code: def exceptAll(self, other):
          
          return DataFrame(self._jdf.exceptAll(other._jdf), self.sql_ctx)
docs: Return a new :class:`DataFrame` containing rows in this :class:`DataFrame` but
          not in another :class:`DataFrame` while preserving duplicates.
  
          This is equivalent to `EXCEPT ALL` in SQL.
  
          >>> df1 = spark.createDataFrame(
          ...         [("a", 1), ("a", 1), ("a", 1), ("a", 2), ("b",  3), ("c", 4)], ["C1", "C2"])
          >>> df2 = spark.createDataFrame([("a", 1), ("b", 3)], ["C1", "C2"])
  
          >>> df1.exceptAll(df2).show()
          +---+---+
          | C1| C2|
          +---+---+
          |  a|  1|
          |  a|  1|
          |  a|  2|
          |  c|  4|
          +---+---+
  
          Also as standard in SQL, this function resolves columns by position (not by name).