Some median Python NaNsense

In this context it's interesting to see that Pandas also tries to move away from using numpy.nan (i.e. IEEE silent NaN) as "missing" indicator. Target is to deal with "missing" uniformly across all data types, instead of numpy.nan for floats, pandas.NaT for "not-a-time", and None for everything else (integers, strings, ...). Internally, the "missing" info is kept as a separate, binary mask.

See the documentation of the upcoming 1.0.0 release: https://pandas.pydata.org/pandas-docs/version/1.0.0/user_...

I am looking forward to it, because in my work I currently have to represent integer data as float to efficiently deal with missings.