xorbits.numpy.searchsorted#
- xorbits.numpy.searchsorted(a, v, side='left', sorter=None, combine_size=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(numpy).
- Returns
indices – Array of insertion points with the same shape as v, or an integer if v is a scalar.
- Return type
int or array of ints
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])
- combine_size: int, optional
The number of chunks to combine.
This docstring was copied from numpy.