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def date_range(start=None, end=None, periods=None, freq=None, tz=None, normalize: 'bool' = False, name: 'Hashable | None' = None, inclusive: 'IntervalClosedType' = 'both', *, unit: 'str | None' = None, **kwargs) -> 'DatetimeIndex'
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
:class:`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
----------
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, Timedelta, datetime.timedelta, or DateOffset, default 'D'
Frequency strings can have multiples, e.g. '5h'. See
:ref:`here <timeseries.offset_aliases>` 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 unless timezone-aware datetime-likes are passed.
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.
.. versionadded:: 1.4.0
unit : str, default None
Specify the desired resolution of the result.
.. versionadded:: 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
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
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')
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