title: How to implement MultiLabelBinarizer on this dataframe?
body: <p>I have a dataframe like this:</p>
  
  <pre><code>    mid value   label
  ID          
  192 3   176.6   [9, 6, 8, 0, 8, 8, 7, 9, 2, 19...
  192 4   73.6    [9, 6, 8, 0, 8, 8, 7, 9, 2, 19...
  192 5   15.8    [9, 6, 8, 0, 8, 8, 7, 9, 2, 19...
  194 3   9603.2  [0, 0, 0, 0, 0, 9, 6, 1, 8, ...
  </code></pre>
  
  <p>I want to implement MultiLabelBinarizer after removing the duplicate values in each list of label column.</p>
  
  <p>I have tried by looping the frame and removing duplicates. and also, the multilabel binarizer doesnt work and throws an exception</p>
  
  <pre><code>    from sklearn.preprocessing import MultiLabelBinarizer
      mlb = MultiLabelBinarizer()
      mlb.fit(y_train.data)
      X_train includes the mid and value columns
      y_train includes label values
      id is the index
  
  I expect a prediction from the above values after the duplicate values are removed from each list of label column
  </code></pre>