Rolling Year to Date percentage change with groupby-CodePudding

I have got a big data frame. Below you will find an extract of it:

lst=[['31122020','A',12],['31012021','A',14],['28022021','A',15],['31032021','A',17]]
df2=pd.DataFrame(lst, columns=['Date','FN','AuM'])

I would like to calculate the Year to date (YTD) of the column AuM. The new column should look like this:

lst=[['31122020','A',12,'NaN'],['31012021','A',14,0.167],['28022021','A',15,0.25],['31032021','A',17,0.417]]
df2=pd.DataFrame(lst, columns=['Date','FN','AuM','AuM_YTD_%Change'])

Do you know any pandas function which can reach my goal?

CodePudding user response：

You can create a mask for dates inside one year, then use diff cumsum for the changes, and div for the change rates:

df2['Date'] = pd.to_datetime(df2['Date'], format='%d%m%Y')
msk = df2['Date'] < df2.loc[0, 'Date']   pd.to_timedelta(365, unit='D')
df2['AuM_YTD_%Change'] = df2.loc[msk, 'AuM'].diff().cumsum().div(df2.loc[0,'AuM'])

Output:

        Date FN  AuM  AuM_YTD_%Change
0 2020-12-31  A   12              NaN
1 2021-01-31  A   14         0.166667
2 2021-02-28  A   15         0.250000
3 2021-03-31  A   17         0.416667