title: Filter DataFrame if group of indexes have the same value in another column
body: <p>I have the following dataset which are lines of journal entries (accounting debit/credit entries)</p>
  
  <pre><code>Entry   Debit   Credit   Account
   1        1                 A
   1                1         B
  
   2        2                 A
   2                1         C
   2                1         C
  
   3        4                 C
   3                1         G
   3                1         A
   3                1         D
   3                1         K
  
   4        1                 H
   4                1         J
  </code></pre>
  
  <p>As you can see we just have 4 journals as they are identified by the Entry Number (Index).</p>
  
  <p>What I need is to filter all Journals that contain Account A, not each line that contains A. The problem is if I do it by account I just get the line that has Account A but I lose the other line related that not necessarily will use Account A.</p>
  
  <p>So after the filter is applied, I should get something like this:</p>
  
  <pre><code>Entry   Debit   Credit   Account
   1        1                 A
   1                1         B
  
   2        2                 A
   2                1         C
   2                1         C
  
   3        4                 C
   3                1         G
   3                1         A
   3                1         D
   3                1         K
  </code></pre>
  
  <p>Account 4 will not be displayed because there's no Entry (index) containing account A.</p>
  
  <p>Setup:</p>
  
  <pre><code>d = {'Entry': {0: 1.0, 1: 1.0, 3: 2.0, 4: 2.0, 5: 2.0, 7: 3.0, 8: 3.0, 9: 
  3.0, 10: 3.0, 11: 3.0, 13: 4.0, 14: 4.0}, 
   'Debit': {0: 1.0, 1: np.nan, 3: 2.0, 4: np.nan, 5: np.nan, 7: 4.0, 8: np.nan, 
             9: np.nan, 10: np.nan, 11: np.nan,13: 1.0, 14: np.nan}, 
   'Credit': {0: np.nan, 1: 1.0, 3: np.nan, 4: 1.0, 5: 1.0, 7: np.nan,
              8: 1.0, 9: 1.0, 10: 1.0, 
              11: 1.0, 13: np.nan, 14: 1.0}, 
   'Account': {0: 'A', 1: 'B', 3: 'A', 4: 'C', 5: 'C', 7: 'C', 8: 'G', 
             9: 'A', 10: 'D', 11: 'K', 13: 'H', 14: 'J'}}
  df = pd.DataFrame(d)
  </code></pre>