mirror of
https://github.com/OpenBB-finance/OpenBB.git
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072c413ac0
* Add separate view for finbrain for crypto curreny sentiment analysis * Move json with symbols to separate directory, little refactoring, update readme * Add screenshot of finbrain for PolkaDot * Add bitquery model and view * Move bitquery to onchain module * Bitquery added to onchain controller * Apply long number formatting for big numbers in bitquery commands * Update README with bitquery API key * Add error handling for cases when there is no token found, or dextrades response is empty. Use os library to take care of paths to files * Add helper method to extract dexTrades object from bitquery response. Add timout exception to request function. Add mapping for DECENTRALIZED_EXCHANGES to support lowercase choices: * Refactor bitquery model and views, prettify column names, remove redundant columns, change command names. Add test for api key * Update website docs for bitquery * Add difflib implementation for exchanges in bitquery ttcp command * Remove menu() from code * Remove tokens with less than 3 trades (doesn't make sense to show them, as most of them are scams). Add difflib for -coin parameter in baas command
140 lines
3.3 KiB
Python
140 lines
3.3 KiB
Python
"""Dataframe helpers"""
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__docformat__ = "numpy"
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import re
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from typing import Union, Any
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import textwrap
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import pandas as pd
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from gamestonk_terminal.helper_funcs import long_number_format
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def wrap_text_in_df(df: pd.DataFrame, w: int = 55) -> pd.DataFrame: # pragma: no cover
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"""
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Parameters
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----------
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df: pd.DataFrame
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Data Frame with some data
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w: int
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length of text in column after which text is wrapped into new line
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Returns
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-------
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pd.DataFrame
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"""
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return df.applymap(
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lambda x: "\n".join(textwrap.wrap(x, width=w)) if isinstance(x, str) else x
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)
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def percent_to_float(s: str) -> float:
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"""Helper method to replace string pct like "123.56%" to float 1.2356
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Parameters
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----------
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s: string
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string to replace
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Returns
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-------
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float
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"""
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s = str(float(s.rstrip("%")))
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i = s.find(".")
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if i == -1:
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return int(s) / 100
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if s.startswith("-"):
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return -percent_to_float(s.lstrip("-"))
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s = s.replace(".", "")
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i -= 2
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if i < 0:
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return float("." + "0" * abs(i) + s)
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return float(s[:i] + "." + s[i:])
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def create_df_index(df: pd.DataFrame, name: str = "rank") -> None:
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"""Helper method that creates new index for given data frame, with provided index name
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Parameters
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----------
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df:
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pd.DataFrame
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name: str
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index name
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"""
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df.index = df.index + 1
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df.reset_index(inplace=True)
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df.rename(columns={"index": name}, inplace=True)
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def long_number_format_with_type_check(x: Union[int, float]) -> Union[str, Any]:
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"""Helper which checks if type of x is int or float and it's smaller then 10^18.
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If yes it apply long_num_format
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Parameters
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----------
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x: int/float
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number to apply long_number_format method
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Returns
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-------
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Union[str, Any]
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"""
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if isinstance(x, (int, float)) and x < 10 ** 18:
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return long_number_format(x)
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return x
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def replace_underscores_in_column_names(string: str) -> str:
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return string.title().replace("_", " ")
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def very_long_number_formatter(num: Union[str, int, float]) -> str:
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"""Apply nice string format for very big numbers like Trillions, Quadrillions, Billions etc.
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Parameters
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----------
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num: Union[str, int, float]
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number to format
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Returns
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-------
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str:
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formatted number
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"""
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if isinstance(num, str):
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try:
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num = float(num)
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except (TypeError, ValueError):
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return str(num)
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if isinstance(num, (int, float)):
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num = int(num)
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magnitude = 0
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while abs(num) >= 1000 and magnitude <= 3:
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magnitude += 1
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num /= 1000.0
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num = round(num, 1)
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formatted_num = f"{num:f}".rstrip("0").rstrip(".")
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return f'{formatted_num}{["", "K", "M", "B", "T"][magnitude]}'
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return num
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def prettify_column_names(columns: list) -> list:
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"""Helper method that change column names into more human readable format. E.g.
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- tradeAmount => Trade amount,
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- tokenValue => Token value
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- mediumGasPrice => Medium Gas Price
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Parameters
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----------
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columns: list
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list of column names
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Returns
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-------
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list with reformatted columns
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"""
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return [" ".join(re.findall(".[^A-Z]*", val)).capitalize() for val in columns]
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