bundles / numpy 2.4.3 / numpy / nonzero
_ArrayFunctionDispatcher
numpy:nonzero
source: /numpy/_core/fromnumeric.py:1993
Signature
def nonzero(a)Summary
Return the indices of the elements that are non-zero.
Extended Summary
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.
Parameters
a: array_likeInput array.
Returns
tuple_of_arrays: tupleIndices of elements that are non-zero.
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 np.nonzero(x)✓
(array([0, 1, 2, 2]), array([0, 1, 0, 1]))
x[np.nonzero(x)] 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 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)] 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]))
See also
- count_nonzero
Counts the number of non-zero elements in the input array.
- flatnonzero
Return indices that are non-zero in the flattened version of the input array.
- ndarray.nonzero
Equivalent ndarray method.
Aliases
numpy.nonzero