mirror of
https://github.com/OpenBB-finance/OpenBB.git
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30372676bf
* Add API Key, test function * Fix API key * Add free NFT endpoints * Add free Dapp Endpoints * Add free DeFi Endpoint * Add free Token Endpoint * Menu cleanup * Update unit tests * Update integration test * ruff * Description * Update SDK model * Trail map * Fix map * Fix map * Update SDK cache * Linting * fix --------- Co-authored-by: Chavithra PARANA <chavithra@gmail.com>
387 lines
10 KiB
Python
387 lines
10 KiB
Python
"""DappRadar model"""
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__docformat__ = "numpy"
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# pylint: disable=C0301,E1137
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import logging
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from typing import Optional
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import pandas as pd
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from openbb_terminal.core.session.current_user import get_current_user
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from openbb_terminal.decorators import check_api_key, log_start_end
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from openbb_terminal.helper_funcs import get_user_agent, request
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from openbb_terminal.rich_config import console
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logger = logging.getLogger(__name__)
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NFT_COLUMNS = [
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"Name",
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"Dapp ID",
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"Logo",
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"Chains",
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"Avg Price [$]",
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"Avg Price Change [%]",
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"Volume [$]",
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"Volume Change [%]",
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"Traders",
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"Traders Change [%]",
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]
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@log_start_end(log=logger)
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def _make_request(url: str, verbose: bool = False) -> Optional[dict]:
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"""Helper method handles dappradar api requests. [Source: https://dappradar.com/]
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Parameters
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----------
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url: str
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endpoint url
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verbose: bool
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whether to print the text from the response
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Returns
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-------
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Optional[dict]:
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dictionary with response data
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"""
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current_user = get_current_user()
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headers = {
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"Accept": "application/json",
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"User-Agent": get_user_agent(),
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# "referer": "https://api.dappradar.com/4tsxo4vuhotaojtl/",
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"X-BLOBR-KEY": current_user.credentials.API_DAPPRADAR_KEY,
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}
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response = request(url, headers=headers)
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if not 200 <= response.status_code < 300:
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if verbose:
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console.print(f"[red]dappradar api exception: {response.text}[/red]")
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return None
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try:
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return response.json()
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except Exception as e: # noqa: F841
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logger.exception("Invalid Response: %s", str(e))
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if verbose:
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console.print(f"[red]Invalid Response:: {response.text}[/red]")
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return None
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@log_start_end(log=logger)
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@check_api_key(["API_DAPPRADAR_KEY"])
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def get_nft_marketplaces(
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chain: str = "", sortby: str = "", order: str = "", limit: int = 10
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) -> pd.DataFrame:
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"""Get top nft collections [Source: https://dappradar.com/]
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Parameters
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----------
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chain: str
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Name of the chain
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sortby: str
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Key by which to sort data
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order: str
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Order of sorting
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limit: int
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Number of records to display
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Returns
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-------
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pd.DataFrame
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Columns: Name, Dapp ID, Logo, Chains, Avg Price [$], Avg Price Change [%],
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Volume [$], Volume Change [%], Traders, Traders Change [%]
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"""
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args = {
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"chain": chain,
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"order": order,
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"sort": sortby,
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"resultsPerPage": limit,
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}
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args = {k: v for k, v in args.items() if v}
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query_string = "&".join([f"{k}={v}" for k, v in args.items()])
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response = _make_request(
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f"https://api.dappradar.com/4tsxo4vuhotaojtl/nfts/marketplaces?{query_string}"
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)
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if response:
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data = response.get("results")
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return pd.DataFrame(
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data,
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columns=[
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"name",
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"dappId",
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"logo",
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"chains",
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"avgPrice",
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"avgPricePercentageChange",
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"volume",
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"volumePercentageChange",
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"traders",
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"tradersPercentageChange",
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],
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).rename(
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columns={
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"name": "Name",
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"dappId": "Dapp ID",
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"logo": "Logo",
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"chains": "Chains",
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"avgPrice": "Avg Price [$]",
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"avgPricePercentageChange": "Avg Price Change [%]",
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"volume": "Volume [$]",
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"volumePercentageChange": "Volume Change [%]",
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"traders": "Traders",
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"tradersPercentageChange": "Traders Change [%]",
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}
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)
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return pd.DataFrame()
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@log_start_end(log=logger)
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@check_api_key(["API_DAPPRADAR_KEY"])
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def get_nft_marketplace_chains() -> pd.DataFrame:
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"""Get nft marketplaces chains [Source: https://dappradar.com/]
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Returns
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-------
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pd.DataFrame
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Columns: Chain
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"""
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response = _make_request(
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"https://api.dappradar.com/4tsxo4vuhotaojtl/nfts/marketplaces/chains"
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)
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if response:
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data = response.get("chains")
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return pd.DataFrame(
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data,
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columns=["Chain"],
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)
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return pd.DataFrame()
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@log_start_end(log=logger)
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@check_api_key(["API_DAPPRADAR_KEY"])
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def get_dapps(chain: str = "", page: int = 1, resultPerPage: int = 15):
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"""Get dapps [Source: https://dappradar.com/]
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Parameters
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----------
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chain: str
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Name of the chain
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page: int
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Page number
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resultPerPage: int
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Number of records to display
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Returns
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-------
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pd.DataFrame
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Columns: Dapp ID, Name, Description, Full Description, Logo, Link, Website,
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Chains, Categories
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"""
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args = {
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"chain": chain,
