diff --git a/python/pyspark/rddsampler.py b/python/pyspark/rddsampler.py
index 122bc38b03b0c..7ff1c316c7623 100644
--- a/python/pyspark/rddsampler.py
+++ b/python/pyspark/rddsampler.py
@@ -18,13 +18,16 @@
 import sys
 import random
 
+
 class RDDSampler(object):
     def __init__(self, withReplacement, fraction, seed=None):
         try:
             import numpy
             self._use_numpy = True
         except ImportError:
-            print >> sys.stderr, "NumPy does not appear to be installed. Falling back to default random generator for sampling."
+            print >> sys.stderr, (
+                "NumPy does not appear to be installed. "
+                "Falling back to default random generator for sampling.")
             self._use_numpy = False
 
         self._seed = seed if seed is not None else random.randint(0, sys.maxint)
@@ -61,7 +64,7 @@ def getUniformSample(self, split):
     def getPoissonSample(self, split, mean):
         if not self._rand_initialized or split != self._split:
             self.initRandomGenerator(split)
-        
+
         if self._use_numpy:
             return self._random.poisson(mean)
         else:
@@ -80,30 +83,27 @@ def getPoissonSample(self, split, mean):
                 num_arrivals += 1
 
             return (num_arrivals - 1)
-    
+
     def shuffle(self, vals):
         if self._random is None:
             self.initRandomGenerator(0)  # this should only ever called on the master so
             # the split does not matter
-        
+
         if self._use_numpy:
             self._random.shuffle(vals)
         else:
             self._random.shuffle(vals, self._random.random)
 
     def func(self, split, iterator):
-        if self._withReplacement:            
+        if self._withReplacement:
             for obj in iterator:
-                # For large datasets, the expected number of occurrences of each element in a sample with
-                # replacement is Poisson(frac). We use that to get a count for each element.                                   
-                count = self.getPoissonSample(split, mean = self._fraction)
+                # For large datasets, the expected number of occurrences of each element in
+                # a sample with replacement is Poisson(frac). We use that to get a count for
+                # each element.
+                count = self.getPoissonSample(split, mean=self._fraction)
                 for _ in range(0, count):
                     yield obj
         else:
             for obj in iterator:
                 if self.getUniformSample(split) <= self._fraction:
                     yield obj
-
-            
-            
-