Summing rows in grouped pandas dataframe and return NaN

I think it's inherent to pandas. A workaround can be :

df.groupby('l')['v'].apply(array).apply(sum)

to mimic the numpy way,

or

df.groupby('l')['v'].apply(pd.Series.sum,skipna=False) # for series, or
df.groupby('l')['v'].apply(pd.DataFrame.sum,skipna=False) # for dataframes.

to call the good function.


I'm not sure where this falls on the ugliness scale, but it works:

>>> series_sum = pd.core.series.Series.sum
>>> df.groupby('l')['v'].agg(series_sum, skipna=False)
l
left     -3
right   NaN
Name: v, dtype: float64

I just dug up the sum method you used when you took df.v.sum, which supports the skipna option:

>>> help(df.v.sum)
Help on method sum in module pandas.core.generic:

sum(axis=None, skipna=None, level=None, numeric_only=None, **kwargs) method 
of pandas.core.series.Series instance