class_docstr: A column composed of numerical data.
  
  This class encodes a standard interface for different types of columns
  containing numerical types of data. In particular, mathematical operations
  that make sense whether a column is integral or real, fixed or floating
  point, should be encoded here.
code: def skew(self, skipna: Optional[bool] = None) -> ScalarLike:
      skipna = True if skipna is None else skipna
  
      if len(self) == 0 or self._can_return_nan(skipna=skipna):
          return cudf.utils.dtypes._get_nan_for_dtype(self.dtype)
  
      self = self.nans_to_nulls().dropna()  # type: ignore
  
      if len(self) < 3:
          return cudf.utils.dtypes._get_nan_for_dtype(self.dtype)
  
      n = len(self)
      miu = self.mean()
      m3 = (((self - miu) ** self.normalize_binop_value(3)).sum()) / n
      m2 = self.var(ddof=0)
  
      if m2 == 0:
          return 0
  
      unbiased_coef = ((n * (n - 1)) ** 0.5) / (n - 2)
      skew = unbiased_coef * m3 / (m2 ** (3 / 2))
      return skew
  
imports: from __future__ import annotations
  from typing import Optional, cast
  import numpy
  import cudf
  from cudf import _lib
  from cudf._typing import ScalarLike
  from cudf.core.column import ColumnBase
  from cudf.core.missing import NA
  from cudf.core.mixins import Scannable
label_desc: Use image node_id 3 for calling the NumericalBaseColumn obj's underlying member method code with example usage: obj.skew(skipna) and returns: skew, cudf, cudf, int