如何让pd.Grouper（）包含空组

Question

I have a dataset that I want to groupby a column AND every month of data in the dataset. I'm using pd.Grouper() for the groupby date per month part of it.

df.groupby(['A',pd.Grouper(key='date', freq='M')]).agg({'B':list})

But this returns only the months for each A , B that actually have data. I also want every month where there was no data for that A , B combo. I don't see this option in the pd.Grouper() documentation.

Answer 1

Given this DataFrame:

date        A  B
2018-01-01  1  3
2018-03-01  2  4

After the groupby you can use resample BUT in order to resample unfortunately you need to create the MultiIndex yourself:

In [11]: res = df.groupby(['A',pd.Grouper(key='date', freq='M')]).agg({'B':list})

In [12]: m = pd.MultiIndex.from_product([df.A.unique(), pd.date_range(df.date.min(), df.date.max() + pd.offsets.MonthEnd(1), freq='M')])

In [13]: m
Out[13]:
MultiIndex(levels=[[1, 2], [2018-01-31 00:00:00, 2018-02-28 00:00:00, 2018-03-31 00:00:00]],
           labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]])

In [14]: res.reindex(m)
Out[14]:
                B
1 2018-01-31  [3]
  2018-02-28  NaN
  2018-03-31  NaN
2 2018-01-31  NaN
  2018-02-28  NaN
  2018-03-31  [4]

Note: to fillna with [] is a little tricky, ideally you'd be able to work around this (in general having lists inside a DataFrame is not recommended).