std

Calculates the standard deviation 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 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.	None
correction		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.	0.0
keepdims		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.	False

Returns:

Name	Type	Description
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.])

Source code in 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)