diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index ae09dbff02a36..8c7f5074a7ce4 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -25,6 +25,7 @@
 import sys
 import shlex
 import traceback
+from bisect import bisect_right
 from subprocess import Popen, PIPE
 from tempfile import NamedTemporaryFile
 from threading import Thread
@@ -572,7 +573,6 @@ def func(iterator):
 
     # TODO: aggregate
         
-
     def max(self):
         """
         Find the maximum item in this RDD.
@@ -666,6 +666,65 @@ def sampleVariance(self):
         """
         return self.stats().sampleVariance()
 
+    def _getBuckets(self, bucketCount):
+        #use the statscounter as a quick way of getting max and min
+        mm_stats = self.stats()
+        min = mm_stats.min()
+        max = mm_stats.max()
+
+        increment = (max-min)/bucketCount
+        buckets = range(min,min)
+        if increment != 0:
+            buckets = range(min,max, increment)
+
+        return {"min":min, "max":max, "buckets":buckets}
+
+    def histogram(self, bucketCount, buckets=None):
+        """
+        Compute a histogram of the data using bucketCount number of buckets
+        evenly spaced between the min and max of the RDD.
+
+        >>> sc.parallelize([1,49, 23, 100, 12, 13, 20, 22, 75, 50]).histogram(3)
+        defaultdict(<type 'int'>, {(67, 100): 2, (1, 33): 6, (34, 66): 2})
+        """
+        min = float("-inf")
+        max = float("inf")
+        evenBuckets = False
+        if not buckets:
+            b = self._getBuckets(bucketCount)
+            buckets = b["buckets"]
+            min = b["min"]
+            max = b["max"]
+        
+        if len(buckets) < 2:
+            raise ValueError("requires more than 1 bucket")
+        if len(buckets) % 2 == 0:
+            evenBuckets = True
+        
+        def histogramPartition(iterator):
+            counters = defaultdict(int)
+            for obj in iterator:
+                k = bisect_right(buckets, obj)
+                if k < len(buckets) and k > 0:
+                    key = (buckets[k-1], buckets[k]-1)
+                elif k == len(buckets):
+                    key = (buckets[k-1], max)
+                elif k == 0:
+                    key = (min, buckets[k]-1)
+                print obj, k, key
+                counters[key] += 1
+            yield counters
+            
+        
+        def mergeCounters(d1, d2):
+            for k in d2.keys():
+                if k in d1:
+                    d1[k] += d2[k]
+            return d1
+        
+        return self.mapPartitions(histogramPartition).reduce(mergeCounters)
+
+
     def countByValue(self):
         """
         Return the count of each unique value in this RDD as a dictionary of