + + +async def afetch_commodity(commodity: str, **kwargs) -> list[dict]: + """Download and parse one commodity's tidy CSV through the ERS disk cache. + + Parameters + ---------- + commodity : str + Commodity key from COMMODITY_FILES. + + Returns + ------- + list[dict] + Row records from parse_rows. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + content = await afetch_ers_file(media_path(commodity), product=PRODUCT_PAGE) + return parse_rows(content.decode("utf-8-sig", errors="replace"), commodity) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_commuting_zones_and_labor_market_areas.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_commuting_zones_and_labor_market_areas.py new file mode 100644 index 00000000000..9ec217592cb --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_commuting_zones_and_labor_market_areas.py @@ -0,0 +1,416 @@ +"""USDA ERS Commuting Zones and Labor Market Areas vintage catalog and parsers.""" + +import csv +import io +import math +import zipfile +from io import StringIO + +BASE_URL = "https://www.ers.usda.gov" +PRODUCT_PAGE = "data-products/commuting-zones-and-labor-market-areas" + +DEFAULT_VINTAGE = "2020" + +VINTAGE_FILES: dict[str, dict] = { + "2020": { + "media_path": "/media/6968/2020-commuting-zones.csv", + "format": "csv", + "label": "2020 Commuting Zones", + }, + "preliminary_2020": { + "media_path": "/media/6969/preliminary-2020-commuting-zones.zip", + "format": "zip_csv", + "member_prefix": "preliminary-2020-commuting-zones", + "label": "Preliminary 2020 Commuting Zones", + }, + "2000": { + "media_path": "/media/5688/2000-commuting-zones.xls", + "format": "xls", + "sheet": "CZ00Equiv", + "label": "2000 Commuting Zones", + }, + "1980_1990": { + "media_path": "/media/5687/1980-and-1990-commuting-zones" + + "-and-labor-market-areas.xls", + "format": "xls", + "sheet": "CZLMA903", + "label": "1980 and 1990 Commuting Zones and Labor Market Areas", + }, +} + +STATE_FIPS: dict[str, str] = { + "01": "Alabama", + "02": "Alaska", + "04": "Arizona", + "05": "Arkansas", + "06": "California", + "08": "Colorado", + "09": "Connecticut", + "10": "Delaware", + "11": "District of Columbia", + "12": "Florida", + "13": "Georgia", + "15": "Hawaii", + "16": "Idaho", + "17": "Illinois", + "18": "Indiana", + "19": "Iowa", + "20": "Kansas", + "21": "Kentucky", + "22": "Louisiana", + "23": "Maine", + "24": "Maryland", + "25": "Massachusetts", + "26": "Michigan", + "27": "Minnesota", + "28": "Mississippi", + "29": "Missouri", + "30": "Montana", + "31": "Nebraska", + "32": "Nevada", + "33": "New Hampshire", + "34": "New Jersey", + "35": "New Mexico", + "36": "New York", + "37": "North Carolina", + "38": "North Dakota", + "39": "Ohio", + "40": "Oklahoma", + "41": "Oregon", + "42": "Pennsylvania", + "44": "Rhode Island", + "45": "South Carolina", + "46": "South Dakota", + "47": "Tennessee", + "48": "Texas", + "49": "Utah", + "50": "Vermont", + "51": "Virginia", + "53": "Washington", + "54": "West Virginia", + "55": "Wisconsin", + "56": "Wyoming", + "72": "Puerto Rico", +} + +STATE_NAMES: tuple[str, ...] = tuple(sorted(set(STATE_FIPS.values()))) + +RECORD_KEYS: tuple[str, ...] = ( + "fips", + "state", + "county_name", + "commuting_zone", + "commuting_zone_name", + "cz_containment", + "cz_avg_containment", + "cz_1990", + "cz_1980", + "metro_area", + "county_population", + "commuting_zone_population", + "distance", + "beale_code", + "msa_code", + "msa_name", + "place_code", +) + + +def state_name(fips: str) -> str | None: + """Return the full state name for a county FIPS code. + + Parameters + ---------- + fips : str + Five-digit county FIPS code, e.g. '01001'. + + Returns + ------- + str | None + The full state name, or None when the two-digit prefix is unknown. + """ + return STATE_FIPS.get(fips[:2]) + + +def _is_missing(value: object) -> bool: + """Return whether a cell is a null, blank, or NaN placeholder.""" + if value is None: + return True + if isinstance(value, float) and math.isnan(value): + return True + return isinstance(value, str) and not value.strip() + + +def _blank_record() -> dict: + """Return a record with every union field initialized to None.""" + return dict.fromkeys(RECORD_KEYS) + + +def _text(value: object) -> str | None: + """Return a stripped string, or None for a blank or NaN cell.""" + if _is_missing(value): + return None + return str(value).strip() or None + + +def _code(value: object) -> str | None: + """Return an integer-like cell as a decimal-free string, or None. + + Parameters + ---------- + value : object + Cell holding a code as an int, a float such as 60.0, or a string. + + Returns + ------- + str | None + The code without a trailing '.0', or None for a blank or NaN cell. + """ + if _is_missing(value): + return None + if isinstance(value, str): + text = value.strip() + return text[:-2] if text.endswith(".0") else text + if isinstance(value, float): + return str(int(value)) + return str(value) + + +def _padded(value: object, width: int) -> str | None: + """Return an integer-like code left-padded with zeros to a fixed width.""" + code = _code(value) + return None if code is None else code.zfill(width) + + +def _msa_code(value: object) -> str | None: + """Return a four-digit MSA code, treating an all-zero code as no MSA.""" + code = _padded(value, 4) + if code is None or set(code) == {"0"}: + return None + return code + + +def _beale(value: object) -> str | None: + """Return a Beale rural-urban continuum code, coercing '.' to None.""" + if _is_missing(value): + return None + text = str(value).strip() + if text.endswith(".0"): + text = text[:-2] + return None if text == "." else text or None + + +def _num(value: object) -> float | None: + """Return a cell as a float, or None for a blank or non-numeric cell.""" + if _is_missing(value): + return None + if isinstance(value, str): + try: + return float(value.strip()) + except ValueError: + return None + if isinstance(value, (int, float)): + return float(value) + return float(str(value)) + + +def _int(value: object) -> int | None: + """Return a cell as an integer count, or None for a blank cell.""" + number = _num(value) + return None if number is None else int(number) + + +def parse_2020(text: str) -> list[dict]: + """Parse the 2020 commuting-zones CSV into county records. + + Parameters + ---------- + text : str + Decoded CSV text with columns FIPStxt, CountyName, StateName, CZ2020, + CZName, CZContainment, and CZAvgContainment. + + Returns + ------- + list[dict] + One record per county, keyed by RECORD_KEYS. + """ + records: list[dict] = [] + for row in csv.DictReader(StringIO(text)): + fips = (row.get("FIPStxt") or "").strip() + if not fips: + continue + record = _blank_record() + record.update( + fips=fips, + state=state_name(fips) or _text(row.get("StateName")), + county_name=_text(row.get("CountyName")), + commuting_zone=_code(row.get("CZ2020")), + commuting_zone_name=_text(row.get("CZName")), + cz_containment=_num(row.get("CZContainment")), + cz_avg_containment=_num(row.get("CZAvgContainment")), + ) + records.append(record) + return records + + +def parse_preliminary_2020(text: str) -> list[dict]: + """Parse the preliminary 2020 commuting-zones CSV into county records. + + Parameters + ---------- + text : str + Decoded CSV text with columns FIPStxt, State, CountyName, and + PreliminaryCZ2020. + + Returns + ------- + list[dict] + One record per county, keyed by RECORD_KEYS. + """ + records: list[dict] = [] + for row in csv.DictReader(StringIO(text)): + fips = (row.get("FIPStxt") or "").strip() + if not fips: + continue + record = _blank_record() + record.update( + fips=fips, + state=state_name(fips), + county_name=_text(row.get("CountyName")), + commuting_zone=_code(row.get("PreliminaryCZ2020")), + ) + records.append(record) + return records + + +def parse_2000(content: bytes) -> list[dict]: + """Parse the 2000 commuting-zones workbook into county records. + + Parameters + ---------- + content : bytes + Raw legacy .xls bytes of the CZ00Equiv sheet. + + Returns + ------- + list[dict] + One record per county, keyed by RECORD_KEYS. + """ + import pandas as pd + + frame = pd.read_excel(io.BytesIO(content), sheet_name="CZ00Equiv", engine="xlrd") + records: list[dict] = [] + for rec in frame.to_dict(orient="records"): + fips = _padded(rec.get("FIPS"), 5) + if fips is None: + continue + record = _blank_record() + record.update( + fips=fips, + state=state_name(fips), + county_name=_text(rec.get("County name")), + commuting_zone=_code(rec.get("Commuting Zone ID, 2000")), + cz_1990=_code(rec.get("Commuting Zone ID, 1990")), + cz_1980=_code(rec.get("Commuting Zone ID, 1980")), + metro_area=_text(rec.get("Metropolitan area, 2003")), + county_population=_int(rec.get("County population 2000")), + commuting_zone_population=_int(rec.get("Commuting zone population 2000")), + ) + records.append(record) + return records + + +def parse_1980_1990(content: bytes) -> list[dict]: + """Parse the 1980/1990 commuting-zones workbook into county records. + + Parameters + ---------- + content : bytes + Raw legacy .xls bytes of the CZLMA903 sheet. + + Returns + ------- + list[dict] + One record per county, keyed by RECORD_KEYS. + """ + import pandas as pd + + frame = pd.read_excel(io.BytesIO(content), sheet_name="CZLMA903", engine="xlrd") + records: list[dict] = [] + for rec in frame.to_dict(orient="records"): + fips = _padded(rec.get("County FIPS Code"), 5) + if fips is None: + continue + record = _blank_record() + record.update( + fips=fips, + state=state_name(fips), + county_name=_text(rec.get("County name")), + commuting_zone=_code(rec.get("CZ90")), + cz_1980=_code(rec.get("CZ80")), + commuting_zone_name=_text( + rec.get("Name of largest place in commuting zone") + ), + county_population=_int(rec.get("Population 1990")), + distance=_num(rec.get("Distance")), + beale_code=_beale(rec.get("Rural-urban Continuum Code 1993 (Beale Code)")), + msa_code=_msa_code(rec.get("MSA 1993")), + msa_name=_text(rec.get("MSA name")), + place_code=_padded(rec.get("State place code"), 7), + ) + records.append(record) + return records + + +def extract_inner_csv(zip_bytes: bytes, prefix: str) -> str: + """Extract and decode one CSV member from a commuting-zones zip archive. + + Parameters + ---------- + zip_bytes : bytes + Raw bytes of the zip archive. + prefix : str + Inner-file basename prefix of the CSV member to read. + + Returns + ------- + str + The decoded CSV text of the matching '.csv' member. + """ + with zipfile.ZipFile(io.BytesIO(zip_bytes)) as archive: + name = next( + member + for member in archive.namelist() + if member.rsplit("/", 1)[-1].startswith(prefix) + and member.lower().endswith(".csv") + ) + raw = archive.read(name) + return raw.decode("utf-8-sig", errors="replace") + + +async def afetch_vintage(vintage: str) -> list[dict]: + """Download and parse one vintage's county lookup through the ERS cache. + + Parameters + ---------- + vintage : str + Vintage key from VINTAGE_FILES. + + Returns + ------- + list[dict] + One record per county, keyed by RECORD_KEYS. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + config = VINTAGE_FILES[vintage] + content = await afetch_ers_file(config["media_path"], product=PRODUCT_PAGE) + if vintage == "2020": + return parse_2020(content.decode("utf-8-sig", errors="replace")) + if vintage == "preliminary_2020": + return parse_preliminary_2020( + extract_inner_csv(content, config["member_prefix"]) + ) + if vintage == "2000": + return parse_2000(content) + return parse_1980_1990(content) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_cost_estimates_of_foodborne_illnesses.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_cost_estimates_of_foodborne_illnesses.py new file mode 100644 index 00000000000..d7570c18010 --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_cost_estimates_of_foodborne_illnesses.py @@ -0,0 +1,182 @@ +"""USDA ERS Cost Estimates of Foodborne Illnesses file catalog and wide parser.""" + +import csv +from io import StringIO + +from openbb_government_us.utils.serializers import is_null_token + +PRODUCT_PAGE = "data-products/cost-estimates-of-foodborne-illnesses" + +COST_ESTIMATES_TABLES: dict[str, dict] = { + "cases_and_cost": { + "media_path": "/media/5317/cases-total-cost-and-per-case-cost-by-pathogen.csv", + "label": "Cases, total cost, and per-case cost, by pathogen", + "value_columns": ( + { + "source": "Mean number of cases", + "label": "Mean number of cases", + "kind": "int", + }, + { + "source": "Mean total cost (millions)", + "label": "Mean total cost (2023 $ millions)", + "kind": "float", + }, + { + "source": " Mean per-case cost", + "label": "Mean per-case cost (2023 $)", + "kind": "float", + }, + ), + }, + "health_outcome_cost": { + "media_path": "/media/5319/mean-cost-for-specific-health-outcomes-by-pathogen.csv", + "label": "Mean cost for specific health outcomes, by pathogen", + "value_columns": ( + { + "source": "No physician's visit mean", + "label": "No physician's visit", + "kind": "float", + }, + { + "source": "No physician's visit CrI", + "label": "No physician's visit (CrI)", + "kind": "text", + }, + { + "source": "Physician's visit only mean", + "label": "Physician's visit only", + "kind": "float", + }, + { + "source": "Physician's visit only CrI", + "label": "Physician's visit only (CrI)", + "kind": "text", + }, + { + "source": "Hospitalized, recovered", + "label": "Hospitalized, recovered", + "kind": "float", + }, + { + "source": "Hospitalized, recovered Crl", + "label": "Hospitalized, recovered (CrI)", + "kind": "text", + }, + { + "source": "Hospitalized, died", + "label": "Hospitalized, died", + "kind": "float", + }, + { + "source": "Hospitalized, died Crl", + "label": "Hospitalized, died (CrI)", + "kind": "text", + }, + { + "source": "Chronic outcomes", + "label": "Chronic outcomes", + "kind": "float", + }, + { + "source": "Chronic outcomes Crl", + "label": "Chronic outcomes (CrI)", + "kind": "text", + }, + { + "source": "Total", + "label": "Total", + "kind": "float", + }, + { + "source": "Total Crl", + "label": "Total (CrI)", + "kind": "text", + }, + ), + }, +} + + +def coerce_value(raw: str | None, kind: str): + """Coerce a raw cell to an int, float, stripped text, or None. + + Parameters + ---------- + raw : str | None + The raw cell value. + kind : str + The target kind: 'int', 'float', or 'text'. + + Returns + ------- + int | float | str | None + The coerced value; None for empty cells and placeholder tokens. + """ + value = (raw or "").strip() + if not value or is_null_token(value): + return None + if kind == "text": + return value + number = float(value) + return int(number) if kind == "int" else number + + +def parse_table(text: str, table: str) -> list[dict]: + """Parse one cost-estimates CSV into wide per-pathogen records. + + Parameters + ---------- + text : str + Decoded text of the selected table's CSV. + table : str + Table slug from COST_ESTIMATES_TABLES. + + Returns + ------- + list[dict] + One record per pathogen, carrying the source order, table slug, + pathogen classification, pathogen name, and the table's + human-labeled value columns. Rows without a pathogen are skipped. + """ + config = COST_ESTIMATES_TABLES[table] + records: list[dict] = [] + for order, row in enumerate(csv.DictReader(StringIO(text))): + pathogen = (row.get("Pathogen") or "").strip() + if not pathogen: + continue + record: dict = { + "_order": order, + "_table": table, + "pathogen_classification": ( + row.get("Pathogen classification") or "" + ).strip() + or None, + "pathogen": pathogen, + } + for column in config["value_columns"]: + record[column["label"]] = coerce_value( + row.get(column["source"]), column["kind"] + ) + records.append(record) + return records + + +async def afetch_table(table: str, **kwargs) -> list[dict]: + """Download and parse one cost-estimates table through the ERS disk cache. + + Parameters + ---------- + table : str + Table slug from COST_ESTIMATES_TABLES. + + Returns + ------- + list[dict] + Wide per-pathogen records from parse_table. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + media_path = COST_ESTIMATES_TABLES[table]["media_path"] + content = await afetch_ers_file(media_path, product=PRODUCT_PAGE) + return parse_table(content.decode("utf-8-sig", errors="replace"), table) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_cotton_wool_and_textile_data.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_cotton_wool_and_textile_data.py new file mode 100644 index 00000000000..aed0bf2cf90 --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_cotton_wool_and_textile_data.py @@ -0,0 +1,485 @@ +"""USDA ERS Cotton, Wool, and Textile Data file catalog and long-format parsers.""" + +import csv +from io import StringIO + +BASE_URL = "https://www.ers.usda.gov" +YEARBOOK_PRODUCT = "data-products/cotton-wool-and-textile-data/cotton-and-wool-yearbook" +RFE_PRODUCT = ( + "data-products/cotton-wool-and-textile-data/" + "raw-fiber-equivalents-of-us-textile-trade-data" +) + +M_US_COTTON = "/media/7122/us-cotton-supply-and-demand.csv" +M_PRICES = "/media/7123/cotton-prices.csv" +M_WORLD = "/media/7124/world-cotton-supply-and-demand.csv" +M_FIBER_DEMAND = "/media/7127/us-fiber-demand.csv" +M_WOOL = "/media/7126/us-wool-supply-and-demand.csv" +M_TEXTILE = "/media/7125/us-textile-fiber-trade.csv" +M_RFE_IMPORTS_BY_FIBER = "/media/5485/table-1-us-textile-imports-by-fiber.csv" +M_RFE_EXPORTS_BY_FIBER = "/media/5487/table-2-us-textile-exports-by-fiber.csv" +M_RFE_IMPORTS_BY_ORIGIN = ( + "/media/5489/table-3-us-cotton-textile-and-apparel-imports-by-origin.csv" +) +M_RFE_EXPORTS_BY_DESTINATION = ( + "/media/5491/table-4-us-cotton-textile-and-apparel-exports-by-destination.csv" +) + +ROW_DIM_FIELDS = ( + "geography", + "measure", + "trade_category", + "textile_group", + "item", +) + +COLUMN_DIM_FIELDS = ("measure", "trade_category", "textile_group", "item") + +MONTH_NAMES = { + "1": "Jan", + "2": "Feb", + "3": "Mar", + "4": "Apr", + "5": "May", + "6": "Jun", + "7": "Jul", + "8": "Aug", + "9": "Sep", + "10": "Oct", + "11": "Nov", + "12": "Dec", +} + +MONTH_ORDER = {name: index for index, name in enumerate(MONTH_NAMES.values(), start=1)} + +FREQUENCY_RANK = {"Annual": 0, "Quarterly": 1, "Monthly": 2} + +TABLE_NUMBER_COLS = ("table_number", "table_num") + + +def classify_frequency(period) -> str: + """Classify a within-year period token into a reporting frequency. + + Parameters + ---------- + period : object + The record's period token: None, 'annual', 'Season', a month number + '1'..'12', or a quarter label 'Q1'..'Q4'. + + Returns + ------- + str + 'Monthly', 'Quarterly', or 'Annual'. + """ + if period is None: + return "Annual" + token = str(period).strip() + if token in MONTH_NAMES: + return "Monthly" + if token.upper() in ("Q1", "Q2", "Q3", "Q4"): + return "Quarterly" + return "Annual" + + +async def table_frequencies(table: str, **kwargs) -> list[str]: + """List the reporting frequencies a table publishes, coarsest first. + + Parameters + ---------- + table : str + Table key from CWT_TABLES. + + Returns + ------- + list[str] + Distinct frequencies present in the table, ordered Annual, Quarterly, + Monthly. + """ + records = await afetch_table(table) + found = {classify_frequency(record["period"]) for record in records} + return sorted(found, key=lambda freq: FREQUENCY_RANK.get(freq, 9)) + + +_DEFAULTS = { + "product": YEARBOOK_PRODUCT, + "year_col": "period", + "period_col": None, + "series_col": "category", + "unit_col": "units", + "dims": (), + "normalize_measure": False, + "pivot": "series", +} + + +def _cfg(label: str, media: str, number: str, **overrides) -> dict: + """Build one table configuration, filling the shared defaults.""" + return {"label": label, "media": media, "number": number, **_DEFAULTS, **overrides} + + +CWT_TABLES: dict[str, dict] = { + "cotton_supply_and_use": _cfg("U.S. cotton - 1. Supply and use", M_US_COTTON, "1"), + "upland_cotton_supply_and_use": _cfg( + "U.S. cotton - 2. Upland supply and use", M_US_COTTON, "2" + ), + "els_cotton_supply_and_use": _cfg( + "U.S. cotton - 3. Extra Long Staple supply and use", M_US_COTTON, "3" + ), + "upland_planted_acreage_by_state": _cfg( + "U.S. cotton - 4. Upland planted acreage by State", + M_US_COTTON, + "4", + series_col="geography", + ), + "upland_harvested_acreage_by_state": _cfg( + "U.S. cotton - 5. Upland harvested acreage by State", + M_US_COTTON, + "5", + series_col="geography", + ), + "upland_lint_yield_by_state": _cfg( + "U.S. cotton - 6. Upland lint yield by State", + M_US_COTTON, + "6", + series_col="geography", + ), + "upland_production_by_state": _cfg( + "U.S. cotton - 7. Upland production by State", + M_US_COTTON, + "7", + series_col="geography", + ), + "els_acreage_by_state": _cfg( + "U.S. cotton - 8. Extra Long Staple acreage by State", + M_US_COTTON, + "8", + series_col="geography", + dims=(("measure", "category"),), + normalize_measure=True, + ), + "els_production_and_yield_by_state": _cfg( + "U.S. cotton - 9. Extra Long Staple production and yield by State", + M_US_COTTON, + "9", + series_col="geography", + dims=(("measure", "category"),), + normalize_measure=True, + ), + "cotton_supply_and_disappearance_monthly": _cfg( + "U.S. cotton - 10. Supply and disappearance, by month", + M_US_COTTON, + "10", + period_col="month", + ), + "upland_farm_spot_mill_prices": _cfg( + "Prices - 11. Upland farm, spot, and mill prices", M_PRICES, "11" + ), + "fiber_prices": _cfg("Prices - 12. Fiber prices", M_PRICES, "12"), + "cotton_price_quotes_monthly": _cfg( + "Prices - 13. Cotton price quotations, monthly", + M_PRICES, + "13", + period_col="time_period", + ), + "cotton_price_quotes_annual": _cfg( + "Prices - 14. Cotton price quotations, annual", M_PRICES, "14" + ), + "world_cotton_supply_and_use": _cfg( + "World - 15. World cotton supply and use", M_WORLD, "15" + ), + "foreign_cotton_supply_and_use": _cfg( + "World - 16. Foreign cotton supply and use", M_WORLD, "16" + ), + "cotton_exports_major_exporters": _cfg( + "World - 17. Exports, major foreign exporters", + M_WORLD, + "17", + series_col="geography", + ), + "cotton_imports_major_importers": _cfg( + "World - 18. Imports, major importers", + M_WORLD, + "18", + series_col="geography", + ), + "former_soviet_union_cotton_supply_and_use": _cfg( + "World - 19. Former Soviet Union supply and use", M_WORLD, "19" + ), + "brazil_cotton_supply_and_use": _cfg( + "World - 20. Brazil supply and use", M_WORLD, "20" + ), + "turkey_cotton_supply_and_use": _cfg( + "World - 21. Turkey supply and use", M_WORLD, "21" + ), + "china_cotton_supply_and_use": _cfg( + "World - 22. China supply and use", M_WORLD, "22" + ), + "india_cotton_supply_and_use": _cfg( + "World - 23. India supply and use", M_WORLD, "23" + ), + "pakistan_cotton_supply_and_use": _cfg( + "World - 24. Pakistan supply and use", M_WORLD, "24" + ), + "cotton_fiber_demand_total_and_per_capita": _cfg( + "U.S. fiber demand - 25. Cotton fiber demand, total and per capita", + M_FIBER_DEMAND, + "25", + ), + "per_capita_cotton_demand": _cfg( + "U.S. fiber demand - 26. Per capita domestic cotton demand", + M_FIBER_DEMAND, + "26", + ), + "cotton_and_synthetic_mill_use": _cfg( + "U.S. fiber demand - 27. Cotton and synthetic staple mill use", + M_FIBER_DEMAND, + "27", + ), + "wool_supply_and_use": _cfg("Wool - 28. Supply and use", M_WOOL, "28"), + "raw_wool_imports_for_mill_use": _cfg( + "Wool - 29. Raw wool imports for mill use", M_WOOL, "29" + ), + "raw_wool_imports_by_origin": _cfg( + "Wool - 30. Raw wool imports by country of origin", + M_WOOL, + "30", + dims=(("geography", "geography"),), + ), + "raw_wool_exports_by_destination": _cfg( + "Wool - 31. Raw wool exports by country of destination", + M_WOOL, + "31", + dims=(("geography", "geography"),), + ), + "wool_tops_trade": _cfg( + "Wool - 32. Trade in wool tops", + M_WOOL, + "32", + dims=(("geography", "geography"),), + ), + "shorn_wool_prices": _cfg("Wool - 33. Shorn wool prices", M_WOOL, "33"), + "mohair_exports_by_destination": _cfg( + "Wool - 34. Mohair exports by country of destination", + M_WOOL, + "34", + series_col="geography", + ), + "raw_fiber_equivalent_textile_manufactures": _cfg( + "Textile trade - 35. Raw-fiber equivalent of manufactures", + M_TEXTILE, + "35", + series_col="fiber", + dims=(("trade_category", "trade_category"),), + ), + "raw_cotton_equivalent_imports": _cfg( + "Textile trade - 36. Raw-cotton equivalent of imports", + M_TEXTILE, + "36", + series_col="textile_category", + period_col="month", + dims=(("textile_group", "textile_group"),), + ), + "raw_cotton_equivalent_exports": _cfg( + "Textile trade - 37. Raw-cotton equivalent of exports", + M_TEXTILE, + "37", + series_col="textile_category", + period_col="month", + dims=(("textile_group", "textile_group"),), + ), + "raw_linen_equivalent_imports": _cfg( + "Textile trade - 38. Raw-linen equivalent of imports", + M_TEXTILE, + "38", + series_col="textile_category", + period_col="month", + dims=(("textile_group", "textile_group"),), + ), + "raw_linen_equivalent_exports": _cfg( + "Textile trade - 39. Raw-linen equivalent of exports", + M_TEXTILE, + "39", + series_col="textile_category", + period_col="month", + dims=(("textile_group", "textile_group"),), + ), + "raw_wool_equivalent_imports": _cfg( + "Textile trade - 40. Raw-wool equivalent of imports", + M_TEXTILE, + "40", + series_col="textile_category", + period_col="month", + dims=(("textile_group", "textile_group"),), + ), + "raw_wool_equivalent_exports": _cfg( + "Textile trade - 41. Raw-wool equivalent of exports", + M_TEXTILE, + "41", + series_col="textile_category", + period_col="month", + dims=(("textile_group", "textile_group"),), + ), + "raw_silk_equivalent_imports": _cfg( + "Textile trade - 42. Raw-silk equivalent of imports", + M_TEXTILE, + "42", + series_col="textile_category", + period_col="month", + dims=(("textile_group", "textile_group"),), + ), + "raw_silk_equivalent_exports": _cfg( + "Textile trade - 43. Raw-silk equivalent of exports", + M_TEXTILE, + "43", + series_col="textile_category", + period_col="month", + dims=(("textile_group", "textile_group"),), + ), + "raw_synthetic_equivalent_imports": _cfg( + "Textile trade - 44. Raw-synthetic equivalent of imports", + M_TEXTILE, + "44", + series_col="textile_category", + period_col="month", + dims=(("textile_group", "textile_group"),), + ), + "raw_synthetic_equivalent_exports": _cfg( + "Textile trade - 45. Raw-synthetic equivalent of exports", + M_TEXTILE, + "45", + series_col="textile_category", + period_col="month", + dims=(("textile_group", "textile_group"),), + ), + "rfe_textile_imports_by_fiber": _cfg( + "Raw-fiber equivalent - T1. Textile imports, by fiber", + M_RFE_IMPORTS_BY_FIBER, + "1", + product=RFE_PRODUCT, + series_col="fiber", + unit_col="unit", + dims=(("item", "Item"),), + ), + "rfe_textile_exports_by_fiber": _cfg( + "Raw-fiber equivalent - T2. Textile exports, by fiber", + M_RFE_EXPORTS_BY_FIBER, + "2", + product=RFE_PRODUCT, + year_col="year", + series_col="fiber", + unit_col="unit", + dims=(("item", "Item"),), + ), + "rfe_cotton_textile_imports_by_origin": _cfg( + "Raw-fiber equivalent - T3. Cotton textile imports, by origin", + M_RFE_IMPORTS_BY_ORIGIN, + "3", + product=RFE_PRODUCT, + year_col="year", + series_col=None, + unit_col="unit", + dims=(("geography", "Region/country"),), + pivot="years", + ), + "rfe_cotton_textile_exports_by_destination": _cfg( + "Raw-fiber equivalent - T4. Cotton textile exports, by destination", + M_RFE_EXPORTS_BY_DESTINATION, + "4", + product=RFE_PRODUCT, + year_col="year", + series_col=None, + unit_col="unit", + dims=(("geography", "Region/country"),), + pivot="years", + ), +} + + +def _table_number(row: dict) -> str: + """Return the row's table-number cell across the two source column names.""" + for col in TABLE_NUMBER_COLS: + value = row.get(col) + if value is not None: + return value.strip() + return "" + + +def parse_table(text: str, table: str) -> list[dict]: + """Parse one table's rows from its source CSV into long-format records. + + Parameters + ---------- + text : str + Decoded CSV text of the source file, which may hold several tables. + table : str + Table key from CWT_TABLES. + + Returns + ------- + list[dict] + Records carrying the table key, integer year, within-year period, the + five row-dimension fields, the unit, the series name, and the numeric + value. Rows for other tables, or whose value is blank or non-numeric, + are skipped. + """ + config = CWT_TABLES[table] + number = config["number"] + series_col = config["series_col"] + period_col = config["period_col"] + year_col = config["year_col"] + unit_col = config["unit_col"] + normalize = config["normalize_measure"] + records: list[dict] = [] + for row in csv.DictReader(StringIO(text)): + if _table_number(row) != number: + continue + raw_value = (row.get("value") or "").strip() + try: + value = float(raw_value) + except ValueError: + continue + year_label = (row.get(year_col) or "").strip() + if not year_label[:4].isdigit(): + continue + record: dict = { + "table": table, + "year": int(year_label[:4]), + "period": ( + ((row.get(period_col) or "").strip() or None) if period_col else None + ), + "geography": None, + "measure": None, + "trade_category": None, + "textile_group": None, + "item": None, + "unit": (row.get(unit_col) or "").strip() or None, + "series": (row.get(series_col) or "").strip() if series_col else None, + "value": value, + } + for out_field, source_col in config["dims"]: + raw = (row.get(source_col) or "").strip() + if out_field == "measure" and normalize: + raw = raw.casefold() + record[out_field] = raw or None + records.append(record) + return records + + +async def afetch_table(table: str, **kwargs) -> list[dict]: + """Download and parse one cotton-wool-textile table through the ERS cache. + + Parameters + ---------- + table : str + Table key from CWT_TABLES. + + Returns + ------- + list[dict] + Long-format records from parse_table. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + config = CWT_TABLES[table] + content = await afetch_ers_file(config["media"], product=config["product"]) + return parse_table(content.decode("utf-8-sig", errors="replace"), table) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_county_typology_codes.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_county_typology_codes.py new file mode 100644 index 00000000000..5ece9b2fcc9 --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_county_typology_codes.py @@ -0,0 +1,587 @@ +"""USDA ERS County Typology Codes file catalog and per-vintage parsers.""" + +import csv +import math +from io import BytesIO, StringIO +from typing import Any + +PRODUCT_PAGE = "data-products/county-typology-codes" +DEFAULT_VINTAGE = "2025" + +VINTAGES: dict[str, dict] = { + "2025": { + "label": "2025 Edition", + "media_path": "/media/6174/ers-county-typology-codes-2025-edition.csv", + "format": "long_2025", + }, + "2015": { + "label": "2015 Edition", + "media_path": "/media/6176/ers-county-typology-codes-2015-edition.csv", + "format": "wide_2015", + }, + "2004": { + "label": "2004 Edition", + "media_path": "/media/6177/2004-county-typology-codes.xls", + "format": "wide_2004", + "sheet": "all_final_codes", + }, + "1989": { + "label": "1989 Edition", + "media_path": "/media/6178/1989-county-typology-codes.xls", + "format": "wide_1989", + "sheet": "Data", + }, + "1979_1986_1983def": { + "label": "1979 and 1986 (1983 nonmetro definition)", + "media_path": "/media/6179/1979-and-1986-county-typology-codes-uses-the" + + "-1983-nonmetro-definition.xls", + "format": "wide_1979", + "sheet": "Data", + "rucc_col": "RURALURB83", + }, + "1979_1986_1974def": { + "label": "1979 and 1986 (1974 nonmetro definition)", + "media_path": "/media/6180/1979-and-1986-county-typology-codes-uses-the" + + "-1974-nonmetro-definition.xls", + "format": "wide_1979", + "sheet": "Data", + "rucc_col": "RURALURB74", + }, +} + +STATE_FIPS_ABBR: dict[str, str] = { + "01": "AL", + "02": "AK", + "04": "AZ", + "05": "AR", + "06": "CA", + "08": "CO", + "09": "CT", + "10": "DE", + "11": "DC", + "12": "FL", + "13": "GA", + "15": "HI", + "16": "ID", + "17": "IL", + "18": "IN", + "19": "IA", + "20": "KS", + "21": "KY", + "22": "LA", + "23": "ME", + "24": "MD", + "25": "MA", + "26": "MI", + "27": "MN", + "28": "MS", + "29": "MO", + "30": "MT", + "31": "NE", + "32": "NV", + "33": "NH", + "34": "NJ", + "35": "NM", + "36": "NY", + "37": "NC", + "38": "ND", + "39": "OH", + "40": "OK", + "41": "OR", + "42": "PA", + "44": "RI", + "45": "SC", + "46": "SD", + "47": "TN", + "48": "TX", + "49": "UT", + "50": "VT", + "51": "VA", + "53": "WA", + "54": "WV", + "55": "WI", + "56": "WY", +} + +STATE_NAMES: dict[str, str] = { + "AL": "Alabama", + "AK": "Alaska", + "AZ": "Arizona", + "AR": "Arkansas", + "CA": "California", + "CO": "Colorado", + "CT": "Connecticut", + "DE": "Delaware", + "DC": "District of Columbia", + "FL": "Florida", + "GA": "Georgia", + "HI": "Hawaii", + "ID": "Idaho", + "IL": "Illinois", + "IN": "Indiana", + "IA": "Iowa", + "KS": "Kansas", + "KY": "Kentucky", + "LA": "Louisiana", + "ME": "Maine", + "MD": "Maryland", + "MA": "Massachusetts", + "MI": "Michigan", + "MN": "Minnesota", + "MS": "Mississippi", + "MO": "Missouri", + "MT": "Montana", + "NE": "Nebraska", + "NV": "Nevada", + "NH": "New Hampshire", + "NJ": "New Jersey", + "NM": "New Mexico", + "NY": "New York", + "NC": "North Carolina", + "ND": "North Dakota", + "OH": "Ohio", + "OK": "Oklahoma", + "OR": "Oregon", + "PA": "Pennsylvania", + "RI": "Rhode Island", + "SC": "South Carolina", + "SD": "South Dakota", + "TN": "Tennessee", + "TX": "Texas", + "UT": "Utah", + "VT": "Vermont", + "VA": "Virginia", + "WA": "Washington", + "WV": "West Virginia", + "WI": "Wisconsin", + "WY": "Wyoming", +} + +STATE_ABBRS: frozenset[str] = frozenset(STATE_NAMES) + +ECON_2025: dict[int, str] = { + 0: "Nonspecialized", + 1: "Farming", + 2: "Mining", + 3: "Manufacturing", + 4: "Government", + 5: "Recreation", +} + +ECON_2015: dict[int, str] = { + 0: "Nonspecialized", + 1: "Farming", + 2: "Mining", + 3: "Manufacturing", + 4: "Federal/State Government", + 5: "Recreation", +} + +ECON_2004: dict[int, str] = { + 1: "Farming", + 2: "Mining", + 3: "Manufacturing", + 4: "Federal/State Government", + 5: "Services", + 6: "Nonspecialized", +} + +ECON_FLAGS_1989: tuple[tuple[str, str], ...] = ( + ("FM", "Farming"), + ("MI", "Mining"), + ("MF", "Manufacturing"), + ("GV", "Government"), + ("TS", "Services"), + ("NS", "Nonspecialized"), +) + +ECON_FLAGS_1979: tuple[tuple[str, str], ...] = ( + ("AGTP79R", "Farming"), + ("MINTP79R", "Mining"), + ("MFGTP79R", "Manufacturing"), + ("GVTTP79R", "Government"), + ("UNCL79", "Nonspecialized"), +) + +CANONICAL_FIELDS: tuple[str, ...] = ( + "fips", + "state", + "county_name", + "metro", + "economic_type", + "economic_type_code", + "economic_type_label", + "farming", + "mining", + "manufacturing", + "government", + "recreation", + "services", + "nonspecialized", + "low_education", + "low_employment", + "population_loss", + "housing_stress", + "retirement_destination", + "persistent_poverty", + "persistent_child_poverty", + "federal_lands", + "commuting", + "transfers_dependent", + "rural_urban_continuum_code", + "urban_influence_code", + "farming_1986", + "mining_1986", + "manufacturing_1986", + "government_1986", + "nonspecialized_1986", +) + + +def state_options() -> list[dict]: + """Return the 51-entry state filter options (50 states plus DC).""" + return [{"label": name, "value": abbr} for abbr, name in STATE_NAMES.items()] + + +def vintage_options() -> list[dict]: + """Return the vintage select options, newest edition first.""" + return [ + {"label": config["label"], "value": key} for key, config in VINTAGES.items() + ] + + +def cell_int(value: Any) -> int | None: + """Coerce a source cell to an integer code, preserving sentinel values. + + Parameters + ---------- + value : Any + A raw CSV string or Excel cell value. + + Returns + ------- + int | None + The integer value, keeping documented sentinels such as 8, 9, 99, and + -1 unchanged; None for blanks and placeholder tokens. + """ + if value is None: + return None + if isinstance(value, float) and math.isnan(value): + return None + text = str(value).strip() + if not text or text.casefold() in {"na", "n/a", "--", "-"}: + return None + try: + return int(float(text)) + except ValueError: + return None + + +def cell_str(value: Any) -> str | None: + """Strip a source cell to a non-empty string, or None.""" + if value is None: + return None + if isinstance(value, float) and math.isnan(value): + return None + text = str(value).strip() + return text or None + + +def normalize_fips(value: Any) -> str | None: + """Normalize a county FIPS to a five-digit zero-padded string.""" + if value is None: + return None + if isinstance(value, float) and math.isnan(value): + return None + text = str(value).strip() + if not text: + return None + try: + return str(int(float(text))).zfill(5) + except ValueError: + return text.zfill(5) + + +def state_of(fips: str) -> str | None: + """Return the postal abbreviation for a county FIPS's state prefix.""" + return STATE_FIPS_ABBR.get(fips[:2]) + + +def _blank_record() -> dict: + """Return a canonical record with every field defaulted to None.""" + return {field: None for field in CANONICAL_FIELDS} + + +def _flag_economic_type(row: dict, flags: tuple[tuple[str, str], ...]) -> str | None: + """Derive an economic-type label from a flag-based vintage's set flags.""" + labels = [label for column, label in flags if cell_int(row.get(column)) == 1] + return " / ".join(labels) if labels else None + + +def parse_2025(text: str) -> list[dict]: + """Parse the 2025 long CSV into one canonical record per county. + + Parameters + ---------- + text : str + Decoded text of the 2025-edition long CSV. + + Returns + ------- + list[dict] + Canonical records keyed by CANONICAL_FIELDS, in source county order. + """ + county_name: dict[str, str | None] = {} + metro: dict[str, int | None] = {} + attrs_by_fips: dict[str, dict[str, Any]] = {} + order: list[str] = [] + for row in csv.DictReader(StringIO(text)): + fips = normalize_fips(row.get("FIPStxt")) + if fips is None: + continue + attrs = attrs_by_fips.get(fips) + if attrs is None: + attrs = {} + attrs_by_fips[fips] = attrs + county_name[fips] = cell_str(row.get("County_Name")) + metro[fips] = cell_int(row.get("Metro2023")) + order.append(fips) + attrs[(row.get("Attribute") or "").strip()] = row.get("Value") + records: list[dict] = [] + for fips in order: + attrs = attrs_by_fips[fips] + code = cell_int(attrs.get("Industry_Dependence_2025")) + record = _blank_record() + record.update( + fips=fips, + state=state_of(fips), + county_name=county_name[fips], + metro=metro[fips], + economic_type=ECON_2025.get(code) if code is not None else None, + economic_type_code=code, + farming=cell_int(attrs.get("High_Farming_2025")), + mining=cell_int(attrs.get("High_Mining_2025")), + manufacturing=cell_int(attrs.get("High_Manufacturing_2025")), + government=cell_int(attrs.get("High_Government_2025")), + recreation=cell_int(attrs.get("High_Recreation_2025")), + nonspecialized=cell_int(attrs.get("Nonspecialized_2025")), + low_education=cell_int(attrs.get("Low_PostSecondary_Ed_2025")), + low_employment=cell_int(attrs.get("Low_Employment_2025")), + population_loss=cell_int(attrs.get("Population_Loss_2025")), + housing_stress=cell_int(attrs.get("Housing_Stress_2025")), + retirement_destination=cell_int(attrs.get("Retirement_Destination_2025")), + persistent_poverty=cell_int(attrs.get("Persistent_Poverty_1721")), + ) + records.append(record) + return records + + +def parse_2015(text: str) -> list[dict]: + """Parse the 2015 wide CSV into one canonical record per county.""" + records: list[dict] = [] + for row in csv.DictReader(StringIO(text)): + fips = normalize_fips(row.get("FIPStxt")) + if fips is None: + continue + code = cell_int(row.get("Economic Types Type_2015_Update non-overlapping")) + record = _blank_record() + record.update( + fips=fips, + state=state_of(fips), + county_name=cell_str(row.get("County_name")), + metro=cell_int(row.get("Metro-nonmetro status, 2013 0=Nonmetro 1=Metro")), + economic_type=ECON_2015.get(code) if code is not None else None, + economic_type_code=code, + economic_type_label=cell_str(row.get("Economic_Type_Label")), + farming=cell_int(row.get("Farming_2015_Update")), + mining=cell_int(row.get("Mining_2015-Update")), + manufacturing=cell_int(row.get("Manufacturing_2015_Update")), + government=cell_int(row.get("Government_2015_Update")), + recreation=cell_int(row.get("Recreation_2015_Update")), + nonspecialized=cell_int(row.get("Nonspecialized_2015_Update")), + low_education=cell_int(row.get("Low_Education_2015_Update")), + low_employment=cell_int(row.get("Low_Employment_Cnty_2008_2012_25_64")), + population_loss=cell_int(row.get("Pop_Loss_2010")), + retirement_destination=cell_int(row.get("Retirement_Dest_2015_Update")), + persistent_poverty=cell_int(row.get("Persistent_Poverty_2013")), + persistent_child_poverty=cell_int( + row.get("Persistent_Related_Child_Poverty_2013") + ), + ) + records.append(record) + return records + + +def parse_2004(rows: list[dict]) -> list[dict]: + """Parse the 2004 workbook rows into one canonical record per county.""" + records: list[dict] = [] + for row in rows: + fips = normalize_fips(row.get("FIPSTXT")) + if fips is None: + continue + code = cell_int(row.get("econdep")) + record = _blank_record() + record.update( + fips=fips, + state=state_of(fips), + county_name=cell_str(row.get("County")), + metro=cell_int(row.get("metro")), + economic_type=ECON_2004.get(code) if code is not None else None, + economic_type_code=code, + farming=cell_int(row.get("farm")), + mining=cell_int(row.get("mine")), + manufacturing=cell_int(row.get("manf")), + government=cell_int(row.get("fsgov")), + services=cell_int(row.get("serv")), + nonspecialized=cell_int(row.get("nonsp")), + recreation=cell_int(row.get("rec")), + low_education=cell_int(row.get("loweduc")), + low_employment=cell_int(row.get("lowemp")), + population_loss=cell_int(row.get("poploss")), + housing_stress=cell_int(row.get("house")), + retirement_destination=cell_int(row.get("retire")), + persistent_poverty=cell_int(row.get("perpov")), + persistent_child_poverty=cell_int(row.get("perchldpov")), + rural_urban_continuum_code=cell_int(row.get("rururb2003")), + urban_influence_code=cell_int(row.get("urbinf2003")), + ) + records.append(record) + return records + + +def parse_1989(rows: list[dict]) -> list[dict]: + """Parse the 1989 workbook rows into one canonical record per county.""" + records: list[dict] = [] + for row in rows: + fips = normalize_fips(row.get("FIPS")) + if fips is None: + continue + rucc = cell_int(row.get("RuralUrb93")) + record = _blank_record() + record.update( + fips=fips, + state=state_of(fips), + county_name=cell_str(row.get("County name")), + metro=(1 if rucc is not None and rucc <= 3 else 0) + if rucc is not None + else None, + economic_type=_flag_economic_type(row, ECON_FLAGS_1989), + farming=cell_int(row.get("FM")), + mining=cell_int(row.get("MI")), + manufacturing=cell_int(row.get("MF")), + government=cell_int(row.get("GV")), + services=cell_int(row.get("TS")), + nonspecialized=cell_int(row.get("NS")), + retirement_destination=cell_int(row.get("RT")), + federal_lands=cell_int(row.get("FL")), + commuting=cell_int(row.get("CM")), + persistent_poverty=cell_int(row.get("PV")), + transfers_dependent=cell_int(row.get("TP")), + rural_urban_continuum_code=rucc, + ) + records.append(record) + return records + + +def parse_1979(rows: list[dict], rucc_col: str) -> list[dict]: + """Parse a 1979/1986 workbook's rows into one canonical record per county. + + Parameters + ---------- + rows : list[dict] + Row dictionaries from the workbook's 'Data' sheet. + rucc_col : str + Rural-urban continuum column name for the file's nonmetro definition. + + Returns + ------- + list[dict] + Canonical records; the unsuffixed flags carry the 1979 revised + classification and the ``*_1986`` flags carry the 1986 update. + """ + records: list[dict] = [] + for row in rows: + fips = normalize_fips(row.get("FIPS")) + if fips is None: + continue + record = _blank_record() + record.update( + fips=fips, + state=state_of(fips), + county_name=cell_str(row.get("County")), + metro=cell_int(row.get("NMET")), + economic_type=_flag_economic_type(row, ECON_FLAGS_1979), + farming=cell_int(row.get("AGTP79R")), + mining=cell_int(row.get("MINTP79R")), + manufacturing=cell_int(row.get("MFGTP79R")), + government=cell_int(row.get("GVTTP79R")), + nonspecialized=cell_int(row.get("UNCL79")), + federal_lands=cell_int(row.get("FEDTP79")), + retirement_destination=cell_int(row.get("RETTP79")), + persistent_poverty=cell_int(row.get("POVTP79")), + rural_urban_continuum_code=cell_int(row.get(rucc_col)), + farming_1986=cell_int(row.get("AGTP86")), + mining_1986=cell_int(row.get("MINTP86")), + manufacturing_1986=cell_int(row.get("MFGTP86")), + government_1986=cell_int(row.get("GVTTP86")), + nonspecialized_1986=cell_int(row.get("UNCL86")), + ) + records.append(record) + return records + + +def _read_sheet(content: bytes, sheet: str) -> list[dict]: + """Read one Excel sheet into a list of row dictionaries.""" + import pandas as pd + + frame = pd.read_excel(BytesIO(content), sheet_name=sheet, header=0, dtype=object) + return frame.to_dict("records") + + +def parse_vintage(vintage: str, content: bytes) -> list[dict]: + """Parse a vintage's raw file content into canonical county records. + + Parameters + ---------- + vintage : str + Vintage key from VINTAGES. + content : bytes + Raw downloaded file content. + + Returns + ------- + list[dict] + Canonical records keyed by CANONICAL_FIELDS. + """ + config = VINTAGES[vintage] + fmt = config["format"] + if fmt == "long_2025": + return parse_2025(content.decode("utf-8-sig", errors="replace")) + if fmt == "wide_2015": + return parse_2015(content.decode("utf-8-sig", errors="replace")) + rows = _read_sheet(content, config["sheet"]) + if fmt == "wide_2004": + return parse_2004(rows) + if fmt == "wide_1989": + return parse_1989(rows) + return parse_1979(rows, config["rucc_col"]) + + +async def afetch_vintage(vintage: str) -> list[dict]: + """Download and parse one vintage's file through the ERS disk cache. + + Parameters + ---------- + vintage : str + Vintage key from VINTAGES. + + Returns + ------- + list[dict] + Canonical county records for the vintage. