numpy.nonzero

numpy.nonzero(a)[source]

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 corresponding non-zero 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 2-D array, with a row for each non-zero element.

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.

Examples

>>>

>>> x = np.eye(3)>>> xarray([[ 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 > 3array([[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]))