title: How to do group by on a multiindex in pandas?
body: <p>Below is my dataframe.  I made some transformations to create the category column and dropped the original column it was derived from.  Now I need to do a group-by to remove the dups e.g. <code>Love</code> and <code>Fashion</code> can be rolled up via a <code>groupby</code> sum.</p>
  
  <pre><code>df.colunms = array([category, clicks, revenue, date, impressions, size], dtype=object)
  df.values=
  [[Love 0 0.36823 2013-11-04 380 300x250]
   [Love 183 474.81522 2013-11-04 374242 300x250]
   [Fashion 0 0.19434 2013-11-04 197 300x250]
   [Fashion 9 18.26422 2013-11-04 13363 300x250]]
  </code></pre>
  
  <p>Here is the index that is created when I created the dataframe</p>
  
  <pre><code>print df.index
  array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
         17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
         34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48])
  </code></pre>
  
  <p>I assume I want to drop the index, and create date, and category as a <code>multiindex</code> then do a <code>groupby</code> sum of the metrics.  How do I do this in pandas dataframe?</p>
  
  <pre><code>df.head(15).to_dict()= {'category': {0: 'Love', 1: 'Love', 2: 'Fashion', 3: 'Fashion', 4: 'Hair', 5: 'Movies', 6: 'Movies', 7: 'Health', 8: 'Health', 9: 'Celebs', 10: 'Celebs', 11: 'Travel', 12: 'Weightloss', 13: 'Diet', 14: 'Bags'}, 'impressions': {0: 380, 1: 374242, 2: 197, 3: 13363, 4: 4, 5: 189, 6: 60632, 7: 269, 8: 40189, 9: 138, 10: 66590, 11: 2227, 12: 22668, 13: 21707, 14: 229}, 'date': {0: '2013-11-04', 1: '2013-11-04', 2: '2013-11-04', 3: '2013-11-04', 4: '2013-11-04', 5: '2013-11-04', 6: '2013-11-04', 7: '2013-11-04', 8: '2013-11-04', 9: '2013-11-04', 10: '2013-11-04', 11: '2013-11-04', 12: '2013-11-04', 13: '2013-11-04', 14: '2013-11-04'}, 'cpc_cpm_revenue': {0: 0.36823, 1: 474.81522000000001, 2: 0.19434000000000001, 3: 18.264220000000002, 4: 0.00080000000000000004, 5: 0.23613000000000001, 6: 81.391139999999993, 7: 0.27171000000000001, 8: 51.258200000000002, 9: 0.11536, 10: 83.966859999999997, 11: 3.43248, 12: 31.695889999999999, 13: 28.459320000000002, 14: 0.43524000000000002}, 'clicks': {0: 0, 1: 183, 2: 0, 3: 9, 4: 0, 5: 1, 6: 20, 7: 0, 8: 21, 9: 0, 10: 32, 11: 1, 12: 12, 13: 9, 14: 2}, 'size': {0: '300x250', 1: '300x250', 2: '300x250', 3: '300x250', 4: '300x250', 5: '300x250', 6: '300x250', 7: '300x250', 8: '300x250', 9: '300x250', 10: '300x250', 11: '300x250', 12: '300x250', 13: '300x250', 14: '300x250'}}
  </code></pre>
  
  <p>Python is 2.7 and pandas is 0.7.0 on ubuntu 12.04.  Below is the error I get if I run the below</p>
  
  <pre><code>import pandas
  print pandas.__version__
  df = pandas.DataFrame.from_dict(
      {
       'category': {0: 'Love', 1: 'Love', 2: 'Fashion', 3: 'Fashion', 4: 'Hair', 5: 'Movies', 6: 'Movies', 7: 'Health', 8: 'Health', 9: 'Celebs', 10: 'Celebs', 11: 'Travel', 12: 'Weightloss', 13: 'Diet', 14: 'Bags'}, 
       'impressions': {0: 380, 1: 374242, 2: 197, 3: 13363, 4: 4, 5: 189, 6: 60632, 7: 269, 8: 40189, 9: 138, 10: 66590, 11: 2227, 12: 22668, 13: 21707, 14: 229}, 
       'date': {0: '2013-11-04', 1: '2013-11-04', 2: '2013-11-04', 3: '2013-11-04', 4: '2013-11-04', 5: '2013-11-04', 6: '2013-11-04', 7: '2013-11-04', 8: '2013-11-04', 9: '2013-11-04', 10: '2013-11-04', 11: '2013-11-04', 12: '2013-11-04', 13: '2013-11-04', 14: '2013-11-04'}, 'cpc_cpm_revenue': {0: 0.36823, 1: 474.81522000000001, 2: 0.19434000000000001, 3: 18.264220000000002, 4: 0.00080000000000000004, 5: 0.23613000000000001, 6: 81.391139999999993, 7: 0.27171000000000001, 8: 51.258200000000002, 9: 0.11536, 10: 83.966859999999997, 11: 3.43248, 12: 31.695889999999999, 13: 28.459320000000002, 14: 0.43524000000000002}, 'clicks': {0: 0, 1: 183, 2: 0, 3: 9, 4: 0, 5: 1, 6: 20, 7: 0, 8: 21, 9: 0, 10: 32, 11: 1, 12: 12, 13: 9, 14: 2}, 'size': {0: '300x250', 1: '300x250', 2: '300x250', 3: '300x250', 4: '300x250', 5: '300x250', 6: '300x250', 7: '300x250', 8: '300x250', 9: '300x250', 10: '300x250', 11: '300x250', 12: '300x250', 13: '300x250', 14: '300x250'}
      }
  )
  df.set_index(['date', 'category'], inplace=True)
  df.groupby(level=[0,1]).sum()
  
  
  Traceback (most recent call last):
    File "/home/ubuntu/workspace/devops/reports/groupby_sub.py", line 9, in &lt;module&gt;
      df.set_index(['date', 'category'], inplace=True)
    File "/usr/lib/pymodules/python2.7/pandas/core/frame.py", line 1927, in set_index
      raise Exception('Index has duplicate keys: %s' % duplicates)
  Exception: Index has duplicate keys: [('2013-11-04', 'Celebs'), ('2013-11-04', 'Fashion'), ('2013-11-04', 'Health'), ('2013-11-04', 'Love'), ('2013-11-04', 'Movies')]
  </code></pre>