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 capability has been added to pandas (beginning with version 0.24): https://pandas.pydata.org/pandas-docs/version/0.24/whatsnew/v0.24.0.html#optional-integer-na-support

At this point, it requires the use of extension dtype Int64 (capitalized), rather than the default dtype int64 (lowercase).

Answer 2

If performance is not the main issue, you can store strings instead.

df.col = df.col.dropna().apply(lambda x: str(int(x)) )

Then you can mix then with NaN as much as you want. If you really want to have integers, depending on your application, you can use -1, or 0, or 1234567890, or some other dedicated value to represent NaN.

You can also temporarily duplicate the columns: one as you have, with floats; the other one experimental, with ints or strings. Then inserts asserts in every reasonable place checking that the two are in sync. After enough testing you can let go of the floats.