an.tsouchlos/banking-breakdown
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Buchungstag -> Valutadatum

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Andreas Tsouchlos 2024-01-02 01:40:41 +01:00
parent 802bf3e837
commit 1116da8251
1 changed files with 7 additions and 7 deletions
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banking_breakdown/statement_parser.py
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@ -19,23 +19,23 @@ def _tag_with_category(df: pd.DataFrame) -> pd.DataFrame:
def _compute_total_balance(df: pd.DataFrame) -> pd.DataFrame: def _compute_total_balance(df: pd.DataFrame) -> pd.DataFrame:
stripped_df = pd.DataFrame( stripped_df = pd.DataFrame(
{'t': df["Buchungstag"], 'value': df["Saldo nach Buchung"]}) {'t': df["Valutadatum"], 'value': df["Saldo nach Buchung"]})
stripped_df.index = stripped_df['t'] stripped_df.index = stripped_df['t']
gb = stripped_df.groupby(pd.Grouper(freq='M')) gb = stripped_df.groupby(pd.Grouper(freq='M'))
result = gb.tail(1)['value'].reset_index() result = gb.tail(1)['value'].reset_index()
#result['t'] = result['t'].apply(lambda dt: dt.replace(day=1))
return result return result
def _compute_net_income(df: pd.DataFrame) -> pd.DataFrame: def _compute_net_income(df: pd.DataFrame) -> pd.DataFrame:
stripped_df = pd.DataFrame({'t': df["Buchungstag"], 'value': df["Betrag"]}) stripped_df = pd.DataFrame({'t': df["Valutadatum"], 'value': df["Betrag"]})
result = stripped_df.resample(rule='M', on="t").sum().reset_index() stripped_df.index = stripped_df['t']
#result['t'] = result['t'].apply(lambda dt: dt.replace(day=1)) gb = stripped_df.groupby(pd.Grouper(freq='M'))
result = gb["value"].sum().reset_index()
return result return result
@ -53,13 +53,13 @@ def _compute_category_overview(df: pd.DataFrame) -> pd.DataFrame:
def _compute_detailed_balance(df: pd.DataFrame) -> pd.DataFrame: def _compute_detailed_balance(df: pd.DataFrame) -> pd.DataFrame:
return pd.DataFrame({'t': df["Buchungstag"], return pd.DataFrame({'t': df["Valutadatum"],
'value': df["Saldo nach Buchung"]}) 'value': df["Saldo nach Buchung"]})
def parse_statement(filename: str) -> types.ReportData: def parse_statement(filename: str) -> types.ReportData:
df = pd.read_csv(filename, delimiter=';', decimal=",") df = pd.read_csv(filename, delimiter=';', decimal=",")
df["Buchungstag"] = pd.to_datetime(df["Buchungstag"], format='%d.%m.%Y') df["Valutadatum"] = pd.to_datetime(df["Valutadatum"], format='%d.%m.%Y')
category_overview_df = _compute_category_overview(df) category_overview_df = _compute_category_overview(df)
total_balance_df = _compute_total_balance(df) total_balance_df = _compute_total_balance(df)