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.

Attributes

n	Return the count of the number of periods.
normalize	Return boolean whether the frequency can align with midnight.
weekmask	Return the weekmask used for custom business day calculations.
holidays	Return the holidays used for custom business day calculations.
calendar	Return the calendar used for business day calculations.
start	Return the start time(s) of the business hour.
end	Return the end time(s) of the business hour.
offset	Return the time offset applied to the business day.

See also

BusinessHour

DateOffset subclass representing possibly n business hours.

CustomBusinessDay

DateOffset subclass representing custom business days.

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	Return the calendar used for business day calculations.
end	Return the end time(s) of the business hour.
freqstr	Return a string representing the frequency.
holidays	Return the holidays used for custom business day calculations.
kwds	Return a dict of extra parameters for the offset.
n	Return the count of the number of periods.
name	Return a string representing the base frequency.
nanos	Return an integer of the total number of nanoseconds.
next_bday	Used for moving to next business day.
normalize	Return boolean whether the frequency can align with midnight.
offset	Return the time offset applied to the business day.
rule_code	Return a string representing the base frequency.
start	Return the start time(s) of the business hour.
weekmask	Return the weekmask used for custom business day calculations.

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.