pandas - Python - rolling functions for GroupBy object

Question

I have a time series object grouped of the type <pandas.core.groupby.SeriesGroupBy object at 0x03F1A9F0>. grouped.sum() gives the desired result but I cannot get rolling_sum to work with the groupby object. Is there any way to apply rolling functions to groupby objects? For example:

x = range(0, 6)
id = ['a', 'a', 'a', 'b', 'b', 'b']
df = DataFrame(zip(id, x), columns = ['id', 'x'])
df.groupby('id').sum()
id    x
a    3
b   12

However, I would like to have something like:

  id  x
0  a  0
1  a  1
2  a  3
3  b  3
4  b  7
5  b  12

Answer 1

For the Googlers who come upon this old question:

Regarding @kekert's comment on @Garrett's answer to use the new

df.groupby('id')['x'].rolling(2).mean()

rather than the now-deprecated

df.groupby('id')['x'].apply(pd.rolling_mean, 2, min_periods=1)

curiously, it seems that the new .rolling().mean() approach returns a multi-indexed series, indexed by the group_by column first and then the index. Whereas, the old approach would simply return a series indexed singularly by the original df index, which perhaps makes less sense, but made it very convenient for adding that series as a new column into the original dataframe.

So I think I've figured out a solution that uses the new rolling() method and still works the same:

df.groupby('id')['x'].rolling(2).mean().reset_index(0,drop=True)

which should give you the series

0    0.0
1    0.5
2    1.5
3    3.0
4    3.5
5    4.5

which you can add as a column:

df['x'] = df.groupby('id')['x'].rolling(2).mean().reset_index(0,drop=True)