Rolling operations on DataFrameGroupby object

I have found a workable solution but it only works if for each id each date is unique. This is the case in my data with some additional processing:

new_df = df.groupby(['id','date']).mean().reset_index()

which returns:

    id      date      target
0   1.0   2017-01-01    0
1   1.0   2017-01-21    1
2   1.0   2017-10-01    0
3   2.0   2017-01-01    1
4   2.0   2017-01-21    0
5   2.0   2017-10-01    0

I can then use the rolling method on a groupby object to get the desired result:

df = new_df.set_index('date')

df.iloc[::-1].groupby('id')['target'].rolling(window='180D', 
    centre=False).apply(lambda x : x[:-1].sum())

There are two tricks here:

1. I reverse the order of the dates (.iloc[::-1]) to take a forward looking window; this has been suggested in other SO questions.

2. I drop the last entry of the sum to remove the 'current' date from the sum, so it only looks forward.

The second 'hack' means it only works when there are no repeats of dates for a given id.

I would be interested in making a more robust solution (e.g., where dates are repeated for an id).