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diff --git a/examples/src/main/python/mllib/ranking_metrics_example.py b/examples/src/main/python/mllib/ranking_metrics_example.py
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+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# $example on$
+from pyspark.mllib.recommendation import ALS, Rating
+from pyspark.mllib.evaluation import RegressionMetrics, RankingMetrics
+# $example off$
+from pyspark import SparkContext
+
+if __name__ == "__main__":
+    sc = SparkContext(appName="Ranking Metrics Example")
+
+    # Several of the methods available in scala are currently missing from pyspark
+    # $example on$
+    # Read in the ratings data
+    lines = sc.textFile("data/mllib/sample_movielens_data.txt")
+
+    def parseLine(line):
+        fields = line.split("::")
+        return Rating(int(fields[0]), int(fields[1]), float(fields[2]) - 2.5)
+    ratings = lines.map(lambda r: parseLine(r))
+
+    # Train a model on to predict user-product ratings
+    model = ALS.train(ratings, 10, 10, 0.01)
+
+    # Get predicted ratings on all existing user-product pairs
+    testData = ratings.map(lambda p: (p.user, p.product))
+    predictions = model.predictAll(testData).map(lambda r: ((r.user, r.product), r.rating))
+
+    ratingsTuple = ratings.map(lambda r: ((r.user, r.product), r.rating))
+    scoreAndLabels = predictions.join(ratingsTuple).map(lambda tup: tup[1])
+
+    # Instantiate regression metrics to compare predicted and actual ratings
+    metrics = RegressionMetrics(scoreAndLabels)
+
+    # Root mean sqaured error
+    print("RMSE = %s" % metrics.rootMeanSquaredError)
+
+    # R-squared
+    print("R-squared = %s" % metrics.r2)
+    # $example off$