API: sum of Series of all NaN should return 0 or NaN ?
### Summary

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.

So the decision we need to make, is to either:

- adapt pandas internal implementation to return 0, so in all cases 0 is returned for all NaN/empty series.
- workaround bottlenecks behaviour or not use it for nansum, in order to consistently return NaN instead of 0
- choose one of both above as the default, but have an option to switch behaviour




---

Original title: **nansum in bottleneck 1.0 will return 0 for all NaN arrays instead of NaN**

xref https://github.com/kwgoodman/bottleneck/issues/96
xref https://github.com/pydata/pandas/issues/9421
- [ ] Tests are turned off for bottleneck >1.0 (xref https://github.com/pydata/pandas/pull/10986)

This matches a change from numpy 1.8 -> 1.9.

We should address this for pandas 0.16.

Should we work around the new behavior (probably the simplest choice) or change nansum in pandas?