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + config = VINTAGES[vintage] + content = await afetch_ers_file(config["media_path"], product=PRODUCT_PAGE) + return parse_vintage(vintage, content) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_dairy_data.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_dairy_data.py new file mode 100644 index 00000000000..425021ae782 --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_dairy_data.py @@ -0,0 +1,235 @@ +"""USDA ERS Dairy Data file catalog and parsers.""" + +import csv +from calendar import monthrange +from datetime import date +from io import StringIO + +BASE_URL = "https://www.ers.usda.gov" +PRODUCT_PAGE = "data-products/dairy-data" + +DAIRY_DATA_FILES: dict[str, dict] = { + "situation_at_a_glance": { + "media_id": 5501, + "slug": "us-dairy-situation-at-a-glance-monthly-and-annual", + "title": "U.S. dairy situation at a glance (monthly and annual)", + }, + "milk_production_quarterly": { + "media_id": 5503, + "slug": "us-milk-production-and-related-data-quarterly-and-annual", + "title": "U.S. milk production and related data (quarterly and annual)", + }, + "supply_utilization_products": { + "media_id": 5505, + "slug": "supply-and-utilization-of-dairy-product-categories-monthly-and-annual", + "title": "Supply and utilization of dairy product categories" + + " (monthly and annual)", + }, + "supply_utilization_milk": { + "media_id": 5507, + "slug": "supply-and-utilization-of-milk-in-all-products-monthly-and-annual", + "title": "Supply and utilization of milk in all products (monthly and annual)", + }, + "milk_fat_skim_solids_allocation": { + "media_id": 7134, + "slug": "supply-and-allocation-of-milk-fat-and-skim-solids-by-product-annual", + "title": "Supply and allocation of milk fat and skim solids by product" + + " (annual)", + }, + "per_capita_consumption": { + "media_id": 5510, + "slug": "dairy-products-per-capita-consumption-united-states-annual", + "title": "Dairy products: per capita consumption, United States (annual)", + }, + "fluid_milk_sales": { + "media_id": 5512, + "slug": "fluid-beverage-milk-sales-quantities-by-product-annual", + "title": "Fluid beverage milk sales quantities by product (annual)", + }, + "soft_products_domestic_use": { + "media_id": 5514, + "slug": "selected-soft-dairy-products-domestic-use-annual", + "title": "Selected soft dairy products, domestic use (annual)", + }, + "milk_cows_by_state": { + "media_id": 5516, + "slug": "milk-cows-and-production-by-state-and-region-annual", + "title": "Milk cows and production by State and region (annual)", + }, + "production_factors": { + "media_id": 5518, + "slug": "annual-milk-production-and-factors-affecting-supply-annual", + "title": "Annual milk production and factors affecting supply (annual)", + }, + "fluid_milk_plants": { + "media_id": 5520, + "slug": "number-and-average-size-of-u-s-fluid-milk-product-plants", + "title": "Number and average size of U.S. fluid milk product plants (annual)", + }, + "cheese_per_capita": { + "media_id": 5522, + "slug": "per-capita-consumption-of-selected-cheese-varieties-annual", + "title": "Per capita consumption of selected cheese varieties (annual)", + }, +} + + +def build_url(table: str) -> str: + """Build the download URL for one table's tidy CSV. + + Parameters + ---------- + table : str + Table key from DAIRY_DATA_FILES. + + Returns + ------- + str + Full URL of the table's CSV file. + """ + return f"{BASE_URL}{media_path(table)}" + + +def media_path(table: str) -> str: + """Build the media path for one table's tidy CSV. + + Parameters + ---------- + table : str + Table key from DAIRY_DATA_FILES. + + Returns + ------- + str + The '/media/{id}/{slug}.csv' path of the table's CSV file. + """ + entry = DAIRY_DATA_FILES[table] + return f"/media/{entry['media_id']}/{entry['slug']}.csv" + + +def period_end(year: int, timeperiod_id: int) -> date: + """Resolve a Year and Timeperiod_id pair to the period's end date. + + Parameters + ---------- + year : int + Calendar year of the observation. + timeperiod_id : int + ERS time period id: 1-12 for months, 13-16 for quarters, + 17 for annual. + + Returns + ------- + date + Month end for 1-12, quarter end for 13-16, December 31 otherwise. + """ + if 1 <= timeperiod_id <= 12: + return date(year, timeperiod_id, monthrange(year, timeperiod_id)[1]) + if 13 <= timeperiod_id <= 16: + month = (timeperiod_id - 12) * 3 + return date(year, month, monthrange(year, month)[1]) + return date(year, 12, 31) + + +def frequency_of(timeperiod_id: int) -> str: + """Resolve an ERS Timeperiod_id to an observation frequency. + + Parameters + ---------- + timeperiod_id : int + ERS time period id: 1-12 for months, 13-16 for quarters, + 17 for annual. + + Returns + ------- + str + One of 'monthly', 'quarterly', or 'annual'. + """ + if 1 <= timeperiod_id <= 12: + return "monthly" + if 13 <= timeperiod_id <= 16: + return "quarterly" + return "annual" + + +def clean_label(value: str | None) -> str | None: + """Strip stray whitespace, including embedded newlines, from a label. + + Parameters + ---------- + value : str | None + Raw label text from a CSV cell. + + Returns + ------- + str | None + The label with whitespace runs collapsed to single spaces, or None + when the input is missing or blank. + """ + if value is None: + return None + cleaned = " ".join(value.split()) + return cleaned or None + + +def parse_rows(text: str) -> list[dict]: + """Parse one tidy CSV's text into normalized row records. + + Parameters + ---------- + text : str + Decoded CSV text of one Dairy Data machine-readable file. + + Returns + ------- + list[dict] + Records with date, year, period, frequency, sub_table, product, + category, data_item, data_item_id, data_item_description, region, + state, value, and unit keys, skipping rows whose value is empty or + 'NA'. Values pass through raw; the unit is as published. + """ + rows: list[dict] = [] + for row in csv.DictReader(StringIO(text)): + raw_value = (row.get("Value") or row.get("Quantity") or "").strip() + if not raw_value or raw_value.upper() == "NA": + continue + year = int((row.get("Year") or "").strip()) + timeperiod_id = int((row.get("Timeperiod_id") or "17").strip()) + rows.append( + { + "date": period_end(year, timeperiod_id), + "year": year, + "period": clean_label(row.get("Period")), + "frequency": frequency_of(timeperiod_id), + "sub_table": clean_label(row.get("Table")), + "product": clean_label(row.get("Product")), + "category": clean_label(row.get("Category")), + "data_item": clean_label(row.get("Data_item")) or "", + "data_item_id": clean_label(row.get("Data_item_id")), + "data_item_description": clean_label(row.get("Data_item_description")), + "region": clean_label(row.get("Region")), + "state": clean_label(row.get("State")), + "value": float(raw_value), + "unit": clean_label(row.get("Unit") or row.get("Units")) or "", + } + ) + return rows + + +async def afetch_table(table: str, **kwargs) -> list[dict]: + """Download and parse one table's tidy CSV through the ERS disk cache. + + Parameters + ---------- + table : str + Table key from DAIRY_DATA_FILES. + + Returns + ------- + list[dict] + Normalized row records from parse_rows. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + content = await afetch_ers_file(media_path(table), product=PRODUCT_PAGE) + return parse_rows(content.decode("utf-8-sig")) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_eating_and_health_module_atus.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_eating_and_health_module_atus.py new file mode 100644 index 00000000000..ee907b02a80 --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_eating_and_health_module_atus.py @@ -0,0 +1,201 @@ +"""USDA ERS Eating and Health Module (ATUS) file catalog and long-format parser.""" + +import csv +from io import StringIO + +PRODUCT_PAGE = "data-products/eating-and-health-module-atus" +RELEASE_YEARS = ("2023", "2022") +VALUE_SUFFIXES = (", mean", ", standard error") + +EATING_HEALTH_TABLES: dict[str, dict] = { + "eating_and_drinking_time": { + "label": "Table 1 - Eating and drinking, associated activities, and" + + " secondary eating time, age 15+ and 18+", + "media": { + "2023": "/media/6532/table-1-average-minutes-per-day-spent-in-and" + + "-percent-of-us-civilian-population-age-15-and-older-and-age-18-and" + + "-older-engaged-in-eating-and-drinking-associated-activities-and" + + "-secondary-eating-on-an-average-day-in-2023.csv", + "2022": "/media/6516/table-1-time-spent-in-and-percent-of-us-civilian" + + "-population-engaged-in-eating-and-drinking-associated-activities-and" + + "-secondary-eating-on-an-average-day-in-2022-age-15-and-older-and-age" + + "-18-and-older.csv", + }, + }, + "eating_time_by_subgroup": { + "label": "Table 2 - Eating and drinking time by age bracket and" + + " metropolitan status", + "media": { + "2023": "/media/6534/table-2-average-minutes-per-day-spent-in-and" + + "-percent-of-us-civilian-population-engaged-in-eating-and-drinking" + + "-associated-activities-and-secondary-eating-on-an-average-day-in" + + "-2023-by-various-subgroups.csv", + "2022": "/media/6518/table-2-time-spent-in-and-percent-of-us-civilian" + + "-population-engaged-in-eating-and-drinking-associated-activities-and" + + "-secondary-eating-on-an-average-day-in-2022-by-various-subgroups.csv", + }, + }, + "grocery_shopper_and_meal_preparer": { + "label": "Table 3 - Usual grocery shopper and usual meal preparer in the" + + " household", + "media": { + "2023": "/media/6536/table-3-usual-grocery-shopper-and-usual-meal" + + "-preparer-in-the-household-on-an-average-day-in-2023.csv", + "2022": "/media/6520/table-3-usual-grocery-shopper-and-usual-meal" + + "-preparer-in-the-household-on-an-average-day-in-2022.csv", + }, + }, + "fast_food_purchases": { + "label": "Table 4 - Fast-food purchases over the previous 7 days", + "media": { + "2023": "/media/6538/table-4-fast-food-purchases-number-of-times-over" + + "-previous-7-days-on-an-average-day-in-2023-for-us-civilian" + + "-population-who-purchased-fast-food-in-the-previous-week.csv", + "2022": "/media/6522/table-4-fast-food-purchases-number-of-times-over" + + "-previous-7-days-on-an-average-day-in-2022-for-us-civilian" + + "-population-who-purchased-fast-food-in-the-previous-week.csv", + }, + }, + "activities_by_grocery_responsibility": { + "label": "Table 5 - Selected activities by grocery-shopping" + + " responsibility, age 18+", + "media": { + "2023": "/media/6540/table-5-average-minutes-per-day-spent-in-selected" + + "-activities-by-how-much-of-the-grocery-shopping-the-interviewee-is" + + "-responsible-for-on-an-average-day-in-2023-age-18-and-older.csv", + "2022": "/media/6524/table-5-average-time-spent-in-selected-activities" + + "-by-how-much-of-the-grocery-shopping-the-interviewee-is-responsible" + + "-for-on-an-average-day-in-2022-age-18-and-older.csv", + }, + }, + "activities_by_meal_prep_responsibility": { + "label": "Table 6 - Selected activities by meal-preparation" + + " responsibility, age 18+", + "media": { + "2023": "/media/6542/table-6-average-minutes-per-day-spent-in-selected" + + "-activities-by-how-much-of-the-meal-preparation-the-interviewee-is" + + "-responsible-for-on-an-average-day-in-2023-age-18-and-older.csv", + "2022": "/media/6526/table-6-average-time-spent-in-selected-activities" + + "-by-how-much-of-the-meal-preparation-the-interviewee-is-responsible" + + "-for-on-an-average-day-in-2022-age-18-and-older.csv", + }, + }, + "activities_by_food_security": { + "label": "Table 7 - Selected food-related activities by food-security" + + " subgroups, age 18+", + "media": { + "2023": "/media/6544/table-7-average-minutes-per-day-spent-in-selected" + + "-food-related-and-other-activities-on-an-average-day-in-2023-age-18" + + "-and-older.csv", + "2022": "/media/6528/table-7-average-minutes-per-day-spent-in-selected" + + "-food-related-and-other-activities-on-an-average-day-in-2022-age-18" + + "-and-older.csv", + }, + }, + "activities_by_bmi_group": { + "label": "Table 8 - Selected food-related activities by BMI group," + + " age 20+", + "media": { + "2023": "/media/6546/table-8-average-minutes-per-day-spent-in-selected" + + "-food-related-and-other-activities-on-an-average-day-in-2023-age-20" + + "-and-older.csv", + "2022": "/media/6530/table-8-average-minutes-per-day-spent-in-selected" + + "-food-related-and-other-activities-on-an-average-day-in-2022-age-20" + + "-and-older.csv", + }, + }, +} + + +def _to_float(raw: str) -> float | None: + """Coerce a source cell to a float, mapping blanks and non-numerics to None.""" + text = raw.strip() + if not text: + return None + try: + return float(text.replace(",", "")) + except ValueError: + return None + + +def parse_table(text: str, table: str) -> list[dict]: + """Parse one wide table CSV into long-format cell records. + + Parameters + ---------- + text : str + Decoded text of the table's CSV. + table : str + Table slug from EATING_HEALTH_TABLES. + + Returns + ------- + list[dict] + One record per data-row and value-column cell, carrying the leading + dimensions mapped to category, subgroup, and measure_type, the value + column header and ordinal, the source row ordinal, and the float value + with blank or suppressed cells coerced to None. The constant leading + title column is dropped; dimension columns are the non-value columns and + value columns are those whose header ends in ', mean' or ', standard + error'. + """ + reader = csv.reader(StringIO(text)) + header = next(reader, None) + if not header: + return [] + dim_indices: list[int] = [] + value_columns: list[tuple[int, str]] = [] + for index, name in enumerate(header): + if index == 0: + continue + if name.strip().endswith(VALUE_SUFFIXES): + value_columns.append((index, name)) + else: + dim_indices.append(index) + records: list[dict] = [] + row_ord = 0 + for row in reader: + if not any(cell.strip() for cell in row): + continue + dims = [(row[i].strip() if i < len(row) else "") for i in dim_indices] + category = dims[0] if len(dims) > 0 and dims[0] else None + subgroup = dims[1] if len(dims) > 1 and dims[1] else None + measure_type = dims[2] if len(dims) > 2 and dims[2] else None + for series_ord, (index, name) in enumerate(value_columns): + raw = row[index] if index < len(row) else "" + records.append( + { + "table": table, + "row_ord": row_ord, + "category": category, + "subgroup": subgroup, + "measure_type": measure_type, + "series": name, + "series_ord": series_ord, + "value": _to_float(raw), + } + ) + row_ord += 1 + return records + + +async def afetch_table(table: str, year: str, **kwargs) -> list[dict]: + """Download and parse one table's release-year CSV through the ERS cache. + + Parameters + ---------- + table : str + Table slug from EATING_HEALTH_TABLES. + year : str + Release year from RELEASE_YEARS. + + Returns + ------- + list[dict] + Long-format records from parse_table. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + media_path = EATING_HEALTH_TABLES[table]["media"][year] + content = await afetch_ers_file(media_path, product=PRODUCT_PAGE) + return parse_table(content.decode("utf-8-sig", errors="replace"), table) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_farm_household_income_and_characteristics.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_farm_household_income_and_characteristics.py new file mode 100644 index 00000000000..0203fcddee8 --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_farm_household_income_and_characteristics.py @@ -0,0 +1,251 @@ +"""USDA ERS Farm Household Income and Characteristics catalog, fetch, and parser.""" + +import csv +from io import StringIO + +PRODUCT_PAGE = "data-products/farm-household-income-and-characteristics" + +DEFAULT_TABLE = "finances_2021_26f" + +TABLE_CONFIG: dict[str, dict] = { + "finances_2021_26f": { + "media_path": "/media/7005/principal-us-farm-operator-household-finances-2021-26f.csv", + "shape": "year", + "item_col": "Item", + "dim_col": "Year", + "label": "Principal U.S. farm operator household finances, 2021-26F", + }, + "mean_median_income": { + "media_path": "/media/7001/mean-and-median-us-farm-operator-household-income-and-ratio-of-farm-household-to-us-household-income-1960-2024.csv", + "shape": "year", + "item_col": "IncomeMeasure", + "dim_col": "Year", + "label": "Mean and median U.S. farm operator household income and ratio of" + " farm household to U.S. household income, 1960-2024", + }, + "farm_size_class": { + "media_path": "/media/6999/all-farms-and-family-farms-by-farm-size-class-gross-sales-1996-2024.csv", + "shape": "year", + "item_col": "FarmType", + "dim_col": "Year", + "label": "All farms and family farms, by farm size class (gross sales)," + " 1996-2024", + }, + "by_farm_type_2024": { + "media_path": "/media/6995/principal-farm-operator-household-finances-by-farm-type-2024.csv", + "shape": "category", + "item_col": "Item", + "dim_col": "FarmType", + "dim_deunderscore": True, + "label": "Principal farm operator household finances, by farm type, 2024", + }, + "by_occupation_2024": { + "media_path": "/media/6997/finances-and-characteristics-of-principal-farm-operator-households-by-major-occupation-2024.csv", + "shape": "category", + "item_col": "Item", + "dim_col": "Occupation", + "dim_prefix": "Major occupation of principal operator: ", + "label": "Finances and characteristics of principal farm operator" + " households, by major occupation, 2024", + }, + "by_age_2024": { + "media_path": "/media/20794/characteristics-of-principal-farm-operator-households-by-age-of-principal-operators-2024.csv", + "shape": "combined", + "label": "Characteristics of principal farm operator households, by age of" + " principal operators, 2024", + }, + "by_experience_2024": { + "media_path": "/media/20796/characteristics-of-principal-farm-operator-households-by-experience-of-operators-2024.csv", + "shape": "combined", + "label": "Characteristics of principal farm operator households, by" + " experience of operators, 2024", + }, + "by_management_team_2024": { + "media_path": "/media/20798/characteristics-of-principal-farm-operator-households-by-type-of-management-team-2024.csv", + "shape": "combined", + "label": "Characteristics of principal farm operator households, by type of" + " management team, 2024", + }, + "by_limited_resource_2024": { + "media_path": "/media/20800/characteristics-of-principal-farm-operator-households-by-limited-resource-farm-status-2024.csv", + "shape": "combined", + "label": "Characteristics of principal farm operator households, by" + " limited-resource farm status, 2024", + }, + "by_sex_2024": { + "media_path": "/media/20802/characteristics-of-principal-farm-operator-households-by-sex-of-principal-operator-2024.csv", + "shape": "combined", + "label": "Characteristics of principal farm operator households, by sex of" + " principal operator, 2024", + }, +} + +TABLE_LABELS: dict[str, str] = { + key: config["label"] for key, config in TABLE_CONFIG.items() +} + +YEAR_TABLES: frozenset[str] = frozenset( + key for key, config in TABLE_CONFIG.items() if config["shape"] == "year" +) + + +def clean_label(value: str | None) -> str: + """Strip no-break spaces and surrounding whitespace from a label. + + Parameters + ---------- + value : str | None + Raw item or category label. + + Returns + ------- + str + The label with no-break-space bytes removed and ends stripped. + """ + return (value or "").replace("\xa0", "").strip() + + +def clean_column(config: dict, raw: str | None) -> str: + """Normalize a cross-tab category value into its wide-column header. + + Parameters + ---------- + config : dict + The table's TABLE_CONFIG entry. + raw : str | None + Raw dimension value, e.g. 'Residence_Farms' or a prefixed occupation. + + Returns + ------- + str + The category with any configured prefix removed and underscores + replaced by spaces, ends stripped. + """ + value = raw or "" + prefix = config.get("dim_prefix") + if prefix and value.startswith(prefix): + value = value[len(prefix) :] + if config.get("dim_deunderscore"): + value = value.replace("_", " ") + return value.strip() + + +def parse_value(raw: str | None) -> int | float | None: + """Parse a raw Value cell into a number, keeping full precision. + + Parameters + ---------- + raw : str | None + Raw Value cell, which may be 'NA' or blank where a value is absent. + + Returns + ------- + int | float | None + The integer or float value, or None when the cell is blank or a + non-numeric placeholder such as 'NA'. + """ + text = (raw or "").strip() + if not text: + return None + try: + return int(text) + except ValueError: + pass + try: + return float(text) + except ValueError: + return None + + +def parse_year(column: str) -> int: + """Return the integer year of a year-column header, forecast suffix dropped. + + Parameters + ---------- + column : str + Year header, e.g. '2024' or the forecast '2025F'. + + Returns + ------- + int + The four-digit year as an integer. + """ + return int(column.rstrip("F")) + + +def year_sort_key(column: str) -> tuple[int, bool]: + """Return a chronological sort key that keeps forecast years last. + + Parameters + ---------- + column : str + Year header, e.g. '2024' or the forecast '2025F'. + + Returns + ------- + tuple[int, bool] + The integer year and whether the header is a forecast, so a forecast + year sorts after a non-forecast year of the same value. + """ + return (parse_year(column), column.endswith("F")) + + +def parse_table(text: str, table: str) -> list[dict]: + """Parse a table's CSV into long records of item, column, and value. + + Parameters + ---------- + text : str + Decoded CSV text of the table file. + table : str + Table key from TABLE_CONFIG. + + Returns + ------- + list[dict] + Records with item, column, and value keys, in source row order. The + column is the year for the time-series tables and the category for the + cross-tab tables. + """ + config = TABLE_CONFIG[table] + shape = config["shape"] + records: list[dict] = [] + for row in csv.DictReader(StringIO(text)): + if shape == "combined": + category, separator, item = (row.get("Combined_Label") or "").partition( + ": " + ) + if not separator: + continue + column = clean_label(category) + item = clean_label(item) + else: + item = clean_label(row.get(config["item_col"])) + column = clean_column(config, row.get(config["dim_col"])) + if not item or not column: + continue + records.append( + {"item": item, "column": column, "value": parse_value(row.get("Value"))} + ) + return records + + +async def afetch_table(table: str) -> list[dict]: + """Download and parse one table's CSV through the ERS disk cache. + + Parameters + ---------- + table : str + Table key from TABLE_CONFIG. + + Returns + ------- + list[dict] + Long records from parse_table for the selected table. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + content = await afetch_ers_file( + TABLE_CONFIG[table]["media_path"], product=PRODUCT_PAGE + ) + return parse_table(content.decode("utf-8-sig", errors="replace"), table) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_farm_income_and_wealth_statistics.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_farm_income_and_wealth_statistics.py new file mode 100644 index 00000000000..8986b5c38bc --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_farm_income_and_wealth_statistics.py @@ -0,0 +1,336 @@ +"""USDA ERS Farm Income and Wealth Statistics catalog, fetch, and CSV parser.""" + +import csv +import io +import re +import zipfile +from io import StringIO + +BASE_URL = "https://www.ers.usda.gov" +PRODUCT_PAGE = ( + "data-products/farm-income-and-wealth-statistics/" + "data-files-us-and-state-level-farm-income-and-wealth-statistics" +) +RELEASE_ZIP = "/media/20808/february-5-2026-release.zip" + +DOWNLOAD_HEADING = "Download All Data in CSV File Format" +RELEASE_ZIP_PATTERN = re.compile(r'href="([^"?]+\.zip)"') +NULL_UNIT_TOKENS = ("�", "\xa0") + +TABLE_PREFIXES: dict[str, tuple[str, ...]] = { + "income_statement": ("FI",), + "production_expenses": ("EX",), + "cash_receipts": ("CR",), + "government_payments": ("GP",), + "farm_related_income": ("FR",), + "home_consumption": ("HC",), + "inventory_change": ("IY",), + "balance_sheet": ("FA", "FD", "FE"), + "financial_ratios": ("RT",), + "farm_business_income": ("FB",), +} + +TABLE_LABELS: dict[str, str] = { + "income_statement": "Income statement (value added)", + "production_expenses": "Production expenses", + "cash_receipts": "Cash receipts", + "government_payments": "Government payments", + "farm_related_income": "Farm-related income", + "home_consumption": "Home consumption", + "inventory_change": "Inventory change", + "balance_sheet": "Balance sheet (U.S.)", + "financial_ratios": "Financial ratios (U.S.)", + "farm_business_income": "Farm business income (U.S.)", +} + +US_ONLY_TABLES: frozenset[str] = frozenset( + {"balance_sheet", "financial_ratios", "farm_business_income"} +) + +STATE_LABELS: dict[str, str] = { + "US": "United States", + "AL": "Alabama", + "AK": "Alaska", + "AZ": "Arizona", + "AR": "Arkansas", + "CA": "California", + "CO": "Colorado", + "CT": "Connecticut", + "DE": "Delaware", + "FL": "Florida", + "GA": "Georgia", + "HI": "Hawaii", + "ID": "Idaho", + "IL": "Illinois", + "IN": "Indiana", + "IA": "Iowa", + "KS": "Kansas", + "KY": "Kentucky", + "LA": "Louisiana", + "ME": "Maine", + "MD": "Maryland", + "MA": "Massachusetts", + "MI": "Michigan", + "MN": "Minnesota", + "MS": "Mississippi", + "MO": "Missouri", + "MT": "Montana", + "NE": "Nebraska", + "NV": "Nevada", + "NH": "New Hampshire", + "NJ": "New Jersey", + "NM": "New Mexico", + "NY": "New York", + "NC": "North Carolina", + "ND": "North Dakota", + "OH": "Ohio", + "OK": "Oklahoma", + "OR": "Oregon", + "PA": "Pennsylvania", + "RI": "Rhode Island", + "SC": "South Carolina", + "SD": "South Dakota", + "TN": "Tennessee", + "TX": "Texas", + "UT": "Utah", + "VT": "Vermont", + "VA": "Virginia", + "WA": "Washington", + "WV": "West Virginia", + "WI": "Wisconsin", + "WY": "Wyoming", +} + +STATE_CODES: tuple[str, ...] = tuple(STATE_LABELS) + + +def allowed_states(table: str) -> tuple[str, ...]: + """Return the state codes a table is published for. + + Parameters + ---------- + table : str + Table key from TABLE_PREFIXES. + + Returns + ------- + tuple[str, ...] + ('US',) for the U.S.-only tables, otherwise every state code led by + the national aggregate. + """ + return ("US",) if table in US_ONLY_TABLES else STATE_CODES + + +def state_options(table: str) -> list[dict]: + """Build the labeled state options a table is published for. + + Parameters + ---------- + table : str + Table key from TABLE_PREFIXES. + + Returns + ------- + list[dict] + Label/value option dictionaries for the widget's state selector. + """ + return [ + {"label": STATE_LABELS[code], "value": code} for code in allowed_states(table) + ] + + +def extract_release_zip(html: str) -> str | None: + """Extract the latest release-zip media path from the data-files subpage. + + Parameters + ---------- + html : str + Decoded HTML of the U.S. and State-level data-files subpage. + + Returns + ------- + str | None + The first ``.zip`` link under the 'Download All Data in CSV File + Format' heading, which is the most recent release, or None when the + heading or a link is absent. + """ + start = html.find(DOWNLOAD_HEADING) + if start == -1: + return None + tail = html[start:] + end = tail.find("") + segment = tail[:end] if end != -1 else tail + match = RELEASE_ZIP_PATTERN.search(segment) + if not match: + return None + path = match.group(1) + return path if path.startswith("/") else "/" + path + + +def clean_unit(unit: str) -> str: + """Strip mis-decoded no-break-space bytes from a published unit label. + + Parameters + ---------- + unit : str + Raw unit_desc value, e.g. '$1,000' trailed by replacement bytes. + + Returns + ------- + str + Cleaned unit, e.g. '$1,000', '$1,000 per farm', 'Ratio', 'Percent', + or '1,000 acres'. + """ + cleaned = unit + for token in NULL_UNIT_TOKENS: + cleaned = cleaned.replace(token, "") + return cleaned.strip() + + +def parse_amount(value: str | None) -> float | None: + """Parse a raw Amount string to a float, keeping full precision. + + Parameters + ---------- + value : str | None + Raw Amount cell. + + Returns + ------- + float | None + The numeric value, or None when the cell is blank or non-numeric. + """ + text = (value or "").strip() + if not text: + return None + try: + return float(text) + except ValueError: + return None + + +def parse_rows(text: str, prefixes: tuple[str, ...], state: str) -> list[dict]: + """Parse the tidy CSV into records for one table's prefixes and one state. + + Parameters + ---------- + text : str + Decoded CSV text of the release file. + prefixes : tuple[str, ...] + The artificialKey two-character prefixes that make up the table. + state : str + Two-letter state code, or 'US', to keep. + + Returns + ------- + list[dict] + Records with prefix, order, year, state, line_item, part_1, part_2, + unit, and amount keys, in source file order. + """ + wanted = set(prefixes) + records: list[dict] = [] + order = 0 + for row in csv.DictReader(StringIO(text)): + prefix = (row.get("artificialKey") or "")[:2] + if prefix not in wanted: + continue + row_state = (row.get("State") or "").strip().upper() + if row_state != state: + continue + records.append( + { + "prefix": prefix, + "order": order, + "year": int((row.get("Year") or "").strip()), + "state": row_state, + "line_item": (row.get("VariableDescriptionTotal") or "").strip(), + "part_1": (row.get("VariableDescriptionPart1") or "").strip() or None, + "part_2": (row.get("VariableDescriptionPart2") or "").strip() or None, + "unit": clean_unit(row.get("unit_desc") or ""), + "amount": parse_amount(row.get("Amount")), + } + ) + order += 1 + return records + + +def extract_csv_text(zip_bytes: bytes) -> str: + """Extract and decode the single CSV member from the release zip bytes. + + Parameters + ---------- + zip_bytes : bytes + Raw bytes of the release zip archive. + + Returns + ------- + str + The decoded CSV text of the archive's single .csv member. + """ + with zipfile.ZipFile(io.BytesIO(zip_bytes)) as archive: + name = next(n for n in archive.namelist() if n.lower().endswith(".csv")) + raw = archive.read(name) + return raw.decode("utf-8-sig", errors="replace") + + +async def aresolve_release_zip() -> str: + """Resolve the latest release-zip media path, cached for a day. + + Returns + ------- + str + The media path of the most recent release zip, falling back to the + hardcoded RELEASE_ZIP when the subpage cannot be parsed. + """ + from openbb_government_us.usda.utils.ers_client import ( + PAGE_TTL, + _download, + get_cache, + ) + + key = f"page:{PRODUCT_PAGE}:release-zip" + with get_cache() as cache: + cached = cache.get(key) + if cached is not None: + return cached + html = (await _download(f"{BASE_URL}/{PRODUCT_PAGE}")).decode( + "utf-8", errors="replace" + ) + path = extract_release_zip(html) or RELEASE_ZIP + with get_cache() as cache: + cache.set(key, path, expire=PAGE_TTL) + return path + + +async def afetch_dataset() -> str: + """Download and decode the release CSV through the ERS disk cache. + + Returns + ------- + str + The decoded tidy CSV text of the current release. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + zip_path = await aresolve_release_zip() + content = await afetch_ers_file(zip_path) + return extract_csv_text(content) + + +async def afetch_table(table: str, state: str) -> list[dict]: + """Download the release CSV and parse one table's rows for one state. + + Parameters + ---------- + table : str + Table key from TABLE_PREFIXES. + state : str + Two-letter state code, or 'US'. + + Returns + ------- + list[dict] + Row records from parse_rows for the selected table and state. + """ + text = await afetch_dataset() + return parse_rows(text, TABLE_PREFIXES[table], state) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_fatus_trade.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_fatus_trade.py new file mode 100644 index 00000000000..26eca2b4a96 --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_fatus_trade.py @@ -0,0 +1,405 @@ +"""USDA ERS U.S. Agricultural Trade Data Update file catalog and parsers.""" + +import csv +import re +from collections import OrderedDict +from io import StringIO + +from openbb_core.app.model.abstract.error import OpenBBError + +BASE_URL = "https://www.ers.usda.gov" +PRODUCT_PAGE = ( + "data-products/foreign-agricultural-trade-of-the-united-states-fatus" + "/us-agricultural-trade-data-update" +) +MEDIA_PATH = "/media/5029/csv-comma-separated-values-format-of-all-data.csv" + +TRADE_TABLES: dict[str, str] = { + "summary": "U.S. agricultural trade, fiscal years, calendar years," + " year-to-date, and current month", + "monthly": "Total value of U.S. agricultural trade and trade balance, monthly", + "exports_ytd": "U.S. agricultural exports, year-to-date and current months", + "imports_ytd": "U.S. agricultural imports, year-to-date and current months", + "top_export_markets": "Top 10 U.S. export markets for soybeans, corn, wheat," + " and cotton, by volume", + "top_import_sources": "Top 10 sources of U.S. imports of fruits and vegetables," + " by value", +} + +TABLE_ALIASES = {name: alias for alias, name in TRADE_TABLES.items()} + +DIRECTIONS = { + "Exports": "exports", + "Imports": "imports", + "Trade balance (exports minus imports)": "balance", +} + +VALUE_TYPES = { + "Customs value": "value", + "Volume": "volume", + "Cost, insurance, and freight (c.i.f.)": "cif", +} + +TABLE_MEASURES: dict[str, list[str]] = { + "summary": ["value"], + "monthly": ["value"], + "exports_ytd": ["value", "volume"], + "imports_ytd": ["value", "volume", "cif"], + "top_export_markets": ["volume"], + "top_import_sources": ["value"], +} + +MEASURE_LABELS: dict[str, str] = { + "value": "Value", + "volume": "Volume", + "cif": "CIF value", +} + +SPREAD_DIMENSION: dict[str, str] = { + "summary": "direction", + "monthly": "direction", + "exports_ytd": "commodity", + "imports_ytd": "commodity", + "top_export_markets": "country", + "top_import_sources": "country", +} + +DIRECTION_LABELS: "OrderedDict[str, str]" = OrderedDict( + [("exports", "Exports"), ("imports", "Imports"), ("balance", "Balance")] +) + +MONTH_INDEX: dict[str, int] = { + "January": 1, + "February": 2, + "March": 3, + "April": 4, + "May": 5, + "June": 6, + "July": 7, + "August": 8, + "September": 9, + "October": 10, + "November": 11, + "December": 12, +} + +LABEL_FIXES = { + "Korea, South": "South Korea", + "Cotton, ex linters": "Cotton, excluding linters", +} + +FISCAL_MONTH_ORDER = { + "October": 1, + "November": 2, + "December": 3, + "January": 4, + "February": 5, + "March": 6, + "April": 7, + "May": 8, + "June": 9, + "July": 10, + "August": 11, + "September": 12, +} + +VINTAGE_PATTERN = re.compile(r"update of ([A-Za-z]+)") + + +def build_url() -> str: + """Build the download URL of the all-data CSV.""" + return f"{BASE_URL}{MEDIA_PATH}" + + +def vintage_order(update_version: str | None) -> int: + """Rank a monthly update vintage by its data month in fiscal-year order. + + Parameters + ---------- + update_version : str | None + Published vintage label, e.g. 'July update of May data'. + + Returns + ------- + int + Position of the data month within the October-September fiscal year, + or 0 when the vintage is missing or unrecognized. + """ + if not update_version: + return 0 + match = VINTAGE_PATTERN.search(update_version) + if match is None: + return 0 + return FISCAL_MONTH_ORDER.get(match[1], 0) + + +def period_rank(period: str) -> int: + """Rank a time period within its year for chronological row ordering. + + Parameters + ---------- + period : str + Published time period, e.g. 'May', 'January-May', 'October-May', or + 'Fiscal year, October-September'. + + Returns + ------- + int + Sort rank placing bare calendar months (1-12) first, then calendar + year-to-date spans (100 + end month), then fiscal year-to-date spans + (200 + end month), then the annual fiscal-year (900) and calendar-year + (901) totals; 500 for an unrecognized period. + """ + if period in MONTH_INDEX: + return MONTH_INDEX[period] + if period.startswith("Fiscal year"): + return 900 + if period.startswith("Calendar year"): + return 901 + parts = period.split("-") + if len(parts) == 2 and parts[0] in MONTH_INDEX and parts[1] in MONTH_INDEX: + if parts[0] == "January": + return 100 + MONTH_INDEX[parts[1]] + return 200 + MONTH_INDEX[parts[1]] + return 500 + + +PERIOD_BASIS_ORDER = ( + "Fiscal year", + "Calendar year", + "Fiscal year-to-date", + "Calendar year-to-date", + "Monthly", +) + + +def classify_period(period: str) -> str | None: + """Classify a published time period into its reporting basis. + + Parameters + ---------- + period : str + Published time period, e.g. 'May', 'January-May', 'October-May', + 'Fiscal year, October-September', or a 'Change, ...' delta row. + + Returns + ------- + str | None + One of 'Fiscal year', 'Calendar year', 'Fiscal year-to-date', + 'Calendar year-to-date', or 'Monthly'; None for change rows and + unrecognized periods. + """ + if period.startswith("Fiscal year"): + return "Fiscal year" + if period.startswith("Calendar year"): + return "Calendar year" + if period in MONTH_INDEX: + return "Monthly" + parts = period.split("-") + if len(parts) == 2 and parts[0] in MONTH_INDEX and parts[1] in MONTH_INDEX: + return ( + "Calendar year-to-date" if parts[0] == "January" else "Fiscal year-to-date" + ) + return None + + +def within_year_rank(basis: str, period: str) -> int: + """Rank a period within its year for chronological ordering under a basis. + + Parameters + ---------- + basis : str + The reporting basis from classify_period. + period : str + Published time period. + + Returns + ------- + int + The month or fiscal-month position that orders the rows within a year; + zero for the annual bases, whose year alone orders the series. + """ + if basis == "Monthly": + return MONTH_INDEX.get(period, 0) + if basis == "Calendar year-to-date": + return MONTH_INDEX.get(period.split("-")[1], 0) + if basis == "Fiscal year-to-date": + return FISCAL_MONTH_ORDER.get(period.split("-")[1], 0) + return 0 + + +async def table_period_bases(table: str, **kwargs) -> list[str]: + """List the period bases a trade table publishes, coarsest first. + + Parameters + ---------- + table : str + Table key from TRADE_TABLES. + + Returns + ------- + list[str] + Distinct reporting bases present in the table, ordered from the annual + totals through the year-to-date spans to the monthly rows. + """ + records = await afetch_trade_data() + present = { + classify_period(record["time_period"]) + for record in records + if record["table"] == table + } + return [basis for basis in PERIOD_BASIS_ORDER if basis in present] + + +def filter_latest(records: list[dict]) -> list[dict]: + """Keep only each table's most recent monthly update vintage. + + Parameters + ---------- + records : list[dict] + Parsed row records from parse_rows. + + Returns + ------- + list[dict] + Rows whose vintage matches the per-table maximum; rows without an + update vintage pass through unchanged. + """ + latest: dict[str, int] = {} + for record in records: + order = vintage_order(record["update_version"]) + if order > latest.get(record["table"], 0): + latest[record["table"]] = order + return [ + record + for record in records + if record["update_version"] is None + or vintage_order(record["update_version"]) == latest.get(record["table"], 0) + ] + + +def _clean(value: str | None) -> str | None: + """Strip a raw CSV cell, mapping empty text and the 'NA' sentinel to None.""" + text = (value or "").strip() + if not text or text == "NA": + return None + return text + + +def parse_rows(text: str) -> list[dict]: + """Parse the all-data CSV text into tidy row records. + + Parameters + ---------- + text : str + Decoded CSV text of the FATUS all-data file. + + Returns + ------- + list[dict] + Records with table, source_table, commodity, country, time_period, + year (raw string), direction, value_type, units, value, + update_version, notes, and data_source keys, skipping rows with an + empty Value. + + Raises + ------ + OpenBBError + If a row carries an unrecognized source table, trade direction, or + value type. + """ + reader = csv.DictReader(StringIO(text)) + reader.fieldnames = [name.strip() for name in reader.fieldnames or []] + rows: list[dict] = [] + for row in reader: + value = (row.get("Value") or "").strip() + if not value: + continue + source_table = (row.get("Source_table") or "").strip() + table = TABLE_ALIASES.get(source_table) + if table is None: + raise OpenBBError( + f"Unrecognized source table: '{source_table}'." + + " Expected one of: " + + "; ".join(sorted(TABLE_ALIASES)) + ) + direction_text = (row.get("US_Trade") or "").strip() + direction = DIRECTIONS.get(direction_text) + if direction is None: + raise OpenBBError( + f"Unrecognized trade direction: '{direction_text}'." + + " Expected one of: " + + "; ".join(sorted(DIRECTIONS)) + ) + value_type_text = (row.get("Value_type") or "").strip() + value_type = VALUE_TYPES.get(value_type_text) + if value_type is None: + raise OpenBBError( + f"Unrecognized value type: '{value_type_text}'." + + " Expected one of: " + + "; ".join(sorted(VALUE_TYPES)) + ) + commodity = (row.get("Commodity") or "").strip() + country = (row.get("Country") or "").strip() + rows.append( + { + "table": table, + "source_table": source_table, + "commodity": LABEL_FIXES.get(commodity, commodity), + "country": LABEL_FIXES.get(country, country), + "time_period": (row.get("Time_period") or "").strip(), + "year": (row.get("Year") or "").strip(), + "direction": direction, + "value_type": value_type, + "units": (row.get("Units") or "").strip(), + "value": float(value), + "update_version": _clean(row.get("Update_version")), + "notes": _clean(row.get("Notes")), + "data_source": _clean(row.get("Data_source")), + } + ) + return rows + + +async def afetch_trade_data(**kwargs) -> list[dict]: + """Download and parse the all-data CSV through the ERS disk cache. + + Returns + ------- + list[dict] + Raw row records from parse_rows. