BUG: groupby().any() returns True instead of False for groups where timedelta column is all null

[sfc-gh-mvashishtha]

Pandas version checks

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

• 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

pd.DataFrame([pd.Timedelta(1), pd.NaT]).groupby([0, 1]).any()

Issue Description

For other dtypes, like integers and strings, groupby().any() returns True for groups where all the values are null, e.g.

pd.DataFrame([1, None]).groupby([0, 1]).any()

pd.DataFrame(["a", None]).groupby([0, 1]).any()

Expected Behavior

groupby().any() should return False for groups where all the timedelta values are null.

Installed Versions

INSTALLED VERSIONS

commit : d9cdd2e
python : 3.9.18.final.0
python-bits : 64
OS : Darwin
OS-release : 23.6.0
Version : Darwin Kernel Version 23.6.0: Mon Jul 29 21:13:04 PDT 2024; root:xnu-10063.141.2~1/RELEASE_ARM64_T6020
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.2.2
numpy : 1.26.3
pytz : 2023.3.post1
dateutil : 2.8.2
setuptools : 68.2.2
pip : 23.3.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 8.18.1
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.4
qtpy : None
pyqt5 : None

[rhshadrach]

Thanks for the report! Confirmed on main. It appears to me the issue is that we view the values as integers prior to computing mask of whether the values are NA or not.

pandas/pandas/core/groupby/ops.py

Lines 375 to 384 in 80b6850

	values = values.view("int64")
	is_numeric = True
	elif dtype.kind == "b":
	values = values.view("uint8")
	if values.dtype == "float16":
	values = values.astype(np.float32)
	if self.how in ["any", "all"]:
	if mask is None:
	mask = isna(values)

I believe switching the order of these will resolve the bug, PRs to fix are welcome!

[rhshadrach]

Hopefully this is an easy fix (and will need a test!), so marking as a good first issue.

[vivrdprasanna]

take

[40gilad]

took it !

[vivrdprasanna]

Hey @40gilad , as a first time contributor, I'd hoped to take a stab at this issue. I see that you took it upon yourself to submit a proposed fix. No worries at all - I'll move onto another issue, but just wanted to flag this for future reference.

[Rahul20037237]

take

[Petroncini]

take

[prafulmaka]

Whats still pending to do here?

[mpvaldez]

Hi! Can I take this? I want to start collaborating and I found the solution