dask 2021.10.0

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NotesParametersReturnsBackRef

This docstring was copied from numpy.nonzero.

Some inconsistencies with the Dask version may exist.

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.

.. deprecated:: 1.17.0
    Use `atleast_1d` explicitly if this behavior is deliberate.

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.

Parameters

a : array_like

Input array.

Returns

tuple_of_arrays : tuple

Indices of elements that are non-zero.

Return the indices of the elements that are non-zero.

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.

Examples

>>> x = np.array([[3, 0, 0], [0, 4, 0], [5, 6, 0]])  # doctest: +SKIP
... x  # doctest: +SKIP
array([[3, 0, 0],
       [0, 4, 0],
       [5, 6, 0]])

>>> np.nonzero(x)  # doctest: +SKIP
(array([0, 1, 2, 2]), array([0, 1, 0, 1]))

>>> x[np.nonzero(x)]  # doctest: +SKIP
array([3, 4, 5, 6])

>>> np.transpose(np.nonzero(x))  # doctest: +SKIP
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]])  # doctest: +SKIP
... a > 3  # doctest: +SKIP
array([[False, False, False],
       [ True,  True,  True],
       [ True,  True,  True]])

>>> np.nonzero(a > 3)  # doctest: +SKIP
(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)]  # doctest: +SKIP
array([4, 5, 6, 7, 8, 9])

>>> a[a > 3]  # prefer this spelling  # doctest: +SKIP
array([4, 5, 6, 7, 8, 9])

nonzero can also be called as a method of the array.

>>> (a > 3).nonzero()  # doctest: +SKIP
(array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

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