parameters: (
          self,
          axis=None,
          skipna=None,
          level=None,
          ddof=1,
          numeric_only=None,
          **kwargs,
      )
return_statement: return self._reduce(
              "var",
              axis=axis,
              skipna=skipna,
              level=level,
              ddof=ddof,
              numeric_only=numeric_only,
              **kwargs,
          )
docstring: Return unbiased variance of the DataFrame.
  
          Normalized by N-1 by default. This can be changed using the
          ddof argument
  
          Parameters
          ----------
  
          axis: {index (0), columns(1)}
              Axis for the function to be applied on.
          skipna: bool, default True
              Exclude NA/null values. If an entire row/column is NA, the result
              will be NA.
          ddof: int, default 1
              Delta Degrees of Freedom. The divisor used in calculations is
              N - ddof, where N represents the number of elements.
  
          Returns
          -------
          scalar
  
          Notes
          -----
          Parameters currently not supported are `level` and
          `numeric_only`
  
          Examples
          --------
          >>> import cudf
          >>> df = cudf.DataFrame({'a': [1, 2, 3, 4], 'b': [7, 8, 9, 10]})
          >>> df.var()
          a    1.666667
          b    1.666667
          dtype: float64
docstring_summary: Return unbiased variance of the DataFrame.
docstring_tokens: Return unbiased variance of the DataFrame .
function: def var(
          self,
          axis=None,
          skipna=None,
          level=None,
          ddof=1,
          numeric_only=None,
          **kwargs,
      ):
          """
          Return unbiased variance of the DataFrame.
  
          Normalized by N-1 by default. This can be changed using the
          ddof argument
  
          Parameters
          ----------
  
          axis: {index (0), columns(1)}
              Axis for the function to be applied on.
          skipna: bool, default True
              Exclude NA/null values. If an entire row/column is NA, the result
              will be NA.
          ddof: int, default 1
              Delta Degrees of Freedom. The divisor used in calculations is
              N - ddof, where N represents the number of elements.
  
          Returns
          -------
          scalar
  
          Notes
          -----
          Parameters currently not supported are `level` and
          `numeric_only`
  
          Examples
          --------
          >>> import cudf
          >>> df = cudf.DataFrame({'a': [1, 2, 3, 4], 'b': [7, 8, 9, 10]})
          >>> df.var()
          a    1.666667
          b    1.666667
          dtype: float64
          """
          return self._reduce(
              "var",
              axis=axis,
              skipna=skipna,
              level=level,
              ddof=ddof,
              numeric_only=numeric_only,
              **kwargs,
          )
function_tokens: def var ( self , axis = None , skipna = None , level = None , ddof = 1 , numeric_only = None , * * kwargs , ) : return self . _reduce ( "var" , axis = axis , skipna = skipna , level = level , ddof = ddof , numeric_only = numeric_only , * * kwargs , )