any

Tests whether any input array element evaluates to True along a specified axis.

Parameters:

Name	Description	Default
`x`	input array.	required
`axis`	axis or axes along which to perform a logical OR reduction. By default, a logical OR reduction is performed over the entire array. If a tuple of integers, logical OR reductions are performed over multiple axes. A valid `axis` must be an integer on the interval `[-N, N)`, where `N` is the rank (number of dimensions) of `x`. If an `axis` is specified as a negative integer, the function determines the axis along which to perform a reduction by counting backward from the last dimension (where `-1` refers to the last dimension). If provided an invalid `axis`, the function raises an exception. Default: `None`.	`None`
`keepdims`	If `True`, the reduced axes (dimensions) are included in the result as singleton dimensions, and, accordingly, the result must is compatible with the input array. Otherwise, if `False`, the reduced axes (dimensions) is not included in the result. Default: `False`.	`False`

Returns:

Name	Type	Description
`out`	`array`	if a logical OR reduction was performed over the entire array, the returned array is a zero-dimensional array containing the test result. Otherwise, the returned array is a non-zero-dimensional array containing the test results. The returned array is of type `bool`.

Special Cases

Positive infinity, negative infinity, and NaN evaluate to True.
If x has a complex floating-point data type, elements having a non-zero component (real or imaginary) evaluate to True.
If x is an empty array or the size of the axis (dimension) along which to evaluate elements is zero, the test result is False.

Examples:

>>> a = sparse.COO.from_numpy(np.array([[0, 1], [2, 0]]))
>>> o = sparse.any(a, axis=1)
>>> o.todense()
array([ True, True])

Source code in sparse/numba_backend/_common.py

def any(x, /, *, axis=None, keepdims=False):
    """
    Tests whether any input array element evaluates to ``True`` along a specified axis.

    Parameters
    ----------
    x: array
        input array.
    axis: Optional[Union[int, Tuple[int, ...]]]
        axis or axes along which to perform a logical OR reduction.
        By default, a logical OR reduction is performed over the entire array.
        If a tuple of integers, logical OR reductions are performed over multiple axes.
        A valid ``axis`` must be an integer on the interval ``[-N, N)``, where ``N`` is the rank (number of
        dimensions) of ``x``. If an ``axis`` is specified as a negative integer, the function determines
        the axis along which to perform a reduction by counting backward from the last dimension (where
        ``-1`` refers to the last dimension).
        If provided an invalid ``axis``, the function raises an exception.
        Default: ``None``.
    keepdims: bool
        If ``True``, the reduced axes (dimensions) are included in the result as singleton dimensions,
        and, accordingly, the result must is compatible with the input array. Otherwise, if ``False``,
        the reduced axes (dimensions) is not included in the result.
        Default: ``False``.

    Returns
    -------
    out: array
        if a logical OR reduction was performed over the entire array, the returned array is a
        zero-dimensional array containing the test result.
        Otherwise, the returned array is a non-zero-dimensional array containing the test results.
        The returned array is of type ``bool``.

    Special Cases
    -------------

       - Positive infinity, negative infinity, and NaN  evaluate to ``True``.

       - If ``x`` has a complex floating-point data type, elements having a non-zero component
        (real or imaginary) evaluate to ``True``.

       - If ``x`` is an empty array or the size of the axis (dimension) along which to evaluate elements
         is zero, the test result is ``False``.

    Examples
    --------
    >>> a = sparse.COO.from_numpy(np.array([[0, 1], [2, 0]]))
    >>> o = sparse.any(a, axis=1)
    >>> o.todense()  # doctest: +NORMALIZE_WHITESPACE
    array([ True, True])
    """
    return x.any(axis=axis, keepdims=keepdims)