From 478b2b7edcf42fa3e16f625d4b8676f2bb31f8dc Mon Sep 17 00:00:00 2001
From: Matei Zaharia <matei@eecs.berkeley.edu>
Date: Wed, 9 Oct 2013 12:08:04 -0700
Subject: Fix PySpark docs and an overly long line of code after fdbae41e

---
 python/pyspark/rdd.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

(limited to 'python/pyspark/rdd.py')

diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 7dfabb0b7d..7019fb8bee 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -117,8 +117,6 @@ class RDD(object):
         else:
             return None
 
-    # TODO persist(self, storageLevel)
-
     def map(self, f, preservesPartitioning=False):
         """
         Return a new RDD containing the distinct elements in this RDD.
@@ -227,7 +225,7 @@ class RDD(object):
             total = num
 
         samples = self.sample(withReplacement, fraction, seed).collect()
-    
+
         # If the first sample didn't turn out large enough, keep trying to take samples;
         # this shouldn't happen often because we use a big multiplier for their initial size.
         # See: scala/spark/RDD.scala
@@ -288,7 +286,7 @@ class RDD(object):
             maxSampleSize = numPartitions * 20.0 # constant from Spark's RangePartitioner
             fraction = min(maxSampleSize / max(rddSize, 1), 1.0)
 
-            samples = self.sample(False, fraction, 1).map(lambda (k, v): k).collect()        
+            samples = self.sample(False, fraction, 1).map(lambda (k, v): k).collect()
             samples = sorted(samples, reverse=(not ascending), key=keyfunc)
 
             # we have numPartitions many parts but one of the them has
@@ -309,7 +307,9 @@ class RDD(object):
         def mapFunc(iterator):
             yield sorted(iterator, reverse=(not ascending), key=lambda (k, v): keyfunc(k))
 
-        return self.partitionBy(numPartitions, partitionFunc=rangePartitionFunc).mapPartitions(mapFunc,preservesPartitioning=True).flatMap(lambda x: x, preservesPartitioning=True)
+        return (self.partitionBy(numPartitions, partitionFunc=rangePartitionFunc)
+                    .mapPartitions(mapFunc,preservesPartitioning=True)
+                    .flatMap(lambda x: x, preservesPartitioning=True))
 
     def glom(self):
         """
@@ -471,7 +471,7 @@ class RDD(object):
         3
         """
         return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
-    
+
     def stats(self):
         """
         Return a L{StatCounter} object that captures the mean, variance
@@ -508,7 +508,7 @@ class RDD(object):
         0.816...
         """
         return self.stats().stdev()
-  
+
     def sampleStdev(self):
         """
         Compute the sample standard deviation of this RDD's elements (which corrects for bias in
@@ -878,7 +878,7 @@ class RDD(object):
         >>> y = sc.parallelize([("a", 3), ("c", None)])
         >>> sorted(x.subtractByKey(y).collect())
         [('b', 4), ('b', 5)]
-        """ 
+        """
         filter_func = lambda (key, vals): len(vals[0]) > 0 and len(vals[1]) == 0
         map_func = lambda (key, vals): [(key, val) for val in vals[0]]
         return self.cogroup(other, numPartitions).filter(filter_func).flatMap(map_func)
-- 
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