sum

Calculates the sum of the input array x.

Parameters:

Name	Type	Description	Default
x		input array of a numeric data type.	required
axis		axis or axes along which sums are computed. By default, the sum is computed over the entire array. If a tuple of integers, sums must 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 is 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 sum was computed over the entire array, a zero-dimensional array containing the sum. Otherwise, an array containing the sums. The returned array has the data type as described by the dtype parameter above.

Special Cases

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

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

Examples:

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

Source code in sparse/numba_backend/_common.py

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

    Parameters
    ----------
    x: array
        input array of a numeric data type.
    axis: Optional[Union[int, Tuple[int, ...]]]
        axis or axes along which sums are computed.
        By default, the sum is computed over the entire array.
        If a tuple of integers, sums must 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 is 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 sum was computed over the entire array, a zero-dimensional array containing the sum.
        Otherwise, an array containing the sums.
        The returned array has the data type as described by the ``dtype`` parameter above.

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

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

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