# 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 int or array of ints

      Array of insertion points with the same shape as v, or an integer if v is a scalar.

  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 uses the same algorithm as 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])