<p>可以用pandas读取数据并将其连接起来。</p>
<p>首先导入数据</p>
<pre><code>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
</code></pre>
<p>而不是连接它:</p>
<pre><code>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
</code></pre>