title: DataFrame of DataFrames with pandas
body: <p>I have the following DataFrame gathering daily stats on 2 measures A and B :</p>
  
  <pre><code>                  A             B
  count  17266.000000  17266.000000
  std        0.179003      0.178781
  75%      101.102251    101.053214
  min      100.700993    100.651956
  mean     101.016747    100.964003
  max      101.540214    101.491178
  50%      100.988465    100.938694
  25%      100.885251    100.830048
  </code></pre>
  
  <p>Below is a piece of code that creates it:</p>
  
  <pre><code>day1 = {
      'A': {
      'count': 17266.0,
      'std': 0.17900265293286116,
      'min': 100.70099294189714,
      'max': 101.54021448871775,
      '50%': 100.98846526697825,
      '25%': 100.88525124427971,
      '75%': 101.10225131847992, 
      'mean': 101.01674677794136
      }, 
      'B': {
      'count': 17266.0, 
      'std': 0.17878125983374854, 
      'min': 100.65195609992342, 
      'max': 101.49117764674403, 
      '50%': 100.93869409089723, 
      '25%': 100.83004837814667, 
      '75%': 101.05321447650618, 
      'mean': 100.96400305527138
      }
  }
  df = pandas.DataFrame.from_dict(day1, orient='index').T
  </code></pre>
  
  <p>The data come right out from a describe(). I have several such describes (one for each day) and I would like to gather them all into a single dataframe that has the date as an index.</p>
  
  <p>The most obvious way to obtain that would be to stack all the daily results into one dataframe, then group it by day and run the stats on the result. However I would like an alternate method because I run into a MemoryError with the amount of data I process.</p>
  
  <p>The final outcome should look like this:</p>
  
  <pre><code>                        A           B    
  2014-12-24 count  15895.000000  15895.000000
          mean      99.943618     99.968860
          std        0.012468      0.011932
          min       99.877695     99.928778
          25%       99.934890     99.960445
          50%       99.943453     99.968847
          75%       99.952340     99.977571
          max       99.982930    100.002507
  2014-12-25 count  16278.000000  16278.000000
          mean      99.937056     99.962203
          std        0.012395      0.012661
          min       99.884501     99.910567
          25%       99.928078     99.953758
          50%       99.936754     99.962411
          75%       99.945914     99.971473
          max       99.981512    100.003770
  </code></pre>