Description
Pandas version checks
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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
pd.cut(
pd.Series([pd.Timestamp('2022-02-26')]),
bins=pd.interval_range(
pd.Timestamp('2022-02-25'),
pd.Timestamp('2022-02-27'),
freq='1D',
)
).value_counts()Issue Description
Binning is not correct in Pandas 1.4.x.
(2022-02-25, 2022-02-26] 0
(2022-02-26, 2022-02-27] 0
Note that the same series gets processed correctly, when the dtype is not datetime64[ns]
pd.cut(
pd.Series(['2022-02-26']),
bins=pd.interval_range(
pd.Timestamp('2022-02-25'),
pd.Timestamp('2022-02-27'),
freq='1D',
)
).value_counts()
Expected Behavior
The expected behavior is
(2022-02-25, 2022-02-26] 1
(2022-02-26, 2022-02-27] 0
Installed Versions
INSTALLED VERSIONS
commit : 06d2301
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.60.1-microsoft-standard-WSL2
Version : #1 SMP Wed Aug 25 23:20:18 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.4.1
numpy : 1.22.1
pytz : 2021.3
dateutil : 2.8.2
pip : 22.0.3
setuptools : 60.7.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.3
IPython : 8.0.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.5.1
numba : None
numexpr : None
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
zstandard : None