pandas.interval_range

pandas.interval_range(start=None, end=None, periods=None, freq=None, name=None, inclusive=None)[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, or DateOffset, default None

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

namestr, default None

Name of the resulting IntervalIndex.

inclusive{“both”, “neither”, “left”, “right”}, default “both”

Include boundaries; Whether to set each bound as inclusive or not.

New in version 1.5.0.

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

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

Deprecated since version 1.5.0: Argument closed has been deprecated to standardize boundary inputs. Use inclusive instead, to set each bound as closed or open.

Returns
IntervalIndex

See also

IntervalIndex

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

Notes

Of the four parameters start, end, periods, and freq, exactly three must be specified. If freq is omitted, the resulting IntervalIndex will have periods linearly spaced elements between start and end, inclusively.

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

Examples

Numeric start and end is supported.

>>> pd.interval_range(start=0, end=5, inclusive="right")
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'), inclusive="right")
IntervalIndex([(2017-01-01, 2017-01-02], (2017-01-02, 2017-01-03],
               (2017-01-03, 2017-01-04]],
              dtype='interval[datetime64[ns], right]')

The freq parameter specifies the frequency between the left and right. endpoints of the individual intervals within the IntervalIndex. For numeric start and end, the frequency must also be numeric.

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

Similarly, for datetime-like start and end, the frequency must be convertible to a DateOffset.

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

Specify start, end, and periods; the frequency is generated automatically (linearly spaced).

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

The inclusive parameter specifies which endpoints of the individual intervals within the IntervalIndex are inclusive.

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