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diff --git a/examples/src/main/python/ml/count_vectorizer_example.py b/examples/src/main/python/ml/count_vectorizer_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.
+#
+
+from __future__ import print_function
+
+from pyspark import SparkContext
+from pyspark.sql import SQLContext
+# $example on$
+from pyspark.ml.feature import CountVectorizer
+# $example off$
+
+if __name__ == "__main__":
+    sc = SparkContext(appName="CountVectorizerExample")
+    sqlContext = SQLContext(sc)
+
+    # $example on$
+    # Input data: Each row is a bag of words with a ID.
+    df = sqlContext.createDataFrame([
+        (0, "a b c".split(" ")),
+        (1, "a b b c a".split(" "))
+    ], ["id", "words"])
+
+    # fit a CountVectorizerModel from the corpus.
+    cv = CountVectorizer(inputCol="words", outputCol="features", vocabSize=3, minDF=2.0)
+    model = cv.fit(df)
+    result = model.transform(df)
+    result.show()
+    # $example off$
+
+    sc.stop()