dask.array.nonzero
dask.array.nonzero¶
- dask.array.nonzero(a)[source]¶
Return the indices of the elements that are non-zero.
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.
Note
When called on a zero-d array or scalar,
nonzero(a)
is treated asnonzero(atleast_1d(a))
.Deprecated since version 1.17.0: Use atleast_1d explicitly if this behavior is deliberate.
- Parameters
- aarray_like
Input array.
- Returns
- tuple_of_arraystuple
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.
Notes
While the nonzero values can be obtained with
a[nonzero(a)]
, it is recommended to usex[x.astype(bool)]
orx[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 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]))