title: Vectorizing Pandas column
body: <p>I'm dealing with a Pandas dataframe with a column that exists of textual descriptions. This column needs to be transformed into something that works with a Support Vector Machine, specifically <code>sklearn SVC</code>. So I want to take each description and replace it with numerical data, for this I'm using the TfidfVectorizer.</p>
  
  <pre><code>vectorizer = TfidfVectorizer()
  df['description'] = vectorizer.fit_transform(df['description'])
  </code></pre>
  
  <p>This results in records looking like the following:</p>
  
  <pre><code>0         (0, 12)\t0.30879049244590173\n  (0, 333)\t0....
  </code></pre>
  
  <p>What this is returning is close but still not usable in an SVM. I suspect I'm misunderstanding how <code>sklearn</code> deals with <code>Pandas</code></p>