NumPy or Pandas: Keeping array type as integer while having a NaN value

Question

Is there a preferred way to keep the data type of a numpy array fixed as int (or int64 or whatever), while still having an element ins

Answer 1

This is not a solution for all cases, but mine (genomic coordinates) I've resorted to using 0 as NaN

a3['MapInfo'] = a3['MapInfo'].fillna(0).astype(int)

This at least allows for the proper 'native' column type to be used, operations like subtraction, comparison etc work as expected