[SPARK-5440][pyspark] Add toLocalIterator to pyspark rdd

Since Java and Scala both have access to iterate over partitions via the "toLocalIterator" function, python should also have that same ability.

Author: Michael Nazario <mnazario@palantir.com>

Closes apache#4237 from mnazario/feature/toLocalIterator and squashes the following commits:

1c58526 [Michael Nazario] Fix documentation off by one error
0cdc8f8 [Michael Nazario] Add toLocalIterator to PySpark

Author: mnazbro

python/pyspark/rdd.py

@@ -2059,6 +2059,20 @@ def countApproxDistinct(self, relativeSD=0.05):
         hashRDD = self.map(lambda x: portable_hash(x) & 0xFFFFFFFF)
         return hashRDD._to_java_object_rdd().countApproxDistinct(relativeSD)
 
+    def toLocalIterator(self):
+        """
+        Return an iterator that contains all of the elements in this RDD.
+        The iterator will consume as much memory as the largest partition in this RDD.
+        >>> rdd = sc.parallelize(range(10))
+        >>> [x for x in rdd.toLocalIterator()]
+        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+        """
+        partitions = xrange(self.getNumPartitions())
+        for partition in partitions:
+            rows = self.context.runJob(self, lambda x: x, [partition])
+            for row in rows:
+                yield row
+
 
 class PipelinedRDD(RDD):