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + content = await afetch_ers_file(MEDIA_PATH, product=PRODUCT_PAGE) + return parse_rows(content.decode("utf-8-sig")) + + +async def distinct_field(field: str, table: str | None = None) -> list[str]: + """List the distinct values of a field, optionally scoped to a table. + + Parameters + ---------- + field : str + Record field to enumerate, e.g. 'commodity' or 'country'. + table : str | None + Table slug, or a comma-separated list, to scope the values to. When + None, values from every table are returned. + + Returns + ------- + list[str] + The distinct non-empty values, in first-seen source order. + """ + records = await afetch_trade_data() + tables = ( + {slug.strip() for slug in table.split(",") if slug.strip()} if table else None + ) + seen: dict[str, None] = {} + for record in records: + if tables is not None and record["table"] not in tables: + continue + value = record.get(field) + if value: + seen.setdefault(value, None) + return list(seen) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_feed_grains.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_feed_grains.py new file mode 100644 index 00000000000..43f387c14e9 --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_feed_grains.py @@ -0,0 +1,314 @@ +"""USDA ERS Feed Grains Yearbook file catalog and parsers.""" + +import csv +import re +from io import StringIO + +from openbb_core.app.model.abstract.error import OpenBBError + +BASE_URL = "https://www.ers.usda.gov" +PRODUCT_PAGE = "data-products/feed-grains-database/feed-grains-yearbook-tables" +MEDIA_PATH = "/media/5766/feed-grains-yearbook-tables-all-years.csv" + +TABLE_GROUPS: dict[str, str] = { + "acreage_and_farm_price": "U.S. Acreage, production, yield, and farm price", + "animal_units": "Processed feeds and animal unit indexes", + "industrial_use": "Feed, seed, and industrial uses", + "prices": "Domestic and international prices", + "production_and_stocks": "U.S. Production, harvested acreage, yield, and stocks", + "rail_shipments": "Rail rates and grain shipments", + "supply_and_use": "U.S. Supply and disappearance", + "trade": "Exports and imports", + "world_supply_and_use": "World production, supply, and disappearance", +} + +FEED_GRAINS_TABLES: dict[str, dict] = { + "acreage_production_and_price": { + "number": 1, + "name": "Table 1--Corn, sorghum, barley, and oats: Planted acreage," + " harvested acreage, production, yield, and price received by farmers", + "group": "acreage_and_farm_price", + }, + "foreign_coarse_grains_supply_and_use": { + "number": 2, + "name": "Table 2--Foreign coarse grains: Supply and disappearance", + "group": "world_supply_and_use", + }, + "feed_grains_supply_and_use": { + "number": 3, + "name": "Table 3--Feed grains (corn, sorghum, barley, and oats):" + " Supply and disappearance, million metric tons", + "group": "supply_and_use", + }, + "corn_supply_and_use": { + "number": 4, + "name": "Table 4--Corn: Supply and disappearance, million bushels", + "group": "supply_and_use", + }, + "sorghum_supply_and_use": { + "number": 5, + "name": "Table 5--Sorghum: Supply and disappearance, million bushels", + "group": "supply_and_use", + }, + "barley_supply_and_use": { + "number": 6, + "name": "Table 6--Barley: Supply and disappearance, million bushels", + "group": "supply_and_use", + }, + "oats_supply_and_use": { + "number": 7, + "name": "Table 7--Oats: Supply and disappearance, million bushels", + "group": "supply_and_use", + }, + "hay_production_and_stocks": { + "number": 8, + "name": "Table 8--Hay: Production, harvested acreage, yield, and stocks", + "group": "production_and_stocks", + }, + "corn_and_sorghum_farm_prices": { + "number": 9, + "name": "Table 9--Corn and sorghum: Prices received by farmers, United States", + "group": "prices", + }, + "barley_and_oats_farm_prices": { + "number": 10, + "name": "Table 10--Barley and oats: Prices received by farmers," + " United States, dollars per bushel", + "group": "prices", + }, + "hay_farm_prices": { + "number": 11, + "name": "Table 11--Hay: Prices received by farmers, United States," + " dollars per ton", + "group": "prices", + }, + "corn_cash_prices": { + "number": 12, + "name": "Table 12--Corn: Cash prices at principal markets, dollars per bushel", + "group": "prices", + }, + "sorghum_cash_prices": { + "number": 13, + "name": "Table 13--Sorghum: Cash prices at principal markets," + " dollars per hundredweight", + "group": "prices", + }, + "barley_and_oats_cash_prices": { + "number": 14, + "name": "Table 14--Barley and Oats: Cash prices at principal markets," + " dollars per bushel", + "group": "prices", + }, + "feed_price_ratios": { + "number": 15, + "name": "Table 15--Feed-price ratios for livestock, poultry, and milk", + "group": "prices", + }, + "byproduct_feed_prices": { + "number": 16, + "name": "Table 16--Byproduct feeds: Wholesale price, bulk," + " specified markets, dollars per ton", + "group": "prices", + }, + "processed_corn_product_prices": { + "number": 17, + "name": "Table 17--Processed corn products: Quoted market prices", + "group": "prices", + }, + "corn_and_sorghum_exports": { + "number": 18, + "name": "Table 18--U.S. corn and sorghum exports", + "group": "trade", + }, + "barley_and_oats_exports": { + "number": 19, + "name": "Table 19--U.S. barley and oats exports", + "group": "trade", + }, + "corn_and_sorghum_imports": { + "number": 20, + "name": "Table 20--U.S. corn and sorghum imports", + "group": "trade", + }, + "barley_and_oats_imports": { + "number": 21, + "name": "Table 21--U.S. barley and oats imports", + "group": "trade", + }, + "corn_and_sorghum_exports_by_destination": { + "number": 22, + "name": "Table 22--U.S. corn and sorghum exports by selected destinations", + "group": "trade", + }, + "barley_and_oats_exports_by_destination": { + "number": 23, + "name": "Table 23--U.S. barley and oats exports by selected destinations", + "group": "trade", + }, + "corn_and_sorghum_imports_by_source": { + "number": 24, + "name": "Table 24--U.S. corn and sorghum imports by selected sources", + "group": "trade", + }, + "barley_and_oats_imports_by_source": { + "number": 25, + "name": "Table 25--U.S. barley and oats imports by selected sources", + "group": "trade", + }, + "white_corn_exports_by_destination": { + "number": 26, + "name": "Table 26--U.S. white corn exports by selected destinations", + "group": "trade", + }, + "world_coarse_grain_trade": { + "number": 27, + "name": "Table 27--World coarse grain trade: Selected exporters" + " and importers by commodity", + "group": "trade", + }, + "rail_rates_and_grain_shipments": { + "number": 28, + "name": "Table 28--Rail rates and grain shipments", + "group": "rail_shipments", + }, + "processed_feeds_fed": { + "number": 29, + "name": "Table 29--Processed feeds: Quantities fed and feed per" + " grain-consuming animal unit, 1,000 metric tons", + "group": "animal_units", + }, + "animal_unit_indexes": { + "number": 30, + "name": "Table 30--Indexes of feed consuming animal units, millions", + "group": "animal_units", + }, + "corn_food_seed_and_industrial_use": { + "number": 31, + "name": "Table 31--Corn: Food, seed, and industrial use, million bushels", + "group": "industrial_use", + }, + "ethanol_exports_by_destination": { + "number": 32, + "name": "Table 32--U.S. exports of ethyl alcohol by selected destinations", + "group": "trade", + }, + "ethanol_imports_by_source": { + "number": 33, + "name": "Table 33--U.S. imports of ethyl alcohol by selected sources", + "group": "trade", + }, + "distillers_dregs_exports_by_destination": { + "number": 34, + "name": "Table 34--U.S. exports of brewers' and distillers' dregs" + " and waste by selected destination", + "group": "trade", + }, + "distillers_dregs_imports_by_source": { + "number": 35, + "name": "Table 35--U.S. imports of brewers' and distillers' dregs" + " and waste by selected sources", + "group": "trade", + }, +} + +TABLE_NUMBER_PATTERN = re.compile(r"^Table (\d+)--") + +NAME_TO_SLUG = {entry["name"]: slug for slug, entry in FEED_GRAINS_TABLES.items()} + + +def build_url() -> str: + """Build the download URL of the all-years tidy yearbook CSV. + + Returns + ------- + str + Full URL of the yearbook CSV file. + """ + return f"{BASE_URL}{MEDIA_PATH}" + + +def table_key(table_name: str) -> tuple[str, int]: + """Resolve a published table name to its catalog slug and table number. + + Parameters + ---------- + table_name : str + Published table name, e.g. 'Table 28--Rail rates and grain shipments'. + + Returns + ------- + tuple[str, int] + Catalog slug and table number; uncataloged names fall back to a + 'table_{number}' slug. + + Raises + ------ + OpenBBError + If the table name does not carry a 'Table N--' prefix. + """ + slug = NAME_TO_SLUG.get(table_name) + if slug is not None: + return slug, FEED_GRAINS_TABLES[slug]["number"] + match = TABLE_NUMBER_PATTERN.match(table_name) + if match is None: + raise OpenBBError(f"Unrecognized table name: '{table_name}'") + number = int(match.group(1)) + return f"table_{number}", number + + +def parse_rows(text: str) -> list[dict]: + """Parse the yearbook CSV text into tidy row records. + + Parameters + ---------- + text : str + Decoded CSV text with columns table_group, table_name, + commodity_group, commodity, attribute, geography, year, frequency, + timeperiod, unit, and amount. + + Returns + ------- + list[dict] + Records with a derived table slug and table_number, stripped label + columns, year as int, and value as float, skipping rows with an + empty amount. + """ + rows: list[dict] = [] + for row in csv.DictReader(StringIO(text)): + amount = (row.get("amount") or "").strip() + if not amount: + continue + table_name = (row.get("table_name") or "").strip() + slug, number = table_key(table_name) + rows.append( + { + "table": slug, + "table_number": number, + "table_name": table_name, + "table_group": (row.get("table_group") or "").strip(), + "commodity_group": (row.get("commodity_group") or "").strip(), + "commodity": (row.get("commodity") or "").strip(), + "attribute": (row.get("attribute") or "").strip(), + "geography": (row.get("geography") or "").strip(), + "year": int((row.get("year") or "").strip()), + "frequency": (row.get("frequency") or "").strip(), + "timeperiod": (row.get("timeperiod") or "").strip(), + "unit": (row.get("unit") or "").strip(), + "value": float(amount), + } + ) + return rows + + +async def afetch_yearbook(**kwargs) -> list[dict]: + """Download and parse the all-years yearbook CSV through the ERS disk cache. + + Returns + ------- + list[dict] + Raw row records from parse_rows. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + content = await afetch_ers_file(MEDIA_PATH, product=PRODUCT_PAGE) + return parse_rows(content.decode("utf-8-sig")) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_fertilizer_use_and_price.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_fertilizer_use_and_price.py new file mode 100644 index 00000000000..e2881c67f9a --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_fertilizer_use_and_price.py @@ -0,0 +1,535 @@ +"""USDA ERS Fertilizer Use and Price workbook catalog and parsers.""" + +import re +from typing import Any + +BASE_URL = "https://www.ers.usda.gov" +PRODUCT_PAGE = "data-products/fertilizer-use-and-price" +WORKBOOK_MEDIA = ( + "/media/5291/all-fertilizer-use-and-price-tables-in-a-single-workbook.xls" +) + +FOOTNOTE_PREFIXES = ("NA =", "NA=", "1/", "2/", "3/", "4/", "Source") +STATE_FOOTNOTE = re.compile(r"\s*\d+/\s*$") + +FERTILIZER_TABLES: dict[str, dict] = { + "us_plant_nutrient_consumption": { + "label": "Table 1. U.S. consumption of plant nutrients", + "sheet": 1, + "group": "A", + "month_col": None, + "columns": { + 1: "Nitrogen (N)", + 2: "Phosphate (P2O5)", + 3: "Potash (K2O)", + 4: "Total", + 6: "Nitrogen share", + 7: "Phosphate share", + 8: "Potash share", + }, + }, + "plant_nutrient_use_by_crop": { + "label": "Table 2. Estimated U.S. plant nutrient use by selected crops", + "sheet": 2, + "group": "A", + "month_col": None, + "columns": { + 1: "Nitrogen - Corn", + 2: "Nitrogen - Cotton", + 3: "Nitrogen - Soybeans", + 4: "Nitrogen - Wheat", + 5: "Nitrogen - Other", + 6: "Phosphate - Corn", + 7: "Phosphate - Cotton", + 8: "Phosphate - Soybeans", + 9: "Phosphate - Wheat", + 10: "Phosphate - Other", + 11: "Potash - Corn", + 12: "Potash - Cotton", + 13: "Potash - Soybeans", + 14: "Potash - Wheat", + 15: "Potash - Other", + }, + }, + "nutrient_material_class_consumption": { + "label": "Table 3. U.S. consumption of single, multiple, and secondary and" + " micro nutrients", + "sheet": 3, + "group": "A", + "month_col": None, + "columns": { + 1: "Multiple-nutrient material", + 2: "Single-nutrient material", + 3: "Secondary and micro-nutrients", + 4: "Total", + 6: "Multiple-nutrient material share", + 7: "Single-nutrient material share", + 8: "Secondary and micronutrients share", + }, + }, + "nitrogen_material_consumption": { + "label": "Table 4. U.S. consumption of selected nitrogen materials", + "sheet": 4, + "group": "A", + "month_col": None, + "columns": { + 1: "Ammonia (Anhydrous)", + 2: "Ammonia (Aqua)", + 4: "Ammonium (Nitrate)", + 5: "Ammonium (Sulfate)", + 6: "Nitrogen solutions", + 7: "Sodium nitrate", + 8: "Urea", + 9: "Other", + }, + }, + "phosphate_potash_material_consumption": { + "label": "Table 5. U.S. consumption of selected phosphate and potash" + " fertilizers", + "sheet": 5, + "group": "A", + "month_col": None, + "columns": { + 1: "Superphosphates, grades 22% and under", + 2: "Superphosphates, grades over 22%", + 3: "Other single phosphates", + 5: "Diammonium phosphate (18-46-0)", + 6: "Monoammonium phosphate (11-(51-55)-0)", + 7: "Other nitrogen-phosphate grades", + 9: "Potassium chloride", + 10: "Other single-nutrient potash", + }, + }, + "secondary_micronutrient_organic_consumption": { + "label": "Table 6. U.S. consumption of selected secondary, micronutrients," + " and natural organic materials", + "sheet": 6, + "group": "A", + "month_col": None, + "columns": { + 1: "Gypsum", + 2: "Sulfur", + 3: "Sulfuric Acid", + 4: "Zinc compound", + 6: "Compost", + 7: "Dried manure", + 8: "Sewage sludge", + 9: "Other organic materials", + }, + }, + "farm_prices": { + "label": "Table 7. Average U.S. farm prices of selected fertilizers", + "sheet": 7, + "group": "A", + "month_col": 1, + "columns": { + 2: "Anhydrous ammonia", + 3: "Nitrogen solutions (30%)", + 4: "Urea 44-46% nitrogen", + 5: "Ammonium nitrate", + 6: "Sulfate of ammonium", + 7: "Super-phosphate 20% phosphate", + 8: "Super-phosphate 44-46% phosphate", + 9: "Diammonium phosphate (18-46-0)", + 10: "Potassium chloride 60% potassium", + }, + }, + "price_indexes": { + "label": "Table 8. Fertilizer price indexes", + "sheet": 8, + "group": "A", + "month_col": None, + "columns": { + 1: "Prices paid by farmers for fertilizer", + 2: "Prices received by farmers for all crops", + 4: "PPI All fertilizers", + 5: "PPI Nitrogen", + 6: "PPI Phosphate", + 7: "PPI Potash", + }, + }, + "corn_nitrogen_share": { + "label": "Table 9. Percent of corn acreage receiving nitrogen fertilizer," + " selected States", + "sheet": 9, + "group": "B", + "unit": "Percent", + }, + "corn_nitrogen_rate": { + "label": "Table 10. Nitrogen used on corn, rate per fertilized acre" + " receiving nitrogen, selected States", + "sheet": 10, + "group": "B", + "unit": "Pounds/acre", + }, + "corn_phosphate_share": { + "label": "Table 11. Percentage of corn acreage receiving phosphate" + " fertilizer, selected States", + "sheet": 11, + "group": "B", + "unit": "Percent", + }, + "corn_phosphate_rate": { + "label": "Table 12. Phosphate used on corn, rate per fertilized acre" + " receiving phosphate fertilizer, selected States", + "sheet": 12, + "group": "B", + "unit": "Pounds/acre", + }, + "corn_potash_share": { + "label": "Table 13. Percentage of corn acreage receiving potash fertilizer," + " selected States", + "sheet": 13, + "group": "B", + "unit": "Percent", + }, + "corn_potash_rate": { + "label": "Table 14. Potash used on corn, rate per fertilized acre receiving" + " potash, selected States", + "sheet": 14, + "group": "B", + "unit": "Pounds/acre", + }, + "cotton_nitrogen_share": { + "label": "Table 15. Percentage of cotton acreage receiving nitrogen" + " fertilizer, selected States", + "sheet": 15, + "group": "B", + "unit": "Percent", + }, + "cotton_nitrogen_rate": { + "label": "Table 16. Nitrogen used on cotton, rate per fertilized acre" + " receiving nitrogen, selected States", + "sheet": 16, + "group": "B", + "unit": "Pounds/acre", + }, + "cotton_phosphate_share": { + "label": "Table 17. Percentage of cotton acreage receiving phosphate" + " fertilizer, selected States", + "sheet": 17, + "group": "B", + "unit": "Percent", + }, + "cotton_phosphate_rate": { + "label": "Table 18. Phosphate used on cotton, rate per fertilized acre" + " receiving phosphate, selected States", + "sheet": 18, + "group": "B", + "unit": "Pounds/acre", + }, + "cotton_potash_share": { + "label": "Table 19. Percentage of cotton acreage receiving potash" + " fertilizer, selected States", + "sheet": 19, + "group": "B", + "unit": "Percent", + }, + "cotton_potash_rate": { + "label": "Table 20. Potash used on cotton, rate per fertilized acre" + " receiving potash, selected States", + "sheet": 20, + "group": "B", + "unit": "Pounds/acre", + }, + "soybeans_nitrogen_share": { + "label": "Table 21. Percentage of soybean acreage receiving nitrogen" + " fertilizer, selected States", + "sheet": 21, + "group": "B", + "unit": "Percent", + }, + "soybeans_nitrogen_rate": { + "label": "Table 22. Nitrogen used on soybeans, rate per fertilized acre" + " receiving nitrogen, selected States", + "sheet": 22, + "group": "B", + "unit": "Pounds/acre", + }, + "soybeans_phosphate_share": { + "label": "Table 23. Percentage of soybean acreage receiving phosphate" + " fertilizer, selected States", + "sheet": 23, + "group": "B", + "unit": "Percent", + }, + "soybeans_phosphate_rate": { + "label": "Table 24. Phosphate used on soybeans, rate per fertilized acre" + " receiving phosphate, selected States", + "sheet": 24, + "group": "B", + "unit": "Pounds/acre", + }, + "soybeans_potash_share": { + "label": "Table 25. Percentage of soybean acreage receiving potash" + " fertilizer, selected States", + "sheet": 25, + "group": "B", + "unit": "Percent", + }, + "soybeans_potash_rate": { + "label": "Table 26. Potash used on soybeans, rate per fertilized acre" + " receiving potash, selected States", + "sheet": 26, + "group": "B", + "unit": "Pounds/acre", + }, + "wheat_nitrogen_share": { + "label": "Table 27. Percentage of wheat acreage receiving nitrogen" + " fertilizer, selected States", + "sheet": 27, + "group": "B", + "unit": "Percent", + }, + "wheat_nitrogen_rate": { + "label": "Table 28. Nitrogen used on wheat, rate per fertilized acre" + " receiving nitrogen, selected States", + "sheet": 28, + "group": "B", + "unit": "Pounds/acre", + }, + "wheat_phosphate_share": { + "label": "Table 29. Percentage of wheat acreage receiving phosphate" + " fertilizer, selected States", + "sheet": 29, + "group": "B", + "unit": "Percent", + }, + "wheat_phosphate_rate": { + "label": "Table 30. Phosphate used on wheat, rate per fertilized acre" + " receiving phosphate, selected States", + "sheet": 30, + "group": "B", + "unit": "Pounds/acre", + }, + "wheat_potash_share": { + "label": "Table 31. Percentage of wheat acreage receiving potash fertilizer," + " selected States", + "sheet": 31, + "group": "B", + "unit": "Percent", + }, + "wheat_potash_rate": { + "label": "Table 32. Potash used on wheat, rate per fertilized acre receiving" + " potash, selected States", + "sheet": 32, + "group": "B", + "unit": "Pounds/acre", + }, +} + + +def _year_of(cell: Any) -> int | None: + """Return the integer year a cell encodes, or None when it is not a year. + + Parameters + ---------- + cell : Any + A worksheet cell value, a float year, or a text label. + + Returns + ------- + int | None + The four-digit year in 1900-2100, else None. + """ + if isinstance(cell, bool): + return None + if isinstance(cell, (int, float)): + year = int(cell) + elif isinstance(cell, str): + text = cell.strip() + if not text[:4].isdigit(): + return None + year = int(text[:4]) + else: + return None + return year if 1900 <= year <= 2100 else None + + +def _numeric(cell: Any) -> float | None: + """Return a cell's value as a float, or None when it is not numeric. + + Parameters + ---------- + cell : Any + A worksheet cell value. + + Returns + ------- + float | None + The float value, else None for blank or non-numeric cells such as 'NA'. + """ + try: + return float(cell) + except (TypeError, ValueError): + return None + + +def parse_group_a(rows: list[list], table: str) -> list[dict]: + """Parse a Group A national worksheet into long-format records. + + Parameters + ---------- + rows : list[list] + Worksheet cells as a list of row lists. + table : str + Table key from FERTILIZER_TABLES. + + Returns + ------- + list[dict] + Records carrying table, year, period, series, and value. Only rows + whose first column parses to a year contribute; non-numeric value + cells are skipped. + """ + config = FERTILIZER_TABLES[table] + columns: dict[int, str] = config["columns"] + month_col = config["month_col"] + records: list[dict] = [] + for row in rows: + if not row: + continue + year = _year_of(row[0]) + if year is None: + continue + period = None + if month_col is not None and month_col < len(row): + period = str(row[month_col]).strip() or None + for col, series in columns.items(): + if col >= len(row): + continue + value = _numeric(row[col]) + if value is None: + continue + records.append( + { + "table": table, + "year": year, + "period": period, + "series": series, + "value": value, + } + ) + return records + + +def parse_group_b(rows: list[list], table: str) -> list[dict]: + """Parse a Group B crop-by-state worksheet into long-format records. + + Parameters + ---------- + rows : list[list] + Worksheet cells as a list of row lists. + table : str + Table key from FERTILIZER_TABLES. + + Returns + ------- + list[dict] + Records carrying table, year, period (always None), series (the State + name), and value. The State x Year matrix is emitted one cell per + record; footnote and blank rows are skipped and 'U.S. average 1/' is + renamed to 'U.S. average'. + """ + header_index = None + for index, row in enumerate(rows): + if row and str(row[0]).strip() == "State": + header_index = index + break + if header_index is None: + return [] + header = rows[header_index] + year_cols: dict[int, int] = {} + for col in range(1, len(header)): + year = _year_of(header[col]) + if year is not None: + year_cols[col] = year + records: list[dict] = [] + for row in rows[header_index + 1 :]: + if not row: + continue + state = str(row[0]).strip() + if not state or state.startswith(FOOTNOTE_PREFIXES): + continue + state = STATE_FOOTNOTE.sub("", state).strip() + for col, year in year_cols.items(): + if col >= len(row): + continue + value = _numeric(row[col]) + if value is None: + continue + records.append( + { + "table": table, + "year": year, + "period": None, + "series": state, + "value": value, + } + ) + return records + + +def parse_sheet(rows: list[list], table: str) -> list[dict]: + """Parse one table's worksheet rows into long-format records. + + Parameters + ---------- + rows : list[list] + Worksheet cells as a list of row lists. + table : str + Table key from FERTILIZER_TABLES. + + Returns + ------- + list[dict] + Long-format records from the group-appropriate parser. + """ + if FERTILIZER_TABLES[table]["group"] == "A": + return parse_group_a(rows, table) + return parse_group_b(rows, table) + + +def _sheet_rows(content: bytes, sheet_index: int) -> list[list]: + """Read one worksheet of the binary .xls workbook into a list of row lists. + + Parameters + ---------- + content : bytes + Raw .xls workbook bytes. + sheet_index : int + Zero-based worksheet index. + + Returns + ------- + list[list] + Cell values, row by row. + """ + import xlrd + + workbook = xlrd.open_workbook(file_contents=content) + sheet = workbook.sheet_by_index(sheet_index) + return [ + [sheet.cell_value(r, c) for c in range(sheet.ncols)] for r in range(sheet.nrows) + ] + + +async def afetch_table(table: str, **kwargs) -> list[dict]: + """Download the workbook and parse one table through the ERS disk cache. + + Parameters + ---------- + table : str + Table key from FERTILIZER_TABLES. + + Returns + ------- + list[dict] + Long-format records from parse_sheet. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + config = FERTILIZER_TABLES[table] + content = await afetch_ers_file(WORKBOOK_MEDIA, product=PRODUCT_PAGE) + rows = _sheet_rows(content, config["sheet"]) + return parse_sheet(rows, table) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_food_at_home_monthly_area_prices.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_food_at_home_monthly_area_prices.py new file mode 100644 index 00000000000..4bd4f264eb6 --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_food_at_home_monthly_area_prices.py @@ -0,0 +1,650 @@ +"""USDA ERS Food-at-Home Monthly Area Prices catalog, fetch, and CSV parser.""" + +import csv +import io +import zipfile +from collections import OrderedDict +from io import StringIO + +BASE_URL = "https://www.ers.usda.gov" +PRODUCT_PAGE = "data-products/food-at-home-monthly-area-prices" + +FMAP_TABLES: "OrderedDict[str, dict]" = OrderedDict( + [ + ( + "monthly_area_prices", + { + "media_path": ( + "/media/5400/food-at-home-monthly-area-prices-2012-to-2018.zip" + ), + "member": "FMAP-Data.csv", + "label": "Monthly area prices (2012-2018)", + }, + ), + ( + "supplemental_price_indexes", + { + "media_path": ( + "/media/5402/food-at-home-monthly-area-prices" + "-supplemental-price-indexes-2016-to-2018.zip" + ), + "member": "FMAP-SupIndex-Data.csv", + "label": "Supplemental price indexes (2016-2018)", + }, + ), + ] +) + +DEFAULT_TABLE = "monthly_area_prices" + +AREAS: "OrderedDict[str, tuple[str, str]]" = OrderedDict( + [ + ("0", ("national", "National")), + ("1", ("northeast", "Northeast")), + ("2", ("midwest", "Midwest")), + ("3", ("south", "South")), + ("4", ("west", "West")), + ("12060", ("atlanta", "Atlanta, GA")), + ("14460", ("boston", "Boston, MA-NH")), + ("16980", ("chicago", "Chicago, IL-IN-WI")), + ("19100", ("dallas", "Dallas-Fort Worth, TX")), + ("19820", ("detroit", "Detroit, MI")), + ("26420", ("houston", "Houston, TX")), + ("31080", ("los_angeles", "Los Angeles, CA")), + ("33100", ("miami", "Miami, FL")), + ("35620", ("new_york", "New York, NY-NJ-PA")), + ("37980", ("philadelphia", "Philadelphia, PA-NJ-DE-MD")), + ] +) + +AREA_FIELDS: tuple[str, ...] = tuple(field for field, _ in AREAS.values()) + +MEASURES: "OrderedDict[str, dict]" = OrderedDict( + [ + ( + "unit_value_mean_wtd", + { + "source": "Unit_value_mean_wtd", + "label": "Weighted mean unit value (dollars per 100 grams)", + "unit": "dollars per 100 grams", + "tables": ("monthly_area_prices",), + }, + ), + ( + "unit_value_mean_unwtd", + { + "source": "Unit_value_mean_unwtd", + "label": "Unweighted mean unit value (dollars per 100 grams)", + "unit": "dollars per 100 grams", + "tables": ("monthly_area_prices",), + }, + ), + ( + "unit_value_se_wtd", + { + "source": "Unit_value_se_wtd", + "label": "Std. error of weighted mean unit value (dollars per 100 g)", + "unit": "dollars per 100 grams", + "tables": ("monthly_area_prices",), + }, + ), + ( + "price_index_geks", + { + "source": "Price_index_GEKS", + "label": "GEKS price index", + "unit": "index", + "tables": ("monthly_area_prices", "supplemental_price_indexes"), + }, + ), + ( + "purchase_dollars_wtd", + { + "source": "Purchase_dollars_wtd", + "label": "Weighted purchases (U.S. dollars)", + "unit": "U.S. dollars", + "tables": ("monthly_area_prices", "supplemental_price_indexes"), + }, + ), + ( + "purchase_dollars_unwtd", + { + "source": "Purchase_dollars_unwtd", + "label": "Unweighted purchases (U.S. dollars)", + "unit": "U.S. dollars", + "tables": ("monthly_area_prices", "supplemental_price_indexes"), + }, + ), + ( + "purchase_grams_wtd", + { + "source": "Purchase_grams_wtd", + "label": "Weighted purchases (grams)", + "unit": "grams", + "tables": ("monthly_area_prices", "supplemental_price_indexes"), + }, + ), + ( + "purchase_grams_unwtd", + { + "source": "Purchase_grams_unwtd", + "label": "Unweighted purchases (grams)", + "unit": "grams", + "tables": ("monthly_area_prices", "supplemental_price_indexes"), + }, + ), + ( + "number_stores", + { + "source": "Number_stores", + "label": "Number of stores", + "unit": "count", + "tables": ("monthly_area_prices", "supplemental_price_indexes"), + }, + ), + ( + "price_index_laspeyres", + { + "source": "Price_index_Laspeyres", + "label": "Laspeyres price index", + "unit": "index", + "tables": ("supplemental_price_indexes",), + }, + ), + ( + "price_index_paasche", + { + "source": "Price_index_Paasche", + "label": "Paasche price index", + "unit": "index", + "tables": ("supplemental_price_indexes",), + }, + ), + ( + "price_index_tornqvist", + { + "source": "Price_index_Tornqvist", + "label": "Tornqvist price index", + "unit": "index", + "tables": ("supplemental_price_indexes",), + }, + ), + ( + "price_index_fisher_ideal", + { + "source": "Price_index_Fisher_Ideal", + "label": "Fisher Ideal price index", + "unit": "index", + "tables": ("supplemental_price_indexes",), + }, + ), + ( + "price_index_ccd", + { + "source": "Price_index_CCD", + "label": "Caves-Christensen-Diewert (CCD) price index", + "unit": "index", + "tables": ("supplemental_price_indexes",), + }, + ), + ] +) + +DEFAULT_MEASURE = "unit_value_mean_wtd" + +DEFAULT_MEASURE_BY_TABLE: dict[str, str] = { + "monthly_area_prices": "unit_value_mean_wtd", + "supplemental_price_indexes": "price_index_geks", +} + +SOURCE_TO_MEASURE: dict[str, str] = { + meta["source"]: key for key, meta in MEASURES.items() +} + +GROUPS: tuple[str, ...] = ( + "Grains", + "Vegetables", + "Fruit", + "Dairy", + "Meat and Protein Foods", + "Prepared meals, sides, and salads", + "Other foods", +) + +DEFAULT_GROUP = "Dairy" +DEFAULT_ITEM = "40000" + +EFPG: "OrderedDict[str, dict]" = OrderedDict( + [ + ("10000", {"name": "Whole-grain breads", "group": "Grains"}), + ("10025", {"name": "Whole-grain rice and pasta", "group": "Grains"}), + ("10050", {"name": "Whole-grain breakfast grains", "group": "Grains"}), + ( + "10075", + { + "name": "Whole-grain flour, bread mixes, and frozen dough", + "group": "Grains", + }, + ), + ("15000", {"name": "Non-whole-grain breads", "group": "Grains"}), + ("15025", {"name": "Non-whole-grain rice and pasta", "group": "Grains"}), + ("15050", {"name": "Non-whole-grain breakfast grains", "group": "Grains"}), + ( + "15075", + { + "name": "Non-whole-grain flour, bread mixes, and frozen dough", + "group": "Grains", + }, + ), + ("20000", {"name": "Potatoes, fresh", "group": "Vegetables"}), + ("20075", {"name": "Potatoes, canned", "group": "Vegetables"}), + ("21500", {"name": "Other starchy vegetables, fresh", "group": "Vegetables"}), + ( + "21525", + {"name": "Other starchy vegetables, fresh cut", "group": "Vegetables"}, + ), + ("21550", {"name": "Other starchy vegetables, frozen", "group": "Vegetables"}), + ("21575", {"name": "Other starchy vegetables, canned", "group": "Vegetables"}), + ("23000", {"name": "Tomatoes, fresh", "group": "Vegetables"}), + ("23075", {"name": "Tomatoes, canned", "group": "Vegetables"}), + ( + "24500", + {"name": "Other red and orange vegetables, fresh", "group": "Vegetables"}, + ), + ( + "24525", + { + "name": "Other red and orange vegetables, fresh cut", + "group": "Vegetables", + }, + ), + ( + "24550", + {"name": "Other red and orange vegetables, frozen", "group": "Vegetables"}, + ), + ( + "24575", + {"name": "Other red and orange vegetables, canned", "group": "Vegetables"}, + ), + ("26000", {"name": "Dark green vegetables, fresh", "group": "Vegetables"}), + ("26525", {"name": "Dark green vegetables, fresh cut", "group": "Vegetables"}), + ("26550", {"name": "Dark green vegetables, frozen", "group": "Vegetables"}), + ("26575", {"name": "Dark green vegetables, canned", "group": "Vegetables"}), + ( + "27500", + {"name": "Beans, lentils, and peas, fresh/dried", "group": "Vegetables"}, + ), + ("27550", {"name": "Beans, lentils, and peas, frozen", "group": "Vegetables"}), + ("27575", {"name": "Beans, lentils, and peas, canned", "group": "Vegetables"}), + ("29000", {"name": "Other/mixed vegetables, fresh", "group": "Vegetables"}), + ("29025", {"name": "Other/mixed vegetables, fresh cut", "group": "Vegetables"}), + ("29050", {"name": "Other/mixed vegetables, frozen", "group": "Vegetables"}), + ("29075", {"name": "Other/mixed vegetables, canned", "group": "Vegetables"}), + ("30000", {"name": "Whole fruit, fresh", "group": "Fruit"}), + ("30025", {"name": "Whole fruit, fresh cut", "group": "Fruit"}), + ("30050", {"name": "Whole fruit, frozen", "group": "Fruit"}), + ("30075", {"name": "Whole fruit, canned", "group": "Fruit"}), + ("30090", {"name": "Whole fruit, dried", "group": "Fruit"}), + ( + "35000", + {"name": "100-percent fruit and vegetable juices, fresh", "group": "Fruit"}, + ), + ( + "35050", + { + "name": "100-percent fruit and vegetable juices, frozen", + "group": "Fruit", + }, + ), + ( + "35075", + { + "name": "100-percent fruit and vegetable juices, canned and " + "shelf-stable", + "group": "Fruit", + }, + ), + ("40000", {"name": "Whole milk", "group": "Dairy"}), + ("40030", {"name": "Whole cream and sour cream", "group": "Dairy"}), + ("40060", {"name": "Whole yogurt", "group": "Dairy"}), + ("43000", {"name": "Reduced-fat, low-fat, and skim milk", "group": "Dairy"}), + ( + "43030", + { + "name": "Reduced-fat, low-fat, and skim cream and sour cream", + "group": "Dairy", + }, + ), + ( + "43060", + {"name": "Reduced-fat, low-fat, and skim yogurt", "group": "Dairy"}, + ), + ("46000", {"name": "Cheese and cream cheese", "group": "Dairy"}), + ("46050", {"name": "Processed cheese", "group": "Dairy"}), + ( + "50000", + { + "name": "Beef, pork, lamb, veal and game, fresh", + "group": "Meat and Protein Foods", + }, + ), + ( + "50050", + { + "name": "Beef, pork, lamb, veal and game, frozen", + "group": "Meat and Protein Foods", + }, + ), + ( + "50075", + { + "name": "Beef, pork, lamb, veal and game, canned", + "group": "Meat and Protein Foods", + }, + ), + ( + "51500", + { + "name": "Chicken, turkey, and game birds, fresh", + "group": "Meat and Protein Foods", + }, + ), + ( + "51550", + { + "name": "Chicken, turkey, and game birds, frozen", + "group": "Meat and Protein Foods", + }, + ), + ( + "51575", + { + "name": "Chicken, turkey, and game birds, canned", + "group": "Meat and Protein Foods", + }, + ), + ( + "53000", + {"name": "Fish and seafood, fresh", "group": "Meat and Protein Foods"}, + ), + ( + "53050", + {"name": "Fish and seafood, frozen", "group": "Meat and Protein Foods"}, + ), + ( + "53075", + {"name": "Fish and seafood, canned", "group": "Meat and Protein Foods"}, + ), + ("54500", {"name": "Nuts and seeds", "group": "Meat and Protein Foods"}), + ( + "54550", + { + "name": "Nut and seed butters and spreads", + "group": "Meat and Protein Foods", + }, + ), + ( + "56000", + { + "name": "Bacon, sausage, and lunch meats", + "group": "Meat and Protein Foods", + }, + ), + ( + "57500", + {"name": "Egg and egg substitutes", "group": "Meat and Protein Foods"}, + ), + ( + "59000", + {"name": "Tofu and meat substitutes", "group": "Meat and Protein Foods"}, + ), + ( + "60000", + { + "name": "Ready-to-eat foods", + "group": "Prepared meals, sides, and salads", + }, + ), + ( + "62500", + { + "name": "Frozen and refrigerated ready-to-heat foods", + "group": "Prepared meals, sides, and salads", + }, + ), + ( + "65000", + { + "name": "Shelf-stable, ready-to-heat foods and soups", + "group": "Prepared meals, sides, and salads", + }, + ), + ( + "67500", + { + "name": "Shelf-stable meal kits", + "group": "Prepared meals, sides, and salads", + }, + ), + ("70000", {"name": "Fats and oils", "group": "Other foods"}), + ("70050", {"name": "Salad dressing", "group": "Other foods"}), + ("71000", {"name": "Condiments, gravies, and sauces", "group": "Other foods"}), + ("71050", {"name": "Dry spices", "group": "Other foods"}), + ("72000", {"name": "Sweetened coffee and tea", "group": "Other foods"}), + ("72010", {"name": "Unsweetened coffee and tea", "group": "Other foods"}), + ( + "72020", + { + "name": "Flavored milk and other sweetened milk-based beverages", + "group": "Other foods", + }, + ), + ("72030", {"name": "Low-calorie beverages", "group": "Other foods"}), + ("72040", {"name": "All other caloric beverages", "group": "Other foods"}), + ("72050", {"name": "Alcohol", "group": "Other foods"}), + ("72060", {"name": "Water", "group": "Other foods"}), + ("73000", {"name": "Sweeteners", "group": "Other foods"}), + ("73010", {"name": "Jellies and jams", "group": "Other foods"}), + ("73020", {"name": "Candy", "group": "Other foods"}), + ("73030", {"name": "Baked goods", "group": "Other foods"}), + ("73040", {"name": "Cake and cookie mixes", "group": "Other foods"}), + ( + "73050", + { + "name": "Ice cream and other milk-based desserts", + "group": "Other foods", + }, + ), + ("73060", {"name": "All other desserts", "group": "Other foods"}), + ("74000", {"name": "Whole-grain breakfast cereal", "group": "Other foods"}), + ("74050", {"name": "All other breakfast cereal", "group": "Other foods"}), + ( + "75000", + {"name": "Savory snacks, whole-grain snacks", "group": "Other foods"}, + ), + ("75050", {"name": "Savory snacks, all other snacks", "group": "Other foods"}), + ("76000", {"name": "Vitamins and meal supplements", "group": "Other foods"}), + ("77000", {"name": "Baby food", "group": "Other foods"}), + ("78000", {"name": "Infant formula", "group": "Other foods"}), + ] +) + + +def media_path(table: str) -> str: + """Return a table's zip media path. + + Parameters + ---------- + table : str + Table key from FMAP_TABLES. + + Returns + ------- + str + The '/media/{id}/{name}.zip' path of the table's archive. + """ + return FMAP_TABLES[table]["media_path"] + + +def build_url(table: str) -> str: + """Return a table's full download URL.""" + return f"{BASE_URL}{media_path(table)}" + + +def table_measures(table: str) -> list[str]: + """List the measure keys a table publishes, in canonical order. + + Parameters + ---------- + table : str + Table key from FMAP_TABLES. + + Returns + ------- + list[str] + Measure keys whose source attribute is present in the table. + """ + return [key for key, meta in MEASURES.items() if table in meta["tables"]] + + +def measure_options(table: str) -> list[dict]: + """Build the labeled measure options a table publishes.""" + return [ + {"label": MEASURES[key]["label"], "value": key} for key in table_measures(table) + ] + + +def group_options() -> list[dict]: + """Build the labeled Tier-1 food-group options.""" + return [{"label": group, "value": group} for group in GROUPS] + + +def item_options(group: str) -> list[dict]: + """List a food group's items as labeled EFPG-code options. + + Parameters + ---------- + group : str + Tier-1 food group from GROUPS. + + Returns + ------- + list[dict] + Label/value options with the EFPG name as label and the EFPG code + string as value, in published order. + """ + return [ + {"label": meta["name"], "value": code} + for code, meta in EFPG.items() + if meta["group"] == group + ] + + +def resolve_measure(table: str, measure: str) -> str: + """Resolve a measure to one the table publishes, falling back per table. + + Parameters + ---------- + table : str + Table key from FMAP_TABLES. + measure : str + Requested measure key. + + Returns + ------- + str + The requested measure when the table publishes it, otherwise the + table's default measure. + """ + meta = MEASURES.get(measure) + if meta is not None and table in meta["tables"]: + return measure + return DEFAULT_MEASURE_BY_TABLE[table] + + +def parse_series(text: str, item_code: str, measure_source: str) -> list[dict]: + """Parse one table's CSV, keeping one item and one measure's observations. + + Parameters + ---------- + text : str + Decoded CSV text of the table's member file. + item_code : str + EFPG code to keep, as a string. + measure_source : str + Source Attribute name to keep, e.g. 'Unit_value_mean_wtd'. + + Returns + ------- + list[dict] + Records with year, month, area_code, and value keys, one per area + and month. + """ + reader = csv.reader(StringIO(text)) + header = next(reader) + idx = {name: position for position, name in enumerate(header)} + year_i = idx["Year"] + month_i = idx["Month"] + efpg_i = idx["EFPG_code"] + area_i = idx["Metroregion_code"] + attribute_i = idx["Attribute"] + value_i = idx["Value"] + item = str(item_code) + records: list[dict] = [] + for row in reader: + if row[efpg_i] != item or row[attribute_i] != measure_source: + continue + records.append( + { + "year": int(row[year_i]), + "month": int(row[month_i]), + "area_code": row[area_i], + "value": float(row[value_i]), + } + ) + return records + + +def extract_csv_text(zip_bytes: bytes, member: str) -> str: + """Extract and decode one CSV member from a table's zip archive. + + Parameters + ---------- + zip_bytes : bytes + Raw bytes of the table's zip archive. + member : str + Member CSV file name to read. + + Returns + ------- + str + The decoded CSV text of the member. + """ + with zipfile.ZipFile(io.BytesIO(zip_bytes)) as archive: + raw = archive.read(member) + return raw.decode("utf-8-sig", errors="replace") + + +async def afetch_series(table: str, item_code: str, measure_source: str) -> list[dict]: + """Download a table and parse one item and measure through the disk cache. + + Parameters + ---------- + table : str + Table key from FMAP_TABLES. + item_code : str + EFPG code to keep, as a string. + measure_source : str + Source Attribute name to keep. + + Returns + ------- + list[dict] + Records from parse_series for the selected item and measure. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + config = FMAP_TABLES[table] + content = await afetch_ers_file(config["media_path"], product=PRODUCT_PAGE) + text = extract_csv_text(content, config["member"]) + return parse_series(text, item_code, measure_source) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_food_availability_per_capita_data_system.