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  • [X] I have confirmed this bug exists on the latest version of pandas.

  • [X] I have confirmed this bug exists on the main branch of pandas.

Reproducible Example

import pandas as pd
import datetime as dt
freq = pd.offsets.CustomBusinessHour(weekmask="Sat Sun", start="00:00", end="16:00")
pd.date_range(dt.datetime(2020,1,1), dt.datetime(2020,1,15), freq=freq)

Issue Description

Expected to return every hour of a day from midnight to 16:00, but when including end='16:00' that last hour isn't captured. Per the doc it states that end defaults to '16:00'. You can put any hour for end and it doesn't capture that final hour.

DatetimeIndex(['2020-01-04 00:00:00', '2020-01-04 01:00:00',
               '2020-01-04 02:00:00', '2020-01-04 03:00:00',
               '2020-01-04 04:00:00', '2020-01-04 05:00:00',
               '2020-01-04 06:00:00', '2020-01-04 07:00:00',
               '2020-01-04 08:00:00', '2020-01-04 09:00:00',
               '2020-01-04 10:00:00', '2020-01-04 11:00:00',
               '2020-01-04 12:00:00', '2020-01-04 13:00:00',
               '2020-01-04 14:00:00', '2020-01-04 15:00:00',
               '2020-01-05 00:00:00', '2020-01-05 01:00:00',
               '2020-01-05 02:00:00', '2020-01-05 03:00:00',
               '2020-01-05 04:00:00', '2020-01-05 05:00:00',
               '2020-01-05 06:00:00', '2020-01-05 07:00:00',
               '2020-01-05 08:00:00', '2020-01-05 09:00:00',
               '2020-01-05 10:00:00', '2020-01-05 11:00:00',
               '2020-01-05 12:00:00', '2020-01-05 13:00:00',
               '2020-01-05 14:00:00', '2020-01-05 15:00:00',
               '2020-01-11 00:00:00', '2020-01-11 01:00:00',
               '2020-01-11 02:00:00', '2020-01-11 03:00:00',
               '2020-01-11 04:00:00', '2020-01-11 05:00:00',
               '2020-01-11 06:00:00', '2020-01-11 07:00:00',
               '2020-01-11 08:00:00', '2020-01-11 09:00:00',
               '2020-01-11 10:00:00', '2020-01-11 11:00:00',
               '2020-01-11 12:00:00', '2020-01-11 13:00:00',
               '2020-01-11 14:00:00', '2020-01-11 15:00:00',
               '2020-01-12 00:00:00', '2020-01-12 01:00:00',
               '2020-01-12 02:00:00', '2020-01-12 03:00:00',
               '2020-01-12 04:00:00', '2020-01-12 05:00:00',
               '2020-01-12 06:00:00', '2020-01-12 07:00:00',
               '2020-01-12 08:00:00', '2020-01-12 09:00:00',
               '2020-01-12 10:00:00', '2020-01-12 11:00:00',
               '2020-01-12 12:00:00', '2020-01-12 13:00:00',
               '2020-01-12 14:00:00', '2020-01-12 15:00:00'],
              dtype='datetime64[ns]', freq='CBH')

Expected Behavior

import pandas as pd import datetime as dt freq = pd.offsets.CustomBusinessHour(weekmask="Sat Sun", start="00:00", end="16:00") pd.date_range(dt.datetime(2020,1,1), dt.datetime(2020,1,15), freq=freq)

Expected:

