DOC: Rolling with method="table" to apply func to all columns and not just individual columns #40374
Description
Is your feature request related to a problem?
With a group.apply the applied function acts on all columns available to provide a result. With rolling.apply it applies the function to each column individually - it would be great if you could apply it to all the columns as well.
e.g. the following is possible with groupby.apply
df = pd.DataFrame({'A': range(10), 'B': range(10,0,-1)}).reset_index()
df['index'] = df['index'] % 3
df.groupby('index').apply(lambda df: df['A'] + df['B'])
However, you can't do the same equivalent with rolling.apply:
df = pd.DataFrame({'A': range(10), 'B': range(10,0,-1)}).reset_index()
df.rolling(3, on='index').apply(lambda df: (df['A'] * df['B']).sum())
Gives the error: KeyError: 'A'
This is because the apply function is applied to each column separately. The series is passed to the function not the dataframe. This feels inconsistent and annoying if you want to apply a function to the grouped rolling data. It would be great if there were a way to do it 😃