1 files changed, 15 insertions, 14 deletions
diff --git a/examples/src/main/python/mllib/sampled_rdds.py b/examples/src/main/python/mllib/sampled_rdds.py
index 92af3af5eb..b7033ab7da 100755
--- a/examples/src/main/python/mllib/sampled_rdds.py
+++ b/examples/src/main/python/mllib/sampled_rdds.py
@@ -18,6 +18,7 @@
 """
 Randomly sampled RDDs.
 """
+from __future__ import print_function
 
 import sys
 
@@ -27,7 +28,7 @@ from pyspark.mllib.util import MLUtils
 
 if __name__ == "__main__":
     if len(sys.argv) not in [1, 2]:
-        print >> sys.stderr, "Usage: sampled_rdds <libsvm data file>"
+        print("Usage: sampled_rdds <libsvm data file>", file=sys.stderr)
         exit(-1)
     if len(sys.argv) == 2:
         datapath = sys.argv[1]
@@ -41,24 +42,24 @@ if __name__ == "__main__":
     examples = MLUtils.loadLibSVMFile(sc, datapath)
     numExamples = examples.count()
     if numExamples == 0:
-        print >> sys.stderr, "Error: Data file had no samples to load."
+        print("Error: Data file had no samples to load.", file=sys.stderr)
         exit(1)
-    print 'Loaded data with %d examples from file: %s' % (numExamples, datapath)
+    print('Loaded data with %d examples from file: %s' % (numExamples, datapath))
 
     # Example: RDD.sample() and RDD.takeSample()
     expectedSampleSize = int(numExamples * fraction)
-    print 'Sampling RDD using fraction %g.  Expected sample size = %d.' \
-        % (fraction, expectedSampleSize)
+    print('Sampling RDD using fraction %g.  Expected sample size = %d.'
+          % (fraction, expectedSampleSize))
     sampledRDD = examples.sample(withReplacement=True, fraction=fraction)
-    print '  RDD.sample(): sample has %d examples' % sampledRDD.count()
+    print('  RDD.sample(): sample has %d examples' % sampledRDD.count())
     sampledArray = examples.takeSample(withReplacement=True, num=expectedSampleSize)
-    print '  RDD.takeSample(): sample has %d examples' % len(sampledArray)
+    print('  RDD.takeSample(): sample has %d examples' % len(sampledArray))
 
-    print
+    print()
 
     # Example: RDD.sampleByKey()
     keyedRDD = examples.map(lambda lp: (int(lp.label), lp.features))
-    print '  Keyed data using label (Int) as key ==> Orig'
+    print('  Keyed data using label (Int) as key ==> Orig')
     #  Count examples per label in original data.
     keyCountsA = keyedRDD.countByKey()
 
@@ -69,18 +70,18 @@ if __name__ == "__main__":
     sampledByKeyRDD = keyedRDD.sampleByKey(withReplacement=True, fractions=fractions)
     keyCountsB = sampledByKeyRDD.countByKey()
     sizeB = sum(keyCountsB.values())
-    print '  Sampled %d examples using approximate stratified sampling (by label). ==> Sample' \
-        % sizeB
+    print('  Sampled %d examples using approximate stratified sampling (by label). ==> Sample'
+          % sizeB)
 
     #  Compare samples
-    print '   \tFractions of examples with key'
-    print 'Key\tOrig\tSample'
+    print('   \tFractions of examples with key')
+    print('Key\tOrig\tSample')
     for k in sorted(keyCountsA.keys()):
         fracA = keyCountsA[k] / float(numExamples)
         if sizeB != 0:
             fracB = keyCountsB.get(k, 0) / float(sizeB)
         else:
             fracB = 0
-        print '%d\t%g\t%g' % (k, fracA, fracB)
+        print('%d\t%g\t%g' % (k, fracA, fracB))
 
     sc.stop()