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merged 5 commits into from
Dec 12, 2022
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DOC: update documentation for CustomBusinessHour #50182

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63e51f0
DOC add examples CustomBusinessHour I
natmokval Dec 10, 2022
2d72295
Update pandas/_libs/tslibs/offsets.pyx
natmokval Dec 11, 2022
4380049
DOC add examples CustomBusinessHour II
natmokval Dec 12, 2022
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DOC add examples CustomBusinessHour III
natmokval Dec 12, 2022
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Merge remote-tracking branch 'origin/main' into 49838-docs-custombusi…
natmokval Dec 12, 2022
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69 changes: 64 additions & 5 deletions pandas/_libs/tslibs/offsets.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -3619,6 +3619,8 @@ cdef class CustomBusinessHour(BusinessHour):
"""
DateOffset subclass representing possibly n custom business days.

In CustomBusinessHour we can use custom weekmask, holidays, and calendar.

Parameters
----------
n : int, default 1
Expand All @@ -3627,24 +3629,81 @@ cdef class CustomBusinessHour(BusinessHour):
Normalize start/end dates to midnight before generating date range.
weekmask : str, Default 'Mon Tue Wed Thu Fri'
Weekmask of valid business days, passed to ``numpy.busdaycalendar``.
holidays : list
List/array of dates to exclude from the set of valid business days,
passed to ``numpy.busdaycalendar``.
calendar : np.busdaycalendar
Calendar to integrate.
start : str, time, or list of str/time, default "09:00"
Start time of your custom business hour in 24h format.
end : str, time, or list of str/time, default: "17:00"
End time of your custom business hour in 24h format.

Examples
--------
>>> from datetime import time
In the example below the default parameters give the next business hour.

>>> ts = pd.Timestamp(2022, 8, 5, 16)
>>> ts + pd.offsets.CustomBusinessHour()
Timestamp('2022-08-08 09:00:00')

We can also change the start and the end of business hours.

>>> ts = pd.Timestamp(2022, 8, 5, 16)
>>> ts + pd.offsets.CustomBusinessHour(start="11:00")
Timestamp('2022-08-08 11:00:00')
>>> ts + pd.offsets.CustomBusinessHour(end=time(19, 0))

>>> from datetime import time as dt_time
>>> ts = pd.Timestamp(2022, 8, 5, 16)
>>> ts + pd.offsets.CustomBusinessHour(end=dt_time(19, 0))
Timestamp('2022-08-05 17:00:00')
>>> ts + pd.offsets.CustomBusinessHour(start=[time(9, 0), "20:00"],
... end=["17:00", time(22, 0)])
Timestamp('2022-08-05 20:00:00')

>>> ts = pd.Timestamp(2022, 8, 5, 22)
>>> ts + pd.offsets.CustomBusinessHour(end=dt_time(19, 0))
Timestamp('2022-08-08 10:00:00')

In the example below we divide our business day hours into several parts.

>>> import datetime as dt
>>> freq = pd.offsets.CustomBusinessHour(start=["06:00", "10:00", "15:00"],
... end=["08:00", "12:00", "17:00"])
>>> pd.date_range(dt.datetime(2022, 12, 9), dt.datetime(2022, 12, 13), freq=freq)
DatetimeIndex(['2022-12-09 06:00:00', '2022-12-09 07:00:00',
'2022-12-09 10:00:00', '2022-12-09 11:00:00',
'2022-12-09 15:00:00', '2022-12-09 16:00:00',
'2022-12-12 06:00:00', '2022-12-12 07:00:00',
'2022-12-12 10:00:00', '2022-12-12 11:00:00',
'2022-12-12 15:00:00', '2022-12-12 16:00:00'],
dtype='datetime64[ns]', freq='CBH')

Business days can be specified by ``weekmask`` parameter. To convert
the returned datetime object to its string representation
the function strftime() is used in the next example.

>>> import datetime as dt
>>> freq = pd.offsets.CustomBusinessHour(weekmask="Mon Wed Fri",
... start="10:00", end="13:00")
>>> pd.date_range(dt.datetime(2022, 12, 10), dt.datetime(2022, 12, 18),
... freq=freq).strftime('%a %d %b %Y %H:%M')
Index(['Mon 12 Dec 2022 10:00', 'Mon 12 Dec 2022 11:00',
'Mon 12 Dec 2022 12:00', 'Wed 14 Dec 2022 10:00',
'Wed 14 Dec 2022 11:00', 'Wed 14 Dec 2022 12:00',
'Fri 16 Dec 2022 10:00', 'Fri 16 Dec 2022 11:00',
'Fri 16 Dec 2022 12:00'],
dtype='object')

In the example below we define custom holidays by using NumPy business day calendar.

>>> import datetime as dt
>>> bdc = np.busdaycalendar(holidays=['2022-12-12', '2022-12-14'])
>>> freq = pd.offsets.CustomBusinessHour(calendar=bdc, start="10:00", end="13:00")
>>> pd.date_range(dt.datetime(2022, 12, 10), dt.datetime(2022, 12, 18), freq=freq)
DatetimeIndex(['2022-12-13 10:00:00', '2022-12-13 11:00:00',
'2022-12-13 12:00:00', '2022-12-15 10:00:00',
'2022-12-15 11:00:00', '2022-12-15 12:00:00',
'2022-12-16 10:00:00', '2022-12-16 11:00:00',
'2022-12-16 12:00:00'],
dtype='datetime64[ns]', freq='CBH')
"""

_prefix = "CBH"
Expand Down