59 lines
2.2 KiB
Python
59 lines
2.2 KiB
Python
import os
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import pandas as pd
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import numpy as np
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from scraper.top100_extractor import programming_crime_list
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from typing import Optional
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from dateutil.parser import isoparse
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DF_EPS_PATH: str = os.path.join(os.path.dirname(__file__), '..', '..', 'Elaborated_Data', 'eps_quarterly_bar_chart.csv')
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def ticker_quarter_list(df_ticker: pd.Series) -> list[int]:
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dfu = df_ticker.unique()
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months = set([isoparse(date).month for date in dfu])
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return list(sorted(months))
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def get_eps(tickers: list[str]) -> list[dict]:
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df = pd.read_csv(DF_EPS_PATH)
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ticker_series = pd.Series(tickers)
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qmap = dict([ \
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[ticker, ticker_quarter_list(df.loc[df.symbol == ticker, 'quarter'])] \
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for ticker in tickers \
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])
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df = df.loc[df.symbol.isin(ticker_series), :] \
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.rename(columns={"epsActual": "quarterlyEps", "symbol": "ticker"}) \
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.reset_index(drop=True)
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def get_quart(x):
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date = isoparse(x.quarter)
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quarter = qmap[x.ticker].index(date.month) + 1
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return f"{date.year}-Q{quarter}"
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df['quarter'] = df.apply(get_quart, axis=1)
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df = df.pivot(index='quarter', columns='ticker', values='quarterlyEps').reset_index(drop=False)
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return df.replace({ np.nan: None }).to_dict('records')
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def get_eps_old(tickers: list[str]) -> list[dict]:
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eps_df = pd.read_csv('Elaborated_Data/eps_quarterly_bar_chart.csv', index_col=[0])
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s_tickers = pd.Series(tickers)
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eps_df = eps_df.loc[eps_df['symbol'].isin(s_tickers), :]
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dates = eps_df['quarter'].to_list()
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for i, date in enumerate(dates):
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eps_df[date] = eps_df['epsActual'].copy().apply(lambda x: int(eval(str(x))[i]))
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eps_df = pd.melt(eps_df, id_vars=['symbol'], value_vars=['quarter'], var_name='quarter').reset_index(drop=True)
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print(eps_df)
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eps_df = eps_df.pivot(index='value', columns='symbol', values='value').reset_index(drop=False)
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exit(1)
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for ticker in tickers:
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filepath = f'Companies_Data/{ticker}_Data/{ticker}earnings.csv'
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eps_quarterly = pd.read_csv(filepath)
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ticker_df = eps_quarterly.loc[eps_quarterly['symbol'] == ticker]
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ticker_df['ticker'] = ticker
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combined_df = combined_df._append(ticker_df)
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