numpy.nonzero
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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 values ina
are always tested and returned in row-major, C-style order. 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.array([[1,0,0], [0,2,0], [1,1,0]]) >>> x array([[1, 0, 0], [0, 2, 0], [1, 1, 0]]) >>> np.nonzero(x) (array([0, 1, 2, 2]), array([0, 1, 0, 1]))
>>> x[np.nonzero(x)] array([1, 2, 1, 1]) >>> 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 arraya
, the conditiona
> 3 is a boolean array and since False is interpreted as 0, np.nonzero(a > 3) yields the indices of thea
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]))
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]))
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Licensed under the 3-clause BSD License.
https://docs.scipy.org/doc/numpy-1.15.4/reference/generated/numpy.nonzero.html