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Module « numpy.matlib »

Fonction where - module numpy.matlib

Signature de la fonction where

Description

help(numpy.matlib.where)

where(condition, [x, y], /)

Return elements chosen from `x` or `y` depending on `condition`.

.. note::
    When only `condition` is provided, this function is a shorthand for
    ``np.asarray(condition).nonzero()``. Using `nonzero` directly should be
    preferred, as it behaves correctly for subclasses. The rest of this
    documentation covers only the case where all three arguments are
    provided.

Parameters
----------
condition : array_like, bool
    Where True, yield `x`, otherwise yield `y`.
x, y : array_like
    Values from which to choose. `x`, `y` and `condition` need to be
    broadcastable to some shape.

Returns
-------
out : ndarray
    An array with elements from `x` where `condition` is True, and elements
    from `y` elsewhere.

See Also
--------
choose
nonzero : The function that is called when x and y are omitted

Notes
-----
If all the arrays are 1-D, `where` is equivalent to::

    [xv if c else yv
     for c, xv, yv in zip(condition, x, y)]

Examples
--------
>>> import numpy as np
>>> a = np.arange(10)
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> np.where(a < 5, a, 10*a)
array([ 0,  1,  2,  3,  4, 50, 60, 70, 80, 90])

This can be used on multidimensional arrays too:

>>> np.where([[True, False], [True, True]],
...          [[1, 2], [3, 4]],
...          [[9, 8], [7, 6]])
array([[1, 8],
       [3, 4]])

The shapes of x, y, and the condition are broadcast together:

>>> x, y = np.ogrid[:3, :4]
>>> np.where(x < y, x, 10 + y)  # both x and 10+y are broadcast
array([[10,  0,  0,  0],
       [10, 11,  1,  1],
       [10, 11, 12,  2]])

>>> a = np.array([[0, 1, 2],
...               [0, 2, 4],
...               [0, 3, 6]])
>>> np.where(a < 4, a, -1)  # -1 is broadcast
array([[ 0,  1,  2],
       [ 0,  2, -1],
       [ 0,  3, -1]])

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