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_food_availability_per_capita_data_system.py new file mode 100644 index 00000000000..4bf0b42ae1a --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_food_availability_per_capita_data_system.py @@ -0,0 +1,852 @@ +"""USDA ERS Food Availability (Per Capita) Data System catalog, fetch, and parsers.""" + +import csv +from collections import OrderedDict +from io import StringIO + +BASE_URL = "https://www.ers.usda.gov" +PRODUCT_PAGE = "data-products/food-availability-per-capita-data-system" + +DATA_SYSTEMS = ("food_availability", "loss_adjusted", "nutrient") + +DATA_SYSTEM_LABELS: dict[str, str] = { + "food_availability": "Food availability", + "loss_adjusted": "Loss-adjusted food availability", + "nutrient": "Nutrient availability", +} + +NULL_TOKENS = frozenset({"", "-", "--", "---", "----", "n/a", "na", "null", "nan"}) + +NUTRIENT_TOTALS_COLUMNS: tuple[tuple[int, str], ...] = ( + (1, "Food energy - Kilocalories"), + (2, "Carbohydrates - Grams"), + (3, "Protein - Grams"), + (4, "Total fat - Grams"), + (5, "Saturated fat - Grams"), + (6, "Monounsaturated fat - Grams"), + (7, "Polyunsaturated fat - Grams"), + (8, "Cholesterol - Milligrams"), + (9, "Dietary fiber - Grams"), + (10, "Vitamin A (RE) - Micrograms RE"), + (11, "Vitamin A (RAE) - Micrograms RAE"), + (12, "Carotene - Micrograms"), + (13, "Vitamin E - Milligrams Alpha-TE"), + (14, "Vitamin C - Milligrams"), + (15, "Thiamin - Milligrams"), + (16, "Riboflavin - Milligrams"), + (17, "Niacin - Milligrams"), + (18, "Vitamin B6 - Milligrams"), + (19, "Folate (DFE) - Micrograms"), + (20, "Vitamin B12 - Micrograms"), + (21, "Calcium - Milligrams"), + (22, "Phosphorus - Milligrams"), + (23, "Magnesium - Milligrams"), + (24, "Iron - Milligrams"), + (25, "Zinc - Milligrams"), + (26, "Copper - Milligrams"), + (27, "Potassium - Milligrams"), + (28, "Selenium - Micrograms"), + (29, "Sodium - Milligrams"), +) + +NUTRIENT_FOODGROUP_NUTRIENTS: tuple[tuple[str, str], ...] = ( + ("Food energy", "Kilocalories"), + ("Carbohydrates", "Grams"), + ("Protein", "Grams"), + ("Total fat", "Grams"), + ("Saturated fat", "Grams"), + ("Monounsaturated fat", "Grams"), + ("Polyunsaturated fat", "Grams"), + ("Cholesterol", "Milligrams"), + ("Dietary fiber", "Grams"), + ("Vitamin A (RAE)", "Micrograms RAE"), + ("Carotene", "Micrograms"), + ("Vitamin E", "Milligrams ATE"), + ("Vitamin C", "Milligrams"), + ("Thiamin", "Milligrams"), + ("Riboflavin", "Milligrams"), + ("Niacin", "Milligrams"), + ("Vitamin B6", "Milligrams"), + ("Folate (DFE)", "Micrograms"), + ("Vitamin B12", "Micrograms"), + ("Calcium", "Milligrams"), + ("Phosphorus", "Milligrams"), + ("Magnesium", "Milligrams"), + ("Iron", "Milligrams"), + ("Zinc", "Milligrams"), + ("Copper", "Milligrams"), + ("Potassium", "Milligrams"), + ("Selenium", "Micrograms"), + ("Sodium", "Milligrams"), +) + +CATALOG: dict[str, "OrderedDict[str, dict]"] = { + "food_availability": OrderedDict( + [ + ( + "coffee_tea_cocoa", + { + "label": "Coffee, tea, cocoa, and spices", + "media": "/media/5327/coffee-tea-cocoa-and-spices-availability.csv", + "format": "wide_columnar", + "year_col": 0, + "block_col": 3, + "id_cols": (0, 1, 2, 3), + "header_row": 0, + "data_start": 1, + }, + ), + ( + "dairy_fluid_milk", + { + "label": "Dairy (fluid milk)", + "media": "/media/5329/dairy-fluid-milk.csv", + "format": "long", + }, + ), + ( + "dairy_products", + { + "label": "Dairy products", + "media": "/media/5331/dairy-products.csv", + "format": "long", + }, + ), + ( + "eggs", + { + "label": "Eggs", + "media": "/media/5333/eggs.csv", + "format": "long", + }, + ), + ( + "fats_and_oils_added", + { + "label": "Fats and oils (added)", + "media": "/media/5335/fats-and-oils-added.csv", + "format": "wide_fixed", + "block": "Added fats and oils (product weight): Per capita food availability", + "year_col": 0, + "data_start": 5, + "columns": ( + (1, "U.S. total population, July 1 - Millions"), + (2, "Butter - Pounds"), + (3, "Margarine - Pounds"), + (4, "Lard - Pounds"), + (5, "Edible tallow - Pounds"), + (6, "Shortening - Pounds"), + (7, "Salad and cooking oils - Pounds"), + (8, "Other edible fats and oils - Pounds"), + (9, "Total - Pounds"), + ), + }, + ), + ( + "fish_and_shellfish", + { + "label": "Fish and shellfish", + "media": "/media/5337/fish-and-shellfish.csv", + "format": "long", + }, + ), + ( + "fruit_all_uses", + { + "label": "Fruit (all uses)", + "media": "/media/5339/fruit-all-uses.csv", + "format": "long", + }, + ), + ( + "fruit_canned", + { + "label": "Fruit (canned)", + "media": "/media/5341/fruit-canned.csv", + "format": "long", + }, + ), + ( + "fruit_dried", + { + "label": "Fruit (dried)", + "media": "/media/5343/fruit-dried.csv", + "format": "long", + }, + ), + ( + "fruit_fresh", + { + "label": "Fruit (fresh)", + "media": "/media/5345/fruit-fresh.csv", + "format": "long", + }, + ), + ( + "fruit_frozen", + { + "label": "Fruit (frozen)", + "media": "/media/5347/fruit-frozen.csv", + "format": "long", + }, + ), + ( + "fruit_juices", + { + "label": "Fruit juices", + "media": "/media/5349/fruit-juices.csv", + "format": "long", + }, + ), + ( + "fruit_and_vegetables", + { + "label": "Fruit and vegetables", + "media": "/media/5351/fruit-and-vegetables.csv", + "format": "long", + }, + ), + ( + "grains", + { + "label": "Grains", + "media": "/media/5353/grains.csv", + "format": "long", + }, + ), + ( + "peanuts_and_tree_nuts", + { + "label": "Peanuts and tree nuts", + "media": "/media/5355/peanuts-and-tree-nuts.csv", + "format": "long", + }, + ), + ( + "population", + { + "label": "Population", + "media": "/media/5357/population.csv", + "format": "wide_fixed", + "block": "Population: Resident and resident plus Armed Forces overseas", + "year_col": 0, + "data_start": 8, + "columns": ( + ( + 1, + "Resident population plus Armed Forces overseas - January 1 - Millions", + ), + ( + 2, + "Resident population plus Armed Forces overseas - July 1 - Millions", + ), + (3, "Resident - January 1 - Millions"), + (4, "Resident - July 1 - Millions"), + ( + 5, + "ERS estimate of resident plus Armed Forces overseas - July 1 - Millions", + ), + ), + }, + ), + ( + "poultry_chicken_and_turkey", + { + "label": "Poultry (chicken and turkey)", + "media": "/media/5359/poultry-chicken-and-turkey.csv", + "format": "long", + }, + ), + ( + "red_meat", + { + "label": "Red meat (beef, veal, pork, lamb, and mutton)", + "media": "/media/5361/red-meat-beef-veal-pork-lamb-and-mutton.csv", + "format": "long", + }, + ), + ( + "red_meat_poultry_and_fish", + { + "label": "Red meat, poultry, and fish", + "media": "/media/5363/red-meat-poultry-and-fish.csv", + "format": "long", + }, + ), + ( + "sugar_and_sweeteners_added", + { + "label": "Sugar and sweeteners (added)", + "media": "/media/5365/sugar-and-sweeteners-added.csv", + "format": "long", + }, + ), + ( + "vegetables_all_uses", + { + "label": "Vegetables (all uses)", + "media": "/media/5367/vegetables-all-uses.csv", + "format": "long", + }, + ), + ( + "vegetables_canned", + { + "label": "Vegetables (canned)", + "media": "/media/5369/vegetables-canned.csv", + "format": "long", + }, + ), + ( + "vegetables_fresh", + { + "label": "Vegetables (fresh)", + "media": "/media/5371/vegetables-fresh.csv", + "format": "long", + }, + ), + ( + "vegetables_frozen", + { + "label": "Vegetables (frozen)", + "media": "/media/5373/vegetables-frozen.csv", + "format": "long", + }, + ), + ( + "vegetables_potatoes", + { + "label": "Vegetables (potatoes)", + "media": "/media/5375/vegetables-potatoes.csv", + "format": "long", + }, + ), + ( + "vegetables_processed", + { + "label": "Vegetables (processed)", + "media": "/media/5377/vegetables-processed.csv", + "format": "long", + }, + ), + ( + "vegetables_pulses", + { + "label": "Vegetables (pulses)", + "media": "/media/5379/vegetables-pulses.csv", + "format": "long", + }, + ), + ] + ), + "loss_adjusted": OrderedDict( + [ + ( + "calories", + { + "label": "Calories", + "media": "/media/5381/calories.csv", + "format": "wide_fixed", + "block": "Average daily per capita calories from the U.S. food availability, adjusted for spoilage and other waste", + "year_col": 0, + "data_start": 5, + "columns": ( + (1, "Meat, eggs, and nuts"), + (2, "Dairy"), + (3, "Fruit"), + (4, "Vegetables"), + (5, "Flour and cereal products"), + (6, "Added fats and oils and dairy fats"), + (7, "Sugar and sweeteners (added)"), + (8, "Total"), + ), + }, + ), + ( + "dairy", + { + "label": "Dairy (fluid milk, cream, and other products)", + "media": "/media/5383/dairy-fluid-milk-cream-and-other-products.csv", + "format": "long", + }, + ), + ( + "fats_and_oils_added", + { + "label": "Fats and oils (added) - butter", + "media": "/media/5385/fats-and-oils-added.csv", + "format": "wide_fixed", + "block": "Butter: Per capita availability adjusted for loss", + "year_col": 0, + "data_start": 6, + "columns": ( + (1, "Primary weight - Lbs/year"), + (2, "Loss from primary to retail weight - Percent"), + (3, "Retail weight - Lbs/year"), + ( + 4, + "Loss from retail/institutional to consumer level - Percent", + ), + (5, "Consumer weight - Lbs/year"), + (6, "Loss at consumer level - Nonedible share - Percent"), + (7, "Loss at consumer level - Edible weight - Lbs/year"), + ( + 8, + "Loss at consumer level - Other (cooking loss and uneaten food) - Percent", + ), + (9, "Total loss, all levels - Percent"), + (10, "Per capita availability adjusted for loss - Lbs/year"), + (11, "Per capita availability adjusted for loss - Oz/day"), + (12, "Per capita availability adjusted for loss - G/day"), + (13, "Calories per fat gram - Number"), + ), + }, + ), + ( + "food_pattern_equivalents", + { + "label": "Food pattern equivalents", + "media": "/media/5387/food-pattern-equivalents.csv", + "format": "wide_fixed", + "block": "Average daily per capita food pattern equivalents from the U.S. food availability, adjusted for spoilage and other waste", + "year_col": 0, + "data_start": 7, + "columns": ( + (1, "Meat, eggs, and nuts - Ounces"), + (2, "Dairy - Cups"), + (3, "Fruit - Cups"), + (4, "Vegetables - Cups"), + (5, "Flour and cereal products - Ounces"), + (6, "Added fats and oils and dairy fats - Grams"), + (7, "Added sugars - Teaspoons"), + ), + }, + ), + ( + "fruit", + { + "label": "Fruit", + "media": "/media/5389/fruit.csv", + "format": "long", + }, + ), + ( + "grains", + { + "label": "Grains", + "media": "/media/5391/grains.csv", + "format": "long", + }, + ), + ( + "meat_poultry_fish_eggs_and_nuts", + { + "label": "Meat, poultry, fish, eggs, and nuts", + "media": "/media/5393/meat-poultry-fish-eggs-and-nuts.csv", + "format": "long", + }, + ), + ( + "sugar_and_sweeteners_added", + { + "label": "Sugar and sweeteners (added)", + "media": "/media/5395/sugar-and-sweeteners-added.csv", + "format": "long", + }, + ), + ( + "vegetables", + { + "label": "Vegetables", + "media": "/media/5397/vegetables.csv", + "format": "long", + }, + ), + ] + ), + "nutrient": OrderedDict( + [ + ( + "totals", + { + "label": "Totals", + "media": "/media/5325/nutrient-availability-food-energy-nutrients-and-dietary-components.xls", + "format": "nutrient_totals", + "sheet": "Totals", + "block": "Totals", + "year_col": 0, + "data_start": 6, + "columns": NUTRIENT_TOTALS_COLUMNS, + }, + ), + ( + "food_group", + { + "label": "By food group", + "media": "/media/5325/nutrient-availability-food-energy-nutrients-and-dietary-components.xls", + "format": "nutrient_foodgroups", + "sheet": "Foodgroups", + "block_col": 0, + "data_start": 4, + }, + ), + ] + ), +} + + +def parse_value(raw: object) -> float | None: + """Parse a source cell into a float, keeping full precision. + + Parameters + ---------- + raw : object + A raw CSV string or a spreadsheet cell value. + + Returns + ------- + float | None + The numeric value with thousands separators removed, or None when the + cell is blank, a null placeholder, or non-numeric. + """ + if isinstance(raw, (int, float)): + return float(raw) + text = str(raw or "").strip() + if text.casefold() in NULL_TOKENS: + return None + try: + return float(text.replace(",", "")) + except ValueError: + return None + + +def is_year_token(token: str) -> bool: + """Return whether a string is a four-digit calendar year.""" + token = token.strip() + return len(token) == 4 and token.isdigit() + + +def clean_block_label(raw: str) -> str: + """Strip surrounding whitespace and trailing footnote digits from a label.""" + return raw.strip().rstrip("0123456789").strip() + + +def parse_long(text: str) -> list[dict]: + """Parse a tidy [Commodity, Year, Attribute, Value] file into records. + + Parameters + ---------- + text : str + Decoded CSV text whose header begins with Commodity, Year, Attribute, + and Value, optionally followed by a Notes column. + + Returns + ------- + list[dict] + Records with block, year, attribute, and value keys. Rows whose year + token is not a four-digit calendar year are skipped. + """ + records: list[dict] = [] + reader = csv.reader(StringIO(text)) + next(reader, None) + for row in reader: + if len(row) < 4: + continue + year_token = row[1].strip() + if not is_year_token(year_token): + continue + records.append( + { + "block": row[0].strip(), + "year": int(year_token), + "attribute": row[2].strip(), + "value": parse_value(row[3]), + } + ) + return records + + +def parse_wide_columnar(text: str, config: dict) -> list[dict]: + """Parse a columnar wide file with a single header row into records. + + Parameters + ---------- + text : str + Decoded CSV text whose first row is a header of measure columns. + config : dict + The catalog entry, carrying header_row, year_col, block_col, id_cols, + and data_start. + + Returns + ------- + list[dict] + Records with block, year, attribute, and value keys, one per measure + column and data row. + """ + rows = list(csv.reader(StringIO(text))) + header = rows[config["header_row"]] + year_col = config["year_col"] + block_col = config["block_col"] + id_cols = set(config["id_cols"]) + measure_cols = [i for i in range(len(header)) if i not in id_cols] + records: list[dict] = [] + for row in rows[config["data_start"] :]: + if len(row) <= year_col or not is_year_token(row[year_col]): + continue + block = row[block_col].strip() if len(row) > block_col else "" + year = int(row[year_col].strip()) + for col in measure_cols: + records.append( + { + "block": block, + "year": year, + "attribute": header[col].strip(), + "value": parse_value(row[col] if col < len(row) else ""), + } + ) + return records + + +def parse_wide_fixed(text: str, config: dict) -> list[dict]: + """Parse a single-block wide file with a hardcoded column map into records. + + Parameters + ---------- + text : str + Decoded CSV text of a titled wide file with year rows. + config : dict + The catalog entry, carrying block, year_col, data_start, and a columns + tuple of (index, attribute-name) pairs. + + Returns + ------- + list[dict] + Records with block, year, attribute, and value keys, one per mapped + column and data row. + """ + rows = list(csv.reader(StringIO(text))) + year_col = config["year_col"] + block = config["block"] + records: list[dict] = [] + for row in rows[config["data_start"] :]: + if len(row) <= year_col or not is_year_token(row[year_col]): + continue + year = int(row[year_col].strip()) + for col, name in config["columns"]: + records.append( + { + "block": block, + "year": year, + "attribute": name, + "value": parse_value(row[col] if col < len(row) else ""), + } + ) + return records + + +def parse_nutrient_totals(content: bytes, config: dict) -> list[dict]: + """Parse the nutrient workbook Totals sheet into records. + + Parameters + ---------- + content : bytes + Raw bytes of the nutrient .xls workbook. + config : dict + The catalog entry, carrying sheet, block, data_start, and a columns + tuple of (index, nutrient-name) pairs. + + Returns + ------- + list[dict] + Records with block, year, attribute, and value keys, one per nutrient + column and year row. + """ + import xlrd + + sheet = xlrd.open_workbook(file_contents=content).sheet_by_name(config["sheet"]) + block = config["block"] + records: list[dict] = [] + for row in range(config["data_start"], sheet.nrows): + year = _year_from_cell(sheet.cell_value(row, 0)) + if year is None: + continue + for col, name in config["columns"]: + records.append( + { + "block": block, + "year": year, + "attribute": name, + "value": parse_value(sheet.cell_value(row, col)), + } + ) + return records + + +def parse_nutrient_foodgroups(content: bytes, config: dict) -> list[dict]: + """Parse the nutrient workbook Foodgroups sheet into records. + + Parameters + ---------- + content : bytes + Raw bytes of the nutrient .xls workbook. + config : dict + The catalog entry, carrying sheet and data_start. + + Returns + ------- + list[dict] + Records with block, year, attribute, and value keys. Each food group is + a block with 1970 and 2010 year rows and paired value and percent-of- + total nutrient columns. + """ + import xlrd + + sheet = xlrd.open_workbook(file_contents=content).sheet_by_name(config["sheet"]) + columns = foodgroup_columns() + records: list[dict] = [] + block: str | None = None + for row in range(config["data_start"], sheet.nrows): + first = sheet.cell_value(row, 0) + year = _year_from_cell(first) + if year is None: + if isinstance(first, str) and first.strip(): + block = clean_block_label(first) + continue + if block is None: + continue + for col, name in columns: + records.append( + { + "block": block, + "year": year, + "attribute": name, + "value": parse_value(sheet.cell_value(row, col)), + } + ) + return records + + +def foodgroup_columns() -> list[tuple[int, str]]: + """Build the Foodgroups sheet value and percent-of-total column map. + + Returns + ------- + list[tuple[int, str]] + (index, attribute-name) pairs: a value column then a percent-of-total + column for each nutrient, in sheet order. + """ + columns: list[tuple[int, str]] = [] + for index, (nutrient, unit) in enumerate(NUTRIENT_FOODGROUP_NUTRIENTS): + value_col = index * 2 + 1 + columns.append((value_col, f"{nutrient} - {unit}")) + columns.append((value_col + 1, f"{nutrient} - Percent of total")) + return columns + + +def _year_from_cell(value: object) -> int | None: + """Return the integer calendar year of a cell, or None when it is not one.""" + if isinstance(value, (int, float)): + year = int(value) + return year if 1500 <= year <= 2100 else None + text = str(value or "").strip() + return int(text) if is_year_token(text) else None + + +def parse_records(content: bytes, config: dict) -> list[dict]: + """Dispatch a file's raw bytes to the parser for its format. + + Parameters + ---------- + content : bytes + Raw bytes of the downloaded file. + config : dict + The catalog entry for the file. + + Returns + ------- + list[dict] + Long-format records with block, year, attribute, and value keys. + """ + fmt = config["format"] + if fmt == "long": + return parse_long(content.decode("utf-8-sig", errors="replace")) + if fmt == "wide_columnar": + return parse_wide_columnar( + content.decode("utf-8-sig", errors="replace"), config + ) + if fmt == "wide_fixed": + return parse_wide_fixed(content.decode("utf-8-sig", errors="replace"), config) + if fmt == "nutrient_totals": + return parse_nutrient_totals(content, config) + return parse_nutrient_foodgroups(content, config) + + +def group_options(data_system: str) -> list[dict]: + """Build the labeled food-group (file) options for a data system. + + Parameters + ---------- + data_system : str + Data system key from DATA_SYSTEMS. + + Returns + ------- + list[dict] + Label/value option dictionaries for the widget's food-group selector. + """ + return [ + {"label": config["label"], "value": slug} + for slug, config in CATALOG[data_system].items() + ] + + +def distinct_blocks(records: list[dict]) -> list[str]: + """List the distinct block names in first-seen order.""" + seen: OrderedDict[str, None] = OrderedDict() + for record in records: + seen.setdefault(record["block"], None) + return list(seen) + + +async def afetch_records(data_system: str, food_group: str) -> list[dict]: + """Download and parse one file into long-format records through the cache. + + Parameters + ---------- + data_system : str + Data system key from DATA_SYSTEMS. + food_group : str + Food-group (file) slug within the data system. + + Returns + ------- + list[dict] + Long-format records with block, year, attribute, and value keys. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + config = CATALOG[data_system][food_group] + content = await afetch_ers_file(config["media"], product=PRODUCT_PAGE) + return parse_records(content, config) + + +async def afetch_blocks(data_system: str, food_group: str) -> list[str]: + """List the block (commodity) options for a file in first-seen order. + + Parameters + ---------- + data_system : str + Data system key from DATA_SYSTEMS. + food_group : str + Food-group (file) slug within the data system. + + Returns + ------- + list[str] + Distinct block names; a single hardcoded block for the wide single-block + files, otherwise the blocks parsed from the file. + """ + config = CATALOG[data_system][food_group] + if config["format"] in ("wide_fixed", "nutrient_totals"): + return [config["block"]] + return distinct_blocks(await afetch_records(data_system, food_group)) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_food_consumption_nutrient_intakes_and_diet_quality.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_food_consumption_nutrient_intakes_and_diet_quality.py new file mode 100644 index 00000000000..b026caa1946 --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_food_consumption_nutrient_intakes_and_diet_quality.py @@ -0,0 +1,389 @@ +"""USDA ERS Food Consumption, Nutrient Intakes, and Diet Quality catalog and parsers.""" + +import csv +from io import StringIO + +BASE_URL = "https://www.ers.usda.gov" +PRODUCT_PAGE = "data-products/food-consumption-nutrient-intakes-and-diet-quality" + +TABLE_FILES: dict[str, str] = { + "1_sample_sizes": "/media/5463/table-1-sample-sizes-by-population-subgroups-for-each-survey-cycle.csv", + "2_nutrient_intake": "/media/5465/table-2-daily-nutrient-intake-by-food-source-1977-2018.csv", + "3_nutrient_intake_share": "/media/5467/table-3-daily-nutrient-intake-and-share-by-food-source-1977-2018.csv", + "4_nutrient_density": "/media/5469/table-4-nutrient-density-by-food-source-1977-2018.csv", + "5_food_group_intake": "/media/5471/table-5-intakes-of-food-group-by-food-source-1977-2018.csv", + "6_food_group_intake_share": "/media/5473/table-6-daily-food-equivalent-group-intake-and-share-by-food-source-1977-2018.csv", + "7_food_group_density": "/media/5475/table-7-density-of-food-group-by-food-source-1977-2018.csv", + "8_recommended_vs_actual_density": "/media/5477/table-8-the-2020-2025-recommended-and-2017-2018-nutrient-and-food-group-density.csv", +} + +TABLE_LABELS: dict[str, str] = { + "1_sample_sizes": "Table 1 - Sample sizes by population subgroup", + "2_nutrient_intake": "Table 2 - Daily nutrient intake by food source", + "3_nutrient_intake_share": "Table 3 - Daily nutrient intake and share by food source", + "4_nutrient_density": "Table 4 - Nutrient density by food source", + "5_food_group_intake": "Table 5 - Food group intake by food source", + "6_food_group_intake_share": "Table 6 - Food group intake and share by food source", + "7_food_group_density": "Table 7 - Food group density by food source", + "8_recommended_vs_actual_density": "Table 8 - Recommended vs. actual density (2017-2018)", +} + +DEFAULT_TABLE = "2_nutrient_intake" +SAMPLE_TABLE = "1_sample_sizes" +RECOMMENDED_TABLE = "8_recommended_vs_actual_density" +NUTRIENT_TABLES = frozenset( + {"2_nutrient_intake", "3_nutrient_intake_share", "4_nutrient_density"} +) +FOOD_GROUP_TABLES = frozenset( + {"5_food_group_intake", "6_food_group_intake_share", "7_food_group_density"} +) +INTAKE_TABLES = NUTRIENT_TABLES | FOOD_GROUP_TABLES + +SURVEY_CYCLES: tuple[str, ...] = ( + "1977-1978", + "1989-1991", + "1994-1998", + "2003-2004", + "2005-2006", + "2007-2008", + "2009-2010", + "2011-2012", + "2013-2014", + "2015-2016", + "2017-2018", +) + +FOOD_SOURCES: tuple[str, ...] = ( + "Total", + "FAH", + "FAFH", + "FAFH: Restaurant", + "FAFH: Fast food", + "FAFH: School", + "FAFH: Others", +) + +DEMOGRAPHICS: tuple[str, ...] = ( + "US consumers aged 2 and above", + "Sex - Males", + "Sex - Females", + "Ages 2-19", + "Ages 20-64", + "Ages 65 and above", + "Cohort - Boys age 2-19", + "Cohort - Girls age 2-19", + "Cohort - Men age 20 and above", + "Cohort - Women age 20 and above", + "Income - low-income households", + "Income - mid-income households", + "Income - high-income households", + "Race - Non-Hispanic White", + "Race - Non-Hispanic Black", + "Race - Hispanic", + "Race - Other racial & ethnic", + "Edu. - Less than high school", + "Edu. - High school degree", + "Edu. - College attended", +) +DEFAULT_DEMOGRAPHIC = "US consumers aged 2 and above" + +STATISTIC_CHOICES: dict[str, str] = {"mean": "Mean", "se_of_mean": "SE of mean"} +DEFAULT_STATISTIC = "mean" + +RECOMMENDED_COLUMNS: tuple[tuple[str, str], ...] = ( + ( + "Recommended density*:Nutrient or food group amount per 1,000 calories", + "Recommended", + ), + ( + "2017-2018 Actual density-Total-Nutrient or food group amount per 1,000 calories", + "Actual: Total", + ), + ( + "2017-2018 Actual density-FAH-Nutrient or food group amount per 1,000 calories", + "Actual: FAH", + ), + ( + "2017-2018 Actual density-FAFH-Nutrient or food group amount per 1,000 calories", + "Actual: FAFH", + ), + ( + "2017-2018 Actual density-Restaurant-Nutrient or food group amount per 1,000 calories", + "Actual: Restaurant", + ), + ( + "2017-2018 Actual density-Fast food-Nutrient or food group amount per 1,000 calories", + "Actual: Fast food", + ), + ( + "2017-2018 Actual density-School-Nutrient or food group amount per 1,000 calories", + "Actual: School", + ), + ( + "2017-2018 density as a ratio of the recommended density-Total-Ratio of actual density to the recommended density", + "Ratio: Total", + ), + ( + "2017-2018 density as a ratio of the recommended density-FAH-Ratio of actual density to the recommended density", + "Ratio: FAH", + ), + ( + "2017-2018 density as a ratio of the recommended density-FAFH-Ratio of actual density to the recommended density", + "Ratio: FAFH", + ), + ( + "2017-2018 density as a ratio of the recommended density-Restaurant-Ratio of actual density to the recommended density", + "Ratio: Restaurant", + ), + ( + "2017-2018 density as a ratio of the recommended density-Fast food-Ratio of actual density to the recommended density", + "Ratio: Fast food", + ), + ( + "2017-2018 density as a ratio of the recommended density-School-Ratio of actual density to the recommended density", + "Ratio: School", + ), +) +RECOMMENDED_COLUMN_MAP: dict[str, str] = { + raw: label for raw, label in RECOMMENDED_COLUMNS +} +RECOMMENDED_COLUMN_ORDER: tuple[str, ...] = tuple( + label for _, label in RECOMMENDED_COLUMNS +) + +UNIT_ALIASES: dict[str, str] = {"Grams per 1,000": "Grams per 1,000 calories"} + + +def item_column(table: str) -> str: + """Return the CSV column holding the row label for an intake table. + + Parameters + ---------- + table : str + Table key from TABLE_FILES. + + Returns + ------- + str + 'Nutrient' for the nutrient tables, 'Food group' for the food-group + tables. + """ + return "Nutrient" if table in NUTRIENT_TABLES else "Food group" + + +def normalize_unit(measurement: str | None) -> str | None: + """Normalize a Measurement label to a canonical unit or None. + + Parameters + ---------- + measurement : str | None + Raw Measurement cell, possibly with trailing spaces or a truncated + 'per 1,000' variant. + + Returns + ------- + str | None + The trimmed, canonical unit label, or None when the cell is empty. + """ + unit = (measurement or "").strip() + if not unit: + return None + return UNIT_ALIASES.get(unit, unit) + + +def split_cycle_variable(value: str | None) -> tuple[str, str]: + """Split a 'Survey years:Variable' cell into its cycle and statistic. + + Parameters + ---------- + value : str | None + Cell such as '1977-1978-Mean' or '2017-2018-SE of mean'. + + Returns + ------- + tuple[str, str] + The survey cycle and the statistic label. + """ + cycle, _, variable = (value or "").rpartition("-") + return cycle.strip(), variable.strip() + + +def to_number(raw: str | None) -> float | None: + """Coerce a source cell to a float, or None when blank or non-numeric. + + Parameters + ---------- + raw : str | None + Raw Value cell; blank, suppressed, and corrupted cells return None. + + Returns + ------- + float | None + The parsed value, or None. + """ + text = (raw or "").strip() + if not text: + return None + try: + return float(text) + except ValueError: + return None + + +def to_number_or_text(raw: str | None) -> float | str | None: + """Coerce a source cell to a float, keeping a non-numeric range as text. + + Parameters + ---------- + raw : str | None + Raw Value cell; a numeric cell returns a float, a range such as + '20-35' returns the raw string, a blank cell returns None. + + Returns + ------- + float | str | None + The parsed value. + """ + text = (raw or "").strip() + if not text: + return None + try: + return float(text) + except ValueError: + return text + + +def parse_sample_sizes(text: str) -> list[dict]: + """Parse the Table 1 sample-size CSV into long records. + + Parameters + ---------- + text : str + Decoded CSV text with Demographics, Survey years, and Sample size. + + Returns + ------- + list[dict] + Records with the demographic subgroup, survey cycle, integer sample + size or None, and source order. + """ + records: list[dict] = [] + for order, row in enumerate(csv.DictReader(StringIO(text))): + size = (row.get("Sample size") or "").strip() + records.append( + { + "demographic": (row.get("Demographics") or "").strip(), + "cycle": (row.get("Survey years") or "").strip(), + "value": int(size) if size.isdigit() else None, + "order": order, + } + ) + return records + + +def parse_intake(text: str, table: str) -> list[dict]: + """Parse an intake table (Tables 2-7) into long records. + + Parameters + ---------- + text : str + Decoded CSV text with the item, Food source, Measurement, Survey + years:Variable, Value, and Demographics columns. + table : str + Table key from INTAKE_TABLES. + + Returns + ------- + list[dict] + Records with the item, food source, unit, demographic subgroup, + statistic, survey cycle, numeric value or None, and source order. + """ + column = item_column(table) + records: list[dict] = [] + for order, row in enumerate(csv.DictReader(StringIO(text))): + cycle, statistic = split_cycle_variable(row.get("Survey years:Variable")) + records.append( + { + "item": (row.get(column) or "").strip(), + "food_source": (row.get("Food source") or "").strip(), + "units": normalize_unit(row.get("Measurement")), + "demographics": (row.get("Demographics") or "").strip(), + "statistic": statistic, + "cycle": cycle, + "value": to_number(row.get("Value")), + "order": order, + } + ) + return records + + +def parse_recommended(text: str) -> list[dict]: + """Parse the Table 8 recommended-versus-actual CSV into long records. + + Parameters + ---------- + text : str + Decoded CSV text with Nutrient or Food group, Variable, and Value. + + Returns + ------- + list[dict] + Records with the item, the wide column label, the numeric or text + value, and source order; rows whose Variable is unmapped are skipped. + """ + records: list[dict] = [] + for order, row in enumerate(csv.DictReader(StringIO(text))): + variable = (row.get("Variable") or "").strip() + column = RECOMMENDED_COLUMN_MAP.get(variable) + if column is None: + continue + records.append( + { + "item": (row.get("Nutrient or Food group") or "").strip(), + "column": column, + "value": to_number_or_text(row.get("Value")), + "order": order, + } + ) + return records + + +def parse_table(text: str, table: str) -> list[dict]: + """Dispatch a table's CSV text to its long-format parser. + + Parameters + ---------- + text : str + Decoded CSV text. + table : str + Table key from TABLE_FILES. + + Returns + ------- + list[dict] + Long-format records for the table. + """ + if table == SAMPLE_TABLE: + return parse_sample_sizes(text) + if table == RECOMMENDED_TABLE: + return parse_recommended(text) + return parse_intake(text, table) + + +async def afetch_table(table: str, **kwargs) -> list[dict]: + """Download and parse one table through the ERS disk cache. + + Parameters + ---------- + table : str + Table key from TABLE_FILES. + + Returns + ------- + list[dict] + Long-format records from parse_table. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + content = await afetch_ers_file(TABLE_FILES[table], product=PRODUCT_PAGE) + return parse_table(content.decode("utf-8-sig", errors="replace"), table) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_food_dollar_series.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_food_dollar_series.py new file mode 100644 index 00000000000..d531f7b1630 --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_food_dollar_series.py @@ -0,0 +1,198 @@ +"""USDA ERS Food Dollar Series file catalog and parser.""" + +import csv +from io import StringIO + +BASE_URL = "https://www.ers.usda.gov" +PRODUCT_PAGE = "data-products/food-dollar-series" + +NULL_STRINGS = frozenset({"", "Not available"}) + +FOOD_DOLLAR_FILES: dict[str, dict] = { + "nominal": { + "media_id": 7055, + "slug": "food-dollar-nominal-data-2011-model-1993-2023", + "value_columns": [ + "Salary_and_benefits", + "Property_income", + "Output_taxes", + "Imports", + "Total", + ], + "units": { + "share": "Cents per Domestic Food Dollar", + "level": "million dollars", + }, + "tables": list(range(1, 23)), + }, + "real": { + "media_id": 7057, + "slug": "food-dollar-real-data-2011-model-1993-2023", + "value_columns": ["Value_added", "Imports", "Total"], + "units": { + "share": "Cents per Domestic Real Food Dollar", + "level": "million 2017 dollars", + }, + "tables": list(range(1, 7)), + }, +} + +TABLE_NAMES: dict[int, str] = { + 1: "Food dollar", + 2: "Food at home dollar", + 3: "Food away from home dollar", + 4: "Food and beverage dollar", + 5: "Home food and beverage dollar", + 6: "Away food and beverage dollar", + 7: "Food at home: Cereals", + 8: "Food at home: Bakery products", + 9: "Food at home: Beef, pork and other meats", + 10: "Food at home: Poultry", + 11: "Food at home: Fish and seafood", + 12: "Food at home: Fresh milk", + 13: "Food at home: Processed dairy products", + 14: "Food at home: Eggs", + 15: "Food at home: Fats and oils", + 16: "Food at home: Fresh Fruits", + 17: "Food at home: Fresh vegetables", + 18: "Food at home: Processed fruits and vegetables", + 19: "Food at home: Sugar and sweets", + 20: "Food at home: Other foods", + 21: "Home food and beverage: Nonalcoholic beverages", + 22: "Home food and beverage: Alcoholic beverages", +} + +COMPONENT_COLUMNS: dict[str, str] = { + "total": "Total", + "salary_and_benefits": "Salary_and_benefits", + "property_income": "Property_income", + "output_taxes": "Output_taxes", + "imports": "Imports", + "value_added": "Value_added", +} + +COMPONENT_LABELS: dict[str, str] = { + "total": "Total", + "salary_and_benefits": "Salary and benefits", + "property_income": "Property income", + "output_taxes": "Output taxes", + "imports": "Imports", + "value_added": "Value added", +} + +SERIES_COMPONENTS: dict[str, list[str]] = { + "nominal": [ + "total", + "salary_and_benefits", + "property_income", + "output_taxes", + "imports", + ], + "real": ["total", "value_added", "imports"], +} + + +def media_path(series: str) -> str: + """Build the media path for one series' tidy CSV. + + Parameters + ---------- + series : str + Series key from FOOD_DOLLAR_FILES, 'nominal' or 'real'. + + Returns + ------- + str + The '/media/{media_id}/{slug}.csv' path of the series' CSV file. + """ + entry = FOOD_DOLLAR_FILES[series] + return f"/media/{entry['media_id']}/{entry['slug']}.csv" + + +def build_url(series: str) -> str: + """Build the download URL for one series' tidy CSV. + + Parameters + ---------- + series : str + Series key from FOOD_DOLLAR_FILES, 'nominal' or 'real'. + + Returns + ------- + str + Full URL of the series' CSV file. + """ + return f"{BASE_URL}{media_path(series)}" + + +def to_float(value: str | None) -> float | None: + """Coerce a source cell to a float, mapping null tokens to None. + + Parameters + ---------- + value : str | None + Raw cell text; the literal 'Not available' and empty strings stand + in for a missing value. + + Returns + ------- + float | None + The parsed value, or None for a null token. + """ + text = (value or "").strip() + if text in NULL_STRINGS: + return None + return float(text) + + +def parse_rows(text: str, series: str) -> list[dict]: + """Parse one series' tidy CSV text into row records. + + Parameters + ---------- + text : str + Decoded CSV text with columns Table_num, Table_name, Category_num, + Category_desc, Year, Units, and the series' value columns. + series : str + Series key from FOOD_DOLLAR_FILES tagged on the parse, selecting the + value columns to coerce. + + Returns + ------- + list[dict] + Records with table_num, category_num, industry_group, year, units, + and one float-or-None entry per series value column. + """ + value_columns = FOOD_DOLLAR_FILES[series]["value_columns"] + rows: list[dict] = [] + for row in csv.DictReader(StringIO(text)): + record = { + "table_num": int(row["Table_num"]), + "category_num": int(row["Category_num"]), + "industry_group": (row["Category_desc"] or "").strip(), + "year": int(row["Year"]), + "units": (row["Units"] or "").strip(), + } + for column in value_columns: + record[column] = to_float(row.get(column)) + rows.append(record) + return rows + + +async def afetch_series(series: str, **kwargs) -> list[dict]: + """Download and parse one series' tidy CSV through the ERS disk cache. + + Parameters + ---------- + series : str + Series key from FOOD_DOLLAR_FILES, 'nominal' or 'real'. + + Returns + ------- + list[dict] + Row records from parse_rows. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + content = await afetch_ers_file(media_path(series), product=PRODUCT_PAGE) + return parse_rows(content.decode("utf-8-sig", errors="replace"), series) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_food_expenditure_series.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_food_expenditure_series.py new file mode 100644 index 00000000000..61a4c048a30 --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_food_expenditure_series.py @@ -0,0 +1,370 @@ +"""USDA ERS Food Expenditure Series file catalog and long-format parser.""" + +import csv +from io import StringIO + +PRODUCT_PAGE = "data-products/food-expenditure-series" + +MONTH_ORDER: dict[str, int] = { + "January": 1, + "February": 2, + "March": 3, + "April": 4, + "May": 5, + "June": 6, + "July": 7, + "August": 8, + "September": 9, + "October": 10, + "November": 11, + "December": 12, +} + +STATES: tuple[str, ...] = ( + "Alabama", + "Alaska", + "Arizona", + "Arkansas", + "California", + "Colorado", + "Connecticut", + "Delaware", + "District of Columbia", + "Florida", + "Georgia", + "Hawaii", + "Idaho", + "Illinois", + "Indiana", + "Iowa", + "Kansas", + "Kentucky", + "Louisiana", + "Maine", + "Maryland", + "Massachusetts", + "Michigan", + "Minnesota", + "Mississippi", + "Missouri", + "Montana", + "Nebraska", + "Nevada", + "New Hampshire", + "New Jersey", + "New Mexico", + "New York", + "North Carolina", + "North Dakota", + "Ohio", + "Oklahoma", + "Oregon", + "Pennsylvania", + "Rhode Island", + "South Carolina", + "South Dakota", + "Tennessee", + "Texas", + "Utah", + "Vermont", + "Virginia", + "Washington", + "West Virginia", + "Wisconsin", + "Wyoming", +) + +FOOD_AND_ALCOHOL_LABELS: tuple[str, ...] = ( + "Food sales at grocery stores", + "Food sales at convenience stores", + "Food sales at other food stores", + "Food sales at warehouse clubs and supercenters", + "Food sales at other stores and foodservice", + "Direct food selling by farmers, manufacturers, and wholesalers", + "Home food production and donations", + "Total food-at-home expenditures", + "Food sales at full-service restaurants", + "Food sales at limited-service restaurants", + "Food sales at drinking places", + "Food sales at hotels and motels", + "Food sales at retail stores", + "Food sales at recreational places", + "Food sales at schools and colleges", + "Food sales at other establishments, not elsewhere classified", + "Food furnished and donated", + "Total food-away-from-home expenditures", + "Alcohol sales at liquor stores", + "Alcohol-at-home sales at food stores", + "Alcohol-at-home sales at other establishments, not elsewhere classified", + "Total alcohol-at-home sales", + "Alcohol sales at eating and drinking places", + "Alcohol sales at hotels and motels", + "Alcohol-away-from-home sales at other establishments, not elsewhere classified", + "Total alcohol-away-from-home sales", +) + +FOOD_EXPENDITURE_FILES: dict[str, dict] = { + "food_and_alcohol": { + "label": "Food and alcohol expenditures, by all purchasers", + "dims": ("year",), + "measure_selects_file": True, + "taxes_selects_file": True, + "files": { + ( + "nominal", + "with", + ): "/media/5188/nominal-food-and-alcohol-expenditures-with-taxes-and-tips-for-all-purchasers.csv", + ( + "nominal", + "without", + ): "/media/5190/nominal-food-and-alcohol-expenditures-without-taxes-and-tips-for-all-purchasers.csv", + ( + "constant", + "with", + ): "/media/5192/constant-dollar-food-and-alcohol-expenditures-with-taxes-and-tips-for-all-purchasers.csv", + ( + "constant", + "without", + ): "/media/5194/constant-dollar-food-and-alcohol-expenditures-without-taxes-and-tips-for-all-purchasers.csv", + }, + "canonical_labels": FOOD_AND_ALCOHOL_LABELS, + }, + "by_final_purchaser": { + "label": "Food expenditures, by final purchaser", + "dims": ("year",), + "measure_selects_file": False, + "taxes_selects_file": False, + "files": {(): "/media/5196/food-expenditures-by-final-purchaser.csv"}, + "canonical_labels": None, + }, + "normalized": { + "label": "Normalized food expenditures and income shares", + "dims": ("year",), + "measure_selects_file": False, + "taxes_selects_file": False, + "files": { + (): "/media/5198/normalized-food-expenditures-by-all-purchasers-and-household-final-users.csv" + }, + "canonical_labels": None, + }, + "monthly": { + "label": "Monthly food sales", + "dims": ("year", "month"), + "measure_selects_file": False, + "taxes_selects_file": False, + "files": { + (): "/media/5202/monthly-sales-of-food-with-taxes-and-tips-for-all-purchasers.csv" + }, + "canonical_labels": None, + }, + "monthly_by_outlet": { + "label": "Monthly food sales, by outlet type", + "dims": ("year", "month"), + "measure_selects_file": False, + "taxes_selects_file": False, + "files": { + (): "/media/5200/monthly-sales-of-food-with-taxes-and-tips-for-all-purchasers-by-outlet-type.csv" + }, + "canonical_labels": None, + }, + "state": { + "label": "State food sales", + "dims": ("year", "state"), + "measure_selects_file": False, + "taxes_selects_file": True, + "files": { + ( + "with", + ): "/media/5204/state-food-sales-with-taxes-and-tips-for-all-purchasers.csv", + ( + "without", + ): "/media/5206/state-food-sales-without-taxes-and-tips-for-all-purchasers.csv", + }, + "canonical_labels": None, + }, + "state_per_capita": { + "label": "State food sales, per capita", + "dims": ("year", "state"), + "measure_selects_file": False, + "taxes_selects_file": True, + "files": { + ( + "with", + ): "/media/5208/state-food-sales-per-capita-with-taxes-and-tips-for-all-purchasers.csv", + ( + "without", + ): "/media/5210/state-food-sales-per-capita-without-taxes-and-tips-for-all-purchasers.csv", + }, + "canonical_labels": None, + }, +} + + +def resolve_media_path(table: str, measure: str, taxes: str) -> str: + """Resolve a table's media path from the measure and tax-treatment selectors. + + Parameters + ---------- + table : str + Table key from FOOD_EXPENDITURE_FILES. + measure : str + 'nominal' or 'constant'; selects the file only when the table splits + its measures across files. + taxes : str + 'with' or 'without'; selects the file only when the table splits its + tax treatment across files. + + Returns + ------- + str + The media path of the selected CSV. + """ + config = FOOD_EXPENDITURE_FILES[table] + key: list[str] = [] + if config["measure_selects_file"]: + key.append(measure) + if config["taxes_selects_file"]: + key.append(taxes) + return config["files"][tuple(key)] + + +def clean_header(header: str) -> tuple[str, str | None]: + """Split a verbose value-column header into a clean label and its measure. + + Parameters + ---------- + header : str + Column header as published, e.g. 'Food-at-home sales with taxes and + tips (millions of nominal U.S. dollars)'. + + Returns + ------- + tuple[str, str | None] + The category label with the tax phrase and unit removed, and the + measure the unit encodes: 'nominal', 'constant', or None for a + percentage share. + """ + text = header.strip() + cut = text.find(" (") + label = text[:cut] if cut != -1 else text + unit = text[cut:].casefold() if cut != -1 else "" + for phrase in (" with taxes and tips", " without taxes and tips"): + if label.endswith(phrase): + label = label[: -len(phrase)] + label = label.strip() + if "percentage" in unit: + return label, None + if "constant" in unit: + return label, "constant" + if "nominal" in unit: + return label, "nominal" + return label, None + + +def extract_unit(header: str) -> str: + """Return a value-column header's parenthetical unit text, or '' when none. + + Parameters + ---------- + header : str + Column header as published, e.g. 