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diff --git a/examples/src/main/python/ml/kmeans_example.py b/examples/src/main/python/ml/kmeans_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
+
+import sys
+import re
+
+import numpy as np
+from pyspark import SparkContext
+from pyspark.ml.clustering import KMeans, KMeansModel
+from pyspark.mllib.linalg import VectorUDT, _convert_to_vector
+from pyspark.sql import SQLContext
+from pyspark.sql.types import Row, StructField, StructType
+
+"""
+A simple example demonstrating a k-means clustering.
+Run with:
+  bin/spark-submit examples/src/main/python/ml/kmeans_example.py <input> <k>
+
+This example requires NumPy (http://www.numpy.org/).
+"""
+
+
+def parseVector(line):
+    array = np.array([float(x) for x in line.split(' ')])
+    return _convert_to_vector(array)
+
+
+if __name__ == "__main__":
+
+    FEATURES_COL = "features"
+
+    if len(sys.argv) != 3:
+        print("Usage: kmeans_example.py <file> <k>", file=sys.stderr)
+        exit(-1)
+    path = sys.argv[1]
+    k = sys.argv[2]
+
+    sc = SparkContext(appName="PythonKMeansExample")
+    sqlContext = SQLContext(sc)
+
+    lines = sc.textFile(path)
+    data = lines.map(parseVector)
+    row_rdd = data.map(lambda x: Row(x))
+    schema = StructType([StructField(FEATURES_COL, VectorUDT(), False)])
+    df = sqlContext.createDataFrame(row_rdd, schema)
+
+    kmeans = KMeans().setK(2).setSeed(1).setFeaturesCol(FEATURES_COL)
+    model = kmeans.fit(df)
+    centers = model.clusterCenters()
+
+    print("Cluster Centers: ")
+    for center in centers:
+        print(center)
+
+    sc.stop()