BUG: pandas 2.2.0 `DataFrame.groupby` regression when group labels of type `pd.Timestamp`

[maread99]

Pandas version checks

• [X] I have checked that this issue has not already been reported.

• [X] I have confirmed this bug exists on the latest version of pandas.

• [ ] I have confirmed this bug exists on the main branch of pandas.

Reproducible Example

import pandas as pd
assert pd.__version__ == "2.2.0"

index = pd.date_range("2024-02-01", "2024-02-03", freq="8h", tz="UTC")

# index
# DatetimeIndex(['2024-02-01 00:00:00+00:00', '2024-02-01 08:00:00+00:00',
#                '2024-02-01 16:00:00+00:00', '2024-02-02 00:00:00+00:00',
#                '2024-02-02 08:00:00+00:00', '2024-02-02 16:00:00+00:00',
#                '2024-02-03 00:00:00+00:00'],
#               dtype='datetime64[ns, UTC]', freq='8h')

df = pd.DataFrame([1] * len(index), index)

def f(ts: pd.Timestamp) -> pd.Timestamp:
    """Group by date, i.e. as timezone naive timestamp."""
    return ts.normalize().tz_convert(None)

grouped = df.groupby(by=f)
keys = list(grouped.groups.keys())
print(keys)

# [Timestamp('2024-02-01 00:00:00+0000', tz='UTC'),
#  Timestamp('2024-02-02 00:00:00+0000', tz='UTC'),
#  Timestamp('2024-02-03 00:00:00+0000', tz='UTC')]

Issue Description

Although the return from the 'by' function is timezone naive, the group labels are timezone aware (UTC).

(I've tried every combination of the as_index and group_keys options, always get the same group keys).

Expected Behavior

Would have expected behaviour as pandas 2.1.4 and prior, i.e,. group keys as returned from the 'by' function...

import pandas as pd
assert pd.__version__ == "2.1.4"

index = pd.date_range("2024-02-01", "2024-02-03", freq="8h", tz="UTC")
df = pd.DataFrame([1] * len(index), index)

def f(ts: pd.Timestamp) -> pd.Timestamp:
    return ts.normalize().tz_convert(None)

grouped = df.groupby(by=f)
keys = list(grouped.groups.keys())
print(keys)

[Timestamp('2024-02-01 00:00:00'),
 Timestamp('2024-02-02 00:00:00'),
 Timestamp('2024-02-03 00:00:00')]

Installed Versions

INSTALLED VERSIONS

commit : f538741432edf55c6b9fb5d0d496d2dd1d7c2457 python : 3.9.13.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.22631 machine : AMD64 processor : Intel64 Family 6 Model 141 Stepping 1, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : English_United Kingdom.1252

pandas : 2.2.0 numpy : 1.26.3 pytz : 2023.4 dateutil : 2.8.2 setuptools : 58.1.0 pip : 23.3.2 Cython : None pytest : 8.0.0 hypothesis : 6.97.3 sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : 4.9.4 html5lib : None pymysql : None psycopg2 : None jinja2 : None IPython : 8.14.0 pandas_datareader : None adbc-driver-postgresql: None adbc-driver-sqlite : None bs4 : 4.12.3 bottleneck : None dataframe-api-compat : None fastparquet : None fsspec : None gcsfs : None matplotlib : None numba : None numexpr : 2.9.0 odfpy : None openpyxl : None pandas_gbq : None pyarrow : 15.0.0 pyreadstat : None python-calamine : None pyxlsb : None s3fs : None scipy : None sqlalchemy : None tables : 3.9.2 tabulate : None xarray : None xlrd : None zstandard : None tzdata : 2023.4 qtpy : None pyqt5 : None

[rhshadrach]

Thanks for the report. Result of a git bisect:

commit 746e5eee860b6e143c33c9b985e095dac2e42677
Author: jbrockmendel
Date:   Mon Oct 16 11:50:28 2023 -0700

    ENH: EA._from_scalars (#53089)

cc @jbrockmendel

[jbrockmendel]

yah i think thats a problem in DTA._from_scalars. _from_scalars itself was a mistake xref #56430

[rhshadrach]

This can be reproduced just using index.map, so taking off the groupby label.