searchsorted(a, v, side='left', sorter=None)

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:

====== ============================ :None:None:`side` returned index i satisfies ====== ============================ left a[i-1] < v <= a[i] right a[i-1] <= v < a[i] ====== ============================

Notes

Binary search is used to find the required insertion points.

As of NumPy 1.4.0 searchsorted works with real/complex arrays containing :None:None:`nan` values. The enhanced sort order is documented in :None:None:`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.

Parameters

a : 1-D array_like

Input array. If :None:None:`sorter` is None, then it must be sorted in ascending order, otherwise :None:None:`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.

versionadded

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.

Find indices where elements should be inserted to maintain order.

See Also

histogram

Produce histogram from 1-D data.

sort

Return a sorted copy of an array.

Examples

This example is valid syntax, but we were not able to check execution

>>> np.searchsorted([1,2,3,4,5], 3)  # doctest: +SKIP
2

This example is valid syntax, but we were not able to check execution

>>> np.searchsorted([1,2,3,4,5], 3, side='right')  # doctest: +SKIP
3

This example is valid syntax, but we were not able to check execution

>>> np.searchsorted([1,2,3,4,5], [-10, 10, 2, 3])  # doctest: +SKIP
array([0, 5, 1, 2])

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

dask.array.routines.searchsorted dask.array.routines.digitize

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