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"page": page,
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}
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args = {k: v for k, v in args.items() if v}
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query_string = "&".join([f"{k}={v}" for k, v in args.items()])
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response = _make_request(
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f"https://api.dappradar.com/4tsxo4vuhotaojtl/dapps?{query_string}"
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)
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if response:
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data = response.get("results")
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return (
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pd.DataFrame(
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data,
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columns=[
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"dappId",
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"name",
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"description",
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"fullDescription",
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"chains",
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"categories",
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"logo",
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"link",
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"website",
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],
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)
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.rename(
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columns={
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"dappId": "Dapp ID",
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"name": "Name",
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"description": "Description",
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"fullDescription": "Full Description",
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"chains": "Chains",
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"categories": "Categories",
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"logo": "Logo",
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"link": "Link",
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"website": "Website",
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}
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)
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.head(resultPerPage) # DappRadar resultsPerPage is broken
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)
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return pd.DataFrame()
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@log_start_end(log=logger)
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@check_api_key(["API_DAPPRADAR_KEY"])
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def get_dapp_categories() -> pd.DataFrame:
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"""Get dapp categories [Source: https://dappradar.com/]
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Returns
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-------
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pd.DataFrame
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Columns: Category
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"""
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response = _make_request(
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"https://api.dappradar.com/4tsxo4vuhotaojtl/dapps/categories"
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)
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if response:
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data = response.get("categories")
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return pd.DataFrame(
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data,
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columns=["Category"],
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)
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return pd.DataFrame()
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@log_start_end(log=logger)
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@check_api_key(["API_DAPPRADAR_KEY"])
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def get_dapp_chains() -> pd.DataFrame:
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"""Get dapp chains [Source: https://dappradar.com/]
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Returns
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-------
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pd.DataFrame
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Columns: Chain
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"""
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response = _make_request("https://api.dappradar.com/4tsxo4vuhotaojtl/dapps/chains")
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if response:
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data = response.get("chains")
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return pd.DataFrame(
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data,
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columns=["Chain"],
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)
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return pd.DataFrame()
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@log_start_end(log=logger)
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@check_api_key(["API_DAPPRADAR_KEY"])
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def get_dapp_metrics(
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dappId: int, chain: str = "", time_range: str = ""
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) -> pd.DataFrame:
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"""Get dapp metrics [Source: https://dappradar.com/]
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Parameters
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----------
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dappId: int
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Dapp ID
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chain: str
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Name of the chain if the dapp is multi-chain
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range: str
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Time range for the metrics. Can be 24h, 7d, 30d
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Returns
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-------
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pd.DataFrame
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Columns: Transactions, Transactions Change [%], Users, UAW, UAW Change [%],
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Volume [$], Volume Change [%], Balance [$], Balance Change [%]
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"""
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if not dappId:
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console.print("[red]Please provide a dappId[/red]")
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return pd.DataFrame()
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query_string = (
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f"range={time_range}" if not chain else f"chain={chain}&range={time_range}"
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)
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response = _make_request(
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f"https://api.dappradar.com/4tsxo4vuhotaojtl/dapps/{dappId}?{query_string}"
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)
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if response:
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data = response["results"].get("metrics")
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return pd.DataFrame(
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data,
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columns=[
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"transactions",
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"transactionsPercentageChange",
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"users",
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"uaw",
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"uawPercentageChange",
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"volume",
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"volumePercentageChange",
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"balance",
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"balancePercentageChange",
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],
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index=[response["results"]["name"]],
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).rename(
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columns={
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"transactions": "Transactions",
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"transactionsPercentageChange": "Transactions Change [%]",
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"users": "Users",
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"uaw": "UAW",
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"uawPercentageChange": "UAW Change [%]",
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"volume": "Volume [$]",
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"volumePercentageChange": "Volume Change [%]",
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"balance": "Balance [$]",
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"balancePercentageChange": "Balance Change [%]",
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}
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)
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return pd.DataFrame()
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@log_start_end(log=logger)
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@check_api_key(["API_DAPPRADAR_KEY"])
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def get_defi_chains() -> pd.DataFrame:
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"""Get defi chains [Source: https://dappradar.com/]
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Returns
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-------
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pd.DataFrame
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Columns: Chains
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"""
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response = _make_request("https://api.dappradar.com/4tsxo4vuhotaojtl/defi/chains")
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if response:
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data = response.get("chains")
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return pd.DataFrame(
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data,
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columns=["Chains"],
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)
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return pd.DataFrame()
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@log_start_end(log=logger)
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@check_api_key(["API_DAPPRADAR_KEY"])
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def get_token_chains() -> pd.DataFrame:
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"""Get chains that support tokens [Source: https://dappradar.com/]
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Returns
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-------
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pd.DataFrame
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Columns: Chains
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"""
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response = _make_request("https://api.dappradar.com/4tsxo4vuhotaojtl/tokens/chains")
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if response:
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data = response.get("chains")
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return pd.DataFrame(
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data,
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columns=["Chains"],
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)
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return pd.DataFrame()
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