prod

Calculates the product of input array x elements.

Parameters:

Name	Type	Description	Default
x		input array of a numeric data type.	required
axis		axis or axes along which products is computed. By default, the product are computed over the entire array. If a tuple of integers, products are computed over multiple axes. Default: None.	None
dtype		data type of the returned array. If None, the returned array has the same data type as x, unless x has an integer data type supporting a smaller range of values than the default integer data type (e.g., x has an int16 or uint32 data type and the default integer data type is int64). In those latter cases: if x has a signed integer data type (e.g., int16), the returned array has the default integer data type. if x has an unsigned integer data type (e.g., uint16), the returned array has an unsigned integer data type having the same number of bits as the default integer data type (e.g., if the default integer data type is int32, the returned array must have a uint32 data type). If the data type (either specified or resolved) differs from the data type of x, the input array is cast to the specified data type before computing the sum (rationale: the dtype keyword argument is intended to help prevent overflows). Default: None.	None
keepdims		if True, the reduced axes (dimensions) are included in the result as singleton dimensions. Accordingly, the result are compatible with the input array. Otherwise, if False, the reduced axes (dimensions) are not included in the result. Default: False.	False

Returns:

Name	Type	Description
out	array	if the product was computed over the entire array, a zero-dimensional array containing the product. Otherwise, a non-zero-dimensional array containing the products. The returned array has a data type as described by the dtype parameter above.

Notes

Special Cases

Let N equal the number of elements over which to compute the product.

• If N is 0, the product is 1 (i.e., the empty product).

Examples:

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

Source code in sparse/numba_backend/_common.py

def prod(x, /, *, axis=None, dtype=None, keepdims=False):
    """
    Calculates the product of input array ``x`` elements.

    Parameters
    ----------
    x: array
        input array of a numeric data type.
    axis: Optional[Union[int, Tuple[int, ...]]]
        axis or axes along which products is computed.
        By default, the product are computed over the entire array.
        If a tuple of integers, products are computed over multiple axes. Default: ``None``.

    dtype: Optional[dtype]
        data type of the returned array.
        If ``None``, the returned array has the same data type as ``x``, unless ``x`` has an integer
        data type supporting a smaller range of values than the default integer data type
        (e.g., ``x`` has an ``int16`` or ``uint32`` data type and the default integer data type is ``int64``).
        In those latter cases:

        -   if ``x`` has a signed integer data type (e.g., ``int16``), the returned array has the
            default integer data type.
        -   if ``x`` has an unsigned integer data type (e.g., ``uint16``), the returned array has an
            unsigned integer data type having the same number of bits as the default integer data type
            (e.g., if the default integer data type is ``int32``, the returned array must have a ``uint32`` data type).

        If the data type (either specified or resolved) differs from the data type of ``x``, the input array is
        cast to the specified data type before computing the sum (rationale: the ``dtype`` keyword argument is
        intended to help prevent overflows). Default: ``None``.

    keepdims: bool
        if ``True``, the reduced axes (dimensions) are included in the result as singleton dimensions.
        Accordingly, the result are compatible with the input array.
        Otherwise, if ``False``, the reduced axes (dimensions)  are not included in the result.
        Default: ``False``.

    Returns
    -------
    out: array
        if the product was computed over the entire array, a zero-dimensional array containing the product.
        Otherwise, a non-zero-dimensional array containing the products.
        The returned array has a data type as described by the ``dtype`` parameter above.

    Notes
    -----

    Special Cases
    -------------
    Let ``N`` equal the number of elements over which to compute the product.

    -   If ``N`` is ``0``, the product is `1` (i.e., the empty product).

    Examples
    --------
    >>> a = sparse.COO.from_numpy(np.array([[0, 2], [-1, 1]]))
    >>> o = sparse.prod(a, axis=1)
    >>> o.todense()
    array([ 0, -1])
    """
    return x.prod(axis=axis, keepdims=keepdims, dtype=dtype)