old_contents: #
  # 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
  # $example on$
  from pyspark.mllib.feature import HashingTF, IDF
  # $example off$
  
  if __name__ == "__main__":
      sc = SparkContext(appName="TFIDFExample")  # SparkContext
  
      # $example on$
      # Load documents (one per line).
      documents = sc.textFile("data/mllib/kmeans_data.txt").map(lambda line: line.split(" "))
  
      hashingTF = HashingTF()
      tf = hashingTF.transform(documents)
  
      # While applying HashingTF only needs a single pass to the data, applying IDF needs two passes:
      # First to compute the IDF vector and second to scale the term frequencies by IDF.
      tf.cache()
      idf = IDF().fit(tf)
      tfidf = idf.transform(tf)
  
      # spark.mllib's IDF implementation provides an option for ignoring terms
      # which occur in less than a minimum number of documents.
      # In such cases, the IDF for these terms is set to 0.
      # This feature can be used by passing the minDocFreq value to the IDF constructor.
      idfIgnore = IDF(minDocFreq=2).fit(tf)
      tfidfIgnore = idf.transform(tf)
      # $example off$
  
      print("tfidf:")
      for each in tfidf.collect():
          print(each)
  
      print("tfidfIgnore:")
      for each in tfidfIgnore.collect():
          print(each)
  
      sc.stop()
  
new_contents: #
  # 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
  # $example on$
  from pyspark.mllib.feature import HashingTF, IDF
  # $example off$
  
  if __name__ == "__main__":
      sc = SparkContext(appName="TFIDFExample")  # SparkContext
  
      # $example on$
      # Load documents (one per line).
      documents = sc.textFile("data/mllib/kmeans_data.txt").map(lambda line: line.split(" "))
  
      hashingTF = HashingTF()
      tf = hashingTF.transform(documents)
  
      # While applying HashingTF only needs a single pass to the data, applying IDF needs two passes:
      # First to compute the IDF vector and second to scale the term frequencies by IDF.
      tf.cache()
      idf = IDF().fit(tf)
      tfidf = idf.transform(tf)
  
      # spark.mllib's IDF implementation provides an option for ignoring terms
      # which occur in less than a minimum number of documents.
      # In such cases, the IDF for these terms is set to 0.
      # This feature can be used by passing the minDocFreq value to the IDF constructor.
      idfIgnore = IDF(minDocFreq=2).fit(tf)
      tfidfIgnore = idfIgnore.transform(tf)
      # $example off$
  
      print("tfidf:")
      for each in tfidf.collect():
          print(each)
  
      print("tfidfIgnore:")
      for each in tfidfIgnore.collect():
          print(each)
  
      sc.stop()
  
subject: Fix example of tf_idf with minDocFreq