title: Merge Target Column back to the original dataframe
body: <p>I have a dataframe and I did some features selection (following a guide) to drop some columns:</p>
  <p><strong>What I did:</strong></p>
  <pre><code>X = df.drop('goal', axis=1).select_dtypes(exclude=['object'])
  y = df['goal']
  </code></pre>
  <p>Then I selected columns using mutual_info_gain:</p>
  <pre><code>from sklearn.feature_selection import mutual_info_regression, mutual_info_classif
  info_gain = mutual_info_classif(X, y)
  </code></pre>
  <p>And finally:</p>
  <pre><code>columns_to_keep = []
  for score, f_name in sorted(zip(info_gain, X.columns), reverse=True)[:50]:
          print(f_name, score)
  
  
          columns_to_keep.append(f_name)
  df_info_gain = X[columns_to_keep]
  </code></pre>
  <p>So now <code>df_info_gain</code> has all 50 features I selected but not the <code>goal</code> column <code>y</code></p>
  <p><strong>What I need:</strong></p>
  <p>What's the correct way to bring back the <code>goal</code> column to this new <code>df_info_gain</code> dataframe?</p>