Efficient pandas rolling aggregation over date range by group - Python 2.7 Windows - Pandas 0.19.2

Question

I\'m trying to find an efficient way to generate rolling counts or sums in pandas given a grouping and a date range. Eventually, I want to be able to add conditions, ie. evaluat

Answer 1

Pandas has support for time-aware rolling via the rolling method, so you can use that instead of writing your own solution from scratch:

def get_rolling_amount(grp, freq):
    return grp.rolling(freq, on='date')['amount'].sum()

df['rolling_sales_180'] = df.groupby('name', as_index=False, group_keys=False) \
                            .apply(get_rolling_amount, '180D')

The resulting output:

    name       date  amount  rolling_sales_180
0  David 2015-01-01     100              100.0
1  David 2015-01-05     500              600.0
2  David 2015-05-30      50              650.0
3  David 2015-07-25      50              100.0
4   Ryan 2014-01-04     100              100.0
5   Ryan 2015-01-19     500              500.0
6   Ryan 2016-03-31      50               50.0
7    Joe 2015-07-01     100              100.0
8    Joe 2015-09-09     500              600.0
9    Joe 2015-10-15      50              650.0