沿着时间序列索引配置concat pandas DataFrame [英] concat pandas DataFrame along timeseries indexes

问题描述

我有两只大熊猫(提供了片段)，它们的日期不相等，它们是我希望与之结合的大熊猫DateFrame:

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

问题是，如果我运行此命令:

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,) 

产生以下DateFrame:

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

有人知道为什么会这样吗?

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

另一方面:是否有任何python库从yahoo提取数据并对其进行规范化?

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

干杯.

供参考:

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)
}

推荐答案

可以使用熊猫读取数据并将其连接起来.

It is possible to read the data with pandas and to concatenate it.

首先导入数据

In [449]: import pandas.io.data as web

In [450]: nab = web.get_data_yahoo('NAB.AX', start='2009-05-25',
                                   end='2009-06-05')[['Close', 'Volume']]

In [451]: cba = web.get_data_yahoo('CBA.AX', start='2009-05-26',
                                   end='2009-06-08')[['Close', 'Volume']]

In [453]: nab
Out[453]: 
            Close    Volume
Date                       
2009-05-25  21.15   9685100
2009-05-26  21.64   8541900
2009-05-27  21.74   9042900
2009-05-28  21.63   9701000
2009-05-29  22.02  14665700
2009-06-01  22.52   6782000
2009-06-02  22.80  10473400
2009-06-03  23.11   9931400
2009-06-04  22.21  17869000
2009-06-05  21.95   8214300

In [454]: cba
Out[454]: 
            Close    Volume
Date                       
2009-05-26  35.45   4529600
2009-05-27  35.13   4521500
2009-05-28  33.95   7945400
2009-05-29  35.14  12548500
2009-06-01  36.16   4509400
2009-06-02  36.33   4304900
2009-06-03  36.80   4845400
2009-06-04  36.79   4592300
2009-06-05  36.51   4417500
2009-06-08  36.51         0

比将其串联起来

In [455]: keys = ['CBA.AX','NAB.AX']

In [456]: pd.concat([cba, nab], axis=1, keys=keys)
Out[456]: 
            CBA.AX            NAB.AX          
             Close    Volume   Close    Volume
Date                                          
2009-05-25     NaN       NaN   21.15   9685100
2009-05-26   35.45   4529600   21.64   8541900
2009-05-27   35.13   4521500   21.74   9042900
2009-05-28   33.95   7945400   21.63   9701000
2009-05-29   35.14  12548500   22.02  14665700
2009-06-01   36.16   4509400   22.52   6782000
2009-06-02   36.33   4304900   22.80  10473400
2009-06-03   36.80   4845400   23.11   9931400
2009-06-04   36.79   4592300   22.21  17869000
2009-06-05   36.51   4417500   21.95   8214300
2009-06-08   36.51         0     NaN       NaN

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