BUG:dataframe.groupby('some_column').timedelta.sum() results wrong when timedelta contains NaT (pandas=1.3.0)

[minientertainment]

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

• [y] 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.

---

Note: Please read this guide detailing how to provide the necessary information for us to reproduce your bug.

Code Sample, a copy-pastable example

this is a tiny example

tmp = pd.DataFrame({'id': ['1624477460271-3908654213', '1624477460271-3908654213'], 'dt':[np.nan, pd.to_timedelta('0:0:2')]})
tmp
Out[6]: 
                         id              dt
0  1624477460271-3908654213             NaT
1  1624477460271-3908654213 0 days 00:00:02

Problem description

dataframe.groupby('some_column').timedelta.sum() results wrong when timedelta contains NaT

tmp.groupby('id').dt.sum()
Out[7]: 
id
1624477460271-3908654213   -106752 days +00:12:45.145224192
Name: dt, dtype: timedelta64[ns]

Expected Output

tmp.dt.sum()
Out[8]: Timedelta('0 days 00:00:02')

Output of pd.show_versions()

[pandas 1.3.0]

[phofl]

Please povide something which is copy and pasteable (see https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports)

also please format your code

[minientertainment]

Please povide something which is copy and pasteable (see https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports)

also please format your code

updated

[nicolaslegrand91]

I also encountered this problem, it seems quite a major bug.

@phofl Here is a full copy-pastable example :

import pandas as pd

df = pd.DataFrame({
    'a': [1, 1, 2, 2],
    'b': [pd.Timedelta('1d'), pd.Timedelta('2d'), pd.Timedelta('3d'), pd.NaT],
})

print('\ndf:')
print(df)

print('\ndf.dtypes:')
print(df.dtypes)

print('\ndf.groupby(\'a\').b.sum():')
print(df.groupby('a').b.sum())

Output of pd.show_versions()

INSTALLED VERSIONS

commit : c7f7443
python : 3.9.5.final.0
python-bits : 64
OS : Darwin
OS-release : 20.4.0
Version : Darwin Kernel Version 20.4.0: Thu Apr 22 21:46:47 PDT 2021; root:xnu-7195.101.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.3.1
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.2.1
setuptools : 49.6.0
Cython : 0.29.24
pytest : 6.2.3
hypothesis : None
sphinx : 3.5.4
blosc : None
feather : None
xlsxwriter : 1.4.4
lxml.etree : 4.6.3
html5lib : None
pymysql : 1.0.2
psycopg2 : 2.9.1 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 7.25.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.0
sqlalchemy : 1.4.22
tables : None
tabulate : 0.8.9
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None

[simonjayhawkins]

first bad commit: [fa8f68e] BUG/API: SeriesGroupBy reduction with numeric_only=True (#41342)

cc @jbrockmendel

[simonjayhawkins]

changing milestone to 1.3.5

[nicolaslegrand91]

@phofl @simonjayhawkins, thanks for handling this request.

Do you think this problem will be fixed, or is it considered to be a bad practice to use groupby & mean/sum on pd.Timedelta values?

[jbrockmendel]

A PR addressing this would be welcome.

[jreback]

good to fix but not necessary for 1.3.x

[jbrockmendel]

Best guess is a is_datetimelike arg in group_add is needed