mean

Calculates the arithmetic mean of the input array x.

Parameters:

Name	Type	Description	Default
x		input array of a real-valued floating-point data type.	required
axis		axis or axes along which arithmetic means must be computed. By default, the mean is computed over the entire array. If a tuple of integers, arithmetic means are computed over multiple axes. Default: None.	None
keepdims		if True, the reduced axes (dimensions) are included in the result as singleton dimensions. Accordingly, the result is compatible is the input array. Otherwise, if False, the reduced axes (dimensions) are not be included in the result. Default: False.	False

Returns:

Name	Type	Description
out	array	if the arithmetic mean was computed over the entire array, a zero-dimensional array with the arithmetic mean. Otherwise, a non-zero-dimensional array containing the arithmetic means. The returned array has the same data type as x.

Special Cases

Let N equal the number of elements over which to compute the arithmetic mean. If N is 0, the arithmetic mean is NaN. If x_i is NaN, the arithmetic mean is NaN (i.e., NaN values propagate).

Examples:

>>> a = sparse.COO.from_numpy(np.array([[0, 1], [2, 0]]))
>>> o = sparse.mean(a, axis=1)
>>> o.todense()
array([0.5, 1. ])

Source code in sparse/numba_backend/_common.py

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

    Parameters
    ----------
    x: array
        input array of  a real-valued floating-point data type.
    axis: Optional[Union[int, Tuple[int, ...]]]
        axis or axes along which arithmetic means must be computed.
        By default, the mean is computed over the entire array.
        If a tuple of integers, arithmetic means are computed over multiple axes. Default: ``None``.
    keepdims: bool
        if ``True``, the reduced axes (dimensions) are included in the result as singleton dimensions.
        Accordingly, the result is compatible is the input array.
        Otherwise, if ``False``, the reduced axes (dimensions) are not be included in the result. Default: ``False``.

    Returns
    -------
    out: array
        if the arithmetic mean was computed over the entire array, a zero-dimensional array with the arithmetic mean.
        Otherwise, a non-zero-dimensional array containing the arithmetic means.
        The returned array has the same data type as ``x``.

    Special Cases
    -------------
    Let ``N`` equal the number of elements over which to compute the arithmetic mean.
    If ``N`` is ``0``, the arithmetic mean is ``NaN``.
    If ``x_i`` is ``NaN``, the arithmetic mean is ``NaN`` (i.e., ``NaN`` values propagate).

    Examples
    --------
    >>> a = sparse.COO.from_numpy(np.array([[0, 1], [2, 0]]))
    >>> o = sparse.mean(a, axis=1)
    >>> o.todense()
    array([0.5, 1. ])
    """

    return x.mean(axis=axis, keepdims=keepdims, dtype=dtype)