均值

计算输入数组 x 的算术平均值。

参数

名称	类型	描述	默认值
x		实值浮点数据类型的输入数组。	必需
axis		必须计算算术平均值的轴。默认情况下，均值是在整个数组上计算的。如果是整数元组，则在多个轴上计算算术平均值。默认值：None。	None
keepdims		如果为 True，则在结果中将缩减的轴（维度）作为单例维度包含在内。因此，结果与输入数组兼容。否则，如果为 False，则结果中不包含缩减的轴（维度）。默认值：False。	False

返回值

名称	类型	描述
out	数组	如果算术平均值是在整个数组上计算的，则返回一个包含算术平均值的零维数组。否则，返回一个包含算术平均值的非零维数组。返回的数组与 x 具有相同的数据类型。

特殊情况

令 N 等于计算算术平均值的元素数量。如果 N 为 0，则算术平均值为 NaN。如果 x_i 为 NaN，则算术平均值为 NaN（即 NaN 值会传播）。

示例

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

源代码位于 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)