pandas.tseries.offsets.CustomBusinessDay#

class pandas.tseries.offsets.CustomBusinessDay#

DateOffset subclass representing possibly n custom business days.

In CustomBusinessDay 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.
`offset`	Return the time offset applied to the business day.

See also

DateOffset: Standard kind of date increment.

Examples

In the example below the default parameters give the next business day.

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

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.CustomBusinessDay(weekmask="Mon Wed Fri")
>>> pd.date_range(dt.datetime(2022, 12, 10), dt.datetime(2022, 12, 21),
...               freq=freq).strftime('%a %d %b %Y %H:%M')
Index(['Mon 12 Dec 2022 00:00', 'Wed 14 Dec 2022 00:00',
       'Fri 16 Dec 2022 00:00', 'Mon 19 Dec 2022 00:00',
       'Wed 21 Dec 2022 00:00'],
       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.CustomBusinessDay(calendar=bdc)
>>> pd.date_range(dt.datetime(2022, 12, 10), dt.datetime(2022, 12, 25), freq=freq)
DatetimeIndex(['2022-12-13', '2022-12-15', '2022-12-16', '2022-12-19',
               '2022-12-20', '2022-12-21', '2022-12-22', '2022-12-23'],
               dtype='datetime64[ns]', freq='C')

If you want to shift the result on n day you can use the parameter offset.

>>> pd.Timestamp(2022, 8, 5, 16) + pd.offsets.CustomBusinessDay(1)
Timestamp('2022-08-08 16:00:00')

>>> import datetime as dt
>>> ts = pd.Timestamp(2022, 8, 5, 16)
>>> ts + pd.offsets.CustomBusinessDay(1, offset=dt.timedelta(days=1))
Timestamp('2022-08-09 16:00:00')

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.
`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`	Returns an integer of the total number of nanoseconds for fixed frequencies.
`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.
`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`(dt)	Roll provided date backward to next offset only if not on offset.
`rollforward`(dt)	Roll provided date forward to next offset only if not on offset.