Update distinct() and repartition() definitions (#138)

* Update repartition functions to allow for Col in numPartitions parameter.

Reference: https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrame.repartition

numPartitions – can be an int to specify the target number of partitions or a Column.
If it is a Column, it will be used as the first partitioning column.
If not specified, the default number of partitions is used.

* Add stub for DataFrame#distinct

* Break apart the Union into overloaded signatures

* Update repartitionByRange by splitting the Union into overloaded signatures

third_party/3/pyspark/sql/dataframe.pyi

@@ -62,8 +62,15 @@ class DataFrame:
     def storageLevel(self) -> StorageLevel: ...
     def unpersist(self, blocking: bool = ...) -> DataFrame: ...
     def coalesce(self, numPartitions: int) -> DataFrame: ...
+    @overload
     def repartition(self, numPartitions: int, *cols: ColumnOrName) -> DataFrame: ...
+    @overload
+    def repartition(self, *cols: ColumnOrName) -> DataFrame: ...
+    @overload
     def repartitionByRange(self, numPartitions: int, *cols: ColumnOrName) -> DataFrame: ...
+    @overload
+    def repartitionByRange(self, *cols: ColumnOrName) -> DataFrame: ...
+    def distinct(self) -> DataFrame: ...
     def sample(self, withReplacement: Optional[bool], fraction: float, seed: Optional[int] = ...) -> DataFrame: ...
     def sampleBy(self, col: str, fractions: float, seed: Optional[int] = ...) -> DataFrame: ...
     def randomSplit(self, weights: List[float], seed: Optional[int] = ...) -> List[DataFrame]: ...