pandas.tseries.offsets.CustomBusinessMonthBegin

class pandas.tseries.offsets.CustomBusinessMonthBegin(n=1, normalize=False, weekmask='Mon Tue Wed Thu Fri', holidays=None, calendar=None, offset=datetime.timedelta(0))

DateOffset subclass representing custom business month(s).

Increments between beginning of month dates.

Parameters:

nint, default 1

The number of months represented.

normalizebool, default False

Normalize start 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.

Attributes

m_offset	Return a MonthBegin or MonthEnd offset.
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 we use the default parameters.

>>> ts = pd.Timestamp(2022, 8, 5)
>>> ts + pd.offsets.CustomBusinessMonthBegin()
Timestamp('2022-09-01 00:00:00')

Custom business month start 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.CustomBusinessMonthBegin(weekmask="Wed Thu")
>>> pd.date_range(dt.datetime(2022, 7, 10), dt.datetime(2022, 12, 18),
...               freq=freq).strftime('%a %d %b %Y %H:%M')
Index(['Wed 03 Aug 2022 00:00', 'Thu 01 Sep 2022 00:00',
       'Wed 05 Oct 2022 00:00', 'Wed 02 Nov 2022 00:00',
       'Thu 01 Dec 2022 00:00'],
       dtype='str')

Using NumPy business day calendar you can define custom holidays.

>>> import datetime as dt
>>> bdc = np.busdaycalendar(holidays=['2022-08-01', '2022-09-30',
...                                   '2022-10-31', '2022-11-01'])
>>> freq = pd.offsets.CustomBusinessMonthBegin(calendar=bdc)
>>> pd.date_range(dt.datetime(2022, 7, 10), dt.datetime(2022, 11, 10), freq=freq)
DatetimeIndex(['2022-08-02', '2022-09-01', '2022-10-03', '2022-11-02'],
               dtype='datetime64[us]', freq='CBMS')