BUG: DataFrame.groupby silently ignores as_index=False when aggregating with multiple functions on a single column

[Demetrio92]

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Reproducible Example

import pandas as pd
df = pd.DataFrame({
    'key': ['10', '10', '20', '20'],
    'value': [1, 2, 1, 1.5]
})

res1 = df.groupby('key', as_index=False)['value'].agg(['min', 'max'])
print('key' in res1.columns)   # False (!)
print(res1)

     min  max
key          
10   1.0  2.0
20   1.0  1.5

Issue Description

After using as_index=False in df.groupby() I was very surprised not to find the grouping column in the columns list. In fact, this option is completely ignored. In the example above as_index=True produces the same result.

Expected Behavior

As a workaround just adding .reset_index() at the end works

res2 = df.groupby('key')['value'].agg(['min', 'max']).reset_index()
print('key' in res2.columns)
print(res2)

  key  min  max
0  10  1.0  2.0
1  20  1.0  1.5

Installed Versions

INSTALLED VERSIONS

commit : 945c9ed
python : 3.9.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.11.0-43-generic
Version : #47~20.04.2-Ubuntu SMP Mon Dec 13 11:06:56 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.4
numpy : 1.21.2
pytz : 2021.3
dateutil : 2.8.2
pip : 21.3.1
setuptools : 58.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.2
IPython : 7.28.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 5.0.0
pyxlsb : None
s3fs : None
scipy : 1.7.1
sqlalchemy : 1.4.25
tables : None
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
numba : None

[mroeschke]

Thanks for the report but this is the expected behavior.

The docs for as_index states: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html

For aggregated output, return object with group labels as the index. Only relevant for DataFrame input

df.groupby('key', as_index=False)['value'] selects the value column from the DataFrame by turning input into a Series; therefore, as_index is a no-op here. Closing as a usage question