Can't convert `Categorical` to 'datetime64[ns, MET]', but can do it with Series

[MarcoGorelli]

Code Sample, a copy-pastable example if possible

from pandas import Categorical

Categorical(['2015-01-01']).astype('datetime64[ns]')  # works
Categorical(['2015-01-01']).astype('datetime64[ns, MET]')  # fails

This is because the astype method returns

       np.array(self, dtype=dtype, copy=copy)

, and numpy arrays don't handle this dtype.

Is this expected behaviour?

Output of pd.show_versions()

INSTALLED VERSIONS ------------------ commit : None python : 3.7.3.final.0 python-bits : 64 OS : Linux OS-release : 4.15.0-60-generic machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_GB.UTF-8 LOCALE : en_GB.UTF-8

pandas : 0.25.1
numpy : 1.16.4
pytz : 2019.2
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : None
pytest : 5.0.1
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : None
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None

[WillAyd]

I think this should be consistent so probably OK to support. If you have a PR you'd like to submit would certainly welcome one

[MarcoGorelli]

Fixed by #28762

[jreback]

hmm do we have a test for this?

[MarcoGorelli]

@jreback sorry for the delay - I've just reopened this and added a test