title: concat pandas DataFrame along timeseries indexes
body: <p>I have two largish (snippets provided) pandas <code>DateFrame</code>s with unequal dates as indexes that I wish to concat into one:</p>
  
  <pre><code>           NAB.AX                                  CBA.AX
              Close    Volume                         Close    Volume
  Date                                    Date
  2009-06-05  36.51   4962900             2009-06-08  21.95         0
  2009-06-04  36.79   5528800             2009-06-05  21.95   8917000
  2009-06-03  36.80   5116500             2009-06-04  22.21  18723600
  2009-06-02  36.33   5303700             2009-06-03  23.11  11643800
  2009-06-01  36.16   5625500             2009-06-02  22.80  14249900
  2009-05-29  35.14  13038600   --AND--   2009-06-01  22.52  11687200
  2009-05-28  33.95   7917600             2009-05-29  22.02  22350700
  2009-05-27  35.13   4701100             2009-05-28  21.63   9679800
  2009-05-26  35.45   4572700             2009-05-27  21.74   9338200
  2009-05-25  34.80   3652500             2009-05-26  21.64   8502900
  </code></pre>
  
  <p>Problem is, if I run this: </p>
  
  <pre><code>keys = ['CBA.AX','NAB.AX']
  mv = pandas.concat([data['CBA.AX'][650:660],data['NAB.AX'][650:660]], axis=1, keys=stocks,) 
  </code></pre>
  
  <p>the following DateFrame is produced:</p>
  
  <pre><code>                                 CBA.AX          NAB.AX        
                                Close  Volume   Close  Volume
  Date                                                      
  2200-08-16 04:24:21.460041     NaN     NaN     NaN     NaN
  2203-05-13 04:24:21.460041     NaN     NaN     NaN     NaN
  2206-02-06 04:24:21.460041     NaN     NaN     NaN     NaN
  2208-11-02 04:24:21.460041     NaN     NaN     NaN     NaN
  2211-07-30 04:24:21.460041     NaN     NaN     NaN     NaN
  2219-10-16 04:24:21.460041     NaN     NaN     NaN     NaN
  2222-07-12 04:24:21.460041     NaN     NaN     NaN     NaN
  2225-04-07 04:24:21.460041     NaN     NaN     NaN     NaN
  2228-01-02 04:24:21.460041     NaN     NaN     NaN     NaN
  2230-09-28 04:24:21.460041     NaN     NaN     NaN     NaN
  2238-12-15 04:24:21.460041     NaN     NaN     NaN     NaN
  </code></pre>
  
  <p>Does anybody have any idea why this might be the case?</p>
  
  <p>On another point: is there any python libraries around that pull data from yahoo and normalise it?</p>
  
  <p>Cheers.</p>
  
  <p>EDIT: For reference: </p>
  
  <pre><code>data = {
  'CBA.AX': &lt;class 'pandas.core.frame.DataFrame'&gt;
      DatetimeIndex: 2313 entries, 2011-12-29 00:00:00 to 2003-01-01 00:00:00
      Data columns:
          Close     2313  non-null values
          Volume    2313  non-null values
      dtypes: float64(1), int64(1),
  
   'NAB.AX': &lt;class 'pandas.core.frame.DataFrame'&gt;
      DatetimeIndex: 2329 entries, 2011-12-29 00:00:00 to 2003-01-01 00:00:00
      Data columns:
          Close     2329  non-null values
          Volume    2329  non-null values
      dtypes: float64(1), int64(1)
  }
  </code></pre>