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pandas - rolling_corr 返回值 > 1

问题描述

我有一个时间序列 DataFrame，我想在 window = 3 的情况下应用 rolling_corr 函数，但我发现一些结果 > 1。我猜这是由于原始值的微小变化。在不修改原始数据集的情况下是否有任何解决方法？

       Index                TA           sn          
2015-09-23 22:30:00  4923.866489  102730.000000
2015-09-23 22:35:00  4928.549856  102730.000000
2015-09-23 22:40:00  4933.126237  102730.000000
2015-09-23 22:45:00  4932.423757  102730.000000
2015-09-23 22:50:00  4930.632884  102730.000000
2015-09-23 22:55:00  4932.184794  102940.000000
2015-09-23 23:00:00  4925.654600  102840.000000
2015-09-24 09:00:00  4914.802897  102675.000000
2015-09-24 09:05:00  4897.657917  102477.142857
2015-09-24 09:10:00  4895.178979  102303.333333
2015-09-24 09:15:00  4893.134804  102435.000000
2015-09-24 09:20:00  4899.745662  102440.000000
2015-09-24 09:25:00  4902.197101  102500.000000
2015-09-24 09:30:00  4900.230251  102490.000000
2015-09-24 09:35:00  4895.271591  102600.000000
2015-09-24 09:40:00  4891.941444  102550.000000
2015-09-24 09:45:00  4885.363355  102550.000000
2015-09-24 09:50:00  4882.384047  102550.000000
2015-09-24 09:55:00  4884.698022  102550.000000
2015-09-24 10:00:00  4884.919459  102550.000000
2015-09-24 10:05:00  4882.617120  102550.000000
2015-09-24 10:10:00  4882.752606  102550.000000
2015-09-24 10:15:00  4883.183232  102550.000000

和输出：

        Index         rolling_corr
2015-09-23 22:30:00 
2015-09-23 22:35:00 
2015-09-23 22:40:00 -inf
2015-09-23 22:45:00 inf
2015-09-23 22:50:00 inf
2015-09-23 22:55:00 0.389793929698
2015-09-23 23:00:00 0.200596075192
2015-09-24 09:00:00 0.999998215741
2015-09-24 09:05:00 0.997050258267
2015-09-24 09:10:00 0.932322658978
2015-09-24 09:15:00 0.285942864418
2015-09-24 09:20:00 0.246531134353
2015-09-24 09:25:00 0.756098005938
2015-09-24 09:30:00 0.762718525006
2015-09-24 09:35:00 -0.935380177853
2015-09-24 09:40:00 -0.635755535941
2015-09-24 09:45:00 0.75794248422
2015-09-24 09:50:00 0.000867697788219
2015-09-24 09:55:00 0.00203505802058
2015-09-24 10:00:00 0.00151086403735
2015-09-24 10:05:00 0.00167037819182
2015-09-24 10:10:00 0.0
2015-09-24 10:15:00 0.0

更新：采用更大的窗口大小可以避免这个问题，但我仍然想知道这个问题的原因和影响。

标签： pandasrolling-computation

解决方案

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