numpy.nonzero(a)
[source]
Return the indices of the elements that are nonzero.
Returns a tuple of arrays, one for each dimension of a
, containing the indices of the nonzero elements in that dimension. The values in a
are always tested and returned in rowmajor, Cstyle order.
To group the indices by element, rather than dimension, use argwhere
, which returns a row for each nonzero element.
Note
When called on a zerod array or scalar, nonzero(a)
is treated as nonzero(atleast1d(a))
.
atleast1d
explicitly if this behavior is deliberate.Parameters: 


Returns: 

See also
flatnonzero
ndarray.nonzero
count_nonzero
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 0d arrays.
>>> 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]))
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https://docs.scipy.org/doc/numpy1.17.0/reference/generated/numpy.nonzero.html