30 lines
1.2 KiB
Python
30 lines
1.2 KiB
Python
import pandas as pd
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import numpy as np
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import os
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ROOT_PATH: str = os.path.join(os.path.dirname(__file__), '..', '..')
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def compare_balance_sheets(ticker_list: list):
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df_ret = pd.DataFrame(columns=['total_assets', 'current_assets', 'total_debt', 'current_debt'])
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for i, ticker in enumerate(ticker_list):
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assets_debt = pd.read_csv(ROOT_PATH + r'/Companies_Data/' + ticker + '_Data/' + ticker + '_balance_sheet_4Y+4Q.csv')
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selected_data = assets_debt
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def get_or_na(column: str):
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row = selected_data.iloc[-2]
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if column in row and not np.isnan(row[column]):
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return row[column]
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return 0
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df_ret.loc[ticker, 'total_assets'] = get_or_na('TotalAssets')
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df_ret.loc[ticker, 'current_assets'] = get_or_na('CurrentAssets')
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df_ret.loc[ticker, 'total_debt'] = get_or_na('TotalDebt')
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df_ret.loc[ticker, 'current_debt'] = get_or_na('CurrentDebt')
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df_ret.loc[:, 'old_assets'] = df_ret['total_assets'].copy() - df_ret['current_assets'].copy()
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df_ret.loc[:, 'old_debt'] = df_ret['total_debt'].copy() - df_ret['current_debt'].copy()
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df_ret.index.name = 'ticker'
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return df_ret.reset_index(drop=False).replace({ np.nan: None }).to_dict('records') |