pandas.interval_range#

pandas.interval_range(start=None,end=None,periods=None,freq=None,name=None,closed='right')[source]#

Return a fixed frequency IntervalIndex.

Parameters:

startnumeric or datetime-like, default None

Left bound for generating intervals.

endnumeric or datetime-like, default None

Right bound for generating intervals.

periodsint, default None

Number of periods to generate.

freqnumeric, str, Timedelta, datetime.timedelta, or DateOffset, default None

The length of each interval. Must be consistent with the type of startand end, e.g. 2 for numeric, or ‘5H’ for datetime-like. Default is 1for numeric and ‘D’ for datetime-like.

namestr, default None

Name of the resulting IntervalIndex.

closed{‘left’, ‘right’, ‘both’, ‘neither’}, default ‘right’

Whether the intervals are closed on the left-side, right-side, bothor neither.

Returns:

IntervalIndex

See also

IntervalIndex

An Index of intervals that are all closed on the same side.

Notes

Of the four parametersstart,end,periods, andfreq,exactly three must be specified. Iffreq is omitted, the resultingIntervalIndex will haveperiods linearly spaced elements betweenstart andend, inclusively.

To learn more about datetime-like frequency strings, please seethis link.

Examples

Numericstart andend is supported.

>>>pd.interval_range(start=0,end=5)IntervalIndex([(0, 1], (1, 2], (2, 3], (3, 4], (4, 5]],              dtype='interval[int64, right]')

Additionally, datetime-like input is also supported.

>>>pd.interval_range(start=pd.Timestamp('2017-01-01'),...end=pd.Timestamp('2017-01-04'))IntervalIndex([(2017-01-01 00:00:00, 2017-01-02 00:00:00],               (2017-01-02 00:00:00, 2017-01-03 00:00:00],               (2017-01-03 00:00:00, 2017-01-04 00:00:00]],              dtype='interval[datetime64[ns], right]')

Thefreq parameter specifies the frequency between the left and right.endpoints of the individual intervals within theIntervalIndex. Fornumericstart andend, the frequency must also be numeric.

>>>pd.interval_range(start=0,periods=4,freq=1.5)IntervalIndex([(0.0, 1.5], (1.5, 3.0], (3.0, 4.5], (4.5, 6.0]],              dtype='interval[float64, right]')

Similarly, for datetime-likestart andend, the frequency must beconvertible to a DateOffset.

>>>pd.interval_range(start=pd.Timestamp('2017-01-01'),...periods=3,freq='MS')IntervalIndex([(2017-01-01 00:00:00, 2017-02-01 00:00:00],               (2017-02-01 00:00:00, 2017-03-01 00:00:00],               (2017-03-01 00:00:00, 2017-04-01 00:00:00]],              dtype='interval[datetime64[ns], right]')

Specifystart,end, andperiods; the frequency is generatedautomatically (linearly spaced).

>>>pd.interval_range(start=0,end=6,periods=4)IntervalIndex([(0.0, 1.5], (1.5, 3.0], (3.0, 4.5], (4.5, 6.0]],          dtype='interval[float64, right]')

Theclosed parameter specifies which endpoints of the individualintervals within theIntervalIndex are closed.

>>>pd.interval_range(end=5,periods=4,closed='both')IntervalIndex([[1, 2], [2, 3], [3, 4], [4, 5]],              dtype='interval[int64, both]')