1 files changed, 9 insertions, 7 deletions
diff --git a/examples/src/main/python/pagerank.py b/examples/src/main/python/pagerank.py
index a5f25d78c1..2fdc9773d4 100755
--- a/examples/src/main/python/pagerank.py
+++ b/examples/src/main/python/pagerank.py
@@ -19,6 +19,7 @@
 This is an example implementation of PageRank. For more conventional use,
 Please refer to PageRank implementation provided by graphx
 """
+from __future__ import print_function
 
 import re
 import sys
@@ -42,11 +43,12 @@ def parseNeighbors(urls):
 
 if __name__ == "__main__":
     if len(sys.argv) != 3:
-        print >> sys.stderr, "Usage: pagerank <file> <iterations>"
+        print("Usage: pagerank <file> <iterations>", file=sys.stderr)
         exit(-1)
 
-    print >> sys.stderr,  """WARN: This is a naive implementation of PageRank and is
-          given as an example! Please refer to PageRank implementation provided by graphx"""
+    print("""WARN: This is a naive implementation of PageRank and is
+          given as an example! Please refer to PageRank implementation provided by graphx""",
+          file=sys.stderr)
 
     # Initialize the spark context.
     sc = SparkContext(appName="PythonPageRank")
@@ -62,19 +64,19 @@ if __name__ == "__main__":
     links = lines.map(lambda urls: parseNeighbors(urls)).distinct().groupByKey().cache()
 
     # Loads all URLs with other URL(s) link to from input file and initialize ranks of them to one.
-    ranks = links.map(lambda (url, neighbors): (url, 1.0))
+    ranks = links.map(lambda url_neighbors: (url_neighbors[0], 1.0))
 
     # Calculates and updates URL ranks continuously using PageRank algorithm.
-    for iteration in xrange(int(sys.argv[2])):
+    for iteration in range(int(sys.argv[2])):
         # Calculates URL contributions to the rank of other URLs.
         contribs = links.join(ranks).flatMap(
-            lambda (url, (urls, rank)): computeContribs(urls, rank))
+            lambda url_urls_rank: computeContribs(url_urls_rank[1][0], url_urls_rank[1][1]))
 
         # Re-calculates URL ranks based on neighbor contributions.
         ranks = contribs.reduceByKey(add).mapValues(lambda rank: rank * 0.85 + 0.15)
 
     # Collects all URL ranks and dump them to console.
     for (link, rank) in ranks.collect():
-        print "%s has rank: %s." % (link, rank)
+        print("%s has rank: %s." % (link, rank))
 
     sc.stop()