Function nanstd

pandas/core/nanops.py:912–955

Compute the standard deviation along given axis while ignoring NaNs Parameters ---------- values : ndarray axis : int, optional skipna : bool, default True ddof : int, default 1 Delta Degrees of Freedom. The divisor used in calculations is N - ddof, wher

(
    values,
    *,
    axis: AxisInt | None = None,
    skipna: bool = True,
    ddof: int = 1,
    mask=None,
)

@bottleneck_switch(ddof=1)
def nanstd(
    values,
    *,
    axis: AxisInt | None = None,
    skipna: bool = True,
    ddof: int = 1,
    mask=None,
):
    """
    Compute the standard deviation along given axis while ignoring NaNs

    Parameters
    ----------
    values : ndarray
    axis : int, optional
    skipna : bool, default True
    ddof : int, default 1
        Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
        where N represents the number of elements.
    mask : ndarray[bool], optional
        nan-mask if known

    Returns
    -------
    result : float
        Unless input is a float array, in which case use the same
        precision as the input array.

    Examples
    --------
    >>> from pandas.core import nanops
    >>> s = pd.Series([1, np.nan, 2, 3])
    >>> nanops.nanstd(s.values)
    1.0
    """
    if values.dtype.kind == "M":
        unit = np.datetime_data(values.dtype)[0]
        values = values.view(f"m8[{unit}]")

    orig_dtype = values.dtype
    values, mask = _get_values(values, skipna, mask=mask)

    result = np.sqrt(nanvar(values, axis=axis, skipna=skipna, ddof=ddof, mask=mask))
    return _wrap_results(result, orig_dtype)


@disallow("M8", "m8")