BUG: inconsistent treatment of numpy.Inf between groupby.sum() and groupby.apply(lambda: _grp: _grp.sum())

[jrowley8013]

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 numpy as np
>>> import pandas as pd
>>> s = pd.Series([np.Inf, np.Inf, np.Inf])
>>> s.groupby([0, 1, 1]).sum()  # np.Inf treated as NaN
0   inf
1   NaN
dtype: float64

>>> s.groupby([0, 1, 1]).apply(lambda _grp: _grp.sum()) # np.Inf + np.Inf = np.Inf
0   inf
1   inf
dtype: float64

Issue Description

When summing together multiple np.Inf values, groupby.sum() appears to treat np.Inf as NaN while groupby.apply(lambda _grp: _grp.sum()) does not.

Call Tracing

I traced the calls used in groupby.sum(), and verified that the flag maybe_use_numba evaluated to False when calling the GroupBy.sum method. Instead, we delegate to the pandas cython function group_sum.

As for groupby,apply(), we are just iterating over each of the groups and calling a reduce operation on the pandas Series. This delegates to the equivalent of np.nansum, which treats infinity in a different manner.

Expected Behavior

Using either groupby.sum() or groupby.apply() should produce the same result. I believe that the expected output should be equal to that obtained with groupby.apply(), which delegates to calling np.nansum:

>>> import numpy as np
>>> import pandas as pd
>>> s = pd.Series([np.Inf, np.Inf, np.Inf])
>>> s.groupby([0, 1, 1]).sum()
0   inf
1   inf
dtype: float64

Installed Versions

INSTALLED VERSIONS

commit : 965ceca
python : 3.10.11.final.0
python-bits : 64
OS : Linux
OS-release : 5.19.0-42-generic
Version : #43~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Fri Apr 21 16:51:08 UTC 2
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.0.2
numpy : 1.24.3
pytz : 2023.3
dateutil : 2.8.2
setuptools : 65.5.0
pip : 23.1.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.9.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 8.13.2
pandas_datareader: None
bs4 : 4.12.2
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.7.1
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 12.0.0
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.10.1
snappy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None

[rhshadrach]

Thanks for the report. #50367 handled the mean case, I think something similar can be done for sum.

[Charlie-XIAO]

take