As a summary of what this is about:

The question is what the sum of a Series of all NaNs should return (which is equivalent to an empty Series after skipping the NaNs): NaN or 0?

In [1]: s = Series([np.nan])                 

In [2]: s.sum(skipna=True)  # skipping NaNs is the default
Out[2]: nan or 0     <---- DISCUSSION POINT

In [3]: s.sum(skipna=False)
Out[3]: nan

The reason this is a discussion point has the following cause: the internal nansum implementation of pandas returns NaN. But, when bottleneck is installed, pandas will use bottlenecks implementation of nansum, which returns 0 (for the versions >= 1.0).
Bottleneck changed the behaviour from returning NaN to returning 0 to model it after numpy's nansum function.

This has the very annoying consequence that depending on whether bottleneck is installed or not (which is only an optional dependency), you get a different behaviour.

This inconsistency is bad, and should be solved, and so we have to choose between both behaviours.