title: time slice on second level of multiindex
body: <p>pandas allows for cool slicing on time indexes.  For example, I can slice a dataframe <code>df</code> for the months from Janurary 2012 to March 2012 by doing:</p>
  
  <pre><code>df['2012-01':'2012-03']
  </code></pre>
  
  <p>However, I have a dataframe <code>df</code> with a multiindex where the time index is the second level.  It looks like:</p>
  
  <pre><code>                     A         B         C         D         E
  a 2001-01-31  0.864841  0.789273  0.370031  0.448256  0.178515
    2001-02-28  0.991861  0.079215  0.900788  0.666178  0.693887
    2001-03-31  0.016674  0.855109  0.984115  0.436574  0.480339
    2001-04-30  0.120924  0.046013  0.659807  0.210534  0.694029
    2001-05-31  0.788149  0.296244  0.478201  0.845042  0.437814
  b 2001-01-31  0.497646  0.349958  0.223227  0.812331  0.975012
    2001-02-28  0.542572  0.472267  0.276186  0.970909  0.138683
    2001-03-31  0.960813  0.666942  0.069349  0.282741  0.127992
    2001-04-30  0.491422  0.678742  0.048784  0.612312  0.713472
    2001-05-31  0.718721  0.504403  0.069047  0.253682  0.836386
  </code></pre>
  
  <p>I can still slice using the method above on any specific level by:</p>
  
  <pre><code>df.loc['a']['2012-01':'2012-03']
  </code></pre>
  
  <p>But this is only for <code>level0 == 'a'</code>.</p>
  
  <p>How do I do this for all values in <code>level0</code>?  I expect something like this:</p>
  
  <pre><code>                     A         B         C         D         E
  a 2001-01-31  0.864841  0.789273  0.370031  0.448256  0.178515
    2001-02-28  0.991861  0.079215  0.900788  0.666178  0.693887
    2001-03-31  0.016674  0.855109  0.984115  0.436574  0.480339
  b 2001-01-31  0.497646  0.349958  0.223227  0.812331  0.975012
    2001-02-28  0.542572  0.472267  0.276186  0.970909  0.138683
    2001-03-31  0.960813  0.666942  0.069349  0.282741  0.127992
  </code></pre>