Non-Reducing Return Values in Rolling .apply

[hayd]

related #2813, #4964 (somewhat)

At the moment rolling_apply (et al) the passed function has to return a value (unlike apply which can return a Series and overall return a DataFrame).

If you could pass it a Series you could, for example, do this:

from pandas.core.algorithms import value_counts
s = Series([1, 1, 2, 3, 1, 2, 3, 2, 1])

pd.rolling_apply(s, 3, value_counts)

from the mailing list.

[jreback]

Here's the example, only issue is that you need to test first iteration to see what structure is returned

In [5]: s = Series([1, 1, 2, 3, 1, 2, 3, 2, 1])

In [6]: def f(x):
   ...:     c = x.value_counts()
   ...:     return 100*c/c.sum()
   ...: 

In [7]: concat([ f(s.iloc[i:i+3]) for i in range(len(s)-3) ],axis=1)
Out[7]: 
    0   1   2   3   4   5
1  66  33  33  33  33 NaN
2  33  33  33  33  33  66
3 NaN  33  33  33  33  33

[WillAyd]

A series can be produced now via the keyword argument raw=False, though the original issue does not work on master with a relatively cryptic message:

>>> 
s.rolling(3).apply(lambda x: x[:2], raw=False) 
TypeError: cannot convert the series to <class 'float'>

Not sure it is even feasible to support non-reducing functions like value_counts provided in the original message, but keeping open in case others have an idea on how to support