code: def eval(self, expr, **kwargs):
          
          columns = self.index if self._is_transposed else self.columns
          index = self.columns if self._is_transposed else self.index
  
          # Make a copy of columns and eval on the copy to determine if result type is
          # series or not
          columns_copy = pandas.DataFrame(columns=self.columns)
          columns_copy = columns_copy.eval(expr, inplace=False, **kwargs)
          expect_series = isinstance(columns_copy, pandas.Series)
  
          def eval_builder(df, **kwargs):
              # pop the `axis` parameter because it was needed to build the mapreduce
              # function but it is not a parameter used by `eval`.
              kwargs.pop("axis", None)
              df.columns = columns
              result = df.eval(expr, inplace=False, **kwargs)
              return result
  
          func = self._build_mapreduce_func(eval_builder, axis=1, **kwargs)
          new_data = self._map_across_full_axis(1, func)
  
          if expect_series:
              new_columns = [columns_copy.name]
              new_index = index
          else:
              new_columns = columns_copy.columns
              new_index = self.index
          return self.__constructor__(new_data, new_index, new_columns)
docs: Returns a new QueryCompiler with expr evaluated on columns.
  
          Args:
              expr: The string expression to evaluate.
  
          Returns:
              A new QueryCompiler with new columns after applying expr.