BUG: Regression from 1.2.5 to 1.3.x: groupby using sum on DataFrame containing lists fails

[Dr-Irv]

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• (optional) I have confirmed this bug exists on the master branch of pandas.

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Code Sample, a copy-pastable example

Version 1.3.2:

>>> df=pd.DataFrame([["M", [1]], ["M", [2]], ["W", [10]], ["W", [20]]], columns=["MW", "v"])
>>> df.groupby("MW").sum()
Empty DataFrame
Columns: []
Index: [M, W]
>>> df.groupby("MW").v.sum()
MW
M      [1, 2]
W    [10, 20]
Name: v, dtype: object

Version 1.2.5:

>>> df=pd.DataFrame([["M", [1]], ["M", [2]], ["W", [10]], ["W", [20]]], columns=["MW", "v"])
>>> df.groupby("MW").sum()
           v
MW
M     [1, 2]
W   [10, 20]
>>> df.groupby("MW").v.sum()
MW
M      [1, 2]
W    [10, 20]
Name: v, dtype: object

Problem description

With 1.2.5 and earlier, if the elements of one of the columns are objects that are lists, then the groupby operation with sum creates the aggregation of the lists. With 1.3.2, it doesn't.

Expected Output

Same as 1.2.5

Output of pd.show_versions()

INSTALLED VERSIONS

commit : 3af1a4f
python : 3.8.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252

pandas : 1.4.0.dev0+475.g3af1a4fa27
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.3
setuptools : 49.6.0.post20210108
Cython : 0.29.23
pytest : 6.2.4
hypothesis : 6.14.1
sphinx : 3.5.4
blosc : None
feather : None
xlsxwriter : 1.4.3
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.25.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.05.0
fastparquet : 0.6.3
gcsfs : 2021.05.0
matplotlib : 3.3.3
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : 0.4.2
scipy : 1.7.0
sqlalchemy : 1.4.20
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.18.2
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.52.0

[simonjayhawkins]

looks similar to #42395. change in behavior reported for non-numeric column

[kurchi1205]

I am new to OpenSource Projects . Can I work on this issue?

[jbrockmendel]

The default for DataFrameGroupBy.sum's numeric_only is True according to the docstring (in the code it is lib.no_default for code-sharing). Failing to exclude non-numeric columns was a bug pre-1.3

[kurchi1205]

Yeah I got that , I am working on the case when the dataframe has no numeric feature , but by default numeric_only is set to True like in the above one .

[Dr-Irv]

The default for DataFrameGroupBy.sum's numeric_only is True according to the docstring (in the code it is lib.no_default for code-sharing). Failing to exclude non-numeric columns was a bug pre-1.3

Yes, but based on the comment here: #42395 (comment) it seems that we might want to consider changing the default to have numeric_only=False .

[simonjayhawkins]

Yes, but based on the comment here: #42395 (comment) it seems that we might want to consider changing the default to have numeric_only=False .

closing this to keep discussion in on place.

[Priyanshi-Chauhan]

Can I work on this issue. Perhaps come up with the contribute to code

[simonjayhawkins]

Thanks @Priyanshi-Chauhan. This issue is closed. Discussion on this issue now in #42395. There is an open PR to fix #43154.

There are many other issues that could be worked on. I'd recommend any from https://github.com/pandas-dev/pandas/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22+label%3A%22Needs+Tests%22