numpy.nonzero¶

numpy.
nonzero
(a)[source]¶ Return the indices of the elements that are nonzero.
Returns a tuple of arrays, one for each dimension of a, containing the indices of the nonzero elements in that dimension. The values in a are always tested and returned in rowmajor, Cstyle order. The corresponding nonzero values can be obtained with:
a[nonzero(a)]
To group the indices by element, rather than dimension, use:
transpose(nonzero(a))
The result of this is always a 2D array, with a row for each nonzero element.
Parameters: a : array_like
Input array.
Returns: tuple_of_arrays : tuple
Indices of elements that are nonzero.
See also
flatnonzero
 Return indices that are nonzero in the flattened version of the input array.
ndarray.nonzero
 Equivalent ndarray method.
count_nonzero
 Counts the number of nonzero elements in the input array.
Examples
>>> x = np.eye(3) >>> x array([[ 1., 0., 0.], [ 0., 1., 0.], [ 0., 0., 1.]]) >>> np.nonzero(x) (array([0, 1, 2]), array([0, 1, 2]))
>>> x[np.nonzero(x)] array([ 1., 1., 1.]) >>> np.transpose(np.nonzero(x)) array([[0, 0], [1, 1], [2, 2]])
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]], dtype=bool) >>> np.nonzero(a > 3) (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))
The
nonzero
method of the boolean array can also be called.>>> (a > 3).nonzero() (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))