BUG: df.groupby().head() missing rows when grouping on several columns with NA

[LucasG0]

Pandas version checks

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Reproducible Example

>>> df = pd.DataFrame([["a", "z"], ["b", None], ["c", None]], columns=["X", "Y"])
>>> df
   X     Y
0  a     z
1  b  None
2  c  None
>>> df.groupby(by=["X", "Y"]).head(n=1)
   X     Y
0  a     z
1  b  None

# Other example
>>> df = pd.DataFrame([["a", "z"], ["b", None], ["c", None], ["c", None], ["c", None]], columns=["X", "Y"])
>>> df
   X     Y
0  a     z
1  b  None
2  c  None
3  c  None
4  c  None
>>> df.groupby(by=["X", "Y"]).head(n=3)
   X     Y
0  a     z
1  b  None
2  c  None
3  c  None

Issue Description

1 row having ("c", None) values is missing in both examples.

Expected Behavior

In both above examples we expect the whole dataframe to be returned. It seems the number of rows of the first group having NA in ("b", None) is deduced from the number of rows that should be returned for the second group having NA ("c", None).

Installed Versions

INSTALLED VERSIONS

commit : db08276
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.11.0-43-generic
Version : #47~20.04.2-Ubuntu SMP Mon Dec 13 11:06:56 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : fr_FR.UTF-8
LOCALE : fr_FR.UTF-8

pandas : 1.1.3
numpy : 1.19.5
pytz : 2019.3
dateutil : 2.8.2
pip : 20.0.2
setuptools : 45.2.0
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.25.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.10.1
fastparquet : 0.7.1
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : 6.0.0
pytables : None
pyxlsb : None
s3fs : 2021.10.1
scipy : None
sqlalchemy : None
tables : None
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
numba : None

[phofl]

You have to set dropna=False, then this will work as expected.

This should not return rows with None at all in your examples.