nonzero

Returns the indices of the array elements which are non-zero.

If x has a complex floating-point data type, non-zero elements are those elements having at least one component (real or imaginary) which is non-zero.

If x has a boolean data type, non-zero elements are those elements which are equal to True.

Parameters:

Name	Type	Description	Default
x		input array having a positive rank. If x is zero-dimensional, the function raises an exception.	required

Returns:

Name	Type	Description
out	Tuple[array, ...]	a tuple of k arrays, one for each dimension of x and each of size n (where n is the total number of non-zero elements), containing the indices of the non-zero elements in that dimension. The indices must are returned in row-major, C-style order.

Examples:

>>> a = sparse.COO.from_numpy(np.array([[0, 1], [2, 0]]))
>>> o = sparse.nonzero(a)
>>> o
(array([0, 1]), array([1, 0]))

Source code in sparse/numba_backend/_common.py

def nonzero(x, /):
    """
    Returns the indices of the array elements which are non-zero.

    If ``x`` has a complex floating-point data type, non-zero elements are those elements having at least
    one component (real or imaginary) which is non-zero.

    If ``x`` has a boolean data type, non-zero elements are those elements which are equal to ``True``.

    Parameters
    ----------
    x: array
        input array having a positive rank.
        If ``x`` is zero-dimensional, the function raises an exception.

    Returns
    -------
    out: Tuple[array, ...]
        a tuple of ``k`` arrays, one for each dimension of ``x`` and each of size ``n`` (where ``n`` is
        the total number of non-zero elements), containing the indices of the non-zero elements in that
        dimension. The indices must are returned in row-major, C-style order.

    Examples
    --------
    >>> a = sparse.COO.from_numpy(np.array([[0, 1], [2, 0]]))
    >>> o = sparse.nonzero(a)
    >>> o
    (array([0, 1]), array([1, 0]))
    """
    return x.nonzero()