BUG: Inconsistent behavior with groupby/min and observed=False on categoricals between 2.0 and 2.1

[j-bennet]

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
import numpy as np

df = pd.DataFrame({
    "cat_1": pd.Categorical(list("AB"), categories=list("ABCDE"), ordered=True),
    "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=True),
    "value_1": np.random.uniform(size=2),
})

chunk1 = df[df.cat_1 == "A"]
chunk2 = df[df.cat_1 == "B"]

df1 = chunk1.groupby("cat_1", observed=False).min()
df2 = chunk2.groupby("cat_1", observed=False).min()
df3 = pd.concat([df1, df2], ignore_index=False)

res3 = df3.groupby(level=0, observed=False).min()
print(f"\n{res3}")

Issue Description

When performing a groupby/min with a categorical dtype and observed=False, the results differ between 1.5.3 (and 2.0) and 2.1.

Output with 1.5.3 or 2.0:

      cat_2   value_1
cat_1
A         1  0.384993
B         2  0.955231
C       NaN       NaN
D       NaN       NaN
E       NaN       NaN

Output with the latest main:

      cat_2   value_1
cat_1
A         1  0.297557
B         1  0.081856
C         1       NaN
D         1       NaN
E         1       NaN

The change can be traced to this PR:

• PERF: Groupby aggregations with Categorical #52120

Expected Behavior

I'm not sure if the changed behavior is intended. Please advise.

Installed Versions

commit : d22d1f2
python : 3.8.16.final.0
python-bits : 64
OS : Darwin
OS-release : 22.3.0
Version : Darwin Kernel Version 22.3.0: Thu Jan 5 20:50:36 PST 2023; root:xnu-8792.81.2~2/RELEASE_ARM64_T6020
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.1.0.dev0+293.gd22d1f2db0
numpy : 1.23.5
pytz : 2022.7.1
dateutil : 2.8.2
setuptools : 67.4.0
pip : 23.0.1
Cython : 0.29.33
pytest : 7.2.1
hypothesis : 6.68.2
sphinx : 4.5.0
blosc : None
feather : None
xlsxwriter : 3.0.8
lxml.etree : 4.9.2
html5lib : 1.1
pymysql : 1.0.2
psycopg2 : 2.9.3
jinja2 : 3.1.2
IPython : 8.11.0
pandas_datareader: None
bs4 : 4.11.2
bottleneck : 1.3.6
brotli :
fastparquet : 2023.2.0
fsspec : 2023.1.0
gcsfs : 2023.1.0
matplotlib : 3.6.3
numba : 0.56.4
numexpr : 2.8.3
odfpy : None
openpyxl : 3.1.0
pandas_gbq : None
pyarrow : 11.0.0
pyreadstat : 1.2.1
pyxlsb : 1.0.10
s3fs : 2023.1.0
scipy : 1.10.1
snappy :
sqlalchemy : 2.0.4
tables : 3.7.0
tabulate : 0.9.0
xarray : 2023.1.0
xlrd : 2.0.1
zstandard : 0.19.0
tzdata : None
qtpy : None
pyqt5 : None

[DeaMariaLeon]

Thank you for reporting @j-bennet.

I get this when I run your example, on pandas version 2.1.0.dev0+171.gc293caf2e9:

      cat_2   value_1
cat_1                
A         1  0.837825
B         2  0.687282
C       NaN       NaN
D       NaN       NaN
E       NaN       NaN 

[MarcoGorelli]

I can't reproduce this either, I'm also getting NaN for categories C D E with the latest release candidate

      cat_2   value_1
cat_1
A         1  0.251036
B         2  0.204336
C       NaN       NaN
D       NaN       NaN
E       NaN       NaN

[j-bennet]

It's reproducible with the latest main, 2.1.0.dev0+295.g25c1942c39, or with the latest nightly, 2.1.0.dev0+293.gd22d1f2db0.

[MarcoGorelli]

ah yes, can reproduce with the latest nightly, thanks!

so, the regression is between 2.0 and 2.1 - updating the title then, thanks for the report!

cc @jbrockmendel

[j-bennet]

git bisect says it was introduced here:

14affe0

[jbrockmendel]

Not intended.

In WrappedCythonOp._ea_wrap_cython_operation there is a case for isinstance(values, Categorical). The last few lines of that case are

            if self.how in self.cast_blocklist:
                return res_values
            return values._from_backing_data(res_values)

I think the fix here is:

            if self.how in self.cast_blocklist:
                return res_values
+            elif self.how in ["first", "last", "min", "max"]:
+                res_values[result_mask==1] = -1
            return values._from_backing_data(res_values)

[DeaMariaLeon]

@j-bennet do you want to do the PR to fix it? If you don't I'll do it.
Could you let me know please?

[j-bennet]

@DeaMariaLeon Go ahead, I'm not familiar enough with the codebase. Thank you.

[j-bennet]

Thank you @DeaMariaLeon .