title: Python pandas idxmax for multiple indexes in a dataframe
body: <p>I have a series that looks like this:</p>
  
  <pre><code>            delivery
  2007-04-26  706           23
  2007-04-27  705           10
              706         1089
              708           83
              710           13
              712           51
              802            4
              806            1
              812            3
  2007-04-29  706           39
              708            4
              712            1
  2007-04-30  705            3
              706         1016
              707            2
  ...
  2014-11-04  1412          53
              1501           1
              1502           1
              1512           1
  2014-11-05  1411          47
              1412        1334
              1501          40
              1502         433
              1504         126
              1506         100
              1508           7
              1510           6
              1512          51
              1604           1
              1612           5
  Length: 26255, dtype: int64
  </code></pre>
  
  <p>where the query is: <code>df.groupby([df.index.date, 'delivery']).size()</code></p>
  
  <p>For each day, I need to pull out the delivery number which has the most volume. I feel like it would be something like:</p>
  
  <pre><code>df.groupby([df.index.date, 'delivery']).size().idxmax(axis=1)
  </code></pre>
  
  <p>However, this just returns me the idxmax for the entire dataframe; instead, I need the second-level idmax (not the date but rather the delivery number) for each day, not the entire dataframe (ie. it returns a vector).</p>
  
  <p>Any ideas on how to accomplish this?</p>