title: tf-idf results analysis with python
body: <p>I am trying to produce tf-idf on plain corpus of about 200k tokens. I produced vector counter at first that term frequency. Then I produced tf-idf matrix and got following results. My code is </p>
  
  <pre><code>from sklearn.feature_extraction.text import TfidfVectorizer
  with open("D:\history.txt", encoding='utf8') as infile:
      contents = infile.readlines()
  #define vectorizer parameters
  tfidf_vectorizer = TfidfVectorizer(max_df=1.0, max_features=200000,
                                   min_df=0.0,
                                   use_idf=True, ngram_range=(1,3))
  tfidf_matrix = tfidf_vectorizer.fit_transform(contents) #fit the vectorizer to contents
  
  print(tfidf_matrix)
  </code></pre>
  
  <p>Results</p>
  
  <pre><code>  (0, 8371)     0.0296607326158
    (0, 27755)    0.159032195629
    (0, 59369)    0.0871403881289
     :    :
    (551, 64746)  0.0324104689629
    (551, 10118)  0.0324104689629
    (551, 9308)   0.0324104689629
  </code></pre>
  
  <p>While I want to get results in following form</p>
  
  <pre><code>   (551, good ) 0.0324104689629
  </code></pre>