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Module « numpy.matlib »

Fonction nonzero - module numpy.matlib

Signature de la fonction nonzero

def nonzero(a) 

Description

help(numpy.matlib.nonzero)

Return the indices of the elements that are non-zero.

Returns a tuple of arrays, one for each dimension of `a`,
containing the indices of the non-zero elements in that
dimension. The values in `a` are always tested and returned in
row-major, C-style order.

To group the indices by element, rather than dimension, use `argwhere`,
which returns a row for each non-zero element.

.. note::

   When called on a zero-d array or scalar, ``nonzero(a)`` is treated
   as ``nonzero(atleast_1d(a))``.

   .. deprecated:: 1.17.0

      Use `atleast_1d` explicitly if this behavior is deliberate.

Parameters
----------
a : array_like
    Input array.

Returns
-------
tuple_of_arrays : tuple
    Indices of elements that are non-zero.

See Also
--------
flatnonzero :
    Return indices that are non-zero in the flattened version of the input
    array.
ndarray.nonzero :
    Equivalent ndarray method.
count_nonzero :
    Counts the number of non-zero elements in the input array.

Notes
-----
While the nonzero values can be obtained with ``a[nonzero(a)]``, it is
recommended to use ``x[x.astype(bool)]`` or ``x[x != 0]`` instead, which
will correctly handle 0-d arrays.

Examples
--------
>>> import numpy as np
>>> x = np.array([[3, 0, 0], [0, 4, 0], [5, 6, 0]])
>>> x
array([[3, 0, 0],
       [0, 4, 0],
       [5, 6, 0]])
>>> np.nonzero(x)
(array([0, 1, 2, 2]), array([0, 1, 0, 1]))

>>> x[np.nonzero(x)]
array([3, 4, 5, 6])
>>> np.transpose(np.nonzero(x))
array([[0, 0],
       [1, 1],
       [2, 0],
       [2, 1]])

A common use for ``nonzero`` is to find the indices of an array, where
a condition is True.  Given an array `a`, the condition `a` > 3 is a
boolean array and since False is interpreted as 0, np.nonzero(a > 3)
yields the indices of the `a` where the condition is true.

>>> a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
>>> a > 3
array([[False, False, False],
       [ True,  True,  True],
       [ True,  True,  True]])
>>> np.nonzero(a > 3)
(array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))

Using this result to index `a` is equivalent to using the mask directly:

>>> a[np.nonzero(a > 3)]
array([4, 5, 6, 7, 8, 9])
>>> a[a > 3]  # prefer this spelling
array([4, 5, 6, 7, 8, 9])

``nonzero`` can also be called as a method of the array.

>>> (a > 3).nonzero()
(array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))

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