'Food-at-home sales with taxes and + tips (millions of nominal U.S. dollars)'. + + Returns + ------- + str + The unit inside the trailing parentheses with the outer pair removed, + e.g. 'millions of nominal U.S. dollars'; empty when the header has no + parenthetical. + """ + text = header.strip() + cut = text.find(" (") + if cut == -1: + return "" + return text[cut + 2 :].rstrip().removesuffix(")") + + +def parse_table(text: str, table: str) -> list[dict]: + """Parse one table's wide CSV text into long-format observation records. + + Parameters + ---------- + text : str + Decoded CSV text of the selected file. + table : str + Table key from FOOD_EXPENDITURE_FILES. + + Returns + ------- + list[dict] + One record per non-blank value cell, carrying the integer year, the + optional month and state dimensions, the category label, the measure + the column encodes, the source unit text, and the numeric value. Cells + with a blank or non-numeric value, or rows with a non-numeric year, + are skipped. + """ + config = FOOD_EXPENDITURE_FILES[table] + dims = config["dims"] + canonical = config["canonical_labels"] + rows = list(csv.reader(StringIO(text))) + if not rows: + return [] + header = rows[0] + n_dims = len(dims) + specs: list[tuple[int, str, str | None, str]] = [] + for offset, col in enumerate(range(n_dims, len(header))): + unit = extract_unit(header[col]) + if canonical is not None: + specs.append((col, canonical[offset], None, unit)) + else: + label, measure = clean_header(header[col]) + specs.append((col, label, measure, unit)) + records: list[dict] = [] + for row in rows[1:]: + if not row or not row[0][:4].isdigit(): + continue + year = int(row[0][:4]) + month = row[1].strip() or None if "month" in dims else None + state = row[1].strip() or None if "state" in dims else None + for col, label, measure, unit in specs: + raw = (row[col] if col < len(row) else "").strip() + try: + value = float(raw) + except ValueError: + continue + records.append( + { + "year": year, + "month": month, + "state": state, + "category": label, + "measure": measure, + "unit": unit, + "value": value, + } + ) + return records + + +async def afetch_table(table: str, measure: str, taxes: str) -> list[dict]: + """Download and parse one table through the ERS disk cache. + + Parameters + ---------- + table : str + Table key from FOOD_EXPENDITURE_FILES. + measure : str + 'nominal' or 'constant'. + taxes : str + 'with' or 'without'. + + Returns + ------- + list[dict] + Long-format records from parse_table. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + path = resolve_media_path(table, measure, taxes) + content = await afetch_ers_file(path, product=PRODUCT_PAGE) + return parse_table(content.decode("utf-8-sig", errors="replace"), table) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_food_price_outlook.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_food_price_outlook.py new file mode 100644 index 00000000000..6941aa991b6 --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_food_price_outlook.py @@ -0,0 +1,386 @@ +"""USDA ERS Food Price Outlook file catalog and parsers.""" + +import csv +import re +from datetime import date +from io import StringIO + +from openbb_core.app.model.abstract.error import OpenBBError + +BASE_URL = "https://www.ers.usda.gov" +PRODUCT_PAGE = "data-products/food-price-outlook" + +FOOD_PRICE_OUTLOOK_FILES: dict[str, dict] = { + "cpi_forecast": { + "media_id": 6460, + "slug": "changes-in-consumer-price-indexes-2023-through-2026", + "kind": "snapshot", + "index": "cpi", + }, + "ppi_forecast": { + "media_id": 6468, + "slug": "changes-in-producer-price-indexes-2023-through-2026", + "kind": "snapshot", + "index": "ppi", + }, + "cpi_annual": { + "media_id": 6462, + "slug": ( + "annual-percent-changes-in-selected-consumer-price-indexes" + "-1974-through-2025" + ), + "kind": "annual", + "index": "cpi", + }, + "ppi_annual": { + "media_id": 6470, + "slug": ( + "annual-percent-changes-in-selected-producer-price-indexes" + "-1974-through-2025" + ), + "kind": "annual", + "index": "ppi", + }, + "cpi_forecast_history": { + "media_id": 6464, + "slug": "historical-consumer-price-index-cpi-forecast-series", + "kind": "history", + "index": "cpi", + }, + "ppi_forecast_history": { + "media_id": 6466, + "slug": "historical-producer-price-index-ppi-forecast-series", + "kind": "history", + "index": "ppi", + }, +} + +CPI_ITEMS: dict[str, str] = { + "all_food": "All food", + "food_away_from_home": "Food away from home", + "food_at_home": "Food at home", + "meats_poultry_and_fish": "Meats, poultry, and fish", + "meats": "Meats", + "beef_and_veal": "Beef and veal", + "pork": "Pork", + "other_meats": "Other meats", + "poultry": "Poultry", + "fish_and_seafood": "Fish and seafood", + "eggs": "Eggs", + "dairy_products": "Dairy products", + "fats_and_oils": "Fats and oils", + "fruits_and_vegetables": "Fruits and vegetables", + "fresh_fruits_and_vegetables": "Fresh fruits and vegetables", + "fresh_fruits": "Fresh fruits", + "fresh_vegetables": "Fresh vegetables", + "processed_fruits_and_vegetables": "Processed fruits and vegetables", + "sugar_and_sweets": "Sugar and sweets", + "cereals_and_bakery_products": "Cereals and bakery products", + "nonalcoholic_beverages": "Nonalcoholic beverages", + "other_foods": "Other foods", +} + +PPI_ITEMS: dict[str, str] = { + "unprocessed_foodstuffs_and_feedstuffs": "Unprocessed foodstuffs and feedstuffs", + "processed_foods_and_feeds": "Processed foods and feeds", + "finished_consumer_foods": "Finished consumer foods", + "farm_level_cattle": "Farm-level cattle", + "wholesale_beef": "Wholesale beef", + "wholesale_pork": "Wholesale pork", + "wholesale_poultry": "Wholesale poultry", + "farm_level_eggs": "Farm-level eggs", + "farm_level_milk": "Farm-level milk", + "wholesale_dairy": "Wholesale dairy", + "farm_level_soybeans": "Farm-level soybeans", + "wholesale_fats_and_oils": "Wholesale fats and oils", + "farm_level_fruits": "Farm-level fruits", + "farm_level_vegetables": "Farm-level vegetables", + "farm_level_wheat": "Farm-level wheat", + "wholesale_wheat_flour": "Wholesale wheat flour", +} + +ALL_ITEMS: dict[str, str] = {**CPI_ITEMS, **PPI_ITEMS} + +ITEM_RENAMES = {"Farm-level fruit": "Farm-level fruits"} + +HIERARCHY_COLUMNS = ("Top-level", "Aggregate", "Mid-level", "Low-level", "Disaggregate") +HIERARCHY_FIELDS = ("top_level", "aggregate", "mid_level", "low_level", "disaggregate") + +SNAPSHOT_YEAR_PATTERN = re.compile( + r"^(?:Annual|(?:Lower bound|Mid point|Upper bound) of prediction interval)" + r" (\d{4})$" +) + +_SUPERSCRIPT_TABLE = str.maketrans("", "", "⁰¹²³⁴⁵⁶⁷⁸⁹*") + + +def table_items(table: str) -> dict[str, str]: + """Map item slugs to published names for one table's item universe. + + Parameters + ---------- + table : str + Table key from FOOD_PRICE_OUTLOOK_FILES. + + Returns + ------- + dict[str, str] + CPI_ITEMS for CPI tables, PPI_ITEMS for PPI tables. + """ + entry = FOOD_PRICE_OUTLOOK_FILES[table] + return CPI_ITEMS if entry["index"] == "cpi" else PPI_ITEMS + + +def media_path(table: str) -> str: + """Build the media path of one table's tidy CSV. + + Parameters + ---------- + table : str + Table key from FOOD_PRICE_OUTLOOK_FILES. + + Returns + ------- + str + The '/media/{id}/{slug}.csv' path of the table's CSV file. + """ + entry = FOOD_PRICE_OUTLOOK_FILES[table] + return f"/media/{entry['media_id']}/{entry['slug']}.csv" + + +def build_url(table: str) -> str: + """Build the download URL of one table's tidy CSV. + + Parameters + ---------- + table : str + Table key from FOOD_PRICE_OUTLOOK_FILES. + + Returns + ------- + str + Full URL of the table's CSV file. + """ + return f"{BASE_URL}{media_path(table)}" + + +def clean_label(text: str | None) -> str: + """Strip whitespace and footnote superscripts from a label. + + Parameters + ---------- + text : str | None + Raw label text. + + Returns + ------- + str + The cleaned label. + """ + return (text or "").translate(_SUPERSCRIPT_TABLE).strip() + + +def normalize_item(text: str | None) -> str: + """Clean an item label and normalize known cross-file spelling variants. + + Parameters + ---------- + text : str | None + Raw item label text. + + Returns + ------- + str + The canonical published item name. + """ + label = clean_label(text) + return ITEM_RENAMES.get(label, label) + + +def attribute_year(attribute: str) -> int | None: + """Parse the trailing year out of an annual or prediction-interval attribute. + + Parameters + ---------- + attribute : str + Cleaned attribute label from a snapshot table. + + Returns + ------- + int | None + The four-digit year for 'Annual YYYY' and prediction-interval + attributes, None for every other measure. + """ + match = SNAPSHOT_YEAR_PATTERN.match(attribute) + return int(match.group(1)) if match else None + + +def _item_value(row: dict) -> str: + """Read the price-index item column of one CSV row. + + Parameters + ---------- + row : dict + A csv.DictReader row. + + Returns + ------- + str + The raw item cell value. + + Raises + ------ + OpenBBError + If the row has no 'Price Index item' column. + """ + for key, value in row.items(): + if key and key.strip().endswith("Price Index item"): + return value or "" + raise OpenBBError( + "No 'Price Index item' column found. Columns: " + ", ".join(map(str, row)) + ) + + +def parse_snapshot_rows(text: str) -> list[dict]: + """Parse a current CPI or PPI changes CSV into tidy records. + + Parameters + ---------- + text : str + Decoded CSV text of a 'Changes in ... Price Indexes' file. + + Returns + ------- + list[dict] + Records with item, hierarchy parents (CPI only), attribute, unit, + year, and value keys, skipping rows with an empty Value. + """ + rows: list[dict] = [] + for row in csv.DictReader(StringIO(text)): + value = (row.get("Value") or "").strip() + if not value: + continue + attribute = clean_label(row.get("Attribute")) + record = { + "attribute": attribute, + "unit": clean_label(row.get("Unit")), + "year": attribute_year(attribute), + "value": float(value), + } + if "Top-level" in row: + levels = [clean_label(row.get(col)) for col in HIERARCHY_COLUMNS] + record.update(zip(HIERARCHY_FIELDS, (level or None for level in levels))) + record["item"] = next((level for level in reversed(levels) if level), "") + else: + record["item"] = normalize_item(_item_value(row)) + rows.append(record) + return rows + + +def parse_annual_rows(text: str) -> list[dict]: + """Parse an annual percent-changes CSV into tidy records. + + Parameters + ---------- + text : str + Decoded CSV text of an 'Annual percent changes' file. + + Returns + ------- + list[dict] + Records with item, attribute, unit, year, and value keys, skipping + rows with an empty Percent change. + """ + rows: list[dict] = [] + for row in csv.DictReader(StringIO(text)): + value = (row.get("Percent change") or "").strip() + if not value: + continue + rows.append( + { + "item": normalize_item(_item_value(row)), + "attribute": "Annual percent change", + "unit": "Percent change", + "year": int((row.get("Year") or "").strip()), + "value": float(value), + } + ) + return rows + + +def parse_history_rows(text: str) -> list[dict]: + """Parse a historical forecast-series CSV into tidy records. + + Parameters + ---------- + text : str + Decoded CSV text of a 'Historical ... forecast series' file. + + Returns + ------- + list[dict] + Records with item, attribute, unit, year (the year being forecast), + forecast_date, and value keys, skipping rows with an empty forecast. + """ + rows: list[dict] = [] + for row in csv.DictReader(StringIO(text)): + value = (row.get("Forecast percent change") or "").strip() + if not value: + continue + rows.append( + { + "item": normalize_item(_item_value(row)), + "attribute": clean_label(row.get("Attribute")), + "unit": "Percent change", + "year": int((row.get("Year being forecast") or "").strip()), + "forecast_date": date( + int((row.get("Year of forecast") or "").strip()), + int((row.get("Month of forecast") or "").strip()), + 1, + ), + "value": float(value), + } + ) + return rows + + +def parse_table(text: str, table: str) -> list[dict]: + """Parse one table's CSV text with the parser matching its kind. + + Parameters + ---------- + text : str + Decoded CSV text. + table : str + Table key from FOOD_PRICE_OUTLOOK_FILES. + + Returns + ------- + list[dict] + Tidy records from the kind-specific parser. + """ + kind = FOOD_PRICE_OUTLOOK_FILES[table]["kind"] + if kind == "snapshot": + return parse_snapshot_rows(text) + if kind == "annual": + return parse_annual_rows(text) + return parse_history_rows(text) + + +async def afetch_table(table: str, **kwargs) -> list[dict]: + """Download and parse one table's tidy CSV through the ERS disk cache. + + Parameters + ---------- + table : str + Table key from FOOD_PRICE_OUTLOOK_FILES. + + Returns + ------- + list[dict] + Tidy records from parse_table. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + content = await afetch_ers_file(media_path(table), product=PRODUCT_PAGE) + return parse_table(content.decode("utf-8-sig"), table) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_food_security_in_the_united_states.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_food_security_in_the_united_states.py new file mode 100644 index 00000000000..c8a9db93686 --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_food_security_in_the_united_states.py @@ -0,0 +1,348 @@ +"""USDA ERS Food Security in the United States file catalog and long parsers.""" + +import csv +import re +import zipfile +from collections import OrderedDict +from io import BytesIO, StringIO + +BASE_URL = "https://www.ers.usda.gov" +PRODUCT_PAGE = "data-products/food-security-in-the-united-states" +MEDIA_PATH = "/media/799/food-security-csv-data-files.zip" +MEMBER_PREFIX = "foodsecurity_csv_datafiles/" + +STATE_LABEL_FIXES = {"U.S. total": "United States", "U.S.": "United States"} + +ALL_HOUSEHOLDS_MEASURES = ( + ("Total", "Total households (1,000)"), + ("Food secure-1,000", "Food secure (1,000)"), + ("Food secure-percent", "Food secure (percent)"), + ("Food insecure-1,000", "Food insecure (1,000)"), + ("Food insecure-percent", "Food insecure (percent)"), + ("Low food security-1,000", "Low food security (1,000)"), + ("Low food security-percent", "Low food security (percent)"), + ("Very low food security-1,000", "Very low food security (1,000)"), + ("Very low food security-percent", "Very low food security (percent)"), +) + +CHILDREN_MEASURES = ( + ("Total", "Total households (1,000)"), + ("Food-secure households-1,000", "Food-secure households (1,000)"), + ("Food-secure households-percent", "Food-secure households (percent)"), + ("Food-insecure households-1,000", "Food-insecure households (1,000)"), + ("Food-insecure households-percent", "Food-insecure households (percent)"), + ( + "Households with food-insecure children-1,000", + "Households with food-insecure children (1,000)", + ), + ( + "Households with food-insecure children-percent", + "Households with food-insecure children (percent)", + ), + ( + "Households with very low food security among children-1,000", + "Households with very low food security among children (1,000)", + ), + ( + "Households with very low food security among children-percent", + "Households with very low food security among children (percent)", + ), +) + +EDUCATION_MEASURES = ( + ("Total", "Total households (1,000)"), + ("Food insecure-1,000", "Food insecure (1,000)"), + ("Food insecure-percent", "Food insecure (percent)"), + ("Food insecure-share", "Food insecure (share of food insecure)"), + ("Very low food security-1,000", "Very low food security (1,000)"), + ("Very low food security-percent", "Very low food security (percent)"), + ("Very low food security-share", "Very low food security (share of very low)"), +) + +STATE_MEASURES = ( + ("Food insecurity prevalence", "Food insecurity prevalence (percent)"), + ("Food insecurity margin of error", "Food insecurity margin of error"), + ( + "Very low food security prevalence", + "Very low food security prevalence (percent)", + ), + ( + "Very low food security margin of error", + "Very low food security margin of error", + ), +) + +FOOD_SECURITY_TABLES: "OrderedDict[str, dict]" = OrderedDict( + [ + ( + "national_trend", + { + "label": "National trend - all U.S. households", + "member": "foodsecurity-all-households-2024.csv", + "dims": ( + ("Category", "category"), + ("Subcategory", "subcategory"), + ("Sub-subcategory", "sub_subcategory"), + ), + "measures": ALL_HOUSEHOLDS_MEASURES, + "year_is_period": False, + "category_mode": "only_all", + }, + ), + ( + "by_characteristic", + { + "label": "By household characteristic", + "member": "foodsecurity-all-households-2024.csv", + "dims": ( + ("Category", "category"), + ("Subcategory", "subcategory"), + ("Sub-subcategory", "sub_subcategory"), + ), + "measures": ALL_HOUSEHOLDS_MEASURES, + "year_is_period": False, + "category_mode": "exclude_all", + }, + ), + ( + "households_with_children", + { + "label": "Households with children", + "member": "foodsecurity-hh-with-children-2024.csv", + "dims": ( + ("Category", "category"), + ("Subcategory", "subcategory"), + ), + "measures": CHILDREN_MEASURES, + "year_is_period": False, + "category_mode": None, + }, + ), + ( + "child_trends", + { + "label": "Child food security trends", + "member": "foodsecurity-child-trends-2024.csv", + "dims": (("Category", "category"),), + "measures": CHILDREN_MEASURES, + "year_is_period": False, + "category_mode": None, + }, + ), + ( + "education_employment_disability", + { + "label": "Education, employment, and disability", + "member": "foodsecurity-educ-emp-dis-2024.csv", + "dims": ( + ("Category", "category"), + ("Subcategory", "subcategory"), + ("Sub-subcategory", "sub_subcategory"), + ), + "measures": EDUCATION_MEASURES, + "year_is_period": False, + "category_mode": None, + }, + ), + ( + "by_state", + { + "label": "By State (3-year averages)", + "member": "foodsecurity-state-2024.csv", + "dims": (("State", "state"),), + "measures": STATE_MEASURES, + "year_is_period": True, + "category_mode": None, + }, + ), + ] +) + +DIM_FIELDS = ("category", "subcategory", "sub_subcategory", "state") + + +def normalize_header(header: str) -> str: + """Normalize a source header, replacing the replacement character and spaces. + + Parameters + ---------- + header : str + Header cell as published, which may carry a U+FFFD replacement + character in place of a space and stray leading or trailing spaces. + + Returns + ------- + str + The header with U+FFFD turned into a space, internal whitespace + collapsed, and the ends stripped. + """ + return re.sub(r"\s+", " ", header.replace("�", " ")).strip() + + +def clean_number(cell: str) -> float | None: + """Parse a numeric cell, stripping the replacement character and commas. + + Parameters + ---------- + cell : str + Value cell as published, which may carry a U+FFFD prefix, thousands + separators, or an N/A placeholder token. + + Returns + ------- + float | None + The parsed value, or None when the cell is blank or non-numeric. + """ + text = cell.replace("�", "").replace(",", "").strip() + if not text: + return None + try: + return float(text) + except ValueError: + return None + + +def _extract_dims(config: dict, raw: list[str], index: dict[str, int]) -> dict: + """Read a row's dimension fields, defaulting missing ones to None. + + Parameters + ---------- + config : dict + Table config from FOOD_SECURITY_TABLES. + raw : list[str] + The row cells. + index : dict[str, int] + Header name to column position map. + + Returns + ------- + dict + The four dimension fields, with the national state labels normalized. + """ + dims: dict = {field: None for field in DIM_FIELDS} + for source_col, output_field in config["dims"]: + position = index.get(source_col) + value = ( + raw[position].strip() + if position is not None and position < len(raw) + else "" + ) + if output_field == "state": + value = STATE_LABEL_FIXES.get(value, value) + dims[output_field] = value or None + return dims + + +def _category_excluded(config: dict, category: str | None) -> bool: + """Return whether a row's category is filtered out by the table's mode.""" + mode = config["category_mode"] + if mode == "only_all": + return category != "All households" + if mode == "exclude_all": + return category == "All households" + return False + + +def parse_table(text: str, table: str) -> list[dict]: + """Parse one table's CSV text into long-format observation records. + + Parameters + ---------- + text : str + Decoded CSV text of the table member. + table : str + Table key from FOOD_SECURITY_TABLES. + + Returns + ------- + list[dict] + One record per year, dimension path, and measure, carrying the year + label, its integer sort year, the four dimension fields, the clean + measure label as series, and the numeric value. Rows whose year label + is blank or non-numeric are dropped. + """ + config = FOOD_SECURITY_TABLES[table] + rows = list(csv.reader(StringIO(text))) + if not rows: + return [] + header = [normalize_header(cell) for cell in rows[0]] + index = {name: position for position, name in enumerate(header)} + year_index = index.get("Year") + if year_index is None: + return [] + records: list[dict] = [] + for raw in rows[1:]: + if len(raw) <= year_index: + continue + cell = raw[year_index] + year_label = ( + cell.replace("�", "-").strip() if config["year_is_period"] else cell.strip() + ) + if not year_label[:4].isdigit(): + continue + dims = _extract_dims(config, raw, index) + if _category_excluded(config, dims["category"]): + continue + sort_year = int(year_label[:4]) + for source_col, label in config["measures"]: + position = index.get(source_col) + if position is None or position >= len(raw): + continue + records.append( + { + "table": table, + "year": year_label, + "sort_year": sort_year, + "category": dims["category"], + "subcategory": dims["subcategory"], + "sub_subcategory": dims["sub_subcategory"], + "state": dims["state"], + "series": label, + "value": clean_number(raw[position]), + } + ) + return records + + +async def afetch_table(table: str, **kwargs) -> list[dict]: + """Download and parse one food-security table through the ERS disk cache. + + Parameters + ---------- + table : str + Table key from FOOD_SECURITY_TABLES. + + Returns + ------- + list[dict] + Long-format records from parse_table. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + config = FOOD_SECURITY_TABLES[table] + content = await afetch_ers_file(MEDIA_PATH, product=PRODUCT_PAGE) + with zipfile.ZipFile(BytesIO(content)) as archive: + raw = archive.read(MEMBER_PREFIX + config["member"]) + return parse_table(raw.decode("utf-8-sig", errors="replace"), table) + + +async def afetch_categories(table: str) -> list[str]: + """List a table's distinct category labels in published order. + + Parameters + ---------- + table : str + Table key from FOOD_SECURITY_TABLES. + + Returns + ------- + list[str] + Distinct non-null Category values, in first-seen order. + """ + records = await afetch_table(table) + seen: list[str] = [] + for record in records: + category = record["category"] + if category and category not in seen: + seen.append(category) + return seen diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_frontier_and_remote_area_codes.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_frontier_and_remote_area_codes.py new file mode 100644 index 00000000000..9e9add62ba4 --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_frontier_and_remote_area_codes.py @@ -0,0 +1,274 @@ +"""USDA ERS frontier and remote area codes (ZIP) catalog and parser.""" + +from io import BytesIO + +BASE_URL = "https://www.ers.usda.gov" +PRODUCT_PAGE = "data-products/frontier-and-remote-area-codes" + +DEFAULT_YEAR = "2020" +DEFAULT_STATE = "MT" + +CANONICAL_COLUMNS: tuple[str, ...] = ( + "zip_code", + "state", + "po_name", + "far_level_1", + "far_level_2", + "far_level_3", + "far_level_4", + "grid_population", + "land_area_sq_mi", + "population_density", + "far_level_1_population", + "far_level_2_population", + "far_level_3_population", + "far_level_4_population", + "far_level_1_population_pct", + "far_level_2_population_pct", + "far_level_3_population_pct", + "far_level_4_population_pct", +) + +CANONICAL_COLUMNS_NO_NAME: tuple[str, ...] = tuple( + name for name in CANONICAL_COLUMNS if name != "po_name" +) + +FAR_ZIP_FILES: dict[str, dict] = { + "2020": { + "media_id": 7072, + "slug": "2020-far-codes-zip-codes", + "ext": "xlsx", + "sheet": "FAR2020 ZIP Code Data", + "data_start": 2, + "columns": CANONICAL_COLUMNS, + }, + "2010": { + "media_id": 5621, + "slug": "2010-far-codes-zip-codes", + "ext": "xlsx", + "sheet": "FAR ZIP Code Data", + "data_start": 2, + "columns": CANONICAL_COLUMNS, + }, + "2000": { + "media_id": 5618, + "slug": "2000-far-codes-zip-codes", + "ext": "xls", + "sheet": "FAR ZIP Code Data", + "data_start": 1, + "columns": CANONICAL_COLUMNS_NO_NAME, + }, +} + +STATES_51: tuple[str, ...] = ( + "AK", "AL", "AR", "AZ", "CA", "CO", "CT", "DC", "DE", "FL", "GA", "HI", + "IA", "ID", "IL", "IN", "KS", "KY", "LA", "MA", "MD", "ME", "MI", "MN", + "MO", "MS", "MT", "NC", "ND", "NE", "NH", "NJ", "NM", "NV", "NY", "OH", + "OK", "OR", "PA", "RI", "SC", "SD", "TN", "TX", "UT", "VA", "VT", "WA", + "WI", "WV", "WY", +) # fmt: skip + +STATES_49: tuple[str, ...] = tuple( + state for state in STATES_51 if state not in ("AK", "HI") +) + +FAR_STATES: dict[str, tuple[str, ...]] = { + "2020": STATES_51, + "2010": STATES_51, + "2000": STATES_49, +} + +ALL_STATES: frozenset[str] = frozenset(STATES_51) + +_STR_FIELDS: frozenset[str] = frozenset({"zip_code", "state", "po_name"}) +_INT_FIELDS: frozenset[str] = frozenset( + {"far_level_1", "far_level_2", "far_level_3", "far_level_4"} +) + + +def media_path(year: str) -> str: + """Build the media path for a vintage's ZIP-code workbook. + + Parameters + ---------- + year : str + Vintage key from FAR_ZIP_FILES. + + Returns + ------- + str + The media path, e.g. '/media/7072/2020-far-codes-zip-codes.xlsx'. + """ + config = FAR_ZIP_FILES[year] + return f"/media/{config['media_id']}/{config['slug']}.{config['ext']}" + + +def year_options() -> list[dict]: + """Build the labeled vintage options for the widget's vintage selector. + + Returns + ------- + list[dict] + Label/value option dictionaries, one per published vintage. + """ + return [{"label": year, "value": year} for year in FAR_ZIP_FILES] + + +def state_options(year: str) -> list[dict]: + """Build the labeled state options a vintage publishes. + + Parameters + ---------- + year : str + Vintage key. Unknown vintages fall back to the default vintage. + + Returns + ------- + list[dict] + Label/value option dictionaries for the widget's state selector. + """ + states = FAR_STATES.get(year) or FAR_STATES[DEFAULT_YEAR] + return [{"label": state, "value": state} for state in states] + + +def _to_str(value) -> str | None: + """Coerce a source cell to a stripped string, None when blank.""" + if value is None: + return None + text = str(value).strip() + return text or None + + +def _to_int(value) -> int | None: + """Coerce a source cell to an integer flag, None when blank or non-numeric.""" + if value is None or (isinstance(value, str) and not value.strip()): + return None + try: + return int(float(value)) + except (TypeError, ValueError): + return None + + +def _to_float(value) -> float | None: + """Coerce a source cell to a float, None when blank or non-numeric.""" + if value is None or (isinstance(value, str) and not value.strip()): + return None + try: + return float(value) + except (TypeError, ValueError): + return None + + +def _map_row(cells: list, columns: tuple[str, ...]) -> dict: + """Map one positional source row to the canonical column dictionary. + + Parameters + ---------- + cells : list + Ordered cell values of one data row. + columns : tuple[str, ...] + Canonical column names in positional order for the vintage. + + Returns + ------- + dict + Canonical record with string identifiers, integer FAR flags, and float + population and area attributes. + """ + record: dict = {} + for index, name in enumerate(columns): + value = cells[index] if index < len(cells) else None + if name in _STR_FIELDS: + record[name] = _to_str(value) + elif name in _INT_FIELDS: + record[name] = _to_int(value) + else: + record[name] = _to_float(value) + return record + + +def parse_rows(rows: list, year: str) -> list[dict]: + """Parse a vintage's worksheet rows into canonical ZIP-code records. + + Parameters + ---------- + rows : list + The worksheet's value rows, each an ordered sequence of cell values, + including the title and header rows. + year : str + Vintage key from FAR_ZIP_FILES, selecting the header offset and the + per-vintage positional column map. + + Returns + ------- + list[dict] + One canonical record per ZIP-code row. Rows with a blank ZIP code are + skipped; the 2000 vintage carries no area name so po_name is absent. + """ + config = FAR_ZIP_FILES[year] + columns = config["columns"] + records: list[dict] = [] + for raw in rows[config["data_start"] :]: + cells = list(raw) + if not cells: + continue + first = cells[0] + if first is None or str(first).strip() == "": + continue + records.append(_map_row(cells, columns)) + return records + + +def _read_sheet_rows(content: bytes, year: str) -> list[list]: + """Read a vintage workbook's data sheet into a list of row lists. + + Parameters + ---------- + content : bytes + Raw workbook bytes, an .xlsx for 2020 and 2010 or a legacy .xls for + 2000. + year : str + Vintage key from FAR_ZIP_FILES. + + Returns + ------- + list[list] + Cell values, row by row, including the title and header rows. + """ + config = FAR_ZIP_FILES[year] + if config["ext"] == "xls": + import xlrd + + sheet = xlrd.open_workbook(file_contents=content).sheet_by_name(config["sheet"]) + return [ + [sheet.cell_value(r, c) for c in range(sheet.ncols)] + for r in range(sheet.nrows) + ] + import openpyxl + + workbook = openpyxl.load_workbook(BytesIO(content), read_only=True, data_only=True) + try: + return [ + list(row) for row in workbook[config["sheet"]].iter_rows(values_only=True) + ] + finally: + workbook.close() + + +async def afetch_far_zip(year: str) -> list[dict]: + """Download a vintage's ZIP-code workbook and parse it through the cache. + + Parameters + ---------- + year : str + Vintage key from FAR_ZIP_FILES. + + Returns + ------- + list[dict] + Canonical records from parse_rows for the selected vintage. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + content = await afetch_ers_file(media_path(year), product=PRODUCT_PAGE) + return parse_rows(_read_sheet_rows(content, year), year) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_fruit_and_tree_nuts_data.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_fruit_and_tree_nuts_data.py new file mode 100644 index 00000000000..97432ad7369 --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_fruit_and_tree_nuts_data.py @@ -0,0 +1,303 @@ +"""USDA ERS Fruit and Tree Nuts Yearbook file catalog and long-format parser.""" + +import csv +import re +from io import StringIO + +BASE_URL = "https://www.ers.usda.gov" +PRODUCT_PAGE = ( + "data-products/fruit-and-tree-nuts-data/fruit-and-tree-nuts-yearbook-tables" +) +YEARBOOK_MEDIA = "/media/7009/all-fruit-and-tree-nuts-yearbook-data.csv" + +CODE_PATTERN = re.compile(r"^Table\s+([A-H])\s*-\s*(\d+)") + +MONTH_NUMBERS: dict[str, int] = { + "january": 1, + "february": 2, + "march": 3, + "april": 4, + "may": 5, + "june": 6, + "july": 7, + "august": 8, + "september": 9, + "october": 10, + "november": 11, + "december": 12, +} + +CATEGORY_LABELS: dict[str, str] = { + "A": "U.S. summary and per-capita availability", + "B": "Noncitrus fruit", + "C": "Citrus", + "D": "Berries", + "E": "Melons", + "F": "Tree nuts", + "G": "Supply and per-capita availability", + "H": "Import share", +} + +TABLE_TITLES: dict[str, str] = { + "A-1": "Fruit and tree nuts: Per capita availability, United States, 1976 to date", + "A-2": "Fruit and tree nuts: Area bearing, United States, 1980 to date", + "A-3": "Fruit and tree nuts: Utilized production and value of production, United States, 1980 to date", + "A-4": "Selected citrus and noncitrus fruit: Area bearing, United States, 1980 to date", + "A-5": "Selected citrus and noncitrus fruit: Production, United States, 1980 to date", + "A-6": "Fruit and tree nuts: Price indexes for fruit and nuts, United States, 1980 to date", + "A-7": "Selected citrus and noncitrus fruit, fresh: Retail price, average annual, United States, 1980 to date", + "A-8": "Apples, fresh: U.S. monthly average retail price, marketing spread, and grower price, 1989 to date", + "A-9": "Peaches, fresh: U.S. monthly average retail price, marketing spread, and grower price, 1989 to date", + "A-10": "Pears, fresh: U.S. monthly average retail price, marketing spread, and grower price, 1989 to date", + "A-11": "Grapes, fresh: U.S. monthly average retail price, marketing spread, and grower price, 1995 to date", + "A-12": "Strawberries, fresh: U.S. monthly average retail price, marketing spread, and grower price, 1990 to date", + "A-13": "Oranges, fresh: U.S. monthly average retail price, marketing spread, and grower price, 1989/90 to date", + "A-14": "Grapefruit, fresh: U.S. monthly average retail price, marketing spread, and grower price, 1989/90 to date", + "A-15": "Lemons, fresh: U.S. monthly retail price, marketing spread, and grower price, 1989/90 to date", + "B-1": "Noncitrus fruit: Utilized production and value, United States, 1980 to date", + "B-2": "Noncitrus fruit, dried: Utilized production (dry basis), California, 1980 to 2018", + "B-3": "Apples: Production, utilization, and season-average grower price, United States, 1980 to date", + "B-4": "Apples, fresh: Price received by growers, monthly, United States, 1980 to date", + "B-5": "Apples: Processed utilization and season-average grower price, United States, 1980 to 2017", + "B-6": "Apricots: Production, utilization, and season-average grower price, United States, 1980 to date", + "B-7": "Avocados: Production, season-average grower price, and value, by State, 1980/81 to date", + "B-8": "Bananas: Number of farms, area harvested, production, price, and value, Hawaii, 1980 to date", + "B-9": "Sweet cherries: Production, utilization, and season-average grower price, United States, 1980 to date", + "B-10": "Tart cherries: Production, utilization, and season-average grower price, United States, 1980 to date", + "B-11": "Figs: Production, utilization, and season-average grower price, California, 1980 to 2018", + "B-12": "Grapes: Production, utilization, and season-average grower price, United States, 1980 to date", + "B-13": "Grapes: Utilized production and season-average grower price, California, 1980 to date", + "B-14": "Grapes: Processed utilization and season-average grower price, United States, 1980 to 2017", + "B-15": "Grapes, fresh: Price received by growers, monthly, United States, 1995 to date", + "B-16": "Kiwifruit: Area bearing, production, season-average grower price, and value, California, 1980 to date", + "B-17": "Nectarines: Production, utilization, and season-average grower price, California, 1980 to date", + "B-18": "Nectarines: Production, utilization, and season-average grower price, Washington, 2005 to 2018", + "B-19": "Olives: Area bearing, production, utilization, season-average grower price, and value, California, 1980 to date", + "B-20": "Papayas: Area harvested, yield, production, utilization, and season-average grower price, Hawaii, 1980 to date", + "B-21": "Peaches: Production, utilization, and season-average grower price, United States, 1980 to date", + "B-22": "Peaches, fresh: Price received by growers, monthly, United States, 1980 to date", + "B-23": "Peaches: Processed utilization, and season-average grower price, United States, 1980 to 2018", + "B-24": "Pears: Production, utilization, and season-average grower price, United States, 1980 to date", + "B-25": "Pears, Bartlett: Production, utilization, and season-average grower price, United States, 1980 to 2018", + "B-26": "Pears, fresh: Price received by growers, monthly, United States, 1980 to date", + "B-27": "Plums: Area bearing, production, season-average grower price, and value, California, 1980 to date", + "B-28": "Prunes: Area bearing, production, season-average grower price, and value, California, 1980 to date", + "B-29": "Plums and prunes: Production, utilization, and season-average grower price, four States, 1980 to 2016", + "C-1": "Grapefruit: Area bearing and yield, by State, 1980/81 to date", + "C-2": "Grapefruit: Production, by State, 1980/81 to date", + "C-3": "Grapefruit: Utilized production, by State, 1980/81 to date", + "C-4": "Grapefruit: Price received by growers - equivalent on-tree returns, by State, 1980/81 to date", + "C-5": "Grapefruit: Price received by growers - equivalent on-tree returns, monthly, United States, 1980/81 to date", + "C-6": "Grapefruit, processed: Utilized production, Florida, 1980/81 to date", + "C-7": "Grapefruit, frozen concentrate: Processors stocks, pack, supplies, and movement, Florida, 1980/81 to date", + "C-8": "Grapefruit, chilled juice: Processors stocks, pack, supplies, and movement, Florida, 1985/86 to date", + "C-9": "Lemons: Area bearing, yield, and production, by State, 1980/81 to date", + "C-10": "Lemons: Utilized production, by State, 1980/81 to date", + "C-11": "Lemons: Price received by growers - equivalent on-tree returns, by State, 1980/81 to date", + "C-12": "Lemons: Price received by growers - equivalent on-tree returns, monthly, United States, 1980/81 to date", + "C-13": "Oranges: Area bearing and yield per acre, by State, 1980/81 to date", + "C-14": "Oranges: Production, by State, 1980/81 to date", + "C-15": "Oranges: Utilized production, by State, 1980/81 to date", + "C-16": "Oranges: Price received by growers - equivalent on-tree returns, by State, 1980/81 to date", + "C-17": "Oranges: Price received by growers - equivalent on-tree returns, monthly, United States, 1980/81 to date", + "C-18": "Oranges, processed: Utilized production, Florida, 1980/81 to date", + "C-19": "Oranges, frozen concentrate: Processors stocks, pack, supplies, and movement, Florida, 1980/81 to date", + "C-20": "Oranges, chilled juice: Processors stocks, pack, supplies, and movement, Florida, 1985/86 to date", + "D-1": "Blackberries: Area bearing, yield, production, and season-average grower price, Oregon, 1980 to 2017", + "D-2": "Blueberries: Area harvested, yield, production, and season-average grower price, by State, various years to date", + "D-3": "Boysenberries and loganberries: Area harvested, yield, production, and season-average grower price, California and Oregon, various years to 2018", + "D-4": "Black raspberries: Area harvested, yield, production, and season-average grower price, Oregon, Washington, and California, 1980 to 2017", + "D-5": "Red raspberries: Area harvested, yield, utilized production, and season-average grower price, Oregon, Washington, and California, 1980 to date", + "D-6": "Raspberries: Area harvested, yield, utilized production, grower price, and value, California, 1992 to date", + "D-7": "Cranberries: Area harvested, yield, production, utilization, season-average grower price, and value, United States, 1980 to date", + "D-8": "Strawberries: Area harvested, production, season-average grower price, and value, United States, 1980 to date", + "D-9": "Strawberries, fresh and processed: Production, season-average grower price, and value, United States, 1980 to date", + "D-10": "Strawberries, fresh: Price received by growers, monthly, United States, 1980 to date", + "E-1": "Melons: Per capita availability, 1970 to date", + "E-2": "Melons: Area harvested and production, 1980 to date", + "E-3": "Melons: Production, by country, 2001 to 2024", + "E-4": "Melons: Area harvested, by country, 2001 to 2024", + "E-5": "Melons: Production, selected States, 1990 to date", + "E-6": "Cantaloupe, fresh: Price received by growers, 1995 to date", + "E-7": "Melons: Supply and availability, farm weight, 1970 to date", + "E-8": "Cantaloupe: Supply, availability, and price, farm weight, 1970 to date", + "E-9": "Honeydew: Supply, availability, and price, farm weight, 1970 to date", + "E-10": "Watermelon: Supply, availability, and season-average price received by growers, farm weight, 1970 to date", + "F-1": "Tree nuts: Area bearing, United States, 1980/81 to date", + "F-2": "Tree nuts: Gross returns, United States, 1980/81 to date", + "F-3": "Tree nuts: Utilized production, shelled basis, United States, 1980/81 to date", + "F-4": "Tree nuts: Price received by growers, season-average, United States, 1980/81 to date", + "F-5": "Tree nuts: Value of production, United States, 1980/81 to date", + "F-6": "Tree nuts: Supply and availability, shelled basis, United States, 1980/81 to date", + "F-7": "Almonds: Area bearing, yield, production (shelled basis), season-average grower price, and value, California, 1980 to date", + "F-8": "Almonds: Supply and availability (shelled basis), 1980/81 to date", + "F-9": "Hazelnuts: Area bearing and yield (in-shell), by State, 1980 to date", + "F-10": "Hazelnuts: Production (in-shell), season-average grower price, and value, by State, 1980 to date", + "F-11": "Hazelnuts: Supply and availability (shelled basis), 1980/81 to date", + "F-12": "Macadamias: Area bearing, yield, production (in-shell), season-average grower price, and value, Hawaii, 1980 to date", + "F-13": "Pecans: Production (in-shell), season-average grower price, and value, United States, 1980 to date", + "F-14": "Pecans: Supply and availability (shelled basis), 1980/81 to date", + "F-15": "Pistachios: Production (in-shell), season-average grower price, and value, California, 1980 to date", + "F-16": "Pistachios: Supply and availability (shelled basis), 1980/81 to date", + "F-17": "Walnuts: Production (in-shell), season-average grower price, and value, California, 1980 to date", + "F-18": "Walnuts: Supply and utilization (shelled basis), 1980/81 to date", + "G-1": "Apples, fresh: Supply and availability, 1980/81 to date", + "G-2": "Apricots, fresh: Supply and availability, 1980 to date", + "G-3": "Avocados, fresh: Supply and availability, 1980/81 to date", + "G-4": "Bananas, fresh: Supply and availability, 1980 to date", + "G-5": "Blueberries, fresh: Supply and availability, 1980 to date", + "G-6": "Grapes, fresh: Supply and availability, 1980/81 to date", + "G-7": "Kiwifruit, fresh: Supply and availability, 1988/89 to date", + "G-8": "Mangoes, fresh: Supply and availability, 1980 to date", + "G-9": "Papayas, fresh: Supply and availability, 1980 to date", + "G-10": "Peaches and nectarines, fresh: Supply and availability, 1980 to date", + "G-11": "Pears, fresh: Supply and availability, 1980/81 to date", + "G-12": "Pineapples, fresh: Supply and availability, 1980 to date", + "G-13": "Raspberries, fresh: Supply and availability, 1992 to date", + "G-14": "Strawberries, fresh: Supply and availability, 1980 to date", + "G-15": "Grapefruit, fresh: Supply and availability, 1980/81 to date", + "G-16": "Lemons, fresh: Supply and availability, 1980/81 to date", + "G-17": "Limes, fresh: Supply and availability, 1980/81 to date", + "G-18": "Oranges, fresh: Supply and availability, 1980/81 to date", + "G-19": "Tangerines and tangelos, fresh: Supply and availability, 1980/81 to date", + "G-20": "Apples, canned: Supply and availability, 1980/81 to date", + "G-21": "Apricots, canned: Supply and availability, 1980/81 to date", + "G-22": "Sweet cherries, canned: Supply and availability, 1980/81 to date", + "G-23": "Tart cherries, canned: Supply and availability, 1980/81 to date", + "G-24": "Olives, canned: Supply and availability, 1980/81 to date", + "G-25": "Peaches, canned: Supply and availability, 1980/81 to date", + "G-26": "Pears, canned: Supply and availability, 1980/81 to date", + "G-27": "Pineapples, canned: Supply and availability, processed-weight basis, 1980 to date", + "G-28": "Plums, canned: Supply and availability, 1980/81 to date", + "G-29": "Apples, juice and cider: Supply and availability, 1980/81 to date", + "G-30": "Grapes, juice: Supply and availability, 1980/81 to date", + "G-31": "Grapefruit, juice: Supply and availability, 1980/81 to date", + "G-32": "Oranges, juice: Supply and availability, 1985/86 to date", + "G-33": "Pineapples, juice: Supply and availability, 1980 to date", + "G-34": "Prunes, dried: Supply and availability, 1980/81 to date", + "G-35": "Grapes, dried: Supply and availability, 1980/81 to date", + "G-36": "All fruit, fresh: Per capita availability, 1980 to date", + "G-37": "Noncitrus fruit, canned: Per capita availability, product-weight basis, 1980/81 to date", + "G-38": "All fruit, frozen: Per capita availability, product-weight basis, 1980 to date", + "G-39": "All fruit, dried: Per capita availability, product-weight basis, 1980/81 to date", + "G-40": "Selected fruit, juices: Per capita availability, 1980/81 to date", + "G-41": "Selected noncitrus fruit, processed and fresh: Per capita availability, fresh-weight equivalent, 1980/81 to date", + "G-42": "Citrus fruit, processed and fresh: Per capita availability, fresh-weight equivalent, 1980/81 to date", + "G-43": "Selected fruit, all: Per capita availability, fresh-weight equivalent, 1980 to date", + "G-44": "Tree nuts (shelled basis), all: Per capita availability, 1980/81 to date", + "G-45": "U.S. population", + "H-1": "Import share, fresh fruit: Percent of domestic fresh fruit availability accounted for by imports, 1975 to date", + "H-2": "Import share, canned fruit: Percent of domestic canned fruit availability accounted for by imports, 1975 to date", + "H-3": "Import share, frozen fruit: Percent of domestic frozen fruit availability accounted for by imports, 1976 to date", + "H-4": "Import share, fruit juice: Percent of domestic fruit juice availability accounted for by imports, 1972 to date", + "H-5": "Import share, dried fruit: Percent of domestic dried fruit availability accounted for by imports, 1975/76 to date", +} + + +def table_category(table: str) -> str: + """Return the letter category of a yearbook table code. + + Parameters + ---------- + table : str + Table code such as 'A-1' or 'G-45'. + + Returns + ------- + str + The leading letter, one of A through H. + """ + return table.split("-", 1)[0] + + +def category_tables(category: str | None = None) -> list[str]: + """List the table codes belonging to a category, in published order. + + Parameters + ---------- + category : str | None + Letter category A through H. When None, every table is returned. + + Returns + ------- + list[str] + Table codes, ordered by letter then number. + """ + letter = (category or "").strip().upper() or None + return [ + code + for code in TABLE_TITLES + if letter is None or table_category(code) == letter + ] + + +def parse_records(text: str, table: str) -> list[dict]: + """Parse the yearbook CSV text into one table's long-format records. + + Parameters + ---------- + text : str + Decoded CSV text of the combined yearbook file. + table : str + Table code to extract, e.g. 