diff --git a/examples/src/main/python/streaming/network_wordcount.py b/examples/src/main/python/streaming/network_wordcount.py
index 77fca7ff7657d..a1458e06f13d2 100644
--- a/examples/src/main/python/streaming/network_wordcount.py
+++ b/examples/src/main/python/streaming/network_wordcount.py
@@ -19,10 +19,12 @@
     filtered_lines = fm_lines.filter(lambda line: "Spark" in line)
     mapped_lines = fm_lines.map(lambda x: (x, 1))
     reduced_lines = mapped_lines.reduce(add)
+    counted_lines = reduced_lines.count()
     
     fm_lines.pyprint()
     filtered_lines.pyprint()
     mapped_lines.pyprint()
     reduced_lines.pyprint()
+    counted_lines.pyprint()
     ssc.start()
     ssc.awaitTermination()
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 3f23e65712368..caa62d44a9069 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -20,21 +20,14 @@ def __init__(self, jdstream, ssc, jrdd_deserializer):
         self.ctx = ssc._sc
         self._jrdd_deserializer = jrdd_deserializer
 
-    def generatedRDDs(self):
-        """
-         // RDDs generated, marked as private[streaming] so that testsuites can access it
-         @transient
-        """
-        pass
-
     def count(self):
         """
 
         """
         #TODO make sure count implementation, thiis different from what pyspark does
-        return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum().map(lambda x: x[1])
+        return self.mapPartitions(lambda i: [sum(1 for _ in i)]).map(lambda x: (None, 1))
 
-    def sum(self):
+    def _sum(self):
         """
         """
         return self.mapPartitions(lambda x: [sum(x)]).reduce(operator.add)
@@ -65,8 +58,9 @@ def func(s, iterator): return chain.from_iterable(imap(f, iterator))
     def map(self, f, preservesPartitioning=False):
         """
         """
-        def func(split, iterator): return imap(f, iterator)
-        return PipelinedDStream(self, func, preservesPartitioning)
+        def func(iterator): return imap(f, iterator)
+        return self.mapPartitions(func)
+        #return PipelinedDStream(self, func, preservesPartitioning)
 
     def mapPartitions(self, f):
         """
@@ -74,6 +68,12 @@ def mapPartitions(self, f):
         def func(s, iterator): return f(iterator)
         return self.mapPartitionsWithIndex(func)
 
+    def mapPartitionsWithIndex(self, f, preservesPartitioning=False):
+        """
+
+        """
+        return PipelinedDStream(self, f, preservesPartitioning)
+
     def reduce(self, func, numPartitions=None):
         """
 
@@ -92,8 +92,8 @@ def combineLocally(iterator):
 
                 #TODO for count operation make sure count implementation
                 # This is different from what pyspark does
-                if isinstance(x, int):
-                    x = ("", x)
+                #if isinstance(x, int):
+                #    x = ("", x)
 
                 (k, v) = x
                 if k not in combiners:
@@ -166,12 +166,6 @@ def _defaultReducePartitions(self):
 
         return self._jdstream.partitions().size()
 
-    def mapPartitionsWithIndex(self, f, preservesPartitioning=False):
-        """
-
-        """
-        return PipelinedDStream(self, f, preservesPartitioning)
-
     def _defaultReducePartitions(self):
         """