diff --git a/docs/scala-programming-guide.md b/docs/scala-programming-guide.md
index c1ef46a1cded743d59f7d1e333d3936e001bd3df..7c0f67bc99e8363d6a44e797354fba7f6227578c 100644
--- a/docs/scala-programming-guide.md
+++ b/docs/scala-programming-guide.md
@@ -168,9 +168,9 @@ The following tables list the transformations and actions currently supported (s
     Iterator[T] => Iterator[U] when running on an RDD of type T. </td>
 </tr>
 <tr>
-  <td> <b>mapPartitionsWithSplit</b>(<i>func</i>) </td>
+  <td> <b>mapPartitionsWithIndex</b>(<i>func</i>) </td>
   <td> Similar to mapPartitions, but also provides <i>func</i> with an integer value representing the index of
-  the split, so <i>func</i> must be of type (Int, Iterator[T]) => Iterator[U] when running on an RDD of type T.
+  the partition, so <i>func</i> must be of type (Int, Iterator[T]) => Iterator[U] when running on an RDD of type T.
   </td>
 </tr>
 <tr>
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 6fb4a7b3be25dfaeea2a7bdca0512868ade70a58..1ad4b5298758ba50816fe3946423e87c3d1314c6 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -27,6 +27,7 @@ import traceback
 from subprocess import Popen, PIPE
 from tempfile import NamedTemporaryFile
 from threading import Thread
+import warnings
 
 from pyspark.serializers import NoOpSerializer, CartesianDeserializer, \
     BatchedSerializer, CloudPickleSerializer, pack_long
@@ -179,7 +180,7 @@ class RDD(object):
         [(2, 2), (2, 2), (3, 3), (3, 3), (4, 4), (4, 4)]
         """
         def func(s, iterator): return chain.from_iterable(imap(f, iterator))
-        return self.mapPartitionsWithSplit(func, preservesPartitioning)
+        return self.mapPartitionsWithIndex(func, preservesPartitioning)
 
     def mapPartitions(self, f, preservesPartitioning=False):
         """
@@ -191,10 +192,24 @@ class RDD(object):
         [3, 7]
         """
         def func(s, iterator): return f(iterator)
-        return self.mapPartitionsWithSplit(func)
+        return self.mapPartitionsWithIndex(func)
+
+    def mapPartitionsWithIndex(self, f, preservesPartitioning=False):
+        """
+        Return a new RDD by applying a function to each partition of this RDD,
+        while tracking the index of the original partition.
+
+        >>> rdd = sc.parallelize([1, 2, 3, 4], 4)
+        >>> def f(splitIndex, iterator): yield splitIndex
+        >>> rdd.mapPartitionsWithIndex(f).sum()
+        6
+        """
+        return PipelinedRDD(self, f, preservesPartitioning)
 
     def mapPartitionsWithSplit(self, f, preservesPartitioning=False):
         """
+        Deprecated: use mapPartitionsWithIndex instead.
+
         Return a new RDD by applying a function to each partition of this RDD,
         while tracking the index of the original partition.
 
@@ -203,7 +218,9 @@ class RDD(object):
         >>> rdd.mapPartitionsWithSplit(f).sum()
         6
         """
-        return PipelinedRDD(self, f, preservesPartitioning)
+        warnings.warn("mapPartitionsWithSplit is deprecated; "
+            "use mapPartitionsWithIndex instead", DeprecationWarning, stacklevel=2)
+        return self.mapPartitionsWithIndex(f, preservesPartitioning)
 
     def filter(self, f):
         """
@@ -235,7 +252,7 @@ class RDD(object):
         >>> sc.parallelize(range(0, 100)).sample(False, 0.1, 2).collect() #doctest: +SKIP
         [2, 3, 20, 21, 24, 41, 42, 66, 67, 89, 90, 98]
         """
-        return self.mapPartitionsWithSplit(RDDSampler(withReplacement, fraction, seed).func, True)
+        return self.mapPartitionsWithIndex(RDDSampler(withReplacement, fraction, seed).func, True)
 
     # this is ported from scala/spark/RDD.scala
     def takeSample(self, withReplacement, num, seed):