'A-1'. + + Returns + ------- + list[dict] + Records carrying the table code, year label, integer year, year + unit, month name, the four source dimensions, unit, and the numeric + value. Rows for other tables, blank or non-numeric values, and + unparseable year labels are skipped. + """ + records: list[dict] = [] + for row in csv.DictReader(StringIO(text)): + match = CODE_PATTERN.match(row.get("table_name") or "") + if match is None: + continue + code = f"{match.group(1)}-{match.group(2)}" + if code != table: + continue + raw_value = (row.get("value") or "").strip() + try: + value = float(raw_value) + except ValueError: + continue + year_label = (row.get("year_value") or "").strip() + if not year_label[:4].isdigit(): + continue + month = (row.get("month") or "").strip() + records.append( + { + "table": code, + "year_label": year_label, + "year": int(year_label[:4]), + "year_unit": (row.get("year_unit") or "").strip(), + "month": month if month.casefold() in MONTH_NUMBERS else None, + "variable": (row.get("variable") or "").strip(), + "commodity": (row.get("commodity_element") or "").strip(), + "market_segment": (row.get("market_segment") or "").strip(), + "geography": (row.get("geographic_extent") or "").strip(), + "unit": (row.get("unit") or "").strip(), + "value": value, + } + ) + return records + + +async def afetch_table(table: str, **kwargs) -> list[dict]: + """Download and parse one yearbook table through the ERS disk cache. + + Parameters + ---------- + table : str + Table code from TABLE_TITLES. + + Returns + ------- + list[dict] + Long-format records from parse_records. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + content = await afetch_ers_file(YEARBOOK_MEDIA, product=PRODUCT_PAGE) + return parse_records(content.decode("utf-8-sig", errors="replace"), table) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_fruit_and_vegetable_prices.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_fruit_and_vegetable_prices.py new file mode 100644 index 00000000000..d4cb9fdc44b --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_fruit_and_vegetable_prices.py @@ -0,0 +1,155 @@ +"""USDA ERS Fruit and Vegetable Prices file catalog and parser.""" + +import csv +from io import StringIO + +from openbb_government_us.utils.serializers import is_null_token + +BASE_URL = "https://www.ers.usda.gov" +PRODUCT_PAGE = "data-products/fruit-and-vegetable-prices" + +DATA_YEAR = 2023 + +FVP_TABLES: dict[str, dict] = { + "fruit": { + "media_id": 6210, + "slug": "all-fruits-average-prices-csv-format", + "item_column": "Fruit", + "item_label": "Fruit", + "title": "All fruits, average retail prices and cup-equivalent prices", + }, + "vegetable": { + "media_id": 6240, + "slug": "all-vegetables-average-prices-csv-format", + "item_column": "Vegetable", + "item_label": "Vegetable", + "title": "All vegetables, average retail prices and cup-equivalent prices", + }, +} + + +def media_path(table: str) -> str: + """Build the media path for one table's tidy CSV. + + Parameters + ---------- + table : str + Table key from FVP_TABLES. + + Returns + ------- + str + The '/media/{id}/{slug}.csv' path of the table's CSV file. + """ + entry = FVP_TABLES[table] + return f"/media/{entry['media_id']}/{entry['slug']}.csv" + + +def build_url(table: str) -> str: + """Build the download URL for one table's tidy CSV. + + Parameters + ---------- + table : str + Table key from FVP_TABLES. + + Returns + ------- + str + Full URL of the table's CSV file. + """ + return f"{BASE_URL}{media_path(table)}" + + +def parse_value(raw: str | None) -> float | None: + """Parse a raw CSV value cell into a float. + + Parameters + ---------- + raw : str | None + Raw cell text, which may carry comma thousands separators or a null + token such as '' or 'NA'. + + Returns + ------- + float | None + The parsed value, or None for empty and null-token cells. + """ + text = (raw or "").strip() + if not text or is_null_token(text): + return None + return float(text.replace(",", "")) + + +def parse_rows(text: str, table: str) -> list[dict]: + """Parse one table's tidy CSV text into wide-on-measure records. + + Each source row already carries all six measures for one item and form, so + the parser strips the trailing-space header, renames headers to snake_case, + coerces the four numeric measures to float, and keeps the two unit-label + columns as text. + + Parameters + ---------- + text : str + Decoded CSV text with the item column, a Form column, and the six + measure columns. + table : str + Table key from FVP_TABLES, selecting the item column name. + + Returns + ------- + list[dict] + Records with item, form, average_retail_price, average_retail_price_unit, + preparation_yield_factor, cup_equivalent_size, cup_equivalent_unit, and + average_price_per_cup_equivalent keys, skipping rows with a blank item. + """ + item_column = FVP_TABLES[table]["item_column"] + records: list[dict] = [] + for raw in csv.DictReader(StringIO(text)): + row = {(key or "").strip(): value for key, value in raw.items()} + item = (row.get(item_column) or "").strip() + if not item: + continue + records.append( + { + "item": item, + "form": (row.get("Form") or "").strip() or None, + "average_retail_price": parse_value(row.get("AverageRetailPrice")), + "average_retail_price_unit": ( + row.get("AverageRetailPriceUnitOfMeasure") or "" + ).strip() + or None, + "preparation_yield_factor": parse_value( + row.get("PreparationYieldFactor") + ), + "cup_equivalent_size": parse_value(row.get("SizeOfACupEquivalent")), + "cup_equivalent_unit": ( + row.get("CupEquivalentUnitOfMeasure") or "" + ).strip() + or None, + "average_price_per_cup_equivalent": parse_value( + row.get("AveragePricePerCupEquivalent") + ), + } + ) + return records + + +async def afetch_table(table: str, **kwargs) -> list[dict]: + """Download and parse one table's tidy CSV through the ERS disk cache. + + Parameters + ---------- + table : str + Table key from FVP_TABLES. + + Returns + ------- + list[dict] + Wide-on-measure records from parse_rows. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + content = await afetch_ers_file(media_path(table), product=PRODUCT_PAGE) + return parse_rows(content.decode("utf-8-sig", errors="replace"), table) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_global_food_assessment.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_global_food_assessment.py new file mode 100644 index 00000000000..ca432ab1aaf --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_global_food_assessment.py @@ -0,0 +1,109 @@ +"""USDA ERS Global Food Assessment file catalog and long-format parser.""" + +import csv +from io import StringIO + +BASE_URL = "https://www.ers.usda.gov" +PRODUCT_PAGE = "data-products/global-food-assessment" +MEDIA_PATH = ( + "/media/6171/grain-food-demand-other-demand-total-demand-production-and-" + "implied-additional-supply-required-for-global-food-assessment-countries.csv" +) + +GLOBAL_FOOD_ASSESSMENT_FILES: dict[str, tuple[str, str]] = { + "global_food_assessment": (MEDIA_PATH, PRODUCT_PAGE) +} + +ELEMENTS: tuple[str, ...] = ( + "Food grain demand", + "Other grain demand", + "Grain production", + "Implied additional supply required", + "Total grain demand", +) + +REGIONS: tuple[str, ...] = ( + "Asia", + "Former Soviet Union", + "Latin America and the Caribbean", + "Middle East and North Africa", + "Sub-Saharan Africa", + "GFA countries", +) + +DEFAULT_ELEMENT = "Total grain demand" + +VALUE_COLUMN = "Millions of metric tons" +UNIT = "Millions of metric tons" + + +def build_url() -> str: + """Build the download URL of the global food assessment CSV.""" + return f"{BASE_URL}{MEDIA_PATH}" + + +def parse_amount(raw: str) -> float | None: + """Parse a value cell into a float, or None for a blank or placeholder. + + Parameters + ---------- + raw : str + Value cell as published. + + Returns + ------- + float | None + The numeric amount, with negatives preserved; None when the cell is + blank or a non-numeric placeholder. + """ + text = raw.replace(",", "").strip() + if not text: + return None + try: + return float(text) + except ValueError: + return None + + +def parse_rows(text: str) -> list[dict]: + """Parse the CSV text into tidy long-format observation records. + + Parameters + ---------- + text : str + Decoded CSV text of the global food assessment file. + + Returns + ------- + list[dict] + Records with element, region, subregion, year, and amount keys. The + constant Dataset title column is carried as context only and never + returned as a value; the unit lives in the value-column header and is + supplied as the UNIT constant rather than a per-row value. + """ + records: list[dict] = [] + for row in csv.DictReader(StringIO(text)): + records.append( + { + "element": (row.get("Element") or "").strip(), + "region": (row.get("Region") or "").strip(), + "subregion": (row.get("Subregion") or "").strip(), + "year": (row.get("Year") or "").strip(), + "amount": parse_amount(row.get(VALUE_COLUMN) or ""), + } + ) + return records + + +async def afetch_records() -> list[dict]: + """Download and parse the CSV through the ERS disk cache. + + Returns + ------- + list[dict] + Long-format records from parse_rows. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + content = await afetch_ers_file(MEDIA_PATH, product=PRODUCT_PAGE) + return parse_rows(content.decode("utf-8-sig", errors="replace")) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_international_agricultural_productivity.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_international_agricultural_productivity.py new file mode 100644 index 00000000000..e0f285ed36f --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_international_agricultural_productivity.py @@ -0,0 +1,455 @@ +"""USDA ERS International Agricultural Productivity catalog, fetch, and parser.""" + +import csv +from io import StringIO + +BASE_URL = "https://www.ers.usda.gov" +PRODUCT_PAGE = "data-products/international-agricultural-productivity" +MEDIA_PATH = ( + "/media/5403/machine-readable-and-long-format-file-of-tfp-indices-and" + "-components-for-countries-regions-countries-grouped-by-income-level-and" + "-the-world-1961-2023.csv" +) +DOWNLOAD_TIMEOUT = 120 +YEAR_MIN = 1961 +YEAR_MAX = 2023 + +GROUPINGS: dict[str, str] = { + "sub_saharan_africa": "Sub-Saharan Africa", + "latin_america_caribbean": "Latin America & Caribbean", + "asia_pacific": "Asia & Pacific", + "cwana": "Central/West Asia & North Africa", + "europe": "Europe", + "oceania": "Oceania", + "north_america": "North America", + "income_group": "Income group", + "country_grouping": "Country grouping", +} + +DEFAULT_GROUPING = "country_grouping" +DEFAULT_COUNTRY = 220 +DEFAULT_MEASURE = "productivity_indices" + +MEASURES: dict[str, dict] = { + "productivity_indices": { + "label": "Productivity indices (2015=100)", + "columns": ( + ("TFP_Index", "TFP index (2015=100)"), + ("Outall_Index", "Output index (2015=100)"), + ("Input_Index", "Input index (2015=100)"), + ("Land_Index", "Land input index (2015=100)"), + ("Labor_Index", "Labor input index (2015=100)"), + ("Capital_Index", "Capital input index (2015=100)"), + ("Materials_Index", "Materials input index (2015=100)"), + ), + }, + "physical_quantities": { + "label": "Physical quantities", + "columns": ( + ("Outall_Q", "Total output ($1,000, 2015 prices)"), + ("Outcrop_Q", "Crop output ($1,000, 2015 prices)"), + ("Outanim_Q", "Animal output ($1,000, 2015 prices)"), + ("Outfish_Q", "Aquaculture output ($1,000, 2015 prices)"), + ("Land_Q", "Agricultural land (1,000 ha, rainfed-cropland-equiv.)"), + ("Labor_Q", "Labor (1,000 persons in agriculture)"), + ("Capital_Q", "Capital stock ($ million, 2015 prices)"), + ("Fertilizer_Q", "Fertilizer (metric tons N, P, K)"), + ("Feed_Q", "Feed (10^6 Mcal metabolizable energy)"), + ("Cropland_Q", "Cropland (1,000 ha)"), + ("Pasture_Q", "Permanent pasture (1,000 ha)"), + ("IrrigArea_Q", "Area equipped for irrigation (1,000 ha)"), + ), + }, +} + +VARIABLE_HEADERS: dict[str, str] = { + variable: header + for spec in MEASURES.values() + for variable, header in spec["columns"] +} + +ENTITIES: tuple[tuple[int, str, str], ...] = ( + (1, "Nigeria", "sub_saharan_africa"), + (2, "Benin", "sub_saharan_africa"), + (3, "Côte d'Ivoire", "sub_saharan_africa"), + (4, "Ghana", "sub_saharan_africa"), + (5, "Guinea", "sub_saharan_africa"), + (6, "Guinea-Bissau", "sub_saharan_africa"), + (7, "Liberia", "sub_saharan_africa"), + (8, "Sierra Leone", "sub_saharan_africa"), + (9, "Togo", "sub_saharan_africa"), + (10, "Cameroon", "sub_saharan_africa"), + (11, "Central African Republic", "sub_saharan_africa"), + (12, "Congo DR", "sub_saharan_africa"), + (13, "Congo Republic", "sub_saharan_africa"), + (14, "Equatorial Guinea", "sub_saharan_africa"), + (15, "Gabon", "sub_saharan_africa"), + (16, "Sao Tome and Principe", "sub_saharan_africa"), + (17, "Burundi", "sub_saharan_africa"), + (18, "Kenya", "sub_saharan_africa"), + (19, "Rwanda", "sub_saharan_africa"), + (20, "Tanzania", "sub_saharan_africa"), + (21, "Uganda", "sub_saharan_africa"), + (22, "Djibouti", "sub_saharan_africa"), + (23, "Eritrea", "sub_saharan_africa"), + (24, "Ethiopia", "sub_saharan_africa"), + (25, "Somalia", "sub_saharan_africa"), + (26, "South Sudan", "sub_saharan_africa"), + (27, "Sudan", "sub_saharan_africa"), + (28, "Burkina Faso", "sub_saharan_africa"), + (29, "Cabo Verde", "sub_saharan_africa"), + (30, "Chad", "sub_saharan_africa"), + (31, "Gambia", "sub_saharan_africa"), + (32, "Mali", "sub_saharan_africa"), + (33, "Mauritania", "sub_saharan_africa"), + (34, "Niger", "sub_saharan_africa"), + (35, "Senegal", "sub_saharan_africa"), + (36, "Angola", "sub_saharan_africa"), + (37, "Comoros", "sub_saharan_africa"), + (38, "Madagascar", "sub_saharan_africa"), + (39, "Malawi", "sub_saharan_africa"), + (40, "Mauritius", "sub_saharan_africa"), + (41, "Mozambique", "sub_saharan_africa"), + (42, "Zambia", "sub_saharan_africa"), + (43, "Zimbabwe", "sub_saharan_africa"), + (44, "Botswana", "sub_saharan_africa"), + (45, "Eswatini", "sub_saharan_africa"), + (46, "Lesotho", "sub_saharan_africa"), + (47, "Namibia", "sub_saharan_africa"), + (48, "South Africa", "sub_saharan_africa"), + (49, "Belize", "latin_america_caribbean"), + (50, "Costa Rica", "latin_america_caribbean"), + (51, "El Salvador", "latin_america_caribbean"), + (52, "Guatemala", "latin_america_caribbean"), + (53, "Honduras", "latin_america_caribbean"), + (54, "Mexico", "latin_america_caribbean"), + (55, "Nicaragua", "latin_america_caribbean"), + (56, "Panama", "latin_america_caribbean"), + (57, "Bahamas", "latin_america_caribbean"), + (58, "Cuba", "latin_america_caribbean"), + (59, "Dominican Republic", "latin_america_caribbean"), + (60, "Haiti", "latin_america_caribbean"), + (61, "Jamaica", "latin_america_caribbean"), + (62, "Lesser Antilles", "latin_america_caribbean"), + (63, "Puerto Rico (U.S.)", "latin_america_caribbean"), + (64, "Trinidad and Tobago", "latin_america_caribbean"), + (65, "French Guiana (France)", "latin_america_caribbean"), + (66, "Guyana", "latin_america_caribbean"), + (67, "Suriname", "latin_america_caribbean"), + (68, "Venezuela", "latin_america_caribbean"), + (69, "Bolivia", "latin_america_caribbean"), + (70, "Colombia", "latin_america_caribbean"), + (71, "Ecuador", "latin_america_caribbean"), + (72, "Peru", "latin_america_caribbean"), + (73, "Brazil", "latin_america_caribbean"), + (74, "Argentina", "latin_america_caribbean"), + (75, "Chile", "latin_america_caribbean"), + (76, "Paraguay", "latin_america_caribbean"), + (77, "Uruguay", "latin_america_caribbean"), + (78, "Japan", "asia_pacific"), + (79, "Korea, Republic", "asia_pacific"), + (80, "Taiwan", "asia_pacific"), + (81, "China", "asia_pacific"), + (82, "Korea, DPR", "asia_pacific"), + (83, "Mongolia", "asia_pacific"), + (84, "Brunei Darussalam", "asia_pacific"), + (85, "Cambodia", "asia_pacific"), + (86, "Indonesia", "asia_pacific"), + (87, "Laos", "asia_pacific"), + (88, "Malaysia", "asia_pacific"), + (89, "Myanmar", "asia_pacific"), + (90, "Philippines", "asia_pacific"), + (91, "Thailand", "asia_pacific"), + (92, "Timor-Leste", "asia_pacific"), + (93, "Vietnam", "asia_pacific"), + (94, "Fiji", "asia_pacific"), + (95, "New Caledonia (France)", "asia_pacific"), + (96, "Papua New Guinea", "asia_pacific"), + (97, "Solomon Islands", "asia_pacific"), + (98, "Vanuatu", "asia_pacific"), + (99, "Micronesia", "asia_pacific"), + (100, "Polynesia", "asia_pacific"), + (101, "Bangladesh", "asia_pacific"), + (102, "Bhutan", "asia_pacific"), + (103, "India", "asia_pacific"), + (104, "Nepal", "asia_pacific"), + (105, "Pakistan", "asia_pacific"), + (106, "Sri Lanka", "asia_pacific"), + (107, "Armenia", "cwana"), + (108, "Azerbaijan", "cwana"), + (109, "Georgia", "cwana"), + (110, "Bahrain", "cwana"), + (111, "Cyprus", "cwana"), + (112, "Iran", "cwana"), + (113, "Iraq", "cwana"), + (114, "Israel", "cwana"), + (115, "West Bank and Gaza", "cwana"), + (116, "Jordan", "cwana"), + (117, "Kuwait", "cwana"), + (118, "Lebanon", "cwana"), + (119, "Oman", "cwana"), + (120, "Qatar", "cwana"), + (121, "Saudi Arabia", "cwana"), + (122, "Syria", "cwana"), + (123, "Turkey", "cwana"), + (124, "United Arab Emirates", "cwana"), + (125, "Yemen", "cwana"), + (126, "Afghanistan", "cwana"), + (127, "Kyrgyzstan", "cwana"), + (128, "Tajikistan", "cwana"), + (129, "Turkmenistan", "cwana"), + (130, "Uzbekistan", "cwana"), + (131, "Algeria", "cwana"), + (132, "Egypt", "cwana"), + (133, "Libya", "cwana"), + (134, "Morocco", "cwana"), + (135, "Tunisia", "cwana"), + (136, "Belarus", "europe"), + (137, "Kazakhstan", "europe"), + (138, "Moldova", "europe"), + (139, "Russian Federation", "europe"), + (140, "Ukraine", "europe"), + (141, "Albania", "europe"), + (142, "Bulgaria", "europe"), + (143, "Czechia", "europe"), + (144, "Slovakia", "europe"), + (145, "Hungary", "europe"), + (146, "Poland", "europe"), + (147, "Romania", "europe"), + (148, "Croatia", "europe"), + (149, "Slovenia", "europe"), + (150, "Bosnia and Herzegovina", "europe"), + (151, "North Macedonia", "europe"), + (152, "Montenegro", "europe"), + (153, "Serbia", "europe"), + (154, "Estonia", "europe"), + (155, "Latvia", "europe"), + (156, "Lithuania", "europe"), + (157, "Finland", "europe"), + (158, "Iceland", "europe"), + (159, "Norway", "europe"), + (160, "Sweden", "europe"), + (161, "Greece", "europe"), + (162, "Italy", "europe"), + (163, "Malta", "europe"), + (164, "Portugal", "europe"), + (165, "Spain", "europe"), + (166, "Austria", "europe"), + (167, "Belgium", "europe"), + (168, "Luxembourg", "europe"), + (169, "Denmark", "europe"), + (170, "France", "europe"), + (171, "Germany", "europe"), + (172, "Ireland", "europe"), + (173, "Netherlands", "europe"), + (174, "Switzerland", "europe"), + (175, "United Kingdom", "europe"), + (176, "Australia", "oceania"), + (177, "New Zealand", "oceania"), + (178, "Canada", "north_america"), + (179, "United States", "north_america"), + (182, "SSA, Nigeria", "sub_saharan_africa"), + (183, "SSA, Western", "sub_saharan_africa"), + (184, "SSA, Central", "sub_saharan_africa"), + (185, "SSA, Eastern", "sub_saharan_africa"), + (186, "SSA, Horn", "sub_saharan_africa"), + (187, "SSA, Sahel", "sub_saharan_africa"), + (188, "SSA, Southern", "sub_saharan_africa"), + (189, "SSA, SACU", "sub_saharan_africa"), + (190, "SSA, Total", "sub_saharan_africa"), + (191, "Central America", "latin_america_caribbean"), + (192, "Caribbean", "latin_america_caribbean"), + (193, "SA, Andean", "latin_america_caribbean"), + (194, "SA, Brazil", "latin_america_caribbean"), + (195, "SA, Southern Cone", "latin_america_caribbean"), + (196, "LAC, Total", "latin_america_caribbean"), + (197, "Asia, Developed", "asia_pacific"), + (198, "NE Asia", "asia_pacific"), + (199, "SE Asia", "asia_pacific"), + (200, "Pacific", "asia_pacific"), + (201, "South Asia", "asia_pacific"), + (202, "ASIA, Total LDC", "asia_pacific"), + (203, "ASIA, Total", "asia_pacific"), + (204, "Central Asia", "cwana"), + (205, "West Asia", "cwana"), + (206, "North Africa", "cwana"), + (207, "CWANA", "cwana"), + (208, "Europe, Eastern", "europe"), + (209, "Europe, Central", "europe"), + (210, "Europe, Northern", "europe"), + (211, "Europe, Southern", "europe"), + (212, "Europe, Western", "europe"), + (213, "EUROPE, Total", "europe"), + (214, "OCEANIA", "oceania"), + (215, "NORTH AMERICA", "north_america"), + (217, "Industrialized Countries", "country_grouping"), + (218, "Developing Countries", "country_grouping"), + (220, "World", "country_grouping"), + (223, "Low income", "income_group"), + (224, "Lower-middle income", "income_group"), + (225, "Upper-middle income", "income_group"), + (226, "Upper-middle income, excluding China", "income_group"), + (227, "China", "income_group"), + (228, "High income", "income_group"), + (231, "Ethiopia, former", "sub_saharan_africa"), + (232, "Sudan, former", "sub_saharan_africa"), + (233, "Czechoslovakia, former", "europe"), + (234, "Belgium-Luxembourg", "europe"), + (235, "Serbia and Montenegro", "europe"), + (236, "Yugoslavia, former", "europe"), + (237, "Former Soviet Union", "country_grouping"), + (238, "Transition countries", "country_grouping"), + (239, "EU14 (includes E&W Germany, excludes UK)", "country_grouping"), + (240, "EU27 (27 countries as of 2021 excludes UK)", "country_grouping"), + (241, "OECD (38 countries as of 2021)", "country_grouping"), + (242, "G20 (19 countries 2021)", "country_grouping"), +) + +ENTITY_LABEL: dict[int, str] = {order: label for order, label, _ in ENTITIES} +ENTITY_GROUPING: dict[int, str] = {order: grouping for order, _, grouping in ENTITIES} + + +def grouping_options() -> list[dict]: + """Build the labeled grouping options for the widget's grouping selector. + + Returns + ------- + list[dict] + Label/value option dictionaries, one per grouping bucket. + """ + return [{"label": label, "value": key} for key, label in GROUPINGS.items()] + + +def measure_options() -> list[dict]: + """Build the labeled measure-family options for the widget's measure selector. + + Returns + ------- + list[dict] + Label/value option dictionaries, one per measure family. + """ + return [{"label": spec["label"], "value": key} for key, spec in MEASURES.items()] + + +def entity_options(grouping: str) -> list[dict]: + """List a grouping's entities as labeled options keyed by dataset order. + + Parameters + ---------- + grouping : str + Grouping key from GROUPINGS. + + Returns + ------- + list[dict] + Label/value option dictionaries, the value being the unique dataset + order that disambiguates entities sharing a name, e.g. the country + 'China' (order 81) from the income aggregate 'China' (order 227). + """ + return [ + {"label": label, "value": order} + for order, label, bucket in ENTITIES + if bucket == grouping + ] + + +def parse_entity(text: str, order: int) -> list[dict]: + """Parse the long-format CSV into one entity's observation records. + + Parameters + ---------- + text : str + Decoded long-format CSV text of the release file. + order : int + Dataset order of the entity to keep. + + Returns + ------- + list[dict] + Records with year, variable, and value keys, for the entity's index + and quantity variables. Rows of other entities or variables, blank + years, and non-numeric values are skipped. + """ + target = str(order) + records: list[dict] = [] + for row in csv.DictReader(StringIO(text)): + if (row.get("Order") or "").strip() != target: + continue + variable = (row.get("Variable") or "").strip() + if variable not in VARIABLE_HEADERS: + continue + year_raw = (row.get("Year") or "").strip() + if not (len(year_raw) == 4 and year_raw.isdigit()): + continue + try: + value = float((row.get("Value") or "").strip()) + except ValueError: + continue + records.append({"year": int(year_raw), "variable": variable, "value": value}) + return records + + +async def _adownload() -> bytes: + """Download the release CSV as raw bytes with an extended timeout. + + Returns + ------- + bytes + The raw file content of the 18 MB long-format CSV. + """ + from openbb_core.app.model.abstract.error import OpenBBError + from openbb_core.provider.utils.helpers import amake_request + + url = f"{BASE_URL}{MEDIA_PATH}" + + async def read_bytes(response, _session) -> bytes: + if response.status != 200: + raise OpenBBError( + f"ERS request failed with status {response.status} -> {url}" + ) + return await response.read() + + result = await amake_request( + url, + timeout=DOWNLOAD_TIMEOUT, + response_callback=read_bytes, # ty: ignore[invalid-argument-type] + ) + return result # ty: ignore[invalid-return-type] + + +async def afetch_dataset() -> str: + """Download and decode the long-format CSV through the ERS disk cache. + + Returns + ------- + str + The decoded long-format CSV text of the current release. + """ + from openbb_government_us.usda.utils.ers_client import DEFAULT_FILE_TTL, get_cache + + key = f"file:{MEDIA_PATH}" + with get_cache() as cache: + cached = cache.get(key) + if cached is not None: + return cached.decode("utf-8-sig", errors="replace") + content = await _adownload() + with get_cache() as cache: + cache.set(key, content, expire=DEFAULT_FILE_TTL) + return content.decode("utf-8-sig", errors="replace") + + +async def afetch_entity(order: int) -> list[dict]: + """Download the release CSV and parse one entity's observation records. + + Parameters + ---------- + order : int + Dataset order of the entity to keep. + + Returns + ------- + list[dict] + Records from parse_entity for the selected entity. + """ + text = await afetch_dataset() + return parse_entity(text, order) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_international_baseline_data.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_international_baseline_data.py new file mode 100644 index 00000000000..f0984c62471 --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_international_baseline_data.py @@ -0,0 +1,182 @@ +"""USDA ERS International Baseline Data file catalog and long-format parsers.""" + +import csv +import zipfile +from collections import OrderedDict +from io import BytesIO, StringIO + +from openbb_core.app.model.abstract.error import OpenBBError + +BASE_URL = "https://www.ers.usda.gov" +PRODUCT_PAGE = "data-products/international-baseline-data" +MEDIA_PATH = "/media/5324/international-baseline-data-sets.zip" + +INTERNATIONAL_BASELINE_FILES: dict[str, tuple[str, str]] = { + "international_baseline": (MEDIA_PATH, PRODUCT_PAGE) +} + +COMMODITIES: tuple[str, ...] = ( + "Barley", + "Beef and veal", + "Corn", + "Cotton", + "Pork", + "Poultry", + "Rice", + "Sorghum", + "Soybean meal", + "Soybean oil", + "Soybeans", + "Wheat", +) + +DEFAULT_COMMODITY = "Wheat" +DEFAULT_ATTRIBUTE = "Production" + + +def build_url() -> str: + """Build the download URL of the international baseline data ZIP.""" + return f"{BASE_URL}{MEDIA_PATH}" + + +def select_csv_member(names: list[str]) -> str: + """Return the archive's sole tidy CSV member name. + + Parameters + ---------- + names : list[str] + Member names of the international baseline ZIP. + + Returns + ------- + str + The single member whose name ends in '.csv', the current long-term + projection release; the year in its name changes each annual release + so it is resolved by extension, never hardcoded. + + Raises + ------ + OpenBBError + When the archive holds no CSV member. + """ + for name in names: + if name.lower().endswith(".csv"): + return name + raise OpenBBError("No CSV member found in the international baseline archive.") + + +def parse_amount(raw: str) -> float | None: + """Parse an Amount cell into a float, or None for the 'No data' token. + + Parameters + ---------- + raw : str + Amount cell as published. + + Returns + ------- + float | None + The numeric amount; None when the cell is blank or a non-numeric + placeholder such as 'No data'. + """ + text = raw.replace(",", "").strip() + if not text: + return None + try: + return float(text) + except ValueError: + return None + + +def parse_rows(text: str) -> list[dict]: + """Parse the projection CSV text into tidy long-format observation records. + + Parameters + ---------- + text : str + Decoded CSV text of the long-term projection member. + + Returns + ------- + list[dict] + Records with country, topic, commodity, attribute, unit, year, + year_type, and amount keys. The amount is None for the 'No data' + placeholder; the constant Data_Release_Year is carried as context + only and never returned as a value. + """ + records: list[dict] = [] + for row in csv.DictReader(StringIO(text)): + records.append( + { + "country": (row.get("Country") or "").strip(), + "topic": (row.get("Topic") or "").strip(), + "commodity": (row.get("Commodity") or "").strip(), + "attribute": (row.get("Attribute") or "").strip(), + "unit": (row.get("Unit") or "").strip(), + "year": (row.get("Year") or "").strip(), + "year_type": (row.get("Year_type") or "").strip(), + "amount": parse_amount(row.get("Amount") or ""), + } + ) + return records + + +def attributes_for_commodity(records: list[dict], commodity: str) -> list[str]: + """List a commodity's attributes in published source order. + + Parameters + ---------- + records : list[dict] + Long-format records from parse_rows. + commodity : str + Commodity name, e.g. 'Wheat'. + + Returns + ------- + list[str] + Distinct Attribute values for the commodity, in first-seen order. + """ + seen: OrderedDict[str, None] = OrderedDict() + for record in records: + if record["commodity"] == commodity and record["attribute"]: + seen.setdefault(record["attribute"], None) + return list(seen) + + +def countries_for_commodity(records: list[dict], commodity: str) -> list[str]: + """List a commodity's countries and regions in published source order. + + Parameters + ---------- + records : list[dict] + Long-format records from parse_rows. + commodity : str + Commodity name, e.g. 'Wheat'. + + Returns + ------- + list[str] + Distinct Country values for the commodity, in first-seen order. + """ + seen: OrderedDict[str, None] = OrderedDict() + for record in records: + if record["commodity"] == commodity and record["country"]: + seen.setdefault(record["country"], None) + return list(seen) + + +async def afetch_records() -> list[dict]: + """Download and parse the projection CSV through the ERS disk cache. + + Returns + ------- + list[dict] + Long-format records from parse_rows. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + content = await afetch_ers_file(MEDIA_PATH, product=PRODUCT_PAGE) + with zipfile.ZipFile(BytesIO(content)) as archive: + member = select_csv_member(archive.namelist()) + raw = archive.read(member) + return parse_rows(raw.decode("utf-8-sig", errors="replace")) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_international_macroeconomic_data_set.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_international_macroeconomic_data_set.py new file mode 100644 index 00000000000..8e815f96571 --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_international_macroeconomic_data_set.py @@ -0,0 +1,169 @@ +"""USDA ERS International Macroeconomic Data Set file catalog and parsers.""" + +import csv +from io import StringIO + +from openbb_government_us.utils.serializers import is_null_token + +BASE_URL = "https://www.ers.usda.gov" +PRODUCT_PAGE = "data-products/international-macroeconomic-data-set" + +GROWTH_MARKER = "Percent Change" + +INTERNATIONAL_MACRO_FILES: dict[str, dict] = { + "real_gdp": { + "label": "Real GDP (billions of 2017 dollars)", + "media": "/media/6157/historical-and-projected-real-gross-domestic" + "-product-gdp-and-growth-rates-of-gdp-for-baseline-countriesregions" + "-in-billions-of-2017-dollars-1970-2035.csv", + "mojibake": False, + }, + "real_gdp_per_capita": { + "label": "Real GDP per capita (2017 dollars)", + "media": "/media/6159/historical-and-projected-real-gdp-per-capita-and" + "-growth-rates-for-baseline-countriesregions-in-billions-of-2017" + "-dollars-1970-2035.csv", + "mojibake": False, + }, + "gdp_deflator": { + "label": "GDP deflator (2017 = 100)", + "media": "/media/6161/historical-and-projected-gdp-deflator-and-growth" + "-rates-of-gdp-deflator-for-baseline-countriesregions-in-billions-of" + "-2017-dollars-1970-2035.csv", + "mojibake": False, + }, + "real_gdp_shares": { + "label": "Real GDP shares (share of world GDP)", + "media": "/media/6163/historical-and-projected-real-gdp-shares-and" + "-growth-rates-for-baseline-countriesregions-in-billions-of-2017" + "-dollars-1970-2035.csv", + "mojibake": False, + }, + "real_exchange_rate": { + "label": "Real exchange rate (2017 = 100)", + "media": "/media/6165/historical-and-projected-real-exchange-rate-and" + "-growth-rates-for-baseline-countriesregions-in-billions-of-2017" + "-dollars-1970-2035.csv", + "mojibake": False, + }, + "cpi": { + "label": "Consumer price index (2017 = 100)", + "media": "/media/6167/historical-and-projected-consumer-price-indices" + "-cpi-for-baseline-countriesregions-1970-2035.csv", + "mojibake": False, + }, + "population": { + "label": "Population", + "media": "/media/6169/historical-and-projected-population-and-growth" + "-rates-for-baseline-countriesregions-1970-2035.csv", + "mojibake": True, + }, +} + +DEFAULT_VARIABLE = "real_gdp" +DEFAULT_MEASURE = "level" + +MEASURES: dict[str, str] = { + "level": "Level", + "growth": "Percent change, year to year", +} + +OBSERVATION_RELABEL: dict[str, str] = { + "US Ag. Trade Weighted Exchange Rate, 2017=100": "US Ag. Trade Weighted", +} + + +def is_growth_unit(unit: str) -> bool: + """Return whether a Unit label is a year-to-year percent-change series.""" + return GROWTH_MARKER in unit + + +def repair_mojibake(text: str) -> str: + """Repair a double-encoded UTF-8 label, leaving clean labels unchanged. + + Parameters + ---------- + text : str + Observation label, possibly double-encoded UTF-8 such as + "Côte d'Ivoire". + + Returns + ------- + str + The Latin-1 to UTF-8 round-tripped label, e.g. "Côte d'Ivoire"; + the input unchanged when it is plain ASCII or cannot be repaired. + """ + try: + return text.encode("latin-1").decode("utf-8") + except (UnicodeDecodeError, UnicodeEncodeError): + return text + + +def parse_rows(text: str, mojibake: bool = False) -> list[dict]: + """Parse one variable's CSV text into long-format observation records. + + Parameters + ---------- + text : str + Decoded CSV text with the Observation, Year, Unit, and Value columns. + mojibake : bool + Whether the file's observation labels are double-encoded UTF-8 and + must be repaired. + + Returns + ------- + list[dict] + Records with observation, year, unit, and value keys. Fully blank + padding rows, non-year rows, and rows whose value is a null token or + non-numeric are dropped. + """ + records: list[dict] = [] + for row in csv.DictReader(StringIO(text)): + observation = (row.get("Observation") or "").strip() + year_token = (row.get("Year") or "").strip() + unit = (row.get("Unit") or "").strip() + raw_value = (row.get("Value") or "").strip() + if not (observation or year_token or unit or raw_value): + continue + if not year_token.isdigit(): + continue + if is_null_token(raw_value): + continue + try: + value = float(raw_value) + except ValueError: + continue + if mojibake: + observation = repair_mojibake(observation) + observation = OBSERVATION_RELABEL.get(observation, observation) + records.append( + { + "observation": observation, + "year": int(year_token), + "unit": unit, + "value": value, + } + ) + return records + + +async def afetch_records(variable: str) -> list[dict]: + """Download and parse one variable's CSV through the ERS disk cache. + + Parameters + ---------- + variable : str + Variable key from INTERNATIONAL_MACRO_FILES. + + Returns + ------- + list[dict] + Long-format records from parse_rows. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + config = INTERNATIONAL_MACRO_FILES[variable] + content = await afetch_ers_file(config["media"], product=PRODUCT_PAGE) + return parse_rows( + content.decode("utf-8-sig", errors="replace"), mojibake=config["mojibake"] + ) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_livestock_and_meat_domestic_data.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_livestock_and_meat_domestic_data.py new file mode 100644 index 00000000000..7c3f2ceea5c --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_livestock_and_meat_domestic_data.py @@ -0,0 +1,291 @@ +"""USDA ERS Livestock and Meat Domestic Data file catalog and parsers.""" + +import csv +import zipfile +from io import BytesIO, StringIO + +from openbb_government_us.utils.serializers import is_null_token + +BASE_URL = "https://www.ers.usda.gov" +PRODUCT_PAGE = "data-products/livestock-and-meat-domestic-data" + +ZIP_MEDIA = "/media/5539/livestock-domestic-data-machine-readable-files.zip" +ZIP_DIR = "livestock-data-machine-readable-files" + +LIVESTOCK_AND_MEAT_FILES: dict[str, dict] = { + "supply_and_disappearance": { + "label": "Meat supply and disappearance", + "member": "MeatSD.csv", + "table_name": None, + "fold_dims": (("commodity_desc", "commodity"),), + "col_dims": ("attribute_desc",), + "multi_unit": True, + "period": "timeperiod_desc", + "period_sort": "timeperiod_id", + }, + "slaughter": { + "label": "Livestock and poultry slaughter", + "member": "MeatStats.csv", + "table_name": "Livestock and poultry slaughter", + "fold_dims": (("commodity_desc", "commodity"),), + "col_dims": ("attribute_desc",), + "multi_unit": False, + "period": "month_year_strip", + "period_sort": "timeperiod_id", + }, + "production": { + "label": "Red meat and poultry production", + "member": "MeatStats.csv", + "table_name": "Red meat and poultry production", + "fold_dims": (("commodity_desc", "commodity"),), + "col_dims": ("attribute_desc",), + "multi_unit": False, + "period": "month_year_strip", + "period_sort": "timeperiod_id", + }, + "live_and_dressed_weights": { + "label": "Livestock and poultry live and dressed weights", + "member": "MeatStats.csv", + "table_name": "Livestock and poultry live and dressed weights", + "fold_dims": (("commodity_desc", "commodity"),), + "col_dims": ("attribute_desc",), + "multi_unit": False, + "period": "month_year_strip", + "period_sort": "timeperiod_id", + }, + "cold_storage": { + "label": "Red meat and poultry cold storage stocks", + "member": "MeatStats.csv", + "table_name": "Red meat and poultry beginning cold storage stocks", + "fold_dims": (("commodity_desc", "commodity"),), + "col_dims": ("attribute_desc",), + "multi_unit": False, + "period": "month_year_strip", + "period_sort": "timeperiod_id", + }, + "livestock_prices": { + "label": "Livestock prices", + "member": "LivestockPrices.csv", + "table_name": None, + "fold_dims": (), + "col_dims": ("attribute_desc",), + "multi_unit": True, + "period": "month_desc", + "period_sort": "month_id", + }, + "wholesale_prices": { + "label": "Wholesale prices", + "member": "WholesalePrices.csv", + "table_name": None, + "fold_dims": (), + "col_dims": ("commodity_desc", "geography_desc", "attribute_desc"), + "multi_unit": True, + "period": "month_desc", + "period_sort": "month_id", + }, + "feeder_cattle_outside_feedlots": { + "label": "Feeder cattle supplies outside feedlots", + "member": "FeederCattleSuppliesOutsideFeedlots.csv", + "table_name": None, + "fold_dims": (), + "col_dims": ("attribute_desc",), + "multi_unit": True, + "period": None, + "period_sort": None, + }, + "heifers_entering_the_herd": { + "label": "Heifers entering the herd", + "member": "heifersenteringherd.csv", + "table_name": None, + "fold_dims": (("table_name", "table_name"),), + "col_dims": ("attribute_desc",), + "multi_unit": True, + "period": None, + "period_sort": None, + }, + "high_plains_cattle_feeding_simulator": { + "label": "High Plains cattle feeding simulator", + "member": "highplainscattlefeedingsimulator.csv", + "table_name": None, + "fold_dims": (), + "col_dims": ("geography_desc", "attribute_desc"), + "multi_unit": True, + "period": "timeperiod_desc", + "period_sort": "month_id", + }, +} + +FOLD_DIM_FIELDS = ("commodity", "table_name", "geography") + + +def _period(config: dict, row: dict) -> str | None: + """Derive the row's period label from a CSV row. + + Parameters + ---------- + config : dict + Table configuration from LIVESTOCK_AND_MEAT_FILES. + row : dict + One decoded CSV row. + + Returns + ------- + str | None + The period label, or None for tables without a within-year period. + """ + kind = config["period"] + if kind is None: + return None + if kind == "month_year_strip": + desc = (row.get("timeperiod_desc") or "").strip() + year = (row.get("year_id") or "").strip() + if year and desc.endswith("-" + year): + return desc[: -(len(year) + 1)] + return desc or None + return (row.get(kind) or "").strip() or None + + +def _period_sort(config: dict, row: dict) -> int: + """Derive the row's chronological sort key within a year. + + Parameters + ---------- + config : dict + Table configuration from LIVESTOCK_AND_MEAT_FILES. + row : dict + One decoded CSV row. + + Returns + ------- + int + The numeric period order; zero for tables without a period. + """ + key = config["period_sort"] + if key is None: + return 0 + return int((row.get(key) or "0").strip() or 0) + + +def _amount(raw: str | None) -> float | str | None: + """Parse a raw amount cell into a number, a text value, or None. + + Parameters + ---------- + raw : str | None + Raw cell text, which may carry comma separators, a null token such as + 'NA', or a textual value such as 'f.o.b.'