pandas.tseries.offsets.DateOffset#

class pandas.tseries.offsets.DateOffset#

Standard kind of date increment used for a date range.

Works exactly like the keyword argument form of relativedelta. Note that the positional argument form of relativedelta is not supported. Use of the keyword n is discouraged– you would be better off specifying n in the keywords you use, but regardless it is there for you. n is needed for DateOffset subclasses.

DateOffset works as follows. Each offset specify a set of dates that conform to the DateOffset. For example, Bday defines this set to be the set of dates that are weekdays (M-F). To test if a date is in the set of a DateOffset dateOffset we can use the is_on_offset method: dateOffset.is_on_offset(date).

If a date is not on a valid date, the rollback and rollforward methods can be used to roll the date to the nearest valid date before/after the date.

DateOffsets can be created to move dates forward a given number of valid dates. For example, Bday(2) can be added to a date to move it two business days forward. If the date does not start on a valid date, it is first rolled forward to the next valid date, and that roll counts as the first of the n increments. For example, 2014-08-31 is a Sunday, so Timestamp("2014-08-31") + BDay(1) only rolls forward to Monday 2014-09-01, and adding BDay(2) gives Tuesday 2014-09-02. Equivalently, the date is first rolled back to the previous valid date, then moved n valid dates forward. Thus pseudo code is:

def __add__(date):
  date = rollback(date) # does nothing if date is valid
  return date + <n number of periods>

When a date offset is created for a negative number of periods, the roll is symmetric: rolling back to the previous valid date counts as the first decrement; equivalently, the date is first rolled forward, then moved abs(n) valid dates backward. The pseudo code is:

def __add__(date):
  date = rollforward(date) # does nothing if date is valid
  return date + <n number of periods>

Zero presents a problem. Should it roll forward or back? We arbitrarily have it rollforward:

date + BDay(0) == BDay.rollforward(date)

Since 0 is a bit weird, we suggest avoiding its use. Because the roll counts as an increment, date + BDay(0) and date + BDay(1) give the same result when date is not a business day.

Besides, adding a DateOffsets specified by the singular form of the date component can be used to replace certain component of the timestamp.

Attributes

`n`	Return the count of the number of periods.
`normalize`	Return boolean whether the frequency can align with midnight.

weekday	(int {0, 1, …, 6}, default 0) A specific integer for the day of the week. - 0 is Monday - 1 is Tuesday - 2 is Wednesday - 3 is Thursday - 4 is Friday - 5 is Saturday - 6 is Sunday Instead Weekday type from dateutil.relativedelta can be used. - MO is Monday - TU is Tuesday - WE is Wednesday - TH is Thursday - FR is Friday - SA is Saturday - SU is Sunday.
**kwds	Temporal parameter that add to or replace the offset value. Parameters that add to the offset (like Timedelta): - years - months - weeks - days - hours - minutes - seconds - milliseconds - microseconds - nanoseconds Parameters that replace the offset value: - year - month - day - weekday - hour - minute - second - microsecond - nanosecond.

See also

dateutil.relativedelta.relativedelta: The relativedelta type is designed to be applied to an existing datetime and can replace specific components of that datetime, or represents an interval of time.

Notes

When added to a DatetimeIndex or datetime Series, a DateOffset is applied to each entry independently. Calendar components such as months and years do not represent a fixed duration, so evenly spaced input dates are not guaranteed to remain evenly spaced: dates that would fall on a nonexistent day are clamped to the end of the month. For example, adding DateOffset(months=1) to both 2018-01-30 and 2018-01-31 yields 2018-02-28 in each case.

Examples

>>> from pandas.tseries.offsets import DateOffset
>>> ts = pd.Timestamp('2017-01-01 09:10:11')
>>> ts + DateOffset(months=3)
Timestamp('2017-04-01 09:10:11')

>>> ts = pd.Timestamp('2017-01-01 09:10:11')
>>> ts + DateOffset(months=2)
Timestamp('2017-03-01 09:10:11')
>>> ts + DateOffset(day=31)
Timestamp('2017-01-31 09:10:11')

>>> ts + pd.DateOffset(hour=8)
Timestamp('2017-01-01 08:10:11')

Attributes

`base`	Returns a copy of the calling offset object with n=1 and all other attributes equal.
`freqstr`	Return a string representing the frequency.
`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.
`normalize`	Return boolean whether the frequency can align with midnight.
`rule_code`	Return a string representing the base frequency.

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.