pyspark.pandas.
date_range
Return a fixed frequency DatetimeIndex.
Left bound for generating dates.
Right bound for generating dates.
Number of periods to generate.
Frequency strings can have multiples, e.g. ‘5H’.
Time zone name for returning localized DatetimeIndex, for example ‘Asia/Hong_Kong’. By default, the resulting DatetimeIndex is timezone-naive.
Normalize start/end dates to midnight before generating date range.
Name of the resulting DatetimeIndex.
Make the interval closed with respect to the given frequency to the ‘left’, ‘right’, or both sides (None, the default).
For compatibility. Has no effect on the result.
See also
DatetimeIndex
An immutable container for datetimes.
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).
start
end
periods
freq
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.
>>> ps.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=None)
Specify start and periods, the number of periods (days).
>>> ps.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=None)
Specify end and periods, the number of periods (days).
>>> ps.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=None)
Specify start, end, and periods; the frequency is generated automatically (linearly spaced).
>>> ps.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 'M' (month end frequency).
'M'
>>> ps.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=None)
Multiples are allowed
>>> ps.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=None)
freq can also be specified as an Offset object.
>>> ps.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=None)
closed controls whether to include start and end that are on the boundary. The default includes boundary points on either end.
>>> ps.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=None)
Use closed='left' to exclude end if it falls on the boundary.
closed='left'
>>> ps.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=None)
Use closed='right' to exclude start if it falls on the boundary.
closed='right'
>>> ps.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=None)