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.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]))