From #27929 (comment). In __getitem__ or Categorical.min(..), we always return np.nan as scalar missing value, regardless of the dtype:

In [7]: cat = pd.Categorical([pd.Timestamp("2012"), None], ordered=True) In [8]: cat Out[8]: [2012-01-01, NaT] Categories (1, datetime64[ns]): [2012-01-01] In [9]: cat[1] Out[9]: nan In [10]: cat.min(skipna=False) Out[10]: nan 

In the above, this could also be pd.NaT instead?
(similar issue will come up once we can use the EAs that use the new NA scalar in categoricals)

However, CategoricalDtype.na_value now also returns np.nan (which should be consistent with what we return in the cases above):

In [13]: cat.dtype.na_value Out[13]: nan 

We can of course let the CategoricalDtype.na_value be dependent on the na_value of the dtype of the categories. But I am not fully sure we want such values-dependent behaviour?