[SPARK-14514][DOC] Add python example for VectorSlicer

## What changes were proposed in this pull request?
Add the missing python example for VectorSlicer

## How was this patch tested?
manual tests

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #12282 from zhengruifeng/vecslicer_pe.

docs/ml-features.md

@@ -1191,6 +1191,14 @@ for more details on the API.
 
 {% include_example java/org/apache/spark/examples/ml/JavaVectorSlicerExample.java %}
 </div>
+
+<div data-lang="python" markdown="1">
+
+Refer to the [VectorSlicer Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.VectorSlicer)
+for more details on the API.
+
+{% include_example python/ml/vector_slicer_example.py %}
+</div>
 </div>
 
 ## RFormula

examples/src/main/python/ml/vector_slicer_example.py

+#
+# 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 VectorSlicer
+from pyspark.mllib.linalg import Vectors
+from pyspark.sql.types import Row
+# $example off$
+
+if __name__ == "__main__":
+    sc = SparkContext(appName="VectorSlicerExample")
+    sqlContext = SQLContext(sc)
+
+    # $example on$
+    df = sqlContext.createDataFrame([
+        Row(userFeatures=Vectors.sparse(3, {0: -2.0, 1: 2.3}),),
+        Row(userFeatures=Vectors.dense([-2.0, 2.3, 0.0]),)])
+
+    slicer = VectorSlicer(inputCol="userFeatures", outputCol="features", indices=[1])
+
+    output = slicer.transform(df)
+
+    output.select("userFeatures", "features").show()
+    # $example off$
+
+    sc.stop()