std

计算输入数组 x 的标准差。

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		计算标准差的轴。默认情况下，标准差是在整个数组上计算的。如果是一个整数元组，则标准差在多个轴上计算。默认值：None。	None
correction		自由度调整。将此参数设置为除 0 以外的值，会根据 N-c 调整标准差计算过程中的除数，其中 N 对应于计算标准差的元素总数，c 对应于提供的自由度调整。当计算总体的标准差时，将此参数设置为 0 是标准选择（即，提供的数组包含构成整个总体的所有数据）。当计算修正后的样本标准差时，将此参数设置为 1 是标准选择（即，提供的数组包含从更大总体中抽样的数据；这通常被称为贝塞尔校正）。默认值：0。	0.0
keepdims		如果为 True，则在结果中包含缩减的轴（维度）作为单例维度，因此结果必须与输入数组兼容。否则，如果为 False，则结果中不得包含缩减的轴（维度）。默认值：False。	False

返回值

名称	类型	描述
out	数组	如果标准差是在整个数组上计算的，则为一个包含标准差的零维数组；否则，为一个包含标准差的非零维数组。返回的数组具有与 x 相同的数据类型。

特殊情况

设 N 等于计算标准差的元素数量。

• 如果 N - correction 小于或等于 0，则标准差为 NaN。
• 如果 x_i 是 NaN，则标准差为 NaN（即，NaN 值会传播）。

示例

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

源代码位于 sparse/numba_backend/_common.py

def std(x, /, *, axis=None, correction=0.0, keepdims=False):
    """
    Calculates the standard deviation 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 standard deviations are computed.
        By default, the standard deviation is computed over the entire array.
        If a tuple of integers, standard deviations are computed over multiple axes.
        Default: ``None``.
    correction: Union[int, float]
        degrees of freedom adjustment.
        Setting this parameter to a value other than ``0`` has the effect of adjusting the divisor
        during the calculation of the standard deviation according to ``N-c`` where ``N`` corresponds
        to the total number of elements over which the standard deviation is computed
        and ``c`` corresponds to the provided degrees of freedom adjustment. When computing the
        standard deviation of a population, setting this parameter to ``0`` is the standard choice
        (i.e., the provided array contains data constituting an entire population). When computing
        the corrected sample standard deviation, setting this parameter to ``1`` is the standard
        choice (i.e., the provided array contains data sampled from a larger population; this is
        commonly referred to as Bessel's correction). Default: ``0``.
    keepdims: bool
        if ``True``, the reduced axes (dimensions) are included in the result as singleton
        dimensions, and, accordingly, the result must be compatible with the input array.
        Otherwise, if ``False``, the reduced axes (dimensions) must not
        be included in the result. Default: ``False``.

    Returns
    -------
    out: array
        if the standard deviation was computed over the entire array, a zero-dimensional array containing
        the standard deviation; otherwise, a non-zero-dimensional array containing the standard deviations.
        The returned array has the same data type as ``x``.

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

    -   If ``N - correction`` is less than or equal to ``0``, the standard deviation is ``NaN``.
    -   If ``x_i`` is ``NaN``, the standard deviation is ``NaN`` (i.e., ``NaN`` values propagate).

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