BUG: DataFrameGroupBy.agg has different behavior when input function receives keyword argument

[mldcastro]

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

df = pd.DataFrame({"a": [1, 2], "b": [4, 5]}, index=pd.Index(list("AB"), name="group"))


def _agg_func(*args, **kwargs):
    for arg in args:
        print("ARG:\n")
        print(arg)
        print()
    return None


# _agg_func will receive a pandas Series of column "a", then after it, column "b"
df.groupby(level="group").agg(_agg_func)

# _agg_func will receive a DataFrame with columns "a" and "b" for each group
df.groupby(level="group").agg(_agg_func, some_kwarg="abc")

Issue Description

When performing an aggregation over groups, my colleagues and I observed that DataFrameGroupBy.agg has inconsistent behavior when the function passed to the agg method has or not keyword arguments.

If agg receives the function _agg_func without keyword arguments, then the input to _agg_func will be a pandas.Series. If keyword arguments are passed, then the input to _agg_func is a pandas.DataFrame.

Expected Behavior

As per the docs, the parameter func should be able to receive a pandas.DataFrame as input, hence, I would expect that the input for _agg_func should always be a pandas.DataFrame, independently if the function receives or not a keyword argument.

Installed Versions

INSTALLED VERSIONS

commit : 0691c5c
python : 3.11.10
python-bits : 64
OS : Linux
OS-release : 5.15.133.1-microsoft-standard-WSL2
Version : #1 SMP Thu Oct 5 21:02:42 UTC 2023
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.2.3
numpy : 2.0.1
pytz : 2024.2
dateutil : 2.9.0.post0
pip : 23.3.1
Cython : None
sphinx : None
IPython : 8.29.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : 3.1.4
lxml.etree : None
matplotlib : 3.9.0
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
psycopg2 : 2.9.9
pymysql : None
pyarrow : 18.0.0
pyreadstat : None
pytest : 8.3.2
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.14.1
sqlalchemy : 2.0.32
tables : None
tabulate : None
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
tzdata : 2024.2
qtpy : None
pyqt5 : None

[rhshadrach]

Thanks for the report, closing as a duplicate of #39169.