nanmean

沿给定轴执行跳过 NaN 值的平均操作。默认情况下使用所有轴。

参数

名称	类型	描述	默认值
x	SparseArray	要执行约减操作的数组。	必需
axis	联合[int, 可迭代对象[int]]	计算均值的轴。默认使用所有轴。	None
keepdims	bool_	是否保留原始数组的维度。	False
dtype	dtype	输出数组的数据类型。	None

返回值

类型	描述
COO	归约后的稀疏输出数组。

另请参阅

• sparse.COO.mean : 不跳过 NaN 的函数。
• numpy.nanmean : 等效的 Numpy 函数。

源代码位于 sparse/numba_backend/_coo/common.py

def nanmean(x, axis=None, keepdims=False, dtype=None, out=None):
    """
    Performs a `NaN` skipping mean operation along the given axes. Uses all axes by default.

    Parameters
    ----------
    x : SparseArray
        The array to perform the reduction on.
    axis : Union[int, Iterable[int]], optional
        The axes along which to compute the mean. Uses all axes by default.
    keepdims : bool, optional
        Whether or not to keep the dimensions of the original array.
    dtype : numpy.dtype
        The data type of the output array.

    Returns
    -------
    COO
        The reduced output sparse array.

    See Also
    --------
    - [`sparse.COO.mean`][] : Function without `NaN` skipping.
    - [`numpy.nanmean`][] : Equivalent Numpy function.
    """
    assert out is None
    x = asCOO(x, name="nanmean")

    if not (np.issubdtype(x.dtype, np.floating) or np.issubdtype(x.dtype, np.complexfloating)):
        return x.mean(axis=axis, keepdims=keepdims, dtype=dtype)

    mask = np.isnan(x)
    x2 = where(mask, 0, x)

    # Count the number non-nan elements along axis
    nancount = mask.sum(axis=axis, dtype="i8", keepdims=keepdims)
    if axis is None:
        axis = tuple(range(x.ndim))
    elif not isinstance(axis, tuple):
        axis = (axis,)
    den = reduce(operator.mul, (x.shape[i] for i in axis), 1)
    den -= nancount

    if (den == 0).any():
        warnings.warn("Mean of empty slice", RuntimeWarning, stacklevel=1)

    num = np.sum(x2, axis=axis, dtype=dtype, keepdims=keepdims)

    with np.errstate(invalid="ignore", divide="ignore"):
        if num.ndim:
            return np.true_divide(num, den, casting="unsafe")
        return (num / den).astype(dtype if dtype is not None else x.dtype)