BUG: `dropna` affects `observed` in `DataFrame.groupby()` since v1.5

[pwwang]

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Reproducible Example

# With pandas 1.5

from pandas import DataFrame, Categorical

df = DataFrame({"x": Categorical([1, 2], categories=[1, 2, 3]), "y": [3, 4]})

df.groupby("x", observed=False).grouper.result_index
# CategoricalIndex([1, 2, 3], categories=[1, 2, 3], ordered=False, dtype='category', name='x')

df.groupby("x", observed=False, dropna=False).grouper.result_index
# CategoricalIndex([1, 2], categories=[1, 2, 3], ordered=False, dtype='category', name='x')
# ------------------------------------------------------------------------------------------
# Unexpected result ↑

df.groupby("x", observed=False, dropna=True).grouper.result_index
# CategoricalIndex([1, 2, 3], categories=[1, 2, 3], ordered=False, dtype='category', name='x')


# With pandas 1.4.4 and prior

df.groupby("x", observed=False).grouper.result_index
# CategoricalIndex([1, 2, 3], categories=[1, 2, 3], ordered=False, dtype='category', name='x')

df.groupby("x", observed=False, dropna=False).grouper.result_index
# CategoricalIndex([1, 2, 3], categories=[1, 2, 3], ordered=False, dtype='category', name='x')

df.groupby("x", observed=False, dropna=True).grouper.result_index
# CategoricalIndex([1, 2, 3], categories=[1, 2, 3], ordered=False, dtype='category', name='x')

Issue Description

dropna=False in DataFrame.groupby() should not affect the results when observed=False.

Expected Behavior

Expected the behavior with pandas 1.4.4 and prior.

Installed Versions

INSTALLED VERSIONS

commit : 87cfe4e
python : 3.9.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.15.57.1-microsoft-standard-WSL2
Version : #1 SMP Wed Jul 27 02:20:31 UTC 2022
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.5.0
numpy : 1.23.3
pytz : 2022.1
dateutil : 2.8.2
setuptools : 58.0.0
pip : 22.2.2
Cython : None
pytest : 6.2.5
hypothesis : None
sphinx : 4.5.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.1.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
brotli :
fastparquet : None
fsspec : 2022.02.0
gcsfs : None
matplotlib : 3.5.1
numba : 0.53.1
numexpr : None
odfpy : None
openpyxl : 3.0.8
pandas_gbq : None
pyarrow : 7.0.0
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.8.0
snappy : None
sqlalchemy : 1.4.28
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
zstandard : None
tzdata : None

[phofl]

cc @rhshadrach

Confirmed:

56a71845decdc308d4210d74c7a59e42f0762c31 is the first bad commit
commit 56a71845decdc308d4210d74c7a59e42f0762c31
Author: Richard Shadrach <45562402+rhshadrach@users.noreply.github.com>
Date:   Thu Aug 18 12:09:09 2022 -0400

    BUG: algorithms.factorize moves null values when sort=False (#46601)

#46601

[rhshadrach]

#46601 fixed an issue with dropna and categorical, namely dropna with categorical still drops null values. On 1.4.x:

values, dtype = (["y", None, "x", "y"], "category")
key = pd.Series(values, dtype=dtype)
df = pd.DataFrame({"key": key, "a": [1, 2, 3, 4]})
gb = df.groupby("key", dropna=False)
print(gb.sum())

#      a
# key   
# x    3
# y    5

The null value is included in the result on 1.5.0. As identified, the patch did not correctly implement the case where observed=False.

I've looked into this, and it appears to me our current implementation of categorical with nulls and dropna are incompatible in groupby. Namely, categorical encodes values as nonnegative integers with nulls being represented by -1 while groupby with dropna=False requires nulls be encoded by nonnegative integers.

We could maybe hack in a patch where we add the null value(s?) to the categories only to remove them upon returning the result. This seems like it would be too significant of a change for a patch release, fragile, and prone to bugs. I am wondering if a better direction I think would be to reimplement groupby so that negative codes are only dropped when dropna=True. This may have some drawbacks and would need some experimenting, but again, too large of a change for a patch version in my opinion.

With this, my recommendation is to undo the offending line from #46601, i.e. change

pandas/pandas/core/groupby/grouper.py

Line 663 in 73d15a7

if self._dropna and self._passed_categorical:

to become if self._passed_categorical:. This would make it so that dropna=False does not work with categorical again, but fixing this regression. I will put up a PR for this, but wanted to see if others have any thoughts first.

cc @jbrockmendel @mroeschke @jreback @phofl @jorisvandenbossche