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.

Parameters:

nint, default 1

The number of days 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.

offsettimedelta, default timedelta(0)

Time offset to apply.

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
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.
normalize
offset	Alias for self._offset.
rule_code	Return a string representing the base frequency.
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(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.