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# 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[us]', 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='str')

  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[us]', 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` | (DEPRECATED) 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.