[英]python pandas create correlation matrix from price dataframe
I have a dataframe populated with stock price returns (indexed by Date). 我有一个填充有股票价格回报(按日期编制索引)的数据框。 Could someone let me know how I can get a correlation matrix from this dataframe.
有人可以让我知道如何从该数据框中获得相关矩阵。
The dataframe would look like: 数据框如下所示:
BBG.XSTO BBG.XLON BBG.XETR BBG.XHEL
Date
06/02/2014 0.001418 0.00708 0.019437 0.025848
07/02/2014 0.021329 0.016221 0.006784 0.032683
10/02/2014 0.005299 0.005177 0.007391 0.005111
11/02/2014 -0.006497 0.021656 -0.004109 0.001855
12/02/2014 -0.003844 0.019885 -0.002457 0.004617
13/02/2014 -0.004795 -0.001831 -0.010602 0.00917
14/02/2014 0.003276 0.010801 -0.000341 0.009992
17/02/2014 0.00206 0.003307 -0.002336 0.009443
18/02/2014 -0.010467 0.004102 0.046172 0.002236
19/02/2014 0.002929 0.003037 -0.009944 0.015511
20/02/2014 -0.003969 -0.015961 0.015342 0.003952
21/02/2014 0.004776 -0.001107 0.010403 0.005243
24/02/2014 0.015125 0.025254 0.018505 0.011263
25/02/2014 -0.001546 0.000742 0.004307 0.019623
26/02/2014 -0.000478 -0.000677 0.006721 0.003797
27/02/2014 -0.009898 0.002869 0.038103 0.010052
28/02/2014 0.005288 0.004927 -0.01254 -0.005852
03/03/2014 -0.035165 -0.023916 -0.022374 -0.01563
04/03/2014 0.020213 0.017346 0.016266 0.040465
05/03/2014 0.004067 0.002742 0.010699 0.005709
06/03/2014 -0.000648 -0.012987 0.013513 -0.008984
07/03/2014 -0.008855 -0.015162 -0.003511 -0.019051
10/03/2014 0.003684 0.002893 0.023136 0.004172
11/03/2014 -0.003214 0.020036 -0.013234 -0.004588
12/03/2014 -0.005376 -0.015244 -0.015922 -0.002511
13/03/2014 -0.016978 0.000689 -0.022335 -0.005889
and hopefully the correlation matrix would look like: 希望相关矩阵如下所示:
BBG.XSTO BBG.XLON BBG.XETR BBG.XHEL
BBG.XSTO 1 0.548504179 0.315191057 0.69486495
BBG.XLON 0.548504179 1 0.314246645 0.56176159
BBG.XETR 0.315191057 0.314246645 1 0.414599864
BBG.XHEL 0.69486495 0.56176159 0.414599864 1
Thanks 谢谢
Assuming your dataframe is named df
. 假设您的数据框名为
df
。
df.corr()
Out[106]:
BBG.XSTO BBG.XLON BBG.XETR BBG.XHEL
BBG.XSTO 1.0000 0.5801 0.3057 0.7185
BBG.XLON 0.5801 1.0000 0.1709 0.5366
BBG.XETR 0.3057 0.1709 1.0000 0.3340
BBG.XHEL 0.7185 0.5366 0.3340 1.0000
标准的熊猫函数DataFrame.corr(method='pearson', min_periods=1)
在这种情况下应该可以使用{'pearson','kendall','spearman'}中的方法很好地工作,'pearson'是您的标准关联描述。
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