BUG: Single Index of Tuples as Output on Tuple Groupings

[RahimD]

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

df = pandas.DataFrame({"A" : [1,2,3,1,2,3], "B": [4,5,6,4,5,6], "C" : [7,8,9,7,8,9]})
df = df.set_index(['A', 'B'])
df.groupby(lambda x : (x[0], x[1])).aggregate('sum')

Issue Description

This issue appears to be similar to #24786 except that here a lambda is used to create the tuples. The User Guide for Pandas states that

The result of the aggregation will have the group names as the new index. In the case of multiple keys, the result is a MultiIndex by default.

Thus I was expecting the tuples to be automatically combined to form a MultiIndex as opposed to an Index with tuples as indices. Internally, the Index.map() call converts the produced list of tuples to a MultiIndex, but when it produces the aggregation index, it appears to produce a single ``Index``` instead.

I wasn't sure if this was intended behaviour, or a bug; I apologize if it is the former!

Expected Behavior

df = pandas.DataFrame({"A" : [1,2,3,1,2,3], "B": [4,5,6,4,5,6], "C" : [7,8,9,7,8,9]})
df = df.set_index(['A', 'B'])
df.groupby(['A', 'B']).aggregate('sum')

Installed Versions

INSTALLED VERSIONS

commit : 0691c5c
python : 3.13.1
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18363
machine : AMD64
processor : AMD64 Family 21 Model 96 Stepping 1, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_Canada.1252

pandas : 2.2.3
numpy : 2.2.1
pytz : 2024.2
dateutil : 2.9.0.post0
pip : 24.3.1
Cython : None
sphinx : None
IPython : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : None
lxml.etree : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : None
pyreadstat : None
pytest : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
tzdata : 2024.2
qtpy : None
pyqt5 : None

[rhshadrach]

Thanks for the report. If you want to have multiple keys, you need to supply a list to groupby.

df.groupby([lambda x : x[0], lambda x: x[1]]).aggregate('sum')

This gives the expected MultiIndex. I believe this resolves your issue. If not, comment here as to why and we can reopen.