ENH: enable skipna on groupby reduction ops

https://github.com/pandas-dev/pandas/issues/15674

In [19]: import pandas as pd
    ...: import numpy as np
    ...: d = {'l':  ['left', 'right', 'left', 'right', 'left', 'right'],
    ...:      'r': ['right', 'left', 'right', 'left', 'right', 'left'],
    ...:      'v': [-1, 1, -1, 1, -1, np.nan]}
    ...: df = pd.DataFrame(d)
    ...: 

In [20]: df.groupby('l').v.sum()
Out[20]: 
l
left    -3.0
right    2.0
Name: v, dtype: float64

In [21]: df.groupby('l').v.apply(lambda x: x.sum(skipna=False))
Out[21]: 
l
left    -3.0
right    NaN
Name: v, dtype: float64

ideally write [21] as df.groupby('l').v.sum(skipna=False)

[vladu]

Indeed, I believe this is not just a “nice-to-have” any more, but a necessary step before this FutureWarning can be resolved by users:

FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().

As long as DataFrame.sum() and DataFrame.groupby().sum() (and other agg functions) have inconsistent APIs, dropping the level kwarg from non-grouped classes isn’t really a good step, IMO.

[mullenkamp]

This is a surprisingly old issue, but this functionality would be really nice and consistent with the non-groupby methods.