BUG: tz-aware datetime with column-wise comparisions failing with np.minmum/maximum

Code Sample, a copy-pastable example if possible

>>> import numpy as np
>>> import pandas as pd

>>> naive = pd.to_datetime(['now'])
>>> utc = naive.tz_localize('UTC')

>>> np.minimum(naive, naive)
>>> np.minimum(utc, utc)
>>> np.minimum(pd.Series(naive), pd.Series(naive))
>>> np.minimum(pd.Series(utc), pd.Series(utc))

  File "bug.py", line 10, in <module>
    np.minimum(pd.Series(utc), pd.Series(utc))
  File "~/anaconda3/lib/python3.5/site-packages/pandas/core/series.py", line 498, in __array_prepare__
    op=context[0].__name__))
TypeError: Series with dtype datetime64[ns, UTC] cannot perform the numpy op minimum

Problem description

When a tz-aware datetime is placed in a Series, the numpy operations fmin/fmax/minimum/maximum throw an error, even though these operations work fine on a tz-aware DatetimeIndex.

Output of pd.show_versions()

INSTALLED VERSIONS

commit: None python: 3.5.2.final.0 python-bits: 64 OS: Linux OS-release: 4.9.8-100.fc24.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: C LANG: C LOCALE: None.None

pandas: 0.19.2 nose: 1.3.7 pip: 9.0.1 setuptools: 27.2.0 Cython: 0.25.2 numpy: 1.11.3 scipy: 0.18.1 statsmodels: 0.8.0 xarray: 0.9.1 IPython: 4.2.0 sphinx: 1.5.1 patsy: 0.4.1 dateutil: 2.6.0 pytz: 2016.10 blosc: None bottleneck: 1.2.0 tables: 3.3.0 numexpr: 2.6.2 matplotlib: 2.0.0 openpyxl: 2.4.1 xlrd: 1.0.0 xlwt: 1.2.0 xlsxwriter: 0.9.6 lxml: 3.7.2 bs4: 4.5.3 html5lib: 0.999 httplib2: None apiclient: None sqlalchemy: 1.1.5 pymysql: None psycopg2: None jinja2: 2.9.4 boto: 2.45.0 pandas_datareader: None

[jreback]

and @adbull

When a tz-aware datetime is placed in a Series, the numpy operations fmin/fmax/minimum/maximum throw an error, even though these operations work fine on a tz-aware DatetimeIndex.

this is not true at all, they naively look like they are working, but because of the same issue above (numpy has no clue about timezones, and forget about missing values), these are completely wrong (they are tz shifted incorrectly)

In [78]: i = pd.DatetimeIndex(df.A)
Out[78]: 
0   2013-01-01 00:00:00-05:00
1   2013-01-02 00:00:00-05:00
2   2013-01-03 00:00:00-05:00
3   2013-01-04 00:00:00-05:00
Name: A, dtype: datetime64[ns, US/Eastern]

In [79]: np.maximum(i, i)
Out[79]: DatetimeIndex(['2013-01-01 05:00:00-05:00', '2013-01-02 05:00:00-05:00', '2013-01-03 05:00:00-05:00', '2013-01-04 05:00:00-05:00'], dtype='datetime64[ns, US/Eastern]', name='A', freq='D')

[mroeschke]

This looks fixed on master again:

In [24]: np.minimum(pd.Series(utc), pd.Series(utc))
Out[24]:
0   2019-07-12 05:14:37.896979+00:00
dtype: datetime64[ns, UTC]

In [25]: pd.__version__
Out[25]: '0.25.0rc0+50.g5a7a8e1de'