pandas.tseries.offsets.CustomBusinessHour#

class pandas.tseries.offsets.CustomBusinessHour#

DateOffset subclass representing possibly n custom business days.

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

Parameters:

nint, default 1: The number of hours represented.
normalizebool, default False: Normalize start/end dates to midnight before generating date range.
weekmaskstr, Default ‘Mon Tue Wed Thu Fri’: Weekmask of valid business days, passed to numpy.busdaycalendar.
holidayslist: List/array of dates to exclude from the set of valid business days, passed to numpy.busdaycalendar.
calendarnp.busdaycalendar: Calendar to integrate.
startstr, time, or list of str/time, default “09:00”: Start time of your custom business hour in 24h format.
endstr, time, or list of str/time, default: “17:00”: End time of your custom business hour in 24h format.
offsettimedelta, default timedelta(0): Time offset to apply.

Examples

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')

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

You can divide your 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')

Using NumPy business day calendar you can define custom holidays.

>>> 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')

Attributes

`base`	Returns a copy of the calling offset object with n=1 and all other attributes equal.
`calendar`
`end`
`freqstr`	Return a string representing the frequency.
`holidays`
`kwds`	Return a dict of extra parameters for the offset.
`n`
`name`	Return a string representing the base frequency.
`nanos`	Returns a integer of the total number of nanoseconds for fixed frequencies.
`next_bday`	Used for moving to next business day.
`normalize`
`offset`	Alias for self._offset.
`rule_code`	Return a string representing the base frequency.
`start`
`weekmask`

Methods

`copy`()	Return a copy of the frequency.
`is_month_end`(ts)	Return boolean whether a timestamp occurs on the month end.
`is_month_start`(ts)	Return boolean whether a timestamp occurs on the month start.
`is_on_offset`(dt)	Return boolean whether a timestamp intersects with this frequency.
`is_quarter_end`(ts)	Return boolean whether a timestamp occurs on the quarter end.
`is_quarter_start`(ts)	Return boolean whether a timestamp occurs on the quarter start.
`is_year_end`(ts)	Return boolean whether a timestamp occurs on the year end.
`is_year_start`(ts)	Return boolean whether a timestamp occurs on the year start.
`rollback`(other)	Roll provided date backward to next offset only if not on offset.
`rollforward`(other)	Roll provided date forward to next offset only if not on offset.

pandas.tseries.offsets.CustomBusinessHour#

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