BUG: not possible to 'cumsum' Timedelta with named aggregation

• 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

import pandas as pd
import numpy as np
import random

# Dataset
ts = pd.DatetimeIndex([pd.Timestamp('2021/01/01 00:30'),
                       pd.Timestamp('2021/01/01 00:45'),
                       pd.Timestamp('2021/01/01 02:00'),
                       pd.Timestamp('2021/01/01 03:50'),
                       pd.Timestamp('2021/01/01 05:00')])
length = len(ts)
random.seed(1)
value = random.sample(range(1, length+1), length)
df = pd.DataFrame({'value': value, 'ts': ts})
df['td'] = df['ts'] - df['ts'].shift(1, fill_value=ts[0]-pd.Timedelta('1h'))
df['amount'] = df['value']*10
df.loc[:2,'grps'] = 'a'
df.loc[2:,'grps'] = 'b'

In [16]: df
Out[16]: 
   value                  ts              td  amount grps
0      2 2021-01-01 00:30:00 0 days 01:00:00      20    a
1      1 2021-01-01 00:45:00 0 days 00:15:00      10    a
2      5 2021-01-01 02:00:00 0 days 01:15:00      50    b
3      4 2021-01-01 03:50:00 0 days 01:50:00      40    b
4      3 2021-01-01 05:00:00 0 days 01:10:00      30    b

# cumsum and Timedelta work when used through pd.Series
df['td'].cumsum()

# cumsum and Timedelta do not work when used in groupby with named aggregation
agg_rules = {'td':('td', 'cumsum')}
res = df.groupby('grps').agg(**agg_rules)

Error message that is returned

res = df.groupby('grps').agg(**agg_rules)
Traceback (most recent call last):

  File "<ipython-input-14-c0f4606d675a>", line 2, in <module>
    res = df.groupby('grps').agg(**agg_rules)

  File "/home/pierre/anaconda3/lib/python3.8/site-packages/pandas/core/groupby/generic.py", line 945, in aggregate
    result, how = aggregate(self, func, *args, **kwargs)

  File "/home/pierre/anaconda3/lib/python3.8/site-packages/pandas/core/aggregation.py", line 582, in aggregate
    return agg_dict_like(obj, arg, _axis), True

  File "/home/pierre/anaconda3/lib/python3.8/site-packages/pandas/core/aggregation.py", line 768, in agg_dict_like
    results = {key: obj._gotitem(key, ndim=1).agg(how) for key, how in arg.items()}

  File "/home/pierre/anaconda3/lib/python3.8/site-packages/pandas/core/aggregation.py", line 768, in <dictcomp>
    results = {key: obj._gotitem(key, ndim=1).agg(how) for key, how in arg.items()}

  File "/home/pierre/anaconda3/lib/python3.8/site-packages/pandas/core/groupby/generic.py", line 247, in aggregate
    ret = self._aggregate_multiple_funcs(func)

  File "/home/pierre/anaconda3/lib/python3.8/site-packages/pandas/core/groupby/generic.py", line 315, in _aggregate_multiple_funcs
    results[base.OutputKey(label=name, position=idx)] = obj.aggregate(func)

  File "/home/pierre/anaconda3/lib/python3.8/site-packages/pandas/core/groupby/generic.py", line 241, in aggregate
    return getattr(self, func)(*args, **kwargs)

  File "/home/pierre/anaconda3/lib/python3.8/site-packages/pandas/core/groupby/groupby.py", line 2497, in cumsum
    return self._cython_transform("cumsum", **kwargs)

  File "/home/pierre/anaconda3/lib/python3.8/site-packages/pandas/core/groupby/groupby.py", line 996, in _cython_transform
    raise DataError("No numeric types to aggregate")

DataError: No numeric types to aggregate

Problem description

Cumsum works with Timedelta in some workflows, but for some other workflows (groupby, named aggregation) it does not.

Output of pd.show_versions()

INSTALLED VERSIONS

commit : 2cb96529396d93b46abab7bbc73a208e708c642e python : 3.8.5.final.0 python-bits : 64 OS : Linux OS-release : 5.8.0-53-generic Version : #60~20.04.1-Ubuntu SMP Thu May 6 09:52:46 UTC 2021 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : fr_FR.UTF-8 LOCALE : fr_FR.UTF-8

pandas : 1.2.4 numpy : 1.20.1 pytz : 2021.1 dateutil : 2.8.1 pip : 21.1.1 setuptools : 52.0.0.post20210125 Cython : 0.29.23 pytest : 6.2.3 hypothesis : None sphinx : 4.0.1 blosc : None feather : None xlsxwriter : 1.3.8 lxml.etree : 4.6.3 html5lib : 1.1 pymysql : None psycopg2 : None jinja2 : 3.0.0 IPython : 7.22.0 pandas_datareader: None bs4 : 4.9.3 bottleneck : 1.3.2 fsspec : 0.9.0 fastparquet : 0.6.3 gcsfs : None matplotlib : 3.3.4 numexpr : 2.7.3 odfpy : None openpyxl : 3.0.7 pandas_gbq : None pyarrow : 2.0.0 pyxlsb : None s3fs : None scipy : 1.6.2 sqlalchemy : 1.4.15 tables : 3.6.1 tabulate : None xarray : None xlrd : 2.0.1 xlwt : 1.3.0 numba : 0.51.2

[WillAyd]

No they aren’t the same - the first is just a normal cumulative whereas the latter is a groupby. So should respect the groupings

[seanjedi]

@WillAyd Added a new test here: https://github.com/pandas-dev/pandas/pull/50033 Im sure there will be a lot of things I can improve, as this is my first test with Pandas 😅