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numpy.searchsorted

numpy.searchsorted(a, v, side='left', sorter=None)

Find indices where elements should be inserted to maintain order.

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

Assuming that a is sorted:

side	returned index i satisfies
left	a[i-1] < v <= a[i]
right	a[i-1] <= v < a[i]

Parameters:

a : 1-D array_like

Input array. If sorter is None, then it must be sorted in ascending order, otherwise sorter must be an array of indices that sort it.

v : array_like

Values to insert into a.

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 a).

sorter : 1-D array_like, optional

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

New in version 1.7.0.

Returns:

indices : array of ints

Array of insertion points with the same shape as v.

See also

sort

Return a sorted copy of an array.

histogram

Produce histogram from 1-D data.

Notes

Binary search is used to find the required insertion points.

As of NumPy 1.4.0 searchsorted works with real/complex arrays containing nan values. The enhanced sort order is documented in sort.

This function is a faster version of the builtin python bisect.bisect_left (side='left') and bisect.bisect_right (side='right') functions, which is also vectorized in the v argument.

Examples

>>>

>>> np.searchsorted([1,2,3,4,5], 3)
2
>>> np.searchsorted([1,2,3,4,5], 3, side='right')
3
>>> np.searchsorted([1,2,3,4,5], [-10, 10, 2, 3])
array([0, 5, 1, 2])