. + + Returns + ------- + float | str | None + The parsed number, the stripped text for non-numeric values, or None + for empty and null-token cells. + """ + text = (raw or "").strip() + if not text or is_null_token(text): + return None + try: + return float(text.replace(",", "")) + except ValueError: + return text + + +def column_label(config: dict, row: dict) -> str: + """Build the wide column label for a CSV row. + + Parameters + ---------- + config : dict + Table configuration from LIVESTOCK_AND_MEAT_FILES. + row : dict + One decoded CSV row. + + Returns + ------- + str + The column-dimension values joined by a space, with the unit appended + in parentheses when the table mixes units. + """ + parts = [(row.get(col) or "").strip() for col in config["col_dims"]] + label = " ".join(part for part in parts if part) + if config["multi_unit"]: + unit = (row.get("unit_desc") or "").strip() + if unit: + label = f"{label} ({unit})" + return label + + +def parse_table(text: str, table: str) -> list[dict]: + """Parse one table's CSV text into long-format observation records. + + Parameters + ---------- + text : str + Decoded CSV text of the table member. + table : str + Table key from LIVESTOCK_AND_MEAT_FILES. + + Returns + ------- + list[dict] + Records carrying the table key, the folding-dimension fields, the + integer year, the period and its sort key, the single-unit table's + row unit, the wide column label, and the amount. Rows filtered out by + table name or holding an empty or null-token amount are skipped. + """ + config = LIVESTOCK_AND_MEAT_FILES[table] + table_name = config["table_name"] + fold_dims = config["fold_dims"] + records: list[dict] = [] + for row in csv.DictReader(StringIO(text)): + if ( + table_name is not None + and (row.get("table_name") or "").strip() != table_name + ): + continue + amount = _amount(row.get("amount")) + if amount is None: + continue + year_label = (row.get("year_id") or "").strip() + if not year_label.isdigit(): + continue + record: dict = { + "table": table, + "commodity": None, + "table_name": None, + "geography": None, + "unit": None, + "year": int(year_label), + "period": _period(config, row), + "period_sort": _period_sort(config, row), + "column": column_label(config, row), + "amount": amount, + } + for source_col, field in fold_dims: + record[field] = (row.get(source_col) or "").strip() or None + if not config["multi_unit"]: + record["unit"] = (row.get("unit_desc") or "").strip() or None + records.append(record) + return records + + +async def afetch_table(table: str, **kwargs) -> list[dict]: + """Download and parse one table through the ERS disk cache. + + Parameters + ---------- + table : str + Table key from LIVESTOCK_AND_MEAT_FILES. + + Returns + ------- + list[dict] + Long-format records from parse_table. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + config = LIVESTOCK_AND_MEAT_FILES[table] + content = await afetch_ers_file(ZIP_MEDIA, product=PRODUCT_PAGE) + with zipfile.ZipFile(BytesIO(content)) as archive: + raw = archive.read(f"{ZIP_DIR}/{config['member']}") + return parse_table(raw.decode("utf-8-sig", errors="replace"), table) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_livestock_and_meat_international_trade_data.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_livestock_and_meat_international_trade_data.py new file mode 100644 index 00000000000..44d9b53f6a5 --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_livestock_and_meat_international_trade_data.py @@ -0,0 +1,514 @@ +"""USDA ERS Livestock and Meat International Trade Data catalog, fetch, and parser.""" + +import io +import re + +PRODUCT_PAGE = "data-products/livestock-and-meat-international-trade-data" + +CARCASS_UNIT = "Carcass weight, 1,000 pounds" +HEAD_UNIT = "Head" + +SPECIES: dict[str, dict] = { + "beef_veal": { + "label": "Beef and veal", + "annual": "/media/5598/beef-and-veal-annual-and-cumulative-year-to-date" + "-us-trade-carcass-weight-1000-pounds.xlsx", + "monthly": "/media/5608/beef-and-veal-monthly-us-trade-carcass-weight" + "-1000-pounds.xlsx", + "unit": CARCASS_UNIT, + "unit_in_label": False, + }, + "cattle": { + "label": "Cattle", + "annual": "/media/5599/cattle-annual-and-cumulative-year-to-date" + "-us-trade-head.xlsx", + "monthly": "/media/5609/cattle-monthly-us-trade-head.xlsx", + "unit": HEAD_UNIT, + "unit_in_label": False, + }, + "pork": { + "label": "Pork", + "annual": "/media/5600/pork-annual-and-cumulative-year-to-date" + "-us-trade-carcass-weight-1000-pounds.xlsx", + "monthly": "/media/5610/pork-monthly-us-trade-carcass-weight-1000-pounds.xlsx", + "unit": CARCASS_UNIT, + "unit_in_label": False, + }, + "hogs": { + "label": "Hogs", + "annual": "/media/5601/hogs-annual-and-cumulative-year-to-date" + "-us-trade-head.xlsx", + "monthly": "/media/5611/hogs-monthly-us-trade-head.xlsx", + "unit": HEAD_UNIT, + "unit_in_label": False, + }, + "lamb_mutton": { + "label": "Lamb and mutton", + "annual": "/media/5602/lamb-and-mutton-annual-and-cumulative-year-to-date" + "-us-trade-carcass-weight-1000-pounds.xlsx", + "monthly": "/media/5612/lamb-and-mutton-monthly-us-trade-carcass-weight" + "-1000-pounds.xlsx", + "unit": CARCASS_UNIT, + "unit_in_label": False, + }, + "sheep_goats": { + "label": "Sheep and goats", + "annual": "/media/5603/sheep-and-goats-annual-and-cumulative-year-to-date" + "-us-trade-head.xlsx", + "monthly": "/media/5613/sheep-and-goats-monthly-us-trade-head.xlsx", + "unit": HEAD_UNIT, + "unit_in_label": False, + }, + "poultry_eggs": { + "label": "Chickens, turkeys, and eggs", + "annual": "/media/5604/chickens-turkeys-and-eggs-annual-and-cumulative" + "-year-to-date-us-trade.xlsx", + "monthly": "/media/5614/chickens-turkeys-and-eggs-monthly-us-trade.xlsx", + "unit": None, + "unit_in_label": True, + }, +} + +SPECIES_LABELS: dict[str, str] = {key: cfg["label"] for key, cfg in SPECIES.items()} + +DIRECTIONS: tuple[str, ...] = ("imports", "exports") + +DIRECTION_LABELS: dict[str, str] = {"imports": "Imports", "exports": "Exports"} + +FREQUENCIES: tuple[str, ...] = ("annual", "monthly") + +FREQUENCY_LABELS: dict[str, str] = {"annual": "Annual", "monthly": "Monthly"} + +MONTHS: tuple[str, ...] = ( + "Jan", + "Feb", + "Mar", + "Apr", + "May", + "Jun", + "Jul", + "Aug", + "Sep", + "Oct", + "Nov", + "Dec", +) + +MONTH_ORD: dict[str, int] = {name: index + 1 for index, name in enumerate(MONTHS)} + +HEADER_MARKER = "Import/export" +FOOTER_PREFIXES = ("1/", "2/", "3/", "Source", "Date run", "USSR") +NULL_TOKENS = frozenset({"", "-", "--", "---", "n/a", "na", "null", "none", "nan"}) + +YEAR_CELL = re.compile(r"^\d{4}$") +MONTH_CELL = re.compile(r"^([A-Za-z]{3})-(\d{2})$") +YTD_CELL = re.compile(r"^([A-Za-z]{3}-[A-Za-z]{3})\s+(\d{2})$") +UNIT_PAREN = re.compile(r"\s*\(([^()]*)\)\s*$") + + +def normalize_label(value) -> str: + """Collapse a source label's newlines and repeated spaces into one line. + + Parameters + ---------- + value : object + Raw column-A cell value. + + Returns + ------- + str + The label with newlines turned into spaces and whitespace collapsed. + """ + return re.sub(r"\s+", " ", str(value).replace("\n", " ")).strip() + + +def expand_year(two_digit: int) -> int: + """Expand a two-digit source year to a four-digit calendar year. + + Parameters + ---------- + two_digit : int + Two-digit year from a 'Mon-YY' or year-to-date header cell. + + Returns + ------- + int + The four-digit year, mapping 00-49 to the 2000s and 50-99 to the 1900s. + """ + return 2000 + two_digit if two_digit < 50 else 1900 + two_digit + + +def parse_block_label( + label: str, unit_in_label: bool, default_unit: str | None +) -> tuple[str, str, str | None]: + """Split a block label into trade direction, product, and unit. + + Parameters + ---------- + label : str + Normalized block label, e.g. 'Cattle imports, total'. + unit_in_label : bool + Whether the trailing parenthetical carries the data unit, as for the + chickens-turkeys-and-eggs workbook whose blocks mix meat and egg units. + default_unit : str | None + Unit to use when the label does not carry one. + + Returns + ------- + tuple[str, str, str | None] + The direction ('imports' or 'exports'), the product with the direction + word removed, and the unit. + """ + text = label + unit = default_unit + direction = "exports" if "export" in text.lower() else "imports" + if unit_in_label: + match = UNIT_PAREN.search(text) + if match: + unit = match.group(1).strip() + text = text[: match.start()].strip() + word = "exports" if direction == "exports" else "imports" + product = re.sub(rf"\b{word}\b", "", text, count=1, flags=re.IGNORECASE) + product = re.sub(r"\s+,", ",", product) + product = re.sub(r"\s+", " ", product).strip(" ,") + return direction, product, unit + + +def coerce_value(value) -> float | None: + """Coerce a source cell to a float, keeping full precision. + + Parameters + ---------- + value : object + Raw value cell. + + Returns + ------- + float | None + The numeric value, or None for blank, placeholder, or non-numeric cells. + """ + if value is None or isinstance(value, bool): + return None + if isinstance(value, (int, float)): + return float(value) + text = str(value).strip() + if text.casefold() in NULL_TOKENS: + return None + try: + return float(text.replace(",", "")) + except ValueError: + return None + + +def parse_time_header(header: tuple) -> list[dict]: + """Parse a header row's time cells into ordered column descriptors. + + Parameters + ---------- + header : tuple + The header row values; time cells begin at column D (index 3). + + Returns + ------- + list[dict] + One descriptor per recognized time cell with index, year, month, + month_ord, col_key, and is_ytd keys. + """ + columns: list[dict] = [] + for index in range(3, len(header)): + cell = header[index] + if cell is None: + continue + text = str(cell).strip() + if YEAR_CELL.match(text): + columns.append( + { + "index": index, + "year": int(text), + "month": None, + "month_ord": 0, + "col_key": text, + "is_ytd": False, + } + ) + continue + month_match = MONTH_CELL.match(text) + if month_match: + month = month_match.group(1).title() + year = expand_year(int(month_match.group(2))) + columns.append( + { + "index": index, + "year": year, + "month": month, + "month_ord": MONTH_ORD.get(month, 0), + "col_key": str(year), + "is_ytd": False, + } + ) + continue + ytd_match = YTD_CELL.match(text) + if ytd_match: + year = expand_year(int(ytd_match.group(2))) + columns.append( + { + "index": index, + "year": year, + "month": None, + "month_ord": 0, + "col_key": f"{ytd_match.group(1)} {year}", + "is_ytd": True, + } + ) + return columns + + +def _iter_sheets(content: bytes): + """Yield each worksheet's row tuples from workbook bytes, recent sheet first. + + Parameters + ---------- + content : bytes + Raw XLSX bytes. + + Yields + ------ + list[tuple] + The value rows of one worksheet, iterating sheets in reverse order so + the most recent split sheet establishes the country ranking. + """ + import openpyxl + + workbook = openpyxl.load_workbook( + io.BytesIO(content), data_only=True, read_only=True + ) + for sheet_name in reversed(workbook.sheetnames): + yield list(workbook[sheet_name].iter_rows(values_only=True)) + workbook.close() + + +def _header_index(rows: list[tuple]) -> int: + """Return the index of a sheet's 'Import/export' header row, or -1. + + Parameters + ---------- + rows : list[tuple] + A worksheet's value rows. + + Returns + ------- + int + The header row index, or -1 when the sheet has no header. + """ + for index, row in enumerate(rows): + cell = row[0] if row else None + if cell is not None and str(cell).startswith(HEADER_MARKER): + return index + return -1 + + +def parse_products(content: bytes, entry: dict, direction: str) -> list[str]: + """List a direction's product blocks from an annual workbook, in file order. + + Parameters + ---------- + content : bytes + Raw XLSX bytes of a species annual workbook. + entry : dict + The species catalog entry from SPECIES. + direction : str + 'imports' or 'exports'. + + Returns + ------- + list[str] + Distinct product labels for the direction, in source order. + """ + products: list[str] = [] + seen: set[str] = set() + for rows in _iter_sheets(content): + start = _header_index(rows) + if start < 0: + continue + for row in rows[start + 1 :]: + label = row[0] if row else None + code = row[1] if len(row) > 1 else None + if label is None or code is None: + continue + block_direction, product, _ = parse_block_label( + normalize_label(label), entry["unit_in_label"], entry["unit"] + ) + if block_direction != direction or product in seen: + continue + seen.add(product) + products.append(product) + return products + + +def parse_workbook( + content: bytes, + entry: dict, + want_direction: str, + want_product: str, +) -> list[dict]: + """Parse one species workbook into long records for a single product block. + + Parameters + ---------- + content : bytes + Raw XLSX bytes of the species annual or monthly workbook. + entry : dict + The species catalog entry from SPECIES. + want_direction : str + Trade direction to keep: 'imports' or 'exports'. + want_product : str + Product block to keep, matching parse_block_label's product. + + Returns + ------- + list[dict] + Records with direction, product, unit, geography_code, country, year, + month, month_ord, col_key, is_ytd, rank, and value keys; only non-null + observations are emitted, and 'Total' summary rows are dropped. + """ + records: list[dict] = [] + ranks: dict[str, int] = {} + for rows in _iter_sheets(content): + start = _header_index(rows) + if start < 0: + continue + columns = parse_time_header(rows[start]) + current: tuple[str, str, str | None] | None = None + for row in rows[start + 1 :]: + label = row[0] if row else None + code = row[1] if len(row) > 1 else None + name = row[2] if len(row) > 2 else None + label_text = normalize_label(label) if label not in (None, "") else None + country = str(name).strip() if name is not None else "" + if label_text and code is None and not country: + break + if label_text and code is not None: + current = parse_block_label( + label_text, entry["unit_in_label"], entry["unit"] + ) + if current is None or not country: + continue + direction, product, unit = current + if direction != want_direction or product != want_product: + continue + if country.casefold() == "total" or country.startswith("Total"): + continue + geography_code = ( + int(code) + if isinstance(code, (int, float)) and not isinstance(code, bool) + else None + ) + if country not in ranks: + ranks[country] = len(ranks) + rank = ranks[country] + for column in columns: + value = coerce_value( + row[column["index"]] if column["index"] < len(row) else None + ) + if value is None: + continue + records.append( + { + "direction": direction, + "product": product, + "unit": unit, + "geography_code": geography_code, + "country": country, + "year": column["year"], + "month": column["month"], + "month_ord": column["month_ord"], + "col_key": column["col_key"], + "is_ytd": column["is_ytd"], + "rank": rank, + "value": value, + } + ) + return records + + +async def afetch_products(table: str, direction: str) -> list[dict]: + """Download the species annual workbook and list a direction's products. + + Parameters + ---------- + table : str + Species key from SPECIES. + direction : str + 'imports' or 'exports'. + + Returns + ------- + list[dict] + Label/value option dictionaries for the product selector. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + entry = SPECIES[table] + content = await afetch_ers_file(entry["annual"], product=PRODUCT_PAGE) + return [ + {"label": product, "value": product} + for product in parse_products(content, entry, direction) + ] + + +async def resolve_product( + table: str, direction: str, product: str | None +) -> str | None: + """Resolve a requested product to a valid one, defaulting to the first block. + + Parameters + ---------- + table : str + Species key from SPECIES. + direction : str + 'imports' or 'exports'. + product : str | None + Requested product, or None to use the direction's first product. + + Returns + ------- + str | None + A product present in the workbook, or None when the direction has none. + """ + options = await afetch_products(table, direction) + values = [option["value"] for option in options] + if product in values: + return product + return values[0] if values else None + + +async def afetch_records( + table: str, direction: str, frequency: str, product: str | None +) -> list[dict]: + """Download and parse one species-direction-product block for a frequency. + + Parameters + ---------- + table : str + Species key from SPECIES. + direction : str + 'imports' or 'exports'. + frequency : str + 'annual' or 'monthly'. + product : str | None + Product block, or None to use the direction's first product. + + Returns + ------- + list[dict] + Long records from parse_workbook for the resolved product block. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + entry = SPECIES[table] + resolved = await resolve_product(table, direction, product) + if resolved is None: + return [] + media_path = entry["monthly"] if frequency == "monthly" else entry["annual"] + content = await afetch_ers_file(media_path, product=PRODUCT_PAGE) + return parse_workbook(content, entry, direction, resolved) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_major_land_uses.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_major_land_uses.py new file mode 100644 index 00000000000..cf8f340e4b4 --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_major_land_uses.py @@ -0,0 +1,257 @@ +"""USDA ERS Major Land Uses file catalog and parsers.""" + +import csv +import re +from io import StringIO + +from openbb_core.app.model.abstract.error import OpenBBError + +BASE_URL = "https://www.ers.usda.gov" +PRODUCT_PAGE = "data-products/major-land-uses" + +MAJOR_LAND_USES_FILES: dict[str, dict] = { + "land_use": { + "media_path": "/media/5639/all-data-in-csv-comma-separated-values-format.csv", + "format": "csv", + }, + "cropland_used_for_crops": { + "media_path": "/media/5647/summary-table-3-total-cropland-used-for-crops" + + "-cropland-harvested-including-double-cropped-crop-failure-and-cultivated" + + "-summer-fallow-for-the-united-states-annual-1910-2025.xlsx", + "format": "xlsx", + }, +} + +LAND_USE_CATEGORIES: dict[str, str] = { + "cropland_idled": "Cropland idled", + "cropland_used_for_crops": "Cropland used for crops", + "cropland_used_for_pasture": "Cropland used for pasture", + "defense_and_industrial": "Land in defense and industrial areas", + "farmsteads_roads_and_miscellaneous_farmland": ( + "Farmsteads, roads, and miscellaneous farmland" + ), + "forest_use_land": "Forest-use land (all)", + "grassland_pasture_and_range": "Grassland pasture and range", + "grazed_forest_use_land": "Grazed forest-use land grazed", + "miscellaneous_other_land": "Miscellaneous other land", + "rural_parks_and_wildlife": "Land in rural parks and wildlife areas", + "rural_transportation": "Land in rural transportation facilities", + "special_uses": "All special uses of land", + "total_cropland": "Total cropland", + "total_land": "Total land", + "ungrazed_forest_use_land": "Ungrazed forest-use land", + "urban_areas": "Land in urban areas", +} + +LABEL_TO_SLUG = {label: slug for slug, label in LAND_USE_CATEGORIES.items()} + +GEOGRAPHIES: tuple[str, ...] = ( + "48 States", + "Alabama", + "Alaska", + "Appalachian", + "Arizona", + "Arkansas", + "California", + "Colorado", + "Connecticut", + "Corn Belt", + "Delaware", + "Delta States", + "District of Columbia", + "Florida", + "Georgia", + "Hawaii", + "Idaho", + "Illinois", + "Indiana", + "Iowa", + "Kansas", + "Kentucky", + "Lake States", + "Louisiana", + "Maine", + "Maryland", + "Massachusetts", + "Michigan", + "Minnesota", + "Mississippi", + "Missouri", + "Montana", + "Mountain", + "Nebraska", + "Nevada", + "New Hampshire", + "New Jersey", + "New Mexico", + "New York", + "North Carolina", + "North Dakota", + "Northeast", + "Northern Plains", + "Ohio", + "Oklahoma", + "Oregon", + "Pacific", + "Pennsylvania", + "Rhode Island", + "South Carolina", + "South Dakota", + "Southeast", + "Southern Plains", + "Tennessee", + "Texas", + "U.S. total", + "Utah", + "Vermont", + "Virginia", + "Washington", + "West Virginia", + "Wisconsin", + "Wyoming", +) + +AGGREGATE_GEOGRAPHIES = {"48 States", "U.S. total"} + +CROPLAND_VARIABLES: tuple[str, ...] = ( + "total_crops_harvested", + "double_cropped", + "cropland_harvested", + "crop_failure", + "cultivated_summer_fallow", + "total_cropland_used_for_crops", +) + +YEAR_PATTERN = re.compile(r"^(\d{4})(?:\s|$)") + + +def parse_land_use_csv(text: str) -> list[dict]: + """Parse the all-data CSV into tidy land-use records. + + Parameters + ---------- + text : str + Decoded CSV text with columns ID, Region, Region or State, Year, + Land use, Value, Units, Release date, and Source. + + Returns + ------- + list[dict] + Records with year, geography, region, is_aggregate, land_use, + value (expanded from thousands to acres, 'N.A.' as None), and + is_preliminary keys. + """ + records: list[dict] = [] + for row in csv.DictReader(StringIO(text)): + geography = (row.get("Region or State") or "").strip() + region = (row.get("Region") or "").strip() + label = re.sub(r"\s+", " ", (row.get("Land use") or "").strip()) + raw_value = (row.get("Value") or "").strip() + is_aggregate = region.endswith(" total") or geography in AGGREGATE_GEOGRAPHIES + records.append( + { + "year": int(row["Year"]), + "geography": geography, + "region": None if is_aggregate else region, + "is_aggregate": is_aggregate, + "land_use": LABEL_TO_SLUG.get(label, label), + "value": ( + None if raw_value in {"", "N.A."} else float(raw_value) * 1000 + ), + "is_preliminary": None, + } + ) + return records + + +def parse_cropland_xlsx(content: bytes) -> list[dict]: + """Parse the annual cropland-used-for-crops workbook into tidy records. + + Parameters + ---------- + content : bytes + Raw XLSX bytes of summary table 3. + + Returns + ------- + list[dict] + Records with year, geography ('United States'), region (None), + is_aggregate (True), land_use (one of CROPLAND_VARIABLES), value + (expanded from millions to acres, blanks as None), and + is_preliminary keys. + + Raises + ------ + OpenBBError + If the workbook does not have the expected seven columns. + """ + from io import BytesIO + + import pandas as pd + + frame = ( + pd.read_excel(BytesIO(content), header=None) + .dropna(how="all") + .dropna(axis=1, how="all") + .reset_index(drop=True) + ) + if frame.shape[1] != len(CROPLAND_VARIABLES) + 1: + raise OpenBBError( + "Unexpected column layout in the cropland-used-for-crops workbook:" + + f" found {frame.shape[1]} columns, expected" + + f" {len(CROPLAND_VARIABLES) + 1}." + ) + records: list[dict] = [] + for row in frame.iloc[2:].itertuples(index=False): + label = str(row[0]).strip() + match = YEAR_PATTERN.match(label) + if match is None: + break + year = int(match.group(1)) + is_preliminary = "5/" in label + for variable, value in zip(CROPLAND_VARIABLES, row[1:]): + records.append( + { + "year": year, + "geography": "United States", + "region": None, + "is_aggregate": True, + "land_use": variable, + "value": None if pd.isna(value) else float(value) * 1_000_000, + "is_preliminary": is_preliminary, + } + ) + return records + + +async def afetch_land_use() -> list[dict]: + """Download and parse the all-data CSV through the ERS disk cache. + + Returns + ------- + list[dict] + Tidy records from parse_land_use_csv. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + content = await afetch_ers_file( + MAJOR_LAND_USES_FILES["land_use"]["media_path"], product=PRODUCT_PAGE + ) + return parse_land_use_csv(content.decode("utf-8-sig")) + + +async def afetch_cropland_used_for_crops() -> list[dict]: + """Download and parse summary table 3 through the ERS disk cache. + + Returns + ------- + list[dict] + Tidy records from parse_cropland_xlsx. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + content = await afetch_ers_file( + MAJOR_LAND_USES_FILES["cropland_used_for_crops"]["media_path"], + product=PRODUCT_PAGE, + ) + return parse_cropland_xlsx(content) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_meat_price_spreads.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_meat_price_spreads.py new file mode 100644 index 00000000000..94d173bf680 --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_meat_price_spreads.py @@ -0,0 +1,183 @@ +"""USDA ERS Meat Price Spreads file catalog and parsers.""" + +import csv +from io import StringIO + +from openbb_government_us.utils.serializers import is_null_token + +BASE_URL = "https://www.ers.usda.gov" +PRODUCT_PAGE = "data-products/meat-price-spreads" + +MEAT_PRICE_SPREADS_FILES: dict[str, dict] = { + "summary": { + "media_id": 5025, + "slug": "summary-of-retail-prices-and-price-spreads", + "title": "Summary of retail prices and price spreads", + "period_column": "Month", + "period_number_column": "Month_Number", + }, + "choice_beef": { + "media_id": 5020, + "slug": "choice-beef-values-and-spreads-and-the-all-fresh-retail-value", + "title": "Choice beef values and spreads and the all-fresh retail value", + "period_column": "Period", + "period_number_column": "Period_Number", + }, + "pork": { + "media_id": 5026, + "slug": "pork-values-and-spreads", + "title": "Pork values and spreads", + "period_column": "Period", + "period_number_column": "Period_Number", + }, + "retail_prices": { + "media_id": 5024, + "slug": "retail-prices-for-beef-pork-poultry-cuts-eggs-and-dairy-products", + "title": "Retail prices for beef, pork, poultry cuts, eggs, and dairy products", + "period_column": "Month", + "period_number_column": "Month_Number", + }, + "historical_monthly": { + "media_id": 5028, + "slug": "historical-monthly-price-spread-data-for-beef-pork-broilers", + "title": "Historical monthly price spread data for beef, pork, broilers", + "period_column": "Month", + "period_number_column": "Month-number", + }, +} + +ANNUAL_PERIOD_NUMBER = 17 +QUARTER_PERIOD_NUMBERS = range(13, 17) + + +def media_path(table: str) -> str: + """Build the media path for one table's tidy CSV. + + Parameters + ---------- + table : str + Table key from MEAT_PRICE_SPREADS_FILES. + + Returns + ------- + str + The '/media/{id}/{slug}.csv' path of the table's CSV file. + """ + entry = MEAT_PRICE_SPREADS_FILES[table] + return f"/media/{entry['media_id']}/{entry['slug']}.csv" + + +def build_url(table: str) -> str: + """Build the download URL for one table's tidy CSV. + + Parameters + ---------- + table : str + Table key from MEAT_PRICE_SPREADS_FILES. + + Returns + ------- + str + Full URL of the table's CSV file. + """ + return f"{BASE_URL}{media_path(table)}" + + +def parse_value(raw: str | None) -> float | None: + """Parse a raw CSV value cell into a float. + + Parameters + ---------- + raw : str | None + Raw cell text, which may carry comma thousands separators, e.g. + '1,009.30', or a null token such as '' or 'NA'. + + Returns + ------- + float | None + The parsed value, or None for empty and null-token cells. + """ + text = (raw or "").strip() + if not text or is_null_token(text): + return None + return float(text.replace(",", "")) + + +def frequency_for(period_number: int) -> str: + """Classify a period number into an observation frequency. + + Parameters + ---------- + period_number : int + Source period number: 1-12 for months, 13-16 for quarters, 17 for the + annual row. + + Returns + ------- + str + One of 'annual', 'quarterly', or 'monthly'. + """ + if period_number == ANNUAL_PERIOD_NUMBER: + return "annual" + if period_number in QUARTER_PERIOD_NUMBERS: + return "quarterly" + return "monthly" + + +def parse_rows(text: str, table: str) -> list[dict]: + """Parse one table's tidy CSV text into long row records. + + Parameters + ---------- + text : str + Decoded CSV text with a Year column, the table's period and period + number columns, a Data_Item column, and a Value column. + table : str + Table key from MEAT_PRICE_SPREADS_FILES, selecting the period and + period number column names. + + Returns + ------- + list[dict] + Records with year, period, period_number, frequency, data_item, and + value keys, skipping rows with an empty or null-token Value. + """ + entry = MEAT_PRICE_SPREADS_FILES[table] + period_column = entry["period_column"] + number_column = entry["period_number_column"] + rows: list[dict] = [] + for row in csv.DictReader(StringIO(text)): + value = parse_value(row.get("Value")) + if value is None: + continue + period_number = int((row.get(number_column) or "0").strip() or "0") + rows.append( + { + "year": int((row.get("Year") or "").strip()), + "period": (row.get(period_column) or "").strip(), + "period_number": period_number, + "frequency": frequency_for(period_number), + "data_item": (row.get("Data_Item") or "").strip(), + "value": value, + } + ) + return rows + + +async def afetch_table(table: str, **kwargs) -> list[dict]: + """Download and parse one table's tidy CSV through the ERS disk cache. + + Parameters + ---------- + table : str + Table key from MEAT_PRICE_SPREADS_FILES. + + Returns + ------- + list[dict] + Long row records from parse_rows. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + content = await afetch_ers_file(media_path(table), product=PRODUCT_PAGE) + return parse_rows(content.decode("utf-8-sig", errors="replace"), table) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_milk_cost_of_production.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_milk_cost_of_production.py new file mode 100644 index 00000000000..7790035336f --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_milk_cost_of_production.py @@ -0,0 +1,224 @@ +"""USDA ERS Milk Cost of Production Estimates file catalog and parsers.""" + +import csv +import re +from io import StringIO + +BASE_URL = "https://www.ers.usda.gov" +PRODUCT_PAGE = "data-products/milk-cost-of-production-estimates" + +MILK_COST_FILES: dict[str, dict] = { + "by_state": { + "media_id": 5057, + "slug": "by-state", + "dimension": "state", + }, + "by_size_of_operation": { + "media_id": 5059, + "slug": "by-size-of-operation", + "dimension": "size_of_operation", + }, +} + +STATE_CHOICES: dict[str, str] = { + "us_total": "U.S. total", + "california": "California", + "indiana": "Indiana", + "iowa": "Iowa", + "kentucky": "Kentucky", + "michigan": "Michigan", + "minnesota": "Minnesota", + "new_york": "New York", + "ohio": "Ohio", + "pennsylvania": "Pennsylvania", + "wisconsin": "Wisconsin", +} + +SIZE_CHOICES: dict[str, str] = { + "fewer_than_50_cows": "Fewer than 50 cows", + "50_99_cows": "50–99 cows", + "100_199_cows": "100–199 cows", + "200_499_cows": "200–499 cows", + "500_999_cows": "500–999 cows", + "1000_1999_cows": "1,000–1,999 cows", + "2000_cows_or_more": "2,000 cows or more", + "all_sizes": "All sizes", +} + +CATEGORY_CHOICES: dict[str, str] = { + "gross_value_of_production": "Gross value of production", + "operating_costs": "Operating costs", + "allocated_overhead": "Allocated overhead", + "costs_listed": "Costs listed", + "net_value": "Net value", + "supporting_information": "Supporting information", +} + +ITEM_CHOICES: dict[str, str] = { + "bedding_and_litter": "Bedding and litter", + "capital_recovery_of_machinery_and_equipment": ( + "Capital recovery of machinery and equipment" + ), + "cattle": "Cattle", + "custom_services": "Custom services", + "fuel_lube_and_electricity": "Fuel, lube, and electricity", + "general_farm_overhead": "General farm overhead", + "grazed_feed": "Grazed feed", + "hired_labor": "Hired labor", + "homegrown_harvested_feed": "Homegrown harvested feed", + "interest_on_operating_capital": "Interest on operating capital", + "marketing": "Marketing", + "milk_cows": "Milk cows", + "milk_sold": "Milk sold", + "opportunity_cost_of_land": "Opportunity cost of land", + "opportunity_cost_of_unpaid_labor": "Opportunity cost of unpaid labor", + "other_income": "Other income", + "other_operating_costs": "Other, operating costs", + "output_per_cow": "Output per cow", + "purchased_feed": "Purchased feed", + "repairs": "Repairs", + "taxes_and_insurance": "Taxes and insurance", + "total_allocated_overhead": "Total, allocated overhead", + "total_costs_listed": "Total, costs listed", + "total_feed_costs": "Total, feed costs", + "total_gross_value_of_production": "Total, gross value of production", + "total_operating_costs": "Total, operating costs", + "value_of_production_less_operating_costs": ( + "Value of production less operating costs" + ), + "value_of_production_less_total_costs_listed": ( + "Value of production less total costs listed" + ), + "veterinary_and_medicine": "Veterinary and medicine", +} + +SUPERSCRIPT_PATTERN = re.compile(r"[¹²³]") +WHITESPACE_PATTERN = re.compile(r"\s+") + + +def media_path(report: str) -> str: + """Build the media path for one report's tidy CSV. + + Parameters + ---------- + report : str + Report key from MILK_COST_FILES. + + Returns + ------- + str + The '/media/{id}/{slug}.csv' path of the report's CSV file. + """ + entry = MILK_COST_FILES[report] + return f"/media/{entry['media_id']}/{entry['slug']}.csv" + + +def build_url(report: str) -> str: + """Build the download URL for one report's tidy CSV. + + Parameters + ---------- + report : str + Report key from MILK_COST_FILES. + + Returns + ------- + str + Full URL of the report's CSV file. + """ + return f"{BASE_URL}{media_path(report)}" + + +def clean_item(item: str) -> str: + """Strip footnote superscripts and normalize whitespace in an item label. + + Parameters + ---------- + item : str + Item label as published, e.g. 'Other, operating costs ² '. + + Returns + ------- + str + Cleaned label shared by both reports, e.g. 'Other, operating costs'. + """ + return WHITESPACE_PATTERN.sub(" ", SUPERSCRIPT_PATTERN.sub("", item)).strip() + + +def decode_csv(content: bytes) -> str: + """Decode CSV bytes as UTF-8, falling back to cp1252. + + Parameters + ---------- + content : bytes + Raw file content; the by-size-of-operation CSV is cp1252-encoded. + + Returns + ------- + str + Decoded CSV text. + """ + try: + return content.decode("utf-8-sig") + except UnicodeDecodeError: + return content.decode("cp1252") + + +def parse_rows(text: str, report: str) -> list[dict]: + """Parse one report's tidy CSV text into row records. + + Parameters + ---------- + text : str + Decoded CSV text with columns Commodity, Category, Item, Units, + Size, Region, Country, Year, Value, and Survey base year. + report : str + Report key from MILK_COST_FILES, deciding whether Region or Size + carries the report's dimension. + + Returns + ------- + list[dict] + Records with category, item, unit, state, size_of_operation, year, + value, and survey_base_year keys, skipping rows with an empty Value. + """ + by_state = MILK_COST_FILES[report]["dimension"] == "state" + rows: list[dict] = [] + for row in csv.DictReader(StringIO(text)): + value = (row.get("Value") or "").strip() + if not value: + continue + rows.append( + { + "category": (row.get("Category") or "").strip(), + "item": clean_item(row.get("Item") or ""), + "unit": (row.get("Units") or "").strip(), + "state": (row.get("Region") or "").strip() if by_state else None, + "size_of_operation": ( + None if by_state else (row.get("Size") or "").strip() + ), + "year": int((row.get("Year") or "").strip()), + "value": float(value), + "survey_base_year": (row.get("Survey base year") or "").strip(), + } + ) + return rows + + +async def afetch_report(report: str, **kwargs) -> list[dict]: + """Download and parse one report's tidy CSV through the ERS disk cache. + + Parameters + ---------- + report : str + Report key from MILK_COST_FILES. + + Returns + ------- + list[dict] + Row records from parse_rows. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + content = await afetch_ers_file(media_path(report), product=PRODUCT_PAGE) + return parse_rows(decode_csv(content), report) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_natural_amenities_scale.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_natural_amenities_scale.py new file mode 100644 index 00000000000..f6dc59aeb9c --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_natural_amenities_scale.py @@ -0,0 +1,216 @@ +"""USDA ERS natural amenities scale catalog and county-lookup parser.""" + +BASE_URL = "https://www.ers.usda.gov" +PRODUCT_PAGE = "data-products/natural-amenities-scale" +MEDIA_PATH = ( + "/media/6172/natural-amenities-scale-including-the-6-components-for-us-counties.xls" +) +SHEET = "NATAMENF" +DATA_START = 105 + +COLUMN_SPECS: tuple[tuple[str, str], ...] = ( + ("fips", "text"), + ("fips_used_for_measures", "text"), + ("state", "text"), + ("county_name", "text"), + ("census_division", "code"), + ("rural_urban_continuum_code", "code"), + ("urban_influence_code", "code"), + ("mean_january_temperature", "num"), + ("mean_january_sunlight", "num"), + ("mean_july_temperature", "num"), + ("mean_july_humidity", "num"), + ("topography_code", "code"), + ("percent_water_area", "num"), + ("log_water_area", "num"), + ("january_temperature_z", "num"), + ("january_sunlight_z", "num"), + ("july_temperature_z", "num"), + ("july_humidity_z", "num"), + ("topography_z", "num"), + ("log_water_area_z", "num"), + ("natural_amenity_scale", "num"), + ("natural_amenity_rank", "code"), +) + +CANONICAL_COLUMNS: tuple[str, ...] = tuple(name for name, _ in COLUMN_SPECS) + +STATE_NAMES: dict[str, str] = { + "AL": "Alabama", + "AZ": "Arizona", + "AR": "Arkansas", + "CA": "California", + "CO": "Colorado", + "CT": "Connecticut", + "DE": "Delaware", + "DC": "District of Columbia", + "FL": "Florida", + "GA": "Georgia", + "ID": "Idaho", + "IL": "Illinois", + "IN": "Indiana", + "IA": "Iowa", + "KS": "Kansas", + "KY": "Kentucky", + "LA": "Louisiana", + "ME": "Maine", + "MD": "Maryland", + "MA": "Massachusetts", + "MI": "Michigan", + "MN": "Minnesota", + "MS": "Mississippi", + "MO": "Missouri", + "MT": "Montana", + "NE": "Nebraska", + "NV": "Nevada", + "NH": "New Hampshire", + "NJ": "New Jersey", + "NM": "New Mexico", + "NY": "New York", + "NC": "North Carolina", + "ND": "North Dakota", + "OH": "Ohio", + "OK": "Oklahoma", + "OR": "Oregon", + "PA": "Pennsylvania", + "RI": "Rhode Island", + "SC": "South Carolina", + "SD": "South Dakota", + "TN": "Tennessee", + "TX": "Texas", + "UT": "Utah", + "VT": "Vermont", + "VA": "Virginia", + "WA": "Washington", + "WV": "West Virginia", + "WI": "Wisconsin", + "WY": "Wyoming", +} + +STATES: tuple[str, ...] = tuple(sorted(STATE_NAMES)) +ALL_STATES: frozenset[str] = frozenset(STATE_NAMES) + + +def state_options() -> list[dict]: + """Build the labeled state options for the widget's state selector. + + Returns + ------- + list[dict] + Label/value option dictionaries, one per state or DC, in code order. + """ + return [{"label": STATE_NAMES[code], "value": code} for code in STATES] + + +def _to_text(value) -> str | None: + """Coerce a source cell to a stripped string, None when blank.""" + if value is None: + return None + text = str(value).strip() + return text or None + + +def _to_code(value) -> str | None: + """Coerce an integer-valued source cell to a code string, None when blank. + + Parameters + ---------- + value : object + A raw classification cell, stored as an integer-valued float such as + 6.0, or a blank cell. + + Returns + ------- + str | None + The integer code as a string, e.g. '6', or None when blank; a + non-numeric cell is returned as its stripped text. + """ + if value is None or (isinstance(value, str) and not value.strip()): + return None + try: + return str(int(float(value))) + except (TypeError, ValueError): + return str(value).strip() or None + + +def _to_num(value) -> float | None: + """Coerce a source cell to a float, None when blank or non-numeric.""" + if value is None or (isinstance(value, str) and not value.strip()): + return None + try: + return float(value) + except (TypeError, ValueError): + return None + + +def parse_rows(rows: list, data_start: int = DATA_START) -> list[dict]: + """Parse the worksheet rows into canonical county-lookup records. + + Parameters + ---------- + rows : list + The worksheet's value rows, each an ordered sequence of cell values, + including the leading legend, notes, and header band. + data_start : int + Zero-indexed row where county data begins, after the header band. + + Returns + ------- + list[dict] + One canonical record per county row, keyed by CANONICAL_COLUMNS. Rows + with a blank first cell are skipped. + """ + records: list[dict] = [] + for raw in rows[data_start:]: + cells = list(raw) + if not cells: + continue + first = cells[0] + if first is None or str(first).strip() == "": + continue + record: dict = {} + for index, (name, kind) in enumerate(COLUMN_SPECS): + cell = cells[index] if index < len(cells) else None + if kind == "text": + record[name] = _to_text(cell) + elif kind == "code": + record[name] = _to_code(cell) + else: + record[name] = _to_num(cell) + records.append(record) + return records + + +def _read_sheet_rows(content: bytes) -> list[list]: + """Read the legacy .xls data sheet into a list of row lists via xlrd. + + Parameters + ---------- + content : bytes + Raw legacy .xls bytes of the natural amenities workbook. + + Returns + ------- + list[list] + Cell values, row by row, including the legend, notes, and header band. + """ + import xlrd + + sheet = xlrd.open_workbook(file_contents=content).sheet_by_name(SHEET) + return [ + [sheet.cell_value(r, c) for c in range(sheet.ncols)] for r in range(sheet.nrows) + ] + + +async def afetch_records() -> list[dict]: + """Download and parse the natural amenities workbook through the cache. + + Returns + ------- + list[dict] + One canonical record per county from parse_rows. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + content = await afetch_ers_file(MEDIA_PATH, product=PRODUCT_PAGE) + return parse_rows(_read_sheet_rows(content)) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_normalized_prices.