BUG: Change of behavior in casting of datetime-like types in MultiIndex

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Code Sample, a copy-pastable example

[Edited to inform a much simpler example.]

import datetime
import pandas as pd

print(f"Pandas version:\t{pd.__version__}\n")

df = pd.DataFrame({'date': [datetime.date(2021, 8, 1),
                            datetime.date(2021, 8, 2),
                            datetime.date(2021, 8, 3)],
                   'ticker': ['aapl', 'goog', 'yhoo'],
                   'value': [5.63269, 4.45609, 2.74843]})

df.set_index(['date', 'ticker'], inplace=True)

print(df.index.get_level_values(0))

Output

The output below has been generated with pandas 1.3.0 or higher.

Pandas version:	1.3.0

Index([2021-08-01, 2021-08-02, 2021-08-03], dtype='object', name='date')

Expected Output

The output below has been generated with pandas 1.2.5.

Pandas version:	1.2.5

DatetimeIndex(['2021-08-01', '2021-08-02', '2021-08-03'], dtype='datetime64[ns]', name='date', freq=None)

Problem description

Starting from pandas 1.3.0, the observed behavior changed: in a MultiIndex creation, datetime.date objects are not cast to datetime64 anymore. I fail to find in the What’s new page the reason for that change of behavior. Is it by design or a bug?

Output of pd.show_versions()

INSTALLED VERSIONS
------------------
commit           : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python           : 3.9.6.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 19.6.0
Version          : Darwin Kernel Version 19.6.0: Tue Jun 22 19:49:55 PDT 2021; root:xnu-6153.141.35~1/RELEASE_X86_64
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : en_US.UTF-8
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.3.2
numpy            : 1.21.2
pytz             : 2021.1
dateutil         : 2.8.2
pip              : 21.2.4
setuptools       : 57.4.0
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : 2.9.1 (dt dec pq3 ext lo64)
jinja2           : None
IPython          : 7.26.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : None
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pyxlsb           : None
s3fs             : None
scipy            : None
sqlalchemy       : 1.4.22
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : None

[jbrockmendel]

However, I would also have to check for an inferred type of mixed, for when my column of datetime.date objects contains null dates, right?

I’d go for lib.infer_dtype(col, skipna=True) == "date" instead of checking for “mixed”

[jbrockmendel]

I guess we could maybe pass a convert_dates parameter through to the Categorical constructor from the MultiIndex constructor. @jbrockmendel wdyt?

It’s possible. Though we’d then have a breaking change for anyone relying on the 1.3 behavior.

Would it be to call pd.to_datetime(arg, errors=‘ignore’) where arg takes every column of the DataFrame

I’d check Index(col).inferred_type == "date"