added python api and fixed test

python/pyspark/sql/dataframe.py

@@ -425,14 +425,30 @@ def distinct(self):
     def sample(self, withReplacement, fraction, seed=None):
         """Returns a sampled subset of this :class:`DataFrame`.
 
-        >>> df.sample(False, 0.5, 97).count()
+        >>> df.sample(False, 0.5, 42).count()
         1
         """
         assert fraction >= 0.0, "Negative fraction value: %s" % fraction
         seed = seed if seed is not None else random.randint(0, sys.maxsize)
         rdd = self._jdf.sample(withReplacement, fraction, long(seed))
         return DataFrame(rdd, self.sql_ctx)
 
+    def randomSplit(self, weights, seed=None):
+        """Randomly splits this :class:`DataFrame` with the provided weights.
+
+        >>> splits = df4.randomSplit([1.0, 2.0], 24)
+        >>> splits[0].count()
+        1
+
+        >>> splits[1].count()
+        3
+        """
+        for w in weights:
+            assert w >= 0.0, "Negative weight value: %s" % w
+        seed = seed if seed is not None else random.randint(0, sys.maxsize)
+        rdd_array = self._jdf.randomSplit(_to_seq(self.sql_ctx._sc, weights), long(seed))
+        return [DataFrame(rdd, self.sql_ctx) for rdd in rdd_array]
+
     @property
     def dtypes(self):
         """Returns all column names and their data types as a list.

sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala

@@ -728,7 +728,7 @@ class DataFrame private[sql](
   /**
    * Randomly splits this [[DataFrame]] with the provided weights.
    *
-   * @param weights weights for splits, will be normalized if they don't sum to 1
+   * @param weights weights for splits, will be normalized if they don't sum to 1.
    * @param seed Seed for sampling.
    * @group dfops
    */
@@ -743,13 +743,24 @@ class DataFrame private[sql](
   /**
    * Randomly splits this [[DataFrame]] with the provided weights.
    *
-   * @param weights weights for splits, will be normalized if they don't sum to 1
+   * @param weights weights for splits, will be normalized if they don't sum to 1.
    * @group dfops
    */
   def randomSplit(weights: Array[Double]): Array[DataFrame] = {
     randomSplit(weights, Utils.random.nextLong)
   }
 
+  /**
+   * Randomly splits this [[DataFrame]] with the provided weights. Provided for the Python Api.
+   *
+   * @param weights weights for splits, will be normalized if they don't sum to 1.
+   * @param seed Seed for sampling.
+   * @group dfops
+   */
+  def randomSplit(weights: List[Double], seed: Long): Array[DataFrame] = {
+    randomSplit(weights.toArray, seed)
+  }
+
   /**
    * (Scala-specific) Returns a new [[DataFrame]] where each row has been expanded to zero or more
    * rows by the provided function.  This is similar to a `LATERAL VIEW` in HiveQL. The columns of