Apply rolling function to groupby over several columns - pandas

Question

I'd like to apply rolling functions to a dataframe grouped by two columns with repeated date entries. Specifically, with both "freq" and "window" as datetime values, not simply ints.
In principle, I'm try to combine the methods from How to apply rolling functions in a group by object in pandas and pandas rolling sum of last five minutes.
Input
Here is a sample of the data, with one id=33 although we expect several id's.
X = [{'date': '2017-02-05', 'id': 33, 'item': 'A', 'points': 20},
{'date': '2017-02-05', 'id': 33, 'item': 'B', 'points': 10},
{'date': '2017-02-06', 'id': 33, 'item': 'B', 'points': 10},
{'date': '2017-02-11', 'id': 33, 'item': 'A', 'points': 1},
{'date': '2017-02-11', 'id': 33, 'item': 'A', 'points': 1},
{'date': '2017-02-11', 'id': 33, 'item': 'A', 'points': 1},
{'date': '2017-02-13', 'id': 33, 'item': 'A', 'points': 4}]
# df = pd.DataFrame(X) and reindex df to pd.to_datetime(df['date'])
df
id item points
date
2017-02-05 33 A 20
2017-02-05 33 B 10
2017-02-06 33 B 10
2017-02-11 33 A 1
2017-02-11 33 A 1
2017-02-11 33 A 1
2017-02-13 33 A 4
Goal
Sample each 'id' every 2 days (freq='2d') and return the sum of total points for each item over the previous three days (window='3D'), end-date inclusive
Desired Output
id A B
date
2017-02-05 33 20 10
2017-02-07 33 20 30
2017-02-09 33 0 10
2017-02-11 33 3 0
2017-02-13 33 7 0
E.g. on the right-inclusive end-date 2017-02-13, we sample the 3-day period 2017-02-11 to 2017-02-13. In this period, id=33 had a sum of A points equal to 1+1+1+4 = 7
Attempts
An attempt of groupby with a pd.rolling_sum as follows didn't work, due to repeated dates
df.groupby(['id', 'item'])['points'].apply(pd.rolling_sum, freq='4D', window=3)
ValueError: cannot reindex from a duplicate axis
Also note that from the documentation http://pandas.pydata.org/pandas-docs/version/0.17.0/generated/pandas.rolling_apply.html 'window' is an int representing the size sample period, not the number of days to sample.
We can also try resampling and using last, however the desired look-back of 3 days doesn't seem to be used
df.groupby(['id', 'item'])['points'].resample('2D', label='right', closed='right').\
apply(lambda x: x.last('3D').sum())
id item date
33 A 2017-02-05 20
2017-02-07 0
2017-02-09 0
2017-02-11 3
2017-02-13 4
B 2017-02-05 10
2017-02-07 10
Of course,setting up a loop over unique id's ID, selecting df_id = df[df['id']==ID], and summing over the periods does work but is computationally-intensive and doesn't exploit groupby's nice vectorization.
Thanks to #jezrael for good suggestions so far
Notes
Pandas version = 0.20.1
I'm a little confused as to why the documentation on rolling() here:https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.rolling.html
suggests that the "window" parameter can be in an int or offset but on attempting df.rolling(window='3D',...) I getraise ValueError("window must be an integer")
It appears that the above documentation is not consistent with the latest code for rolling's window from ./core/window.py :
https://github.com/pandas-dev/pandas/blob/master/pandas/core/window.py
elif not is_integer(self.window):
raise ValueError("window must be an integer")

Answer 1

It's easiest to handle resample and rolling with date frequencies when we have a single level datetime index.
However, I can't pivot/unstack appropriately without dealing with duplicate A/Bs so I groupby and sum
I unstack one level date so I can fill_value=0. Currently, I can't fill_value=0 when I unstack more than one level at a time. I make up for it with a transpose T
Now that I've got a single level in the index, I reindex with a date range from the min to max values in the index
Finally, I do a rolling 3 day sum and resample that result every 2 days with resample
I clean this up with a bit of renaming indices and one more pivot.
s = df.set_index(['id', 'item'], append=True).points
s = s.groupby(level=['date', 'id', 'item']).sum()
d = s.unstack('date', fill_value=0).T
tidx = pd.date_range(d.index.min(), d.index.max())
d = d.reindex(tidx, fill_value=0)
d1 = d.rolling('3D').sum().resample('2D').first().astype(d.dtypes).stack(0)
d1 = d1.rename_axis(['date', 'id']).rename_axis(None, 1)
print(d1)
A B
date id
2017-02-05 33 20 10
2017-02-07 33 20 20
2017-02-09 33 0 0
2017-02-11 33 3 0
2017-02-13 33 7 0

Answer 2

df = pd.DataFrame(X)
# group sum by day
df = df.groupby(['date', 'id', 'item'])['points'].sum().reset_index().sort_values(['date', 'id', 'item'])
# convert index to datetime index
df = df.set_index('date')
df.index = DatetimeIndex(df.index)
# rolloing sum by 3D
df['pointsum'] = df.groupby(['id', 'item']).transform(lambda x: x.rolling(window='3D').sum())
# reshape dataframe
df = df.reset_index().set_index(['date', 'id', 'item'])['pointsum'].unstack().reset_index().set_index('date').fillna(0)
df