@@ -1016,7 +1016,7 @@ def interval_range(
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Additionally, datetime-like input is also supported.
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>>> pd.interval_range(start=pd.Timestamp('2017-01-01'),
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- ... end=pd.Timestamp('2017-01-04'))
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+ ... end=pd.Timestamp('2017-01-04'), inclusive="right" )
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IntervalIndex([(2017-01-01, 2017-01-02], (2017-01-02, 2017-01-03],
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(2017-01-03, 2017-01-04]],
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dtype='interval[datetime64[ns], right]')
@@ -1025,23 +1025,23 @@ def interval_range(
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endpoints of the individual intervals within the ``IntervalIndex``. For
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numeric ``start`` and ``end``, the frequency must also be numeric.
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- >>> pd.interval_range(start=0, periods=4, freq=1.5)
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+ >>> pd.interval_range(start=0, periods=4, freq=1.5, inclusive="right" )
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IntervalIndex([(0.0, 1.5], (1.5, 3.0], (3.0, 4.5], (4.5, 6.0]],
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dtype='interval[float64, right]')
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Similarly, for datetime-like ``start`` and ``end``, the frequency must be
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convertible to a DateOffset.
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>>> pd.interval_range(start=pd.Timestamp('2017-01-01'),
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- ... periods=3, freq='MS')
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+ ... periods=3, freq='MS', inclusive="right" )
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IntervalIndex([(2017-01-01, 2017-02-01], (2017-02-01, 2017-03-01],
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(2017-03-01, 2017-04-01]],
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dtype='interval[datetime64[ns], right]')
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Specify ``start``, ``end``, and ``periods``; the frequency is generated
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automatically (linearly spaced).
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- >>> pd.interval_range(start=0, end=6, periods=4)
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+ >>> pd.interval_range(start=0, end=6, periods=4, inclusive="right" )
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IntervalIndex([(0.0, 1.5], (1.5, 3.0], (3.0, 4.5], (4.5, 6.0]],
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dtype='interval[float64, right]')
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