DatetimeIndex(['2020-01-04 00:00:00', '2020-01-04 01:00:00',
               '2020-01-04 02:00:00', '2020-01-04 03:00:00',
               '2020-01-04 04:00:00', '2020-01-04 05:00:00',
               '2020-01-04 06:00:00', '2020-01-04 07:00:00',
               '2020-01-04 08:00:00', '2020-01-04 09:00:00',
               '2020-01-04 10:00:00', '2020-01-04 11:00:00',
               '2020-01-04 12:00:00', '2020-01-04 13:00:00',
               '2020-01-04 14:00:00', '2020-01-04 15:00:00',
               '2020-01-04 16:00:00', '2020-01-05 00:00:00',
               '2020-01-05 01:00:00', '2020-01-05 02:00:00',
               '2020-01-05 03:00:00', '2020-01-05 04:00:00',
               '2020-01-05 05:00:00', '2020-01-05 06:00:00',
               '2020-01-05 07:00:00', '2020-01-05 08:00:00',
               '2020-01-05 09:00:00', '2020-01-05 10:00:00',
               '2020-01-05 11:00:00', '2020-01-05 12:00:00',
               '2020-01-05 13:00:00', '2020-01-05 14:00:00',
               '2020-01-05 15:00:00', '2020-01-05 16:00:00',
               '2020-01-11 00:00:00', '2020-01-11 01:00:00',
               '2020-01-11 02:00:00', '2020-01-11 03:00:00',
               '2020-01-11 04:00:00', '2020-01-11 05:00:00',
               '2020-01-11 06:00:00', '2020-01-11 07:00:00',
               '2020-01-11 08:00:00', '2020-01-11 09:00:00',
               '2020-01-11 10:00:00', '2020-01-11 11:00:00',
               '2020-01-11 12:00:00', '2020-01-11 13:00:00',
               '2020-01-11 14:00:00', '2020-01-11 15:00:00',
               '2020-01-11 16:00:00', '2020-01-12 00:00:00',
               '2020-01-12 01:00:00', '2020-01-12 02:00:00',
               '2020-01-12 03:00:00', '2020-01-12 04:00:00',
               '2020-01-12 05:00:00', '2020-01-12 06:00:00',
               '2020-01-12 07:00:00', '2020-01-12 08:00:00',
               '2020-01-12 09:00:00', '2020-01-12 10:00:00',
               '2020-01-12 11:00:00', '2020-01-12 12:00:00',
               '2020-01-12 13:00:00', '2020-01-12 14:00:00',
               '2020-01-12 15:00:00', '2020-01-12 16:00:00'],
              dtype='datetime64[ns]', freq='CBH')

Installed Versions

INSTALLED VERSIONS ------------------ commit : 91111fd99898d9dcaa6bf6bedb662db4108da6e6 python : 3.9.2.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.19041 machine : AMD64 processor : Intel64 Family 6 Model 165 Stepping 5, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : English_United States.1252 pandas : 1.5.1 numpy : 1.23.4 pytz : 2022.2.1 dateutil : 2.8.2 setuptools : 65.3.0 pip : 22.2.2 Cython : None pytest : 7.2.0 hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : 3.0.3 lxml.etree : 4.9.1 html5lib : None pymysql : None psycopg2 : None jinja2 : 3.1.2 IPython : 8.4.0 pandas_datareader: None bs4 : 4.11.1 bottleneck : None brotli : fastparquet : None fsspec : 2022.8.2 gcsfs : None matplotlib : 3.6.1 numba : None numexpr : None odfpy : None openpyxl : 3.0.10 pandas_gbq : None pyarrow : None pyreadstat : None pyxlsb : 1.0.10 s3fs : None scipy : 1.8.1 snappy : None sqlalchemy : 1.4.40 tables : None tabulate : None xarray : None xlrd : None xlwt : None zstandard : None tzdata : 2022.2 ​

Comment From: Eikinho

take

Comment From: natmokval

Hi @Eikinho. Do you mind if I will work on this issue as well?

Comment From: natmokval

I am not sure that there is a problem here. It looks like expected behavior. The return value contains the beginning of every business hour. That is why 16:00 is not included in this case.

The example from the issue description may be useful to clarify the behavior of CustomBusinessHour. Probably, documentation for CustomBusinessHour can be updated. @MarcoGorelli, what do you think?

Comment From: MarcoGorelli

Agree that this looks expected - and sure, an extra example to clarify would be great, the docs are quite sparse on this at the moment