title: Python Machine Learning - Training/Testing and apply prediction to new dataset
body: <p>I have only done training and testing on a single data set splits.
  I have a supervised learning problem: Data 1 Training/Testing and Data 2: No labels. I am using pandas data frame. </p>
  
  <p>Data set 1: supervised</p>
  
  <pre><code>text        y_variable
  apple       fruit
  orange      fruit
  celery      vegetable
  mango       fruit
  </code></pre>
  
  <p>Data set 2: with no label</p>
  
  <pre><code>text        to_be_predicted
  orange      ?
  celery      ?
  mango       ?
  </code></pre>
  
  <p>I am using scikit learn:</p>
  
  <pre><code>X = df['text']
  y = df['y_variable']
  
  X_train, X_test, y_train, y_test = train_test_split(
  X, y, test_size=0.2
  </code></pre>
  
  <p>This splits the existing dataframe into training and testing. How to I train/test first Data set 1 and apply it to the second data set? Machine Learning.</p>
  
  <p>Data set 2: with no label</p>
  
  <pre><code>text        to_be_predicted
  orange      fruit
  celery      vegetable
  mango       fruit
  </code></pre>