Pandas version checks
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[x] I have checked that this issue has not already been reported.
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[x] I have confirmed this bug exists on the latest version of pandas.
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[ ] I have confirmed this bug exists on the main branch of pandas.
Reproducible Example
import pandas as pd
df = pd.DataFrame({'a': [3,6,1,1,None,6]}, dtype='Int64[pyarrow]')
df['a_mask'] = df['a'].isna()
print(df.groupby('a_mask').rank(method='min'))
Issue Description
On Windows, this outputs
a
0 4.0
1 1.0
2 1.0
3 1.0
4 <NA>
5 5.0
Expected Behavior
On Linux, it outputs
a
0 3.0
1 4.0
2 1.0
3 1.0
4 <NA>
5 4.0
Installed Versions
Windows:
INSTALLED VERSIONS
------------------
commit : c888af6d0bb674932007623c0867e1fbd4bdc2c6
python : 3.11.9
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.26100
machine : AMD64
processor : Intel64 Family 6 Model 141 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United Kingdom.1252
pandas : 2.3.1
numpy : 2.3.1
pytz : 2025.2
dateutil : 2.9.0.post0
pip : None
Cython : None
sphinx : None
IPython : 9.4.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
html5lib : None
hypothesis : 6.135.32
gcsfs : None
jinja2 : 3.1.6
lxml.etree : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : 21.0.0
pyreadstat : None
pytest : 8.4.1
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
tzdata : 2025.2
qtpy : None
pyqt5 : None
Linux:
INSTALLED VERSIONS
------------------
commit : c888af6d0bb674932007623c0867e1fbd4bdc2c6
python : 3.12.8
python-bits : 64
OS : Linux
OS-release : 6.6.87.2-microsoft-standard-WSL2
Version : #1 SMP PREEMPT_DYNAMIC Thu Jun 5 18:30:46 UTC 2025
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : C.UTF-8
pandas : 2.3.1
numpy : 2.3.1
pytz : 2025.2
dateutil : 2.9.0.post0
pip : None
Cython : None
sphinx : None
IPython : 9.1.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2025.7.0
html5lib : None
hypothesis : 6.135.32
gcsfs : None
jinja2 : 3.1.6
lxml.etree : None
matplotlib : 3.10.3
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : 21.0.0
pyreadstat : None
pytest : 8.4.1
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.16.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
tzdata : 2025.2
qtpy : None
pyqt5 : None
Comment From: MarcoGorelli
Note that downgrading PyArrow to version 20 resolves this
I think this may be related to the PyArrow 21 release from 9 hours ago? https://pypi.org/project/pyarrow/21.0.0/
Comment From: rhshadrach
Took a quick look over the changelog for PyArrow 21, nothing jumped out. It'd probably be helpful to narrow down what functionality caused this. From Windows, are you able to post the output of:
df = pd.DataFrame({'a': [3,6,1,1,None,6]}, dtype='Int64[pyarrow]')
df['a_mask'] = df['a'].isna()
gb = df.groupby('a_mask')
print(df['a_mask'])
print(gb._grouper.result_index)
print(gb._grouper.codes)
print(gb._grouper.groupings[0].codes)
If the result_index / codes look right, my best guess it's something ArrowExtensionArray._groupby_op.
Comment From: MarcoGorelli
sure, here you go
0 False
1 False
2 False
3 False
4 True
5 False
Name: a_mask, dtype: bool
Index([False, True], dtype='bool', name='a_mask')
[array([0, 0, 0, 0, 1, 0])]
[0 0 0 0 1 0]