concat DataFrame along timeseries indexes

[mnbbrown]

Please see: http://stackoverflow.com/questions/11714768/concat-pandas-dataframe-along-timeseries-indexes

NumPy v1.6.3
Pandas v0.8.1

I have two largish (snippets provided) pandas DateFrames with unequal dates as indexes that I wish to concat into one:

           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

Problem is, if I run this:

keys = ['CBA.AX','NAB.AX']
mv = pandas.concat([data['CBA.AX'][650:660],data['NAB.AX'][650:660]], axis=1, keys=stocks,) 

the following DateFrame is produced:

                                 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

Does anybody have any idea why this might be the case?

On another point: is there any python libraries around that pull data from yahoo and normalise it?

For reference:

data = {
'CBA.AX': <class 'pandas.core.frame.DataFrame'>
    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': <class 'pandas.core.frame.DataFrame'>
    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)
}

[mnbbrown]

http://cl.ly/2B0h0c1p1W3D Pickled DataFrames.

[wesm]

This has been fixed, referenced in #1745