numpy.nonzero(a)
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 in a are always tested and returned in row-major, C-style order.
To group the indices by element, rather than dimension, use
, which returns a row for each non-zero element.
Parameters:aarray_likeInput array.
Returns:tuple_of_arraystupleIndices of elements that are non-zero.
See also
Return indices that are non-zero in the flattened version of the input array.
Equivalent ndarray method.
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 use x[x.astype(np.bool)] or x[x!=0] instead, which will correctly handle 0-d arrays.
Examples
>>> importnumpyasnp>>> x=np.array([[3,0,0],[0,4,0],[5,6,0]])>>> xarray([[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>3array([[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 spellingarray([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]))