code: def pivot(self, pivot_col, values=None):
          
          if values is None:
              jgd = self._jgd.pivot(pivot_col)
          else:
              jgd = self._jgd.pivot(pivot_col, values)
          return GroupedData(jgd, self._df)
docs: Pivots a column of the current :class:`DataFrame` and perform the specified aggregation.
          There are two versions of pivot function: one that requires the caller to specify the list
          of distinct values to pivot on, and one that does not. The latter is more concise but less
          efficient, because Spark needs to first compute the list of distinct values internally.
  
          :param pivot_col: Name of the column to pivot.
          :param values: List of values that will be translated to columns in the output DataFrame.
  
          # Compute the sum of earnings for each year by course with each course as a separate column
  
          >>> df4.groupBy("year").pivot("course", ["dotNET", "Java"]).sum("earnings").collect()
          [Row(year=2012, dotNET=15000, Java=20000), Row(year=2013, dotNET=48000, Java=30000)]
  
          # Or without specifying column values (less efficient)
  
          >>> df4.groupBy("year").pivot("course").sum("earnings").collect()
          [Row(year=2012, Java=20000, dotNET=15000), Row(year=2013, Java=30000, dotNET=48000)]
          >>> df5.groupBy("sales.year").pivot("sales.course").sum("sales.earnings").collect()
          [Row(year=2012, Java=20000, dotNET=15000), Row(year=2013, Java=30000, dotNET=48000)]