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pandas.IntervalIndex.searchsorted

IntervalIndex.searchsorted(value, side='left', sorter=None)[source]

Find indices where elements should be inserted to maintain order.

Find the indices into a sorted IndexOpsMixin self such that, if the corresponding elements in value were inserted before the indices, the order of self would be preserved.

Parameters:

value : array_like

Values to insert into self.

side : {‘left’, ‘right’}, optional

If ‘left’, the index of the first suitable location found is given. If ‘right’, return the last such index. If there is no suitable index, return either 0 or N (where N is the length of self).

sorter : 1-D array_like, optional

Optional array of integer indices that sort self into ascending order. They are typically the result of np.argsort.

Returns:

indices : array of ints

Array of insertion points with the same shape as value.

Notes

Binary search is used to find the required insertion points.

Examples

>>> x = pd.Series([1, 2, 3])
>>> x
0    1
1    2
2    3
dtype: int64
>>> x.searchsorted(4)
array([3])
>>> x.searchsorted([0, 4])
array([0, 3])
>>> x.searchsorted([1, 3], side='left')
array([0, 2])
>>> x.searchsorted([1, 3], side='right')
array([1, 3])
>>>
>>> x = pd.Categorical(['apple', 'bread', 'bread', 'cheese', 'milk' ])
[apple, bread, bread, cheese, milk]
Categories (4, object): [apple < bread < cheese < milk]
>>> x.searchsorted('bread')
array([1])     # Note: an array, not a scalar
>>> x.searchsorted(['bread'])
array([1])
>>> x.searchsorted(['bread', 'eggs'])
array([1, 4])
>>> x.searchsorted(['bread', 'eggs'], side='right')
array([3, 4])    # eggs before milk
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