Pass Pandas DataFrame as input

[truebrew]

Does it sound like a good idea to accept a Pandas DataFrame with Datetimes as indices and values as column as input? It would save the step of converting to dictionary.

[tarioch]

sounds like a good idea (as far as I can tell we have an indirect dependency to pandas through scipy anyway), feel free to contribute that.

[truebrew]

I have no idea how this works :). I'd suggest to pass an argument 'df' to the function, which by default will be False. Otherwise, when df=True, we can just do df.to_dict() requiring that the datetimes are the index and the values are set in the column. It will more of a series than a dataframe, actually.

[hernanhevia]

Hi, you'd give this a try:

def xirr(df, date_column='date', amount_column='amount'):
valuesPerDate = df[[date_column,amount_column]].set_index(date_column).to_dict(orient='dict')[amount_column]

Cheers

[Kk-ships]

@truebrew May be you can try this approach.

import dateparser
import xirr
def try_parsing_date(text):
    """
    try and parse date
    :param text: string
    :return: date part of datetime object
    """
    try:
        return dateparser.parse(text).date()
    except:
        pass


def xirr_df(df, date = 'date', amount= 'amount'):
      dates = df[date].tolist()
      amounts = df[amount].tolist()
      dates_parsed = list(map(try_parsing_date, dates))
      valuesPerDate = dict(zip(dates_parsed, amounts))
      return xirr.xirr(valuesPerDate)