# numpy.searchsorted¶

`numpy.``searchsorted`(a, v, side='left', sorter=None)[source]

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. indices : array of ints Array of insertion points with the same shape as v.

`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])
```

#### Previous topic

numpy.flatnonzero

numpy.extract