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Module « numpy »

Fonction searchsorted - module numpy

Signature de la fonction searchsorted

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

Description

help(numpy.searchsorted)

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.

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
--------
>>> 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.

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