pandas.date_range¶
-
pandas.
date_range
(start=None, end=None, periods=None, freq=None, tz=None, normalize=False, name=None, closed=None, **kwargs)[source]¶ Return a fixed frequency DatetimeIndex.
Parameters: start : str or datetime-like, optional
Left bound for generating dates.
end : str or datetime-like, optional
Right bound for generating dates.
periods : integer, optional
Number of periods to generate.
freq : str or DateOffset, default ‘D’ (calendar daily)
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.
closed : {None, ‘left’, ‘right’}, optional
Make the interval closed with respect to the given frequency to the ‘left’, ‘right’, or both sides (None, the default).
**kwargs
For compatibility. Has no effect on the result.
Returns: - rng : DatetimeIndex
See also
pandas.DatetimeIndex
- An immutable container for datetimes.
pandas.timedelta_range
- Return a fixed frequency TimedeltaIndex.
pandas.period_range
- Return a fixed frequency PeriodIndex.
pandas.interval_range
- Return a fixed frequency IntervalIndex.
Notes
Of the four parameters
start
,end
,periods
, andfreq
, exactly three must be specified. Iffreq
is omitted, the resultingDatetimeIndex
will haveperiods
linearly spaced elements betweenstart
andend
(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 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'], freq=None)
Other Parameters
Changed the freq (frequency) to
'M'
(month end frequency).>>> pd.date_range(start='1/1/2018', periods=5, freq='M') DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31', '2018-04-30', '2018-05-31'], dtype='datetime64[ns]', freq='M')
Multiples are allowed
>>> pd.date_range(start='1/1/2018', periods=5, freq='3M') DatetimeIndex(['2018-01-31', '2018-04-30', '2018-07-31', '2018-10-31', '2019-01-31'], dtype='datetime64[ns]', freq='3M')
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='3M')
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')
closed controls whether to include start and end that are on the boundary. The default includes boundary points on either end.
>>> pd.date_range(start='2017-01-01', end='2017-01-04', closed=None) DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04'], dtype='datetime64[ns]', freq='D')
Use
closed='left'
to exclude end if it falls on the boundary.>>> pd.date_range(start='2017-01-01', end='2017-01-04', closed='left') DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03'], dtype='datetime64[ns]', freq='D')
Use
closed='right'
to exclude start if it falls on the boundary.>>> pd.date_range(start='2017-01-01', end='2017-01-04', closed='right') DatetimeIndex(['2017-01-02', '2017-01-03', '2017-01-04'], dtype='datetime64[ns]', freq='D')