mars.dataframe.date_range(start=None, end=None, periods=None, freq=None, tz=None, normalize=False, name=None, closed=NoDefault.no_default, inclusive=None, chunk_size=None, **kwargs)[source]#

Return a fixed frequency DatetimeIndex.

  • start (str or datetime-like, optional) – Left bound for generating dates.

  • end (str or datetime-like, optional) – Right bound for generating dates.

  • periods (int, optional) – Number of periods to generate.

  • freq (str or DateOffset, default 'D') – Frequency strings can have multiples, e.g. ‘5H’. See here for a list of frequency aliases.

  • tz (str or tzinfo, optional) – Time zone name for returning localized DatetimeIndex, for example ‘Asia/Hong_Kong’. By default, the resulting DatetimeIndex is timezone-naive.

  • normalize (bool, default False) – Normalize start/end dates to midnight before generating date range.

  • name (str, default None) – Name of the resulting DatetimeIndex.

  • inclusive ({“both”, “neither”, “left”, “right”}, default “both”) – Include boundaries; Whether to set each bound as closed or open.

  • **kwargs – For compatibility. Has no effect on the result.



Return type


See also


An immutable container for datetimes.


Return a fixed frequency TimedeltaIndex.


Return a fixed frequency PeriodIndex.


Return a fixed frequency IntervalIndex.


Of the four parameters start, end, periods, and freq, exactly three must be specified. If freq is omitted, the resulting DatetimeIndex will have periods linearly spaced elements between start and end (closed on both sides).

To learn more about the frequency strings, please see this link.


Specifying the values

The next four examples generate the same DatetimeIndex, but vary the combination of start, end and periods.

Specify start and end, with the default daily frequency. >>> import mars.dataframe as md

>>> md.date_range(start='1/1/2018', end='1/08/2018').execute()
DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
               '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08'],
              dtype='datetime64[ns]', freq='D')

Specify start and periods, the number of periods (days).

>>> md.date_range(start='1/1/2018', periods=8).execute()
DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
               '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08'],
              dtype='datetime64[ns]', freq='D')

Specify end and periods, the number of periods (days).

>>> md.date_range(end='1/1/2018', periods=8).execute()
DatetimeIndex(['2017-12-25', '2017-12-26', '2017-12-27', '2017-12-28',
               '2017-12-29', '2017-12-30', '2017-12-31', '2018-01-01'],
              dtype='datetime64[ns]', freq='D')

Specify start, end, and periods; the frequency is generated automatically (linearly spaced).

>>> md.date_range(start='2018-04-24', end='2018-04-27', periods=3).execute()
DatetimeIndex(['2018-04-24 00:00:00', '2018-04-25 12:00:00',
               '2018-04-27 00:00:00'],
              dtype='datetime64[ns]', freq=None)

Other Parameters

Changed the freq (frequency) to 'M' (month end frequency).

>>> md.date_range(start='1/1/2018', periods=5, freq='M').execute()
DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31', '2018-04-30',
              dtype='datetime64[ns]', freq='M')

Multiples are allowed

>>> md.date_range(start='1/1/2018', periods=5, freq='3M').execute()
DatetimeIndex(['2018-01-31', '2018-04-30', '2018-07-31', '2018-10-31',
              dtype='datetime64[ns]', freq='3M')

freq can also be specified as an Offset object.

>>> md.date_range(start='1/1/2018', periods=5, freq=md.offsets.MonthEnd(3)).execute()
DatetimeIndex(['2018-01-31', '2018-04-30', '2018-07-31', '2018-10-31',
              dtype='datetime64[ns]', freq='3M')

Specify tz to set the timezone.

>>> md.date_range(start='1/1/2018', periods=5, tz='Asia/Tokyo').execute()
DatetimeIndex(['2018-01-01 00:00:00+09:00', '2018-01-02 00:00:00+09:00',
               '2018-01-03 00:00:00+09:00', '2018-01-04 00:00:00+09:00',
               '2018-01-05 00:00:00+09:00'],
              dtype='datetime64[ns, Asia/Tokyo]', freq='D')

inclusive controls whether to include start and end that are on the boundary. The default, “both”, includes boundary points on either end.

>>> md.date_range(start='2017-01-01', end='2017-01-04', inclusive='both').execute()
DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04'],
              dtype='datetime64[ns]', freq='D')

Use inclusive='left' to exclude end if it falls on the boundary.

>>> md.date_range(start='2017-01-01', end='2017-01-04', closed='left').execute()
DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03'],
              dtype='datetime64[ns]', freq='D')

Use inclusive='right' to exclude start if it falls on the boundary, and similarly inclusive=’neither’ will exclude both start and end.

>>> md.date_range(start='2017-01-01', end='2017-01-04', closed='right').execute()
DatetimeIndex(['2017-01-02', '2017-01-03', '2017-01-04'],
              dtype='datetime64[ns]', freq='D')


Pandas 1.4.0 or later is required to use inclusive='neither'. Otherwise an error may be raised.