text: #
  # 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
  
  # $example on$
  from pyspark.ml.feature import VectorSlicer
  from pyspark.ml.linalg import Vectors
  from pyspark.sql.types import Row
  # $example off$
  from pyspark.sql import SparkSession
  
  if __name__ == "__main__":
      spark = SparkSession\
          .builder\
          .appName("VectorSlicerExample")\
          .getOrCreate()
  
      # $example on$
      df = spark.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()
      # $example off$
  
      spark.stop()
  