Added the PrefixSpan MLlib example based on the current documentation

docs/mllib-frequent-pattern-mining.md

@@ -177,6 +177,20 @@ Refer to the [`PrefixSpan` Java docs](api/java/org/apache/spark/mllib/fpm/Prefix
 
 {% include_example java/org/apache/spark/examples/mllib/JavaPrefixSpanExample.java %}
 
+</div>
+
+<div data-lang="python" markdown="1">
+
+[`PrefixSpan`](api/python/pyspark.mllib.html#pyspark.mllib.fpm.PrefixSpan) implements the
+PrefixSpan algorithm.
+Calling `PrefixSpan.run` returns a
+[`PrefixSpanModel`](api/python/pyspark.mllib.html#pyspark.mllib.fpm.PrefixSpanModel)
+that stores the frequent sequences with their frequencies.
+
+Refer to the [`PrefixSpan` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.fpm.PrefixSpan) and [`PrefixSpanModel` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.fpm.PrefixSpanModel) for details on the API.
+
+{% include_example python/mllib/prefix_span_example.py %}
+
 </div>
 </div>
 

examples/src/main/python/mllib/prefix_span_example.py

@@ -0,0 +1,38 @@
+#
+# 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.fpm import PrefixSpan
+# $example off$
+from pyspark import SparkContext
+
+if __name__ == "__main__":
+    sc = SparkContext(appName="PythonPrefixSpanExample")
+
+    # $example on$
+    sequences = sc.parallelize([
+        [[1,2],[3]],
+        [[1],[3,2],[1,2]],
+        [[1,2],[5]],
+        [[6]],
+    ])
+
+    model = PrefixSpan.train(sequences, minSupport=0.5, maxPatternLength=5)
+    result = model.freqSequences().collect()
+    for fs in result:
+        print('{}, {}'.format(fs.sequence,fs.freq))
+    # $example off$