code: def nanstd(values, axis=None, skipna=True, ddof=1, mask=None):
      
      result = np.sqrt(nanvar(values, axis=axis, skipna=skipna, ddof=ddof,
                              mask=mask))
      return _wrap_results(result, values.dtype)
docs: 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
      --------
      >>> import pandas.core.nanops as nanops
      >>> s = pd.Series([1, np.nan, 2, 3])
      >>> nanops.nanstd(s)
      1.0