title: sklearn : TFIDF Transformer : How to get tf-idf values of given words in document
body: <p>I used <b>sklearn</b> for calculating TFIDF (Term frequency inverse document frequency) values for documents using command as :</p>
  
  <pre><code>from sklearn.feature_extraction.text import CountVectorizer
  count_vect = CountVectorizer()
  X_train_counts = count_vect.fit_transform(documents)
  from sklearn.feature_extraction.text import TfidfTransformer
  tf_transformer = TfidfTransformer(use_idf=False).fit(X_train_counts)
  X_train_tf = tf_transformer.transform(X_train_counts)
  </code></pre>
  
  <p><code>X_train_tf</code> is a <code>scipy.sparse</code> matrix of shape <code>(2257, 35788)</code>. </p>
  
  <p>How can I get TF-IDF for words in a particular document? More specific, how to get words with maximum TF-IDF values in a given document?</p>