Python snippet to convert Revolut export to Wallet

revolut2wallet.py

import argparse
import datetime

import pandas as pd

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('input', help='Input revolut export file')
    parser.add_argument('--output', default="rev2wall-{CURRENCY}-{TIMESTAMP}.csv", help='Input Revolut file')
    return parser.parse_args()

def load_df(input_file):
    _df = (pd
       .read_csv(input_file, sep=';', parse_dates=['Completed Date '], thousands=',', na_values=['  '])
       .rename(columns=lambda x: x.strip())
    )
    return _df

def get_currency(df):
    return df.filter(like='Paid Out').columns[0][-4:-1]

def wrangle_format(_df, cur):
    df = (_df
        .assign(
            currency=cur,
            paid_out_currency=lambda x: x['Paid Out ({})'.format(cur)].astype(float),
            paid_in_currency=lambda x: x['Paid In ({})'.format(cur)].astype(float),
            amount=lambda x: x.paid_in_currency.where(x.paid_in_currency.notna(), -x.paid_out_currency),
            date=lambda x: pd.to_datetime(x['Completed Date']).dt.strftime('%Y/%m/%d'),
            note=lambda x: (x['Notes'] + ' ' + x['Category']).str.strip(),
            type=lambda x: (x['paid_in_currency'].notna()).map({True: "Income", False: "Expense"}),
        )
        .rename(columns=lambda x: x.lower())
        .filter(['date', 'currency', 'amount', 'note', 'type'])
    )
    return df

def main(input_file):
    _df = load_df(input_file)
    cur = get_currency(_df)
    df = wrangle_format(_df, cur)
    return df, cur

if __name__ == "__main__":
    args = parse_args()
    df, cur = main(args.input)
    try:
        output = args.output.format(CURRENCY=cur, TIMESTAMP=pd.datetime.now().strftime("%Y%m%dT%H%M%S"))
    except KeyError:
        output = args.output
    df.to_csv(output, index=False, sep=";")

Yeah, that's annoying. I guess that could be solved by changing thousands here.

Yeah, that's annoying. I guess that could be solved by changing thousands here.

Thanks, I've ended up doing it like this:

.read_csv(input_file, sep=';', parse_dates=['Completed Date '], decimal=',', na_values=['  '])

Works as intended now, thank you very much. :)