title: Store Tf-idf matrix and update existing matrix on new articles in pandas
body: <p>I have a pandas dataframe with column <code>text</code> consists of <code>news articles</code>. Given as:-</p>
  
  <pre><code>text
  article1
  article2
  article3
  article4
  </code></pre>
  
  <p>I have calculated the Tf-IDF values for articles as:-</p>
  
  <pre><code>from sklearn.feature_extraction.text import TfidfVectorizer
  tfidf = TfidfVectorizer()
  matrix_1 = tfidf.fit_transform(df['text'])
  </code></pre>
  
  <p>As my dataframe is kept updating from time to time. So, let's say after calculating of-if as matrix_1 my dataframe got updated with more articles. Something like:</p>
  
  <pre><code>text
  article1
  article2
  article3
  article4
  article5
  article6
  article7
  </code></pre>
  
  <p>As I have millions of articles and all I want to store a tf-IDF matrix of the previous article and updating the same with tf-IDF scores of the new article. Running the of-IDF code for all articles, again and again, would be memory consuming. Is there any way I can perform this?</p>