NumPy or Pandas: Keeping array type as integer while having a NaN value
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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 inside listed as `numpy.NaN`?

In particular, I am converting an in-house data structure to a Pandas DataFrame. In our structure, we have integer-type columns that still have NaN's (but the dtype of the column is int). It seems to recast everything as a float if we make this a DataFrame, but we'd really like to be `int`.

Thoughts?