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# pandas.date_range

pandas.date_range(start=None, end=None, periods=None, freq=None, tz=None, normalize=False, name=None, inclusive='both', *, unit=None, **kwargs)

  Return a fixed frequency DatetimeIndex.

  Returns the range of equally spaced time points (where the difference between any two adjacent points is specified by the given frequency) such that they all satisfy start <[=] x <[=] end, where the first one and the last one are, resp., the first and last time points in that range that fall on the boundary of `freq` (if given as a frequency string) or that are valid for `freq` (if given as a `pandas.tseries.offsets.DateOffset`). (If exactly one of `start`, `end`, or `freq` is not specified, this missing parameter can be computed given `periods`, the number of timesteps in the range. See the note below.)

  Parameters:
    startstr or datetime-like, optional

      Left bound for generating dates.

    endstr or datetime-like, optional

      Right bound for generating dates.

    periodsint, optional

      Number of periods to generate.

    freqstr, Timedelta, datetime.timedelta, or DateOffset, default ‘D’

      Frequency strings can have multiples, e.g. ‘5h’. See here for a list of frequency aliases.

    tzstr or tzinfo, optional

      Time zone name for returning localized DatetimeIndex, for example ‘Asia/Hong_Kong’. By default, the resulting DatetimeIndex is timezone-naive unless timezone-aware datetime-likes are passed.

    normalizebool, default False

      Normalize start/end dates to midnight before generating date range.

    namestr, 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.

      Added in version 1.4.0.

    unitstr, default None

      Specify the desired resolution of the result.

      Added in version 2.0.0.

    **kwargs

      For compatibility. Has no effect on the result.

  Returns:
    DatetimeIndex

  See also

  `DatetimeIndex`

    An immutable container for datetimes.

  `timedelta_range`

    Return a fixed frequency TimedeltaIndex.

  `period_range`

    Return a fixed frequency PeriodIndex.

  `interval_range`

    Return a fixed frequency IntervalIndex.

  Notes

  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.

  Examples

  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.

      >>> pd.date_range(start='1/1/2018', end='1/08/2018')
      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 timezone-aware start and end, with the default daily frequency.

      >>> pd.date_range(
      ...     start=pd.to_datetime("1/1/2018").tz_localize("Europe/Berlin"),
      ...     end=pd.to_datetime("1/08/2018").tz_localize("Europe/Berlin"),
      ... )
      DatetimeIndex(['2018-01-01 00:00:00+01:00', '2018-01-02 00:00:00+01:00',
                     '2018-01-03 00:00:00+01:00', '2018-01-04 00:00:00+01:00',
                     '2018-01-05 00:00:00+01:00', '2018-01-06 00:00:00+01:00',
                     '2018-01-07 00:00:00+01:00', '2018-01-08 00:00:00+01:00'],
                    dtype='datetime64[ns, Europe/Berlin]', freq='D')

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

      >>> pd.date_range(start='1/1/2018', periods=8)
      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).

      >>> pd.date_range(end='1/1/2018', periods=8)
      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).

      >>> pd.date_range(start='2018-04-24', end='2018-04-27', periods=3)
      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 `'ME'` (month end frequency).

      >>> pd.date_range(start='1/1/2018', periods=5, freq='ME')
      DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31', '2018-04-30',
                     '2018-05-31'],
                    dtype='datetime64[ns]', freq='ME')

  Multiples are allowed

      >>> pd.date_range(start='1/1/2018', periods=5, freq='3ME')
      DatetimeIndex(['2018-01-31', '2018-04-30', '2018-07-31', '2018-10-31',
                     '2019-01-31'],
                    dtype='datetime64[ns]', freq='3ME')

  freq can also be specified as an Offset object.

      >>> pd.date_range(start='1/1/2018', periods=5, freq=pd.offsets.MonthEnd(3))
      DatetimeIndex(['2018-01-31', '2018-04-30', '2018-07-31', '2018-10-31',
                     '2019-01-31'],
                    dtype='datetime64[ns]', freq='3ME')

  Specify tz to set the timezone.

      >>> pd.date_range(start='1/1/2018', periods=5, tz='Asia/Tokyo')
      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.

      >>> pd.date_range(start='2017-01-01', end='2017-01-04', inclusive="both")
      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.

      >>> pd.date_range(start='2017-01-01', end='2017-01-04', inclusive='left')
      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.

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

  Specify a unit

      >>> pd.date_range(start="2017-01-01", periods=10, freq="100YS", unit="s")
      DatetimeIndex(['2017-01-01', '2117-01-01', '2217-01-01', '2317-01-01',
                     '2417-01-01', '2517-01-01', '2617-01-01', '2717-01-01',
                     '2817-01-01', '2917-01-01'],
                    dtype='datetime64[s]', freq='100YS-JAN')