dask.array.searchsorted — Dask documentation (original) (raw)
Find indices where elements should be inserted to maintain order.
This docstring was copied from numpy.searchsorted.
Some inconsistencies with the Dask version may exist.
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_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.
varray_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).
sorter1-D array_like, optional
Optional array of integer indices that sort array a into ascending order. They are typically the result of argsort.
Returns
indicesint 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.
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 containingnan values. The enhanced sort order is documented in sort.
This function uses the same algorithm as the builtin pythonbisect.bisect_left (side='left'
) and bisect.bisect_right(side='right'
) functions, which is also vectorized in the v argument.
Examples
import numpy as np
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)
sorter
array([1, 2, 3, 0]) # Indices that would sort the array 'a' result = np.searchsorted(a, 25, sorter=sorter)
result
2 a[sorter[result]]
30 # The element at index 2 of the sorted array is 30.