title: How do I apply Tfidf_vectorizer to the whole pandas column?
body: <p>First of all I'm kinda new to NLP, so I might have understood a concept in a wrong way or something</p>
  
  <p>I am trying to find a way to vectorize the whole column as 1 text, and then after getting the results, I would like to fit the model I'm using to my target set.</p>
  
  <p>I am currently using a pipeline to vectorize my dataframe columns, but I believe that they are being vectorized 1 by 1, instead of joining all the columns together and then doing it.</p>
  
  <p>Here's an exaggerated example of my dataset:</p>
  
  <pre><code>   data                                                 target
  1 "conventional normal breast cancer test"              breast cancer test
  2 "regular and conventional normal lung cancer test"    lung cancer test
  </code></pre>
  
  <p>Basically, I would like to give the terms "breast" and "lung" a high tfidf score because it is unique, and I don't want my model to mistake these 2 types of data because they seem similar</p>
  
  <p>My Current Code:</p>
  
  <pre><code>vectorizer = feature_extraction.text.TfidfVectorizer(ngram_range=(1, 3),
                           analyzer='word',)
  
  pipe = pipeline.Pipeline([
  ('vectorizer', vectorizer),
  ('clf', linear_model.LogisticRegression())])
  
  pipe.fit(X_train, y_train)
  
  y_predicted = pipe.predict(X_test)
  </code></pre>