title: Performing CountVectorizer on multiple columns
body: <p>I'm struggling on creating CountVectorizer model on a text dataframe that I have. 
  The dataframe contains 4 columns with a relatively long text.
  For example:</p>
  
  <pre><code>Description     Comments         Summary           System Log
  text text text  text text text   text text text    text text text
  </code></pre>
  
  <p>I created this function that work well on each column separately, but I can't figure out how to do the same for the all df together: </p>
  
  <pre><code>    vectorizer = CountVectorizer(max_features=1500, max_df = 0.90, min_df = 0.05)
      X = vectorizer.fit_transform(df).toarray()
      tfidfconverter = TfidfTransformer()
      X = tfidfconverter.fit_transform(X).toarray()
      df = pd.DataFrame(X, columns = vectorizer.get_feature_names())
  
      return df
  </code></pre>
  
  <p>The output that I'm looking to get is a df that will look something like this:</p>
  
  <pre><code>       able    above   abpwrk  accessor    according   action      activity    actual      without 
  0       0.0     0.0     0.0     0.00000     0.0         0.000000    0.0         0.000000    0.000000    
  1       0.0     0.0     0.0     0.07126     0.0         0.249390    0.0         0.000000    0.000000    
  </code></pre>
  
  <p>It works if I'm merging all the columns into one column of text, but something tells me there must be a smarter solution. Any idea? </p>