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_normalized_prices.py new file mode 100644 index 00000000000..67dc32b5244 --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_normalized_prices.py @@ -0,0 +1,193 @@ +"""USDA ERS Normalized Prices catalog, zip fetch, and long-format parser.""" + +import csv +import io +import zipfile +from io import StringIO + +BASE_URL = "https://www.ers.usda.gov" +PRODUCT_PAGE = "data-products/normalized-prices" +MEDIA_PATH = "/media/5552/all-tables-2025-csv-format.zip" + +INDEX_UNITS = "Index, 2011=100" + +NORMALIZED_PRICES_TABLES: dict[str, dict] = { + "table1_national_prices": { + "label": "Table 1 - National normalized prices by commodity and report year", + "csv_prefix": "table1", + "dimension": "year", + "commodity_field": "Commodity", + "description_field": "Description", + "units_field": "Units", + "units_constant": None, + "column_field": "Report Year", + "value_field": "Price", + }, + "table2_national_indices": { + "label": "Table 2 - National price indices by series and index year (2011=100)", + "csv_prefix": "table2", + "dimension": "year", + "commodity_field": "Commodity", + "description_field": None, + "units_field": None, + "units_constant": INDEX_UNITS, + "column_field": "Price index year", + "value_field": "Price", + }, + "table3_state_prices": { + "label": "Table 3 - State normalized prices by commodity and State", + "csv_prefix": "table3", + "dimension": "state", + "commodity_field": "Commodity", + "description_field": "Description", + "units_field": "Units", + "units_constant": None, + "column_field": "State", + "value_field": "Price", + }, +} + +DEFAULT_TABLE = "table1_national_prices" + + +def parse_value(value: str | None) -> float | None: + """Parse a raw price cell to a float, keeping full precision. + + Parameters + ---------- + value : str | None + Raw price cell; blank or suppressed cells are non-numeric. + + Returns + ------- + float | None + The numeric value, or None when the cell is blank or non-numeric. + """ + text = (value or "").strip() + if not text: + return None + try: + return float(text) + except ValueError: + return None + + +def parse_column(token: str, dimension: str) -> int | str | None: + """Parse a pivot-column token into an integer year or a State name. + + Parameters + ---------- + token : str + Raw report-year, index-year, or State cell. + dimension : str + 'year' to require a four-digit year, 'state' to keep the name. + + Returns + ------- + int | str | None + The integer year, the State name, or None when the token is empty + or not a four-digit year. + """ + text = token.strip() + if dimension == "year": + return int(text) if len(text) == 4 and text.isdigit() else None + return text or None + + +def parse_table(text: str, table: str) -> list[dict]: + """Parse one inner CSV into long-format observation records. + + Parameters + ---------- + text : str + Decoded CSV text of the selected table. + table : str + Table slug from NORMALIZED_PRICES_TABLES. + + Returns + ------- + list[dict] + Records with table, commodity, description, units, dimension, column + (integer year or State name), value, and the stable first-seen order + of the commodity or series row. Rows without a commodity or a valid + pivot column are skipped. + """ + config = NORMALIZED_PRICES_TABLES[table] + dimension = config["dimension"] + records: list[dict] = [] + seen: dict[tuple, int] = {} + for row in csv.DictReader(StringIO(text)): + commodity = (row.get(config["commodity_field"]) or "").strip() + if not commodity: + continue + description = None + if config["description_field"]: + description = (row.get(config["description_field"]) or "").strip() or None + units = config["units_constant"] + if units is None and config["units_field"]: + units = (row.get(config["units_field"]) or "").strip() or None + column = parse_column(row.get(config["column_field"]) or "", dimension) + if column is None: + continue + row_key = (commodity, description, units) + if row_key not in seen: + seen[row_key] = len(seen) + records.append( + { + "table": table, + "commodity": commodity, + "description": description, + "units": units, + "dimension": dimension, + "column": column, + "value": parse_value(row.get(config["value_field"])), + "order": seen[row_key], + } + ) + return records + + +def extract_inner_csv(zip_bytes: bytes, prefix: str) -> str: + """Extract and decode one table's CSV member from the all-tables zip. + + Parameters + ---------- + zip_bytes : bytes + Raw bytes of the all-tables csv-format zip archive. + prefix : str + Inner-file basename prefix, e.g. 'table1', matched year-agnostically. + + Returns + ------- + str + The decoded CSV text of the matching '.csv' member. + """ + with zipfile.ZipFile(io.BytesIO(zip_bytes)) as archive: + name = next( + member + for member in archive.namelist() + if member.rsplit("/", 1)[-1].startswith(prefix) + and member.lower().endswith(".csv") + ) + raw = archive.read(name) + return raw.decode("utf-8-sig", errors="replace") + + +async def afetch_table(table: str, **kwargs) -> list[dict]: + """Download the all-tables zip and parse one table through the ERS cache. + + Parameters + ---------- + table : str + Table slug from NORMALIZED_PRICES_TABLES. + + Returns + ------- + list[dict] + Long-format records from parse_table. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + content = await afetch_ers_file(MEDIA_PATH, product=PRODUCT_PAGE) + text = extract_inner_csv(content, NORMALIZED_PRICES_TABLES[table]["csv_prefix"]) + return parse_table(text, table) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_oil_crops_yearbook.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_oil_crops_yearbook.py new file mode 100644 index 00000000000..119bff3b0a0 --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_oil_crops_yearbook.py @@ -0,0 +1,750 @@ +"""USDA ERS Oil Crops Yearbook file catalog and long-format parser.""" + +import csv +from io import StringIO + +from openbb_government_us.utils.serializers import is_null_token + +BASE_URL = "https://www.ers.usda.gov" +PRODUCT_PAGE = "data-products/oil-crops-yearbook" +OILCROPS_CSV = "/media/5218/all-tables-oil-crops-yearbook.csv" + +DEFAULT_TABLE = "soybeans_supply_disappearance_price" + +OIL_CROPS_TABLES: dict[str, dict] = { + "soybean_stocks_quarterly": { + "label": "Soybean stocks: U.S. on-farm, off-farm, and total, by quarter", + "table_number": "1", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": None, + "geography_col": None, + }, + "soybeans_acreage_production_yield_value": { + "label": "Soybeans: U.S. acreage, yield, production, and value", + "table_number": "2", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": None, + "geography_col": None, + }, + "soybeans_supply_disappearance_price": { + "label": "Soybeans: U.S. supply, disappearance, and price", + "table_number": "3", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": None, + "geography_col": None, + }, + "soybean_meal_supply_disappearance_price": { + "label": "Soybean meal: U.S. supply, disappearance, and price", + "table_number": "4", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": None, + "geography_col": None, + }, + "soybean_oil_supply_disappearance_price": { + "label": "Soybean oil: U.S. supply, disappearance, and price", + "table_number": "5", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": None, + "geography_col": None, + }, + "soybeans_supply_disappearance_by_quarter": { + "label": "Soybeans: U.S. supply and disappearance, by crop-year quarter", + "table_number": "6", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": None, + "geography_col": None, + }, + "soybean_meal_supply_disappearance_by_month": { + "label": "Soybean meal: U.S. supply and disappearance, by month", + "table_number": "7", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": None, + "geography_col": None, + }, + "soybean_oil_supply_disappearance_by_month": { + "label": "Soybean oil: U.S. supply and disappearance, by month", + "table_number": "8", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": None, + "geography_col": None, + }, + "soybean_crush_value_price_spread": { + "label": "Soybeans: value of products per bushel crushed and spot price spread", + "table_number": "9", + "attribute_filter": None, + "series_col": "Attribute_Desc2", + "commodity_col": None, + "geography_col": None, + }, + "peanuts_acreage_production_yield_value": { + "label": "Peanuts: U.S. acreage, yield, production, and value", + "table_number": "10", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": None, + "geography_col": None, + }, + "peanuts_supply_disappearance_price": { + "label": "Peanuts (farmers' stock basis): U.S. supply, disappearance, and price", + "table_number": "11", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": None, + "geography_col": None, + }, + "peanuts_food_use": { + "label": "Peanuts: U.S. food uses, shelled basis", + "table_number": "12", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": None, + "geography_col": None, + }, + "peanuts_planted_acreage_by_state": { + "label": "Peanuts: U.S. planted acreage, by State and region", + "table_number": "13", + "attribute_filter": None, + "series_col": "Geography_Desc2", + "commodity_col": None, + "geography_col": None, + }, + "peanuts_harvested_acreage_by_state": { + "label": "Peanuts: U.S. harvested acreage, by State and region", + "table_number": "14", + "attribute_filter": None, + "series_col": "Geography_Desc2", + "commodity_col": None, + "geography_col": None, + }, + "peanuts_production_by_state": { + "label": "Peanuts: U.S. production, by State and region", + "table_number": "15", + "attribute_filter": None, + "series_col": "Geography_Desc2", + "commodity_col": None, + "geography_col": None, + }, + "peanuts_yield_by_state": { + "label": "Peanuts: U.S. yield per harvested acre, by State and region", + "table_number": "16", + "attribute_filter": None, + "series_col": "Geography_Desc2", + "commodity_col": None, + "geography_col": None, + }, + "cottonseed_acreage_production_yield_value": { + "label": "Cottonseed: U.S. acreage, yield, production, and value", + "table_number": "17", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": None, + "geography_col": None, + }, + "cottonseed_supply_disappearance_price": { + "label": "Cottonseed: U.S. supply, disappearance, and price", + "table_number": "18", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": None, + "geography_col": None, + }, + "cottonseed_meal_supply_disappearance_price": { + "label": "Cottonseed meal: U.S. supply, disappearance, and price", + "table_number": "19", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": None, + "geography_col": None, + }, + "cottonseed_oil_supply_disappearance_price": { + "label": "Cottonseed oil: U.S. supply, disappearance, and price", + "table_number": "20", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": None, + "geography_col": None, + }, + "sunflowerseed_acreage_production_yield_value": { + "label": "Sunflowerseed: U.S. acreage, yield, production, and value, by type", + "table_number": "21", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": "Commodity_Desc2", + "geography_col": None, + }, + "sunflowerseed_supply_disappearance_price": { + "label": "Sunflowerseed: U.S. supply, disappearance, and price", + "table_number": "22", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": None, + "geography_col": None, + }, + "sunflowerseed_meal_supply_disappearance_price": { + "label": "Sunflowerseed meal: U.S. supply, disappearance, and price", + "table_number": "23", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": None, + "geography_col": None, + }, + "sunflowerseed_oil_supply_disappearance_price": { + "label": "Sunflowerseed oil: U.S. supply, disappearance, and price", + "table_number": "24", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": None, + "geography_col": None, + }, + "canola_seed_supply_disappearance_price": { + "label": "Canola seed: U.S. acreage, supply, disappearance, price, and value", + "table_number": "25", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": None, + "geography_col": None, + }, + "canola_oil_supply_disappearance_price": { + "label": "Canola oil: U.S. supply, disappearance, and price", + "table_number": "26", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": None, + "geography_col": None, + }, + "canola_meal_supply_disappearance_price": { + "label": "Canola meal: U.S. supply, disappearance, and price", + "table_number": "27", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": None, + "geography_col": None, + }, + "flaxseed_acreage_production_yield_value": { + "label": "Flaxseed: U.S. acreage, yield, production, and value", + "table_number": "28", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": None, + "geography_col": None, + }, + "flaxseed_supply_disappearance_price": { + "label": "Flaxseed: U.S. supply, disappearance, and price", + "table_number": "29", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": None, + "geography_col": None, + }, + "linseed_meal_supply_disappearance_price": { + "label": "Linseed meal: U.S. supply, disappearance, and price", + "table_number": "30", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": None, + "geography_col": None, + }, + "linseed_oil_supply_disappearance_price": { + "label": "Linseed oil: U.S. supply, disappearance, and price", + "table_number": "31", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": None, + "geography_col": None, + }, + "edible_fats_and_oils_supply_disappearance": { + "label": "Edible fats and oils: U.S. supply and disappearance, by product", + "table_number": "32", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": "Commodity_Desc2", + "geography_col": None, + }, + "corn_oil_supply_disappearance_price": { + "label": "Corn oil: U.S. supply, disappearance, and price", + "table_number": "33", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": None, + "geography_col": None, + }, + "prices_received_by_farmers": { + "label": "Prices received by U.S. farmers, by month", + "table_number": "34", + "attribute_filter": "Received by U.S. farmers", + "series_col": "Attribute_Desc2", + "commodity_col": None, + "geography_col": None, + }, + "prices_terminal_markets": { + "label": "Cash prices at terminal markets, by month", + "table_number": "34", + "attribute_filter": "Cash prices at terminal markets", + "series_col": "Attribute_Desc2", + "commodity_col": None, + "geography_col": None, + }, + "prices_oilmeals": { + "label": "Oilmeal prices, by month", + "table_number": "34", + "attribute_filter": "Meal prices", + "series_col": "Attribute_Desc2", + "commodity_col": None, + "geography_col": None, + }, + "prices_fats_and_oils_wholesale": { + "label": "Wholesale prices of fats and oils, by month", + "table_number": "34", + "attribute_filter": "Wholesale prices", + "series_col": "Attribute_Desc2", + "commodity_col": None, + "geography_col": None, + }, + "prices_producer_price_indexes": { + "label": "Producer price indexes for fats and oils products, by month", + "table_number": "34", + "attribute_filter": "Bureau of Labor Statistics Producer Price Indexes", + "series_col": "Attribute_Desc2", + "commodity_col": None, + "geography_col": None, + }, + "lard_supply_disappearance_price": { + "label": "Lard: U.S. supply, disappearance, and price", + "table_number": "35", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": None, + "geography_col": None, + }, + "edible_tallow_supply_disappearance_price": { + "label": "Edible tallow: U.S. supply, disappearance, and price", + "table_number": "36", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": None, + "geography_col": None, + }, + "world_soybean_complex_supply_and_use": { + "label": "World soybean, meal, and oil supply and use, by region", + "table_number": "37", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": "Commodity_Desc2", + "geography_col": "Geography_Desc2", + }, + "soybean_exports_by_destination": { + "label": "U.S. soybean exports, by selected destination", + "table_number": "38", + "attribute_filter": None, + "series_col": "Geography_Desc2", + "commodity_col": None, + "geography_col": None, + }, + "soybean_meal_exports_by_destination": { + "label": "U.S. soybean meal exports, by selected destination", + "table_number": "39", + "attribute_filter": None, + "series_col": "Geography_Desc2", + "commodity_col": None, + "geography_col": None, + }, + "soybean_oil_exports_by_destination": { + "label": "U.S. soybean oil exports, by selected destination", + "table_number": "40", + "attribute_filter": None, + "series_col": "Geography_Desc2", + "commodity_col": None, + "geography_col": None, + }, + "world_oilseed_supply_and_distribution": { + "label": "World oilseed supply and distribution, by oilseed", + "table_number": "41", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": "Commodity_Desc2", + "geography_col": None, + }, + "world_vegetable_oils_supply_and_distribution": { + "label": "World vegetable oils supply and distribution, by oil", + "table_number": "42", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": "Commodity_Desc2", + "geography_col": None, + }, + "world_protein_meal_supply_and_distribution": { + "label": "World protein meal supply and distribution, by meal", + "table_number": "43", + "attribute_filter": None, + "series_col": "Attribute_Desc", + "commodity_col": "Commodity_Desc2", + "geography_col": None, + }, +} + +MONTH_NUMBERS: dict[str, int] = { + "January": 1, + "February": 2, + "March": 3, + "April": 4, + "May": 5, + "June": 6, + "July": 7, + "August": 8, + "September": 9, + "October": 10, + "November": 11, + "December": 12, +} + +MONTH_ABBREVIATIONS: dict[str, str] = { + "January": "Jan", + "February": "Feb", + "March": "Mar", + "April": "Apr", + "May": "May", + "June": "Jun", + "July": "Jul", + "August": "Aug", + "September": "Sep", + "October": "Oct", + "November": "Nov", + "December": "Dec", +} + +QUARTER_ORDER: list[str] = ["Sep.–Nov.", "Dec.–Feb.", "Mar.–May", "June–Aug."] + +POINT_IN_TIME_ORDER: list[str] = [ + "September 1", + "December 1", + "March 1", + "June 1", +] + +FREQUENCY_ORDER: list[str] = ["point_in_time", "annual", "quarterly", "monthly"] + +FREQUENCY_LABELS: dict[str, str] = { + "point_in_time": "Point-in-time", + "annual": "Annual", + "quarterly": "Quarterly", + "monthly": "Monthly", +} + +_POINT_IN_TIME_TABLES: frozenset[str] = frozenset({"soybean_stocks_quarterly"}) +_QUARTERLY_MONTHLY_ANNUAL_TABLES: frozenset[str] = frozenset( + {"soybeans_supply_disappearance_by_quarter"} +) +_MONTHLY_ANNUAL_TABLES: frozenset[str] = frozenset( + { + "soybean_meal_supply_disappearance_by_month", + "soybean_oil_supply_disappearance_by_month", + "soybean_crush_value_price_spread", + } +) +_MONTHLY_ONLY_TABLES: frozenset[str] = frozenset( + { + "prices_received_by_farmers", + "prices_terminal_markets", + "prices_oilmeals", + "prices_fats_and_oils_wholesale", + "prices_producer_price_indexes", + } +) + + +def _table_frequencies(table: str) -> list[str]: + """Return the frequency tokens present in a table, in fixed order.""" + if table in _POINT_IN_TIME_TABLES: + present = {"point_in_time"} + elif table in _QUARTERLY_MONTHLY_ANNUAL_TABLES: + present = {"annual", "quarterly", "monthly"} + elif table in _MONTHLY_ANNUAL_TABLES: + present = {"annual", "monthly"} + elif table in _MONTHLY_ONLY_TABLES: + present = {"monthly"} + else: + present = {"annual"} + return [freq for freq in FREQUENCY_ORDER if freq in present] + + +FREQUENCIES_BY_TABLE: dict[str, list[str]] = { + table: _table_frequencies(table) for table in OIL_CROPS_TABLES +} + + +def default_frequency(table: str) -> str: + """Return the default frequency for a table, the finest that stays dense.""" + freqs = FREQUENCIES_BY_TABLE[table] + for candidate in ("point_in_time", "quarterly", "monthly"): + if candidate in freqs: + return candidate + return "annual" + + +DEFAULT_FREQUENCY: dict[str, str] = { + table: default_frequency(table) for table in OIL_CROPS_TABLES +} + + +def parse_amount(raw: str | None) -> float | None: + """Parse a raw Amount cell into a float. + + Parameters + ---------- + raw : str | None + Raw cell text, or a null token such as '' or 'NA'. + + Returns + ------- + float | None + The parsed value, or None for empty, null-token, or non-numeric cells. + """ + text = (raw or "").strip() + if not text or is_null_token(text): + return None + try: + return float(text.replace(",", "")) + except ValueError: + return None + + +def coerce_period(raw: str | None) -> str | None: + """Coerce a Timeperiod_Desc cell into a period label or None. + + Parameters + ---------- + raw : str | None + Raw Timeperiod_Desc value; 'MY Total' marks a whole-year total. + + Returns + ------- + str | None + The stripped period label, or None for 'MY Total' and blank cells. + """ + text = (raw or "").strip() + if not text or text == "MY Total": + return None + return text + + +def frequency_of(raw: str | None) -> str: + """Classify a Timeperiod_Desc token into a frequency bucket. + + Parameters + ---------- + raw : str | None + Raw Timeperiod_Desc value. + + Returns + ------- + str + One of 'point_in_time', 'monthly', 'quarterly', or 'annual'. + """ + text = (raw or "").strip() + if not text or text == "MY Total": + return "annual" + if "–" in text or "-" in text: + return "quarterly" + parts = text.split() + if len(parts) == 2 and parts[0] in MONTH_NUMBERS and parts[1].isdigit(): + return "point_in_time" + if text in MONTH_NUMBERS: + return "monthly" + return "annual" + + +def start_month(my_definition: str | None) -> int: + """Return the first calendar month of a marketing-year definition. + + Parameters + ---------- + my_definition : str | None + MY_Definition value, e.g. 'October–September'. + + Returns + ------- + int + The month number of the opening month, or 1 when unknown. + """ + text = (my_definition or "").strip() + for separator in ("–", "-"): + if separator in text: + first = text.split(separator, 1)[0].strip() + return MONTH_NUMBERS.get(first, 1) + return MONTH_NUMBERS.get(text, 1) + + +def annual_descriptor(my_definition: str | None) -> str: + """Build a readable annual period label from a marketing-year definition. + + Parameters + ---------- + my_definition : str | None + MY_Definition value, e.g. 'September–August'. + + Returns + ------- + str + A label such as 'Marketing year (Sep-Aug)', or 'Marketing year'. + """ + text = (my_definition or "").strip() + for separator in ("–", "-"): + if separator in text: + first, last = (part.strip() for part in text.split(separator, 1)) + start = MONTH_ABBREVIATIONS.get(first) + end = MONTH_ABBREVIATIONS.get(last) + if start and end: + return f"Marketing year ({start}-{end})" + return "Marketing year" + + +def format_period( + period: str | None, frequency: str, my_definition: str | None +) -> str | None: + """Format a period token for display given its frequency. + + Parameters + ---------- + period : str | None + The coerced period token, or None for whole-year totals. + frequency : str + The frequency bucket the row belongs to. + my_definition : str | None + MY_Definition value, used to label annual rows. + + Returns + ------- + str | None + The display label, or None when no period applies. + """ + if period is None: + return annual_descriptor(my_definition) if frequency == "annual" else None + if frequency == "quarterly": + cleaned = period.replace(".", "").replace("–", "-") + rank = QUARTER_ORDER.index(period) if period in QUARTER_ORDER else 0 + return f"{cleaned} (Q{rank + 1})" + return period + + +def period_ordinal( + period: str | None, frequency: str, my_definition: str | None +) -> int: + """Return a within-year sort rank for a period token. + + Parameters + ---------- + period : str | None + The coerced period token. + frequency : str + The frequency bucket the row belongs to. + my_definition : str | None + MY_Definition value, giving the opening month of the marketing year. + + Returns + ------- + int + The position of the period within its marketing year. + """ + if period is None: + return 0 + if frequency == "point_in_time": + return POINT_IN_TIME_ORDER.index(period) if period in POINT_IN_TIME_ORDER else 0 + if frequency == "quarterly": + return QUARTER_ORDER.index(period) if period in QUARTER_ORDER else 0 + if frequency == "monthly" and period in MONTH_NUMBERS: + return (MONTH_NUMBERS[period] - start_month(my_definition)) % 12 + return 0 + + +def parse_rows(text: str, table: str) -> list[dict]: + """Parse the all-tables CSV into one table's long-format records. + + Parameters + ---------- + text : str + Decoded CSV text of the all-tables Oil Crops Yearbook file. + table : str + Table key from OIL_CROPS_TABLES, selecting the Table_number, the + Attribute_Desc split filter, and the pivot columns. + + Returns + ------- + list[dict] + Records carrying the year label, integer year, period, frequency, + marketing-year definition, commodity, geography, unit, series name, + and numeric amount for the selected table, skipping rows with a blank + or non-numeric Amount. + """ + config = OIL_CROPS_TABLES[table] + table_number = config["table_number"] + attribute_filter = config["attribute_filter"] + series_col = config["series_col"] + commodity_col = config["commodity_col"] + geography_col = config["geography_col"] + records: list[dict] = [] + for row in csv.DictReader(StringIO(text)): + if row.get("Table_number") != table_number: + continue + if ( + attribute_filter is not None + and row.get("Attribute_Desc") != attribute_filter + ): + continue + amount = parse_amount(row.get("Amount")) + if amount is None: + continue + year_label = (row.get("Marketing_Year") or "").strip() + if not year_label[:4].isdigit(): + continue + timeperiod = row.get("Timeperiod_Desc") + records.append( + { + "table": table, + "marketing_year": year_label, + "year": int(year_label[:4]), + "period": coerce_period(timeperiod), + "frequency": frequency_of(timeperiod), + "my_definition": (row.get("MY_Definition") or "").strip() or None, + "commodity": ( + ((row.get(commodity_col) or "").strip() or None) + if commodity_col + else None + ), + "geography": ( + ((row.get(geography_col) or "").strip() or None) + if geography_col + else None + ), + "unit_desc": (row.get("Unit_Desc") or "").strip() or None, + "series": (row.get(series_col) or "").strip(), + "amount": amount, + } + ) + return records + + +async def afetch_table(table: str, **kwargs) -> list[dict]: + """Download and parse one Oil Crops Yearbook table through the ERS cache. + + Parameters + ---------- + table : str + Table key from OIL_CROPS_TABLES. + + Returns + ------- + list[dict] + Long-format records from parse_rows. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + content = await afetch_ers_file(OILCROPS_CSV, product=PRODUCT_PAGE) + return parse_rows(content.decode("utf-8-sig", errors="replace"), table) diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_poverty_area_measures.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_poverty_area_measures.py new file mode 100644 index 00000000000..0fa77f46913 --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_poverty_area_measures.py @@ -0,0 +1,419 @@ +"""USDA ERS Poverty Area Measures edition catalog and county/tract-lookup parsers.""" + +import csv +from collections import OrderedDict +from io import StringIO +from typing import Any + +PRODUCT_PAGE = "data-products/poverty-area-measures" +DEFAULT_EDITION = "2025" + +STATE_NAMES: dict[str, str] = { + "AL": "Alabama", + "AK": "Alaska", + "AZ": "Arizona", + "AR": "Arkansas", + "CA": "California", + "CO": "Colorado", + "CT": "Connecticut", + "DE": "Delaware", + "DC": "District of Columbia", + "FL": "Florida", + "GA": "Georgia", + "HI": "Hawaii", + "ID": "Idaho", + "IL": "Illinois", + "IN": "Indiana", + "IA": "Iowa", + "KS": "Kansas", + "KY": "Kentucky", + "LA": "Louisiana", + "ME": "Maine", + "MD": "Maryland", + "MA": "Massachusetts", + "MI": "Michigan", + "MN": "Minnesota", + "MS": "Mississippi", + "MO": "Missouri", + "MT": "Montana", + "NE": "Nebraska", + "NV": "Nevada", + "NH": "New Hampshire", + "NJ": "New Jersey", + "NM": "New Mexico", + "NY": "New York", + "NC": "North Carolina", + "ND": "North Dakota", + "OH": "Ohio", + "OK": "Oklahoma", + "OR": "Oregon", + "PA": "Pennsylvania", + "RI": "Rhode Island", + "SC": "South Carolina", + "SD": "South Dakota", + "TN": "Tennessee", + "TX": "Texas", + "UT": "Utah", + "VT": "Vermont", + "VA": "Virginia", + "WA": "Washington", + "WV": "West Virginia", + "WI": "Wisconsin", + "WY": "Wyoming", +} + +STATE_ABBRS: frozenset[str] = frozenset(STATE_NAMES) + +EDITION_CATALOG: "OrderedDict[str, dict]" = OrderedDict( + [ + ( + "2025", + { + "label": "September 2025", + "media": "/media/5041/poverty-area-measures-september-2025-edition.csv", + "flag_sep": "", + "metro_col": "MetNonmet2023", + "beale_col": "Beale2023", + "ruca_col": "Ruca2020", + "subreg_col": "Subreg3", + "tract_fips_col": "TractFIPS23", + "geography_note": "2023 census-tract geography; Beale 2023," + " RUCA 2020, and the 2023 metro definition.", + }, + ), + ( + "2023", + { + "label": "December 2023", + "media": "/media/5043/poverty-area-measures-december-2023-edition.csv", + "flag_sep": "_", + "metro_col": "MetNonmet2023", + "beale_col": "Beale2013", + "ruca_col": "RUCA_2010", + "subreg_col": "subreg3", + "tract_fips_col": None, + "geography_note": "2010 census-tract geography; Beale 2013," + " RUCA 2010, and the 2023 metro definition.", + }, + ), + ( + "2022", + { + "label": "November 2022", + "media": "/media/5045/poverty-area-measures-november-2022-edition.csv", + "flag_sep": "_", + "metro_col": "MetNonmet2013", + "beale_col": "Beale2013", + "ruca_col": "RUCA_2010", + "subreg_col": "subreg3", + "tract_fips_col": None, + "geography_note": "2010 census-tract geography; Beale 2013," + " RUCA 2010, and the 2013 metro definition.", + }, + ), + ] +) + +EDITIONS = tuple(EDITION_CATALOG) + +EDITION_OPTIONS = [ + {"label": config["label"], "value": key} for key, config in EDITION_CATALOG.items() +] + +LEVELS = ("county", "tract") + +LEVEL_OPTIONS = [ + {"label": "County", "value": "county"}, + {"label": "Census Tract", "value": "tract"}, +] + +MEASURE_PREFIX: dict[str, str] = { + "high_poverty": "HiPov", + "extreme_poverty": "ExtPov", + "persistent_poverty": "PerPov", + "enduring_poverty": "EndurePov", +} + +PERIOD_TOKEN: dict[str, str] = { + "1960": "60", + "1970": "70", + "1980": "80", + "1990": "90", + "2000": "00", + "2007_11": "0711", + "2015_19": "1519", + "2017_21": "1721", + "2018_22": "1822", + "2019_23": "1923", +} + +FLAG_FIELDS: "OrderedDict[str, tuple[str, ...]]" = OrderedDict( + [ + ( + "high_poverty", + ( + "1960", + "1970", + "1980", + "1990", + "2000", + "2007_11", + "2015_19", + "2017_21", + "2018_22", + "2019_23", + ), + ), + ( + "extreme_poverty", + ( + "1960", + "1970", + "1980", + "1990", + "2000", + "2007_11", + "2015_19", + "2017_21", + "2018_22", + "2019_23", + ), + ), + ( + "persistent_poverty", + ("1990", "2000", "2007_11", "2015_19", "2017_21"), + ), + ( + "enduring_poverty", + ("2007_11", "2015_19", "2017_21"), + ), + ] +) + + +def edition_options() -> list[dict]: + """Return the edition select options, newest edition first.""" + return list(EDITION_OPTIONS) + + +def state_options() -> list[dict]: + """Return the 51-entry state filter options (50 states plus DC).""" + return [{"label": name, "value": abbr} for abbr, name in STATE_NAMES.items()] + + +def cell_int(value: Any) -> int | None: + """Coerce a source cell to an integer flag, preserving sentinel values. + + Parameters + ---------- + value : Any + A raw CSV string cell. + + Returns + ------- + int | None + The integer value, keeping the documented -1 not-available sentinel and + the 0-3 enduring-poverty categories unchanged; None for blanks and + placeholder tokens. + """ + if value is None: + return None + text = str(value).strip() + if not text or text.casefold() in {"na", "n/a", "--", "-"}: + return None + try: + return int(float(text)) + except ValueError: + return None + + +def cell_str(value: Any) -> str | None: + """Strip a source cell to a non-empty string, or None.""" + if value is None: + return None + text = str(value).strip() + return text or None + + +def normalize_fips(value: Any) -> str | None: + """Normalize a county FIPS to a five-digit zero-padded string.""" + if value is None: + return None + text = str(value).strip() + if not text: + return None + try: + return str(int(float(text))).zfill(5) + except ValueError: + return text.zfill(5) + + +def normalize_tract_fips(value: Any) -> str | None: + """Normalize an eleven-digit census-tract GEOID to a zero-padded string.""" + if value is None: + return None + text = str(value).strip() + if not text: + return None + try: + return str(int(float(text))).zfill(11) + except ValueError: + return text.zfill(11) + + +def flag_column(measure: str, period: str, sep: str, letter: str) -> str: + """Build the source flag column name for a measure, period, and level letter. + + Parameters + ---------- + measure : str + A canonical measure key from MEASURE_PREFIX. + period : str + A canonical period key from PERIOD_TOKEN. + sep : str + The edition's flag separator: '' for the 2025 edition, '_' otherwise. + letter : str + 'c' for county-level flags, 't' for tract-level flags. + + Returns + ------- + str + The source column name, e.g. 'HiPov0711c' or 'HiPov0711_t'. + """ + return f"{MEASURE_PREFIX[measure]}{PERIOD_TOKEN[period]}{sep}{letter}" + + +def tract_fips_of(config: dict, lower: dict, county_fips: str) -> str | None: + """Resolve an eleven-digit tract FIPS from the edition's tract columns. + + Parameters + ---------- + config : dict + The edition config, naming the tract-FIPS source column when present. + lower : dict + The source row keyed by lower-cased column name. + county_fips : str + The row's five-digit county FIPS, used to derive the tract FIPS on + editions that publish only the tract number. + + Returns + ------- + str | None + The eleven-digit tract FIPS, or None when the tract number is blank. + """ + column = config["tract_fips_col"] + if column: + return normalize_tract_fips(lower.get(column.lower())) + tract = lower.get("tract") + if tract is None or not str(tract).strip(): + return None + return county_fips + str(tract).strip().zfill(6) + + +def parse_records(text: str, edition: str, level: str) -> list[dict]: + """Parse the combined county-and-tract CSV into one lookup record per area. + + Parameters + ---------- + text : str + Decoded text of the edition's combined CSV. + edition : str + An edition key from EDITIONS. + level : str + 'county' to deduplicate on county FIPS and read the county ('c') flag + columns, or 'tract' to keep one row per census tract and read the tract + ('t') flag columns. + + Returns + ------- + list[dict] + One record per geographic unit, carrying the identity and geography + columns plus the fixed union of poverty-area classification flags for + the requested level. + """ + config = EDITION_CATALOG[edition] + sep = config["flag_sep"] + letter = "c" if level == "county" else "t" + seen: set[str] = set() + records: list[dict] = [] + for raw in csv.DictReader(StringIO(text)): + lower = {(key or "").strip().lower(): value for key, value in raw.items()} + fips = normalize_fips(lower.get("fips")) + if fips is None: + continue + if level == "county": + if fips in seen: + continue + seen.add(fips) + tract_fips = None + tract = None + tract_name = None + ruca_code = None + bna = None + else: + tract_fips = tract_fips_of(config, lower, fips) + tract = cell_str(lower.get("tract")) + tract_name = cell_str(lower.get("tractname")) + ruca_code = cell_int(lower.get(config["ruca_col"].lower())) + bna = cell_int(lower.get("bna01")) + record: dict[str, Any] = { + "fips": fips, + "state": cell_str(lower.get("stusab")), + "county_name": cell_str(lower.get("countyname")), + "region": cell_int(lower.get("region")), + "subregion": cell_int(lower.get(config["subreg_col"].lower())), + "metro_nonmetro": cell_int(lower.get(config["metro_col"].lower())), + "rucc_code": cell_int(lower.get(config["beale_col"].lower())), + "tract_fips": tract_fips, + "tract": tract, + "tract_name": tract_name, + "ruca_code": ruca_code, + "bna": bna, + } + for measure, periods in FLAG_FIELDS.items(): + for period in periods: + column = flag_column(measure, period, sep, letter).lower() + record[f"{measure}_{period}"] = cell_int(lower.get(column)) + records.append(record) + return records + + +async def afetch_edition(edition: str, level: str) -> list[dict]: + """Download and parse one edition into lookup records through the disk cache. + + Parameters + ---------- + edition : str + An edition key from EDITIONS. + level : str + 'county' or 'tract'. + + Returns + ------- + list[dict] + Lookup records for the edition at the requested geographic level. + """ + from openbb_government_us.usda.utils.ers_client import afetch_ers_file + + config = EDITION_CATALOG[edition] + content = await afetch_ers_file(config["media"], product=PRODUCT_PAGE) + return parse_records(content.decode("utf-8-sig", errors="replace"), edition, level) + + +async def afetch_states(edition: str) -> list[dict]: + """List an edition's states as label/value options in sorted code order. + + Parameters + ---------- + edition : str + An edition key from EDITIONS. + + Returns + ------- + list[dict] + One option per distinct state code present in the edition, labeled with + the full state name. + """ + records = await afetch_edition(edition, "county") + codes = sorted({record["state"] for record in records if record["state"]}) + return [{"label": STATE_NAMES.get(code, code), "value": code} for code in codes] diff --git a/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_price_spreads.py b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_price_spreads.py new file mode 100644 index 00000000000..b28168628a7 --- /dev/null +++ b/openbb_platform/providers/government_us/openbb_government_us/usda/utils/ers_price_spreads.py @@ -0,0 +1,264 @@ +"""USDA ERS Price Spreads from Farm to Consumer file catalog and parsers.""" + +import csv +import re +from io import StringIO + +from openbb_core.app.model.abstract.error import OpenBBError + +BASE_URL = "https://www.ers.usda.gov" +PRODUCT_PAGE = "data-products/price-spreads-from-farm-to-consumer" + +PRICE_SPREADS_FILES: dict[str, dict] = { + "milk_and_dairy_basket": { + "media_id": 5716, + "slug": "milk-and-dairy-basket", + "category": "dairy", + }, + "butter": { + "media_id": 5718, + "slug": "butter-1-pound", + "category": "dairy", + }, + "cheddar_cheese": { + "media_id": 5720, + "slug": "cheddar-cheese-1-pound", + "category": "dairy", + }, + "ice_cream": { + "media_id": 5722, + "slug": "ice-cream-regular-one-half-gallon", + "category": "dairy", + }, + "whole_milk": { + "media_id": 5724, + "slug": "whole-milk-1-gallon", + "category": "dairy", + }, + "fresh_fruit_basket": { + "media_id": 5734, + "slug": "fresh-fruit-basket", + "category": "fresh_fruit", + }, + "fresh_apples": { + "media_id": 5736, + "slug": "fresh-apples", + "category": "fresh_fruit", + }, + "fresh_grapefruit": { + "media_id": 5738, + "slug": "fresh-grapefruit", + "category": "fresh_fruit", + }, + "fresh_grapes": { + "media_id": 5740, + "slug": "fresh-grapes", + "category": "fresh_fruit", + }, + "fresh_oranges": { + "media_id": 5742, + "slug": "fresh-oranges", + "category": "fresh_fruit", + }, + "fresh_peaches": { + "media_id": 5744, + "slug": "fresh-peaches", + "category": "fresh_fruit", + }, + "fresh_strawberries": { + "media_id": 5746, + "slug": "fresh-strawberries", + "category": "fresh_fruit", + }, + "fresh_vegetables_basket": { + "media_id": 5748, + "slug": "fresh-vegetables-basket", + "category": "fresh_vegetables", + }, + "fresh_broccoli": { + "media_id": 5750, + "slug": "fresh-broccoli", + "category": "fresh_vegetables", + }, + "fresh_carrots": { + "media_id": 5752, + "slug": "fresh-carrots", + "category": "fresh_vegetables", + }, + "fresh_lettuce_iceberg": { + "media_id": 5754, + "slug": "fresh-lettuce-iceberg", + "category": "fresh_vegetables", + }, + "fresh_lettuce_romaine": { + "media_id": 5756, + "slug": "fresh-lettuce-romaine", + "category": "fresh_vegetables", + }, + "fresh_potatoes": { + "media_id": 5758, + "slug": "fresh-potatoes", + "category": "fresh_vegetables", + }, + "fresh_tomatoes": { + "media_id": 5760, + "slug": "fresh-tomatoes", + "category": "fresh_vegetables", + }, + "orange_juice_nfc": { + "media_id": 5762, + "slug": "orange-juice-not-from-concentrate-one-gallon", + "category": "processed", + }, + "flour": { + "media_id": 5726, + "slug": "flour-white-all-purpose-per-pound", + "category": "field_crops", + }, + "vegetable_oil": { + "media_id": 5728, + "slug": "vegetable-soybean-oil-per-gallon", + "category": "field_crops", + }, + "sugar": { + "media_id": 5730, + "slug": "sugar-white-per-pound", + "category": "field_crops", + }, + "white_pan_bread": { + "media_id": 5732, + "slug": "white-pan-bread-per-pound", + "category": "field_crops", + }, +} + +ATTRIBUTE_PATTERN = re.compile(r"^(?PSummary.
"}]}, + f"{base}/text": {"textVersions": []}, + f"{base}/titles": {"titles": [{"titleType": "Short Title", "title": "LRA"}]}, + } + + +def test_bill_record_assembles_every_sub_resource(monkeypatch): + """The detail document and its nine sub-collections fold into one record.""" + monkeypatch.setattr(api, "_get", _responder(_detail_routes())) + + record = asyncio.run(api.bill_record(95, "hr", 8410, CREDS)) + + assert record["bill_id"] == "95-hr-8410" + assert record["_detailed"] is True + assert record["title"] == "Labor Reform Act" + assert record["sponsors"][0]["bioguideId"] == "T000001" + assert record["cosponsors"][0]["isOriginalCosponsor"] is False + assert record["actions"][0]["text"] == "Intro" + assert record["committees"][0]["name"] == "Education and Labor" + assert record["relatedBills"][0]["number"] == 77 + assert record["subjects"] == [{"name": "Labor"}] + assert record["policyArea"] == {"name": "Industrial relations"} + assert record["titles"][0]["type"] == "Short Title" + assert record["latestAction"]["text"] == "Recommitted." + + +def test_bill_record_policy_area_prefers_the_detail_document(monkeypatch): + """A policy area on the bill itself wins over the one under subjects.""" + routes = _detail_routes() + routes["bill/95/hr/8410"]["bill"]["policyArea"] = {"name": "From detail"} + monkeypatch.setattr(api, "_get", _responder(routes)) + + record = asyncio.run(api.bill_record(95, "hr", 8410, CREDS)) + + assert record["policyArea"] == {"name": "From detail"} + + +def test_bill_record_tolerates_a_failing_sub_resource(monkeypatch, caplog): + """One unavailable sub-collection empties that field instead of failing the bill.""" + routes = _detail_routes() + del routes["bill/95/hr/8410/summaries"] + monkeypatch.setattr(api, "_get", _responder(routes)) + + with caplog.at_level("WARNING"): + record = asyncio.run(api.bill_record(95, "hr", 8410, CREDS)) + + assert record["summaries"] == [] + assert record["title"] == "Labor Reform Act" + assert "summaries" in caplog.text + + +def test_bill_record_missing_bill_raises_bill_not_found(monkeypatch): + """An absent bill raises the not-found type so callers can answer 404.""" + from openbb_government_us.congress.utils.bulk import BillNotFound + + monkeypatch.setattr(api, "_get", _responder({"bill/95/hr/1": {"bill": None}})) + + with pytest.raises(BillNotFound, match="Bill not found on Congress.gov"): + asyncio.run(api.bill_record(95, "hr", 1, CREDS)) + + +def test_fetch_bill_list_returns_slim_records(monkeypatch): + """The cacheable list fetch yields store-ready records.""" + + async def _paged_filtered(path, key, params): + assert path == "bill/95/hr" + return _bill_page([1, 2])["bills"] + + monkeypatch.setattr(api, "_paged_filtered", _paged_filtered) + + out = asyncio.run(api.fetch_bill_list(95, "HR", CREDS)) + + assert [r["bill_id"] for r in out] == ["95-hr-1", "95-hr-2"] + assert all(r["_detailed"] is False for r in out) + + +def test_paged_stops_once_the_reported_total_is_collected(monkeypatch): + """A sub-collection stops at its count even when a next link is still offered.""" + calls: list = [] + + async def _get(path, key, **params): + calls.append(params["offset"]) + return { + "actions": [{"text": "x"}] * api._PAGE_LIMIT, + "pagination": {"next": "more", "count": api._PAGE_LIMIT}, + } + + monkeypatch.setattr(api, "_get", _get) + + out = asyncio.run(api._paged("bill/95/hr/1/actions", "k", "actions")) + + assert len(out) == api._PAGE_LIMIT + assert calls == [0] diff --git a/openbb_platform/providers/congress_gov/tests/test_bulk.py b/openbb_platform/providers/government_us/tests/test_congress_bulk.py similarity index 73% rename from openbb_platform/providers/congress_gov/tests/test_bulk.py rename to openbb_platform/providers/government_us/tests/test_congress_bulk.py index 7540064a5ca..6e967a68c52 100644 --- a/openbb_platform/providers/congress_gov/tests/test_bulk.py +++ b/openbb_platform/providers/government_us/tests/test_congress_bulk.py @@ -1,17 +1,19 @@ -"""Tests for openbb_congress_gov.utils.bulk (GovInfo bulk-data path).""" +"""Tests for openbb_government_us.congress.utils.bulk (GovInfo bulk-data path).""" import asyncio import io import os +import tempfile import zipfile from datetime import date +from pathlib import Path import pytest from openbb_core.app.model.abstract.error import OpenBBError from openbb_core.provider.utils import helpers as core_helpers -from openbb_congress_gov.utils import bulk -from openbb_congress_gov.utils.helpers import BillsState +from openbb_government_us.congress.utils import bulk +from openbb_government_us.congress.utils.helpers import BillsState _BILLSTATUS_29 = """