How to save rolling window aggregation results back into the original DataFrame?

Question

I have the following DataFrame:

    UserID  Amount      Timestamp
50  1       600.00      2021-05-23 10:00:00
53  1       723.00      2021-05-24 05:12:00
54  2       1.00        2021-05-25 00:24:00
55  2       1000.00     2021-05-25 19:36:00
56  2       10000.00    2021-05-26 14:48:00
58  3       30.00       2021-05-27 10:00:00
60  4       50.00       2021-05-28 05:12:00
64  4       500.00      2021-05-29 00:24:00
65  4       10.00       2021-05-29 19:36:00
66  4       235.52      2021-05-30 14:48:00
69  4       567.12      2021-05-31 10:00:00

And I compute the aggregates like this:

agg = df.groupby(['UserID']).rolling('15d', on='Timestamp')['Amount'].agg(['sum', 'mean', 'std'])

What is returned cannot be added back right away to the original DataFrame. I tried with: df[['a', 'b', 'c']] = agg.values but then the data is ordered incorrectly. I don't know how (better, what's the correct way) to save rolling window aggregation results back to the original DataFrame.

Answer 1

First calculate the aggregates like you were already doing but also do reset_index() in the end, to get back dataframe with all other columns.

Then just apply pd.merge on this dataframe with the original dataframe on UserId, Timestamp , to add back the Amount column:

>>> df2 = df.groupby(['UserID']).rolling('15d', on='Timestamp')['Amount'].agg(['sum', 'mean', 'std']).reset_index()
>>> df = pd.merge(df, df2, on=['UserID','Timestamp'])
>>> df
    UserID    Amount           Timestamp       sum         mean          std
0        1    600.00 2021-05-23 10:00:00    600.00   600.000000          NaN
1        1    723.00 2021-05-24 05:12:00   1323.00   661.500000    86.974134
2        2      1.00 2021-05-25 00:24:00      1.00     1.000000          NaN
3        2   1000.00 2021-05-25 19:36:00   1001.00   500.500000   706.399674
4        2  10000.00 2021-05-26 14:48:00  11001.00  3667.000000  5507.237692
5        3     30.00 2021-05-27 10:00:00     30.00    30.000000          NaN
6        4     50.00 2021-05-28 05:12:00     50.00    50.000000          NaN
7        4    500.00 2021-05-29 00:24:00    550.00   275.000000   318.198052
8        4     10.00 2021-05-29 19:36:00    560.00   186.666667   272.090671
9        4    235.52 2021-05-30 14:48:00    795.52   198.880000   223.499928
10       4    567.12 2021-05-31 10:00:00   1362.64   272.528000   254.134419