BUG: Groupby iteration replaces None with NaN

[Erotemic]

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Reproducible Example

df = pd.DataFrame([
    {'a': None, 'b': 4, 'c': 1},
    {'a': None, 'b': 1, 'c': 1},
    {'a': None, 'b': 1, 'c': 2},
    {'a': 1, 'b': 2, 'c': 2},
    {'a': 1, 'b': 3, 'c': 2},
], dtype=object)
group_keys = ['a', 'c']
grouped = df.groupby(group_keys, dropna=False)
for group_vals, group in grouped:
    group_key1 = dict(zip(group_keys, group_vals))
    group_key2 = group.iloc[0][group_keys].to_dict()
    print('---')
    print(f'group_key1={group_key1}')
    print(f'group_key2={group_key2}')

This results in

---
group_key1={'a': nan, 'c': 1}
group_key2={'a': None, 'c': 1}
---
group_key1={'a': nan, 'c': 2}
group_key2={'a': None, 'c': 2}
---
group_key1={'a': 1.0, 'c': 2}
group_key2={'a': 1, 'c': 2}

The problem is I would expect group_key1 to be the same as group_key2.

Issue Description

When iterating over a pandas.core.groupby.generic.DataFrameGroupBy object, the returned group values replace None with nan.

Expected Behavior

I would have expected group_key1 and group_key2 to be the same in each iteration. Namely, I would think the value for "a" should always be "None" because that's what's in my DataFrame, but the set of unique values returned when iterating over the result of groupby seems to be replacing Nones with nans.

I did do a search for this issue, and I didn't find anything that matched it, but I'm also not confident that I didn't miss anything. I'm also not sure that this isn't expected behavior. Looking at the docstring for pandas.core.groupby.generic.DataFrameGroupBy or pandas.core.groupby.generic.DataFrameGroupBy.__iter__ didn't show anything. If this is expected behavior, any pointer to documenation would be helpful.

In the meantime simply using the first set of values in the returned group does seem to work fine, but it's odd to me the tuple of "group values" doesn't come directly from the underlying dataframe.

Installed Versions

INSTALLED VERSIONS

commit : e86ed37
python : 3.12.0.final.0
python-bits : 64
OS : Linux
OS-release : 6.2.0-33-generic
Version : #33~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Thu Sep 7 10:33:52 UTC 2
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.1.1
numpy : 1.26.0
pytz : 2023.3.post1
dateutil : 2.8.2
setuptools : None
pip : 23.2.1
Cython : None
pytest : 7.4.2
hypothesis : None
sphinx : 7.2.6
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.16.1
pandas_datareader : 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
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None

[paulreece]

This seems like it is intended behavior please see here:

pandas/pandas/core/groupby/generic.py

Lines 1881 to 1895 in 40f219c

	def filter(self, func, dropna: bool = True, *args, **kwargs):
	"""
	Filter elements from groups that don't satisfy a criterion.
	Elements from groups are filtered if they do not satisfy the
	boolean criterion specified by func.
	Parameters
	----------
	func : function
	Criterion to apply to each group. Should return True or False.
	dropna : bool
	Drop groups that do not pass the filter. True by default; if False,
	groups that evaluate False are filled with NaNs.

One of the maintainers will be able to give a more detailed explanation of why this is the case.

[rhshadrach]

@Erotemic - I think this is essentially #48476.