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:a1-D array_likeInput array. If sorter is None, then it must be sorted in ascending order, otherwise sorter must be an array of indices that sort it.
varray_likeValues to insert into a.
side{‘left’, ‘right’}, optionalIf ‘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).
sorter1-D array_like, optionalOptional array of integer indices that sort array a into ascending order. They are typically the result of argsort.
Returns:indicesint or array of intsArray of insertion points with the same shape as v, or an integer if v is a scalar.
See also
Return a sorted copy of an array.
Produce histogram from 1-D data.
Notes
Binary search is used to find the required insertion points.
As of NumPy 1.4.0
works with real/complex arrays containing
values. The enhanced sort order is documented in
.
This function uses the same algorithm as the builtin python
(side='left') and
(side='right') functions, which is also vectorized in the v argument.
Examples
>>> importnumpyasnp>>> np.searchsorted([11,12,13,14,15],13)2>>> np.searchsorted([11,12,13,14,15],13,side='right')3>>> np.searchsorted([11,12,13,14,15],[-10,20,12,13])array([0, 5, 1, 2])When sorter is used, the returned indices refer to the sorted array of a and not a itself:
>>> a=np.array([40,10,20,30])>>> sorter=np.argsort(a)>>> sorterarray([1, 2, 3, 0]) # Indices that would sort the array 'a'>>> result=np.searchsorted(a,25,sorter=sorter)>>> result2>>> a[sorter[result]]30 # The element at index 2 of the sorted array is 30.