BUG: Using pd.cut with IntervalIndex containing Timedeltas returns only NaN

[oalfred]

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 pandas as pd

one_to_seven_days_timedeltas = [pd.Timedelta(days=d) for d in [1, 2, 3, 4, 5, 6, 7]]
even_days_timedeltas = [pd.Timedelta(days=d) for d in [0, 2, 4, 6, 8]]

s = pd.Series(one_to_seven_days_timedeltas)
i = pd.IntervalIndex.from_breaks(even_days_timedeltas, closed='left')

print(pd.cut(s, i))

Issue Description

Using pd.cut when the bins are an IntervalIndex that contains Timedelta returns only NaN. Output from the example is

0    NaN
1    NaN
2    NaN
3    NaN
4    NaN
5    NaN
6    NaN
dtype: category
Categories (4, interval[timedelta64[ns], left]): [ <
                                                  [0 days 00:00:00, 2 days 00:00:00) <
                                                  [2 days 00:00:00, 4 days 00:00:00) <
                                                  [4 days 00:00:00, 6 days 00:00:00) <
                                                  [6 days 00:00:00, 8 days 00:00:00)]

I believe the problem might be that the data, s in the example, is converted to a numeric data type but the bins are not.

Expected Behavior

The data should be binned according to the intervals in the IntervalIndex. Output from the example should be

0    [0 days 00:00:00, 2 days 00:00:00)
1    [2 days 00:00:00, 4 days 00:00:00)
2    [2 days 00:00:00, 4 days 00:00:00)
3    [4 days 00:00:00, 6 days 00:00:00)
4    [4 days 00:00:00, 6 days 00:00:00)
5    [6 days 00:00:00, 8 days 00:00:00)
6    [6 days 00:00:00, 8 days 00:00:00)
dtype: category
Categories (4, interval[timedelta64[ns], left]): [ <
                                                  [0 days 00:00:00, 2 days 00:00:00) <
                                                  [2 days 00:00:00, 4 days 00:00:00) <
                                                  [4 days 00:00:00, 6 days 00:00:00) <
                                                  [6 days 00:00:00, 8 days 00:00:00)]

Installed Versions

Running pd.show_versions() fails on assert '_distutils' in core.__file__, core.__file__ in .../_distutils_hack/__init__.py:59 which is quite confusing but I was able to get around this by setting the environment variable SETUPTOOLS_USE_DISTUTILS=stdlib.

INSTALLED VERSIONS

commit : 4bfe3d0
python : 3.10.4.final.0
python-bits : 64
OS : Darwin
OS-release : 21.5.0
Version : Darwin Kernel Version 21.5.0: Tue Apr 26 21:08:22 PDT 2022; root:xnu-8020.121.3~4/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8

pandas : 1.4.2
numpy : 1.22.3
pytz : 2022.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 61.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.1
IPython : 8.2.0
pandas_datareader: None
bs4 : 4.11.1
bottleneck : None
brotli : None
fastparquet : 0.8.1
fsspec : 2022.3.0
gcsfs : None
markupsafe : 2.1.1
matplotlib : 3.5.1
numba : None
numexpr : 2.8.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 7.0.0
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.8.0
snappy : None
sqlalchemy : 1.4.35
tables : 3.7.0
tabulate : None
xarray : None
xlrd : None
xlwt : None
zstandard : None

[iansheng]

Pandas 1.3.5 behaves as expected, this problem has appeared since 1.4.0.

[doodger]

Same issue has happened to me on Pandas 1.4.2; using cut and groupby with time intervals works as expected on pandas 1.3.4

[simonjayhawkins]

Thanks @oalfred for the report. This looks like a duplicate of #46218.

[simonjayhawkins]

moving to 1.4.4

[mroeschke]

Closing as a duplicate of #46218. Discussion of a solution seems further along there