Python有很多计算相关性的方法，scipy有自己的分析工具，pandas也有很方便的多变量相关性分析。今天我们就来说说这两个工具的用法。一、数据采集本文采集的数据来自东方财富网，以二手房价格指数为例：数据起始于2011年1月1日，每个数据点为当月价格指数那时。采集方法是使用开发者工具找到请求发回的JSON数据，方法如下：数据如下（2011/1/1-2019/10/1）：#北京：bj=[100.3,100.4,99.9,100.1,99.8,99.9,100.1,100,99.6,99.5,99.3,99.2,99.1,99.8,100.2,100.4,99.9,100.2,100.3,100.3,100.1,100,100.3,101,101,102.2,103.1,102,101.7,101.3,101.4,101.2,101.3,101.1,101.2,100.6,99.9,100.2,99.8,99.1,98.7,99.2,98.6,100.7,100.2,100,99.9,100.5,104.3,102.6,101.12.13,13,13、102.3、3.2.3.2。103.7,102.3,101.4,101.6,103.9,105.7,101.1,100.2,100.2,100.8,101.3,102.2,100,99.1,98.9,99.2,99.1,99.4,99.5,99.5,99.6,99.99,99.5,100.3,100.1,100.4,100,99.8,99.8,99.4,99.8,99.9,100.2,100.4,100.6,100,100,99.7,99.6,99.5,99.4]#广州：gz=[101.2,100.6,99.5,101,99.8100.1,100.2,100.6,99.5,99.2,99.6,99.6,99.6,99.8,99.6,99.9,100.5,100.7,100.9,100.6,100.4,100.5,100.5,100.4,101.7,100.19,5,101.1101,101,100.4,101,101.2,100.6,101,100.3,100.2,100.7,100.1,99.7,98.9,98.6,98.7,100,100,100.2,99.8,99.7,100,100,101.1,102.3,101.8,101.8,101.3,101.3,101,101.2,101.2,101.1,100.7,100.7,101.3,101.3,3,11.3,101.2,103.5,102.6,102.6,101.6,101.6,101.6,10.6,10.6,101.6,101.6,101.6,10.6,101.6,10.6,10.6,10.6,10.6,10.6,10.6,10.6.6,10.6.6.6in103.3,101,100.5.100.8,100.100.100.2,99.7,100.100.100.9.9.9.9,100.2,100.5,101,100.3,100.6,100.2,99.7,99.7,99.5,99.6,99,0,7,00,0,000,00,00,00,00,00,00,00,00,00,00,00,00,0069.7,99.7,999.7,999.7,00,00,100,00,00,00,00,00,00,00,00,00,00,00,00,00,00,00,00,006999.7,999.7,99999.7,9999999.7,99999999999999999,9999999999,9999999999,999999,9999.100,99.7,99.9]#上海：sh=[100.5,100.4,100.4,100.6,100.2,100.2,100.3,100.1,100.1,99.8,99.5,99.6,99.3,99.7,99.5,100.1,100.3,100.2,11，100.2、100.2、100.2、100.4、100.8、101.6、102.6、101.3、100.9、101.1、100.8、100.8、101、100.9、100.9、100.7、100.5、100.1、100.1、100.6、100.6、100.2、100.2、100.9100.3,100.1,100,100.6,102.2,101.2,101.6,101.1,101,100.8,101,101.2,102.7,105.3,106.2,102.5,101.4,102.2,102,103.7,103.4,100.3,99.8,99.5,99.6,100.2,100.7,100.8,100,99.9,99.6,99.8,99.9,100.3,99.7,99.9,100.1,99.6,99.4,99.8,99.7,99.7,99.9,99.9,99.8,99.8,99.9,99.7,100,99.9,100.3,100.5,100。99.9,100.4,100,100.6,99.8]#深圳：sz=[100.6,102.6,100.6,100.5,100.3,100,99.5,100,99.8,100,99.2,99.6,99.2,100,100.1,100,100,100.2,100.2,100.1,100.1,100.4,100.3,100.6,100.5,101.4,102.3,101.1,101,101.3,101,101.6,101.3,100.9,100.8,100.7,100.8,100.8,100.100.100.100.2,99.4,99.5,99.3,100.4,100.7,100.3,100.5,106.3,106.3,103.3.3.11.9,03.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3。103.3,104.7,99,100.100.8,101.8,102,99.4,99.3,99.9.9.9.3,100.8,100.3,99.7,99.8,100.100.4,100.9,100.7,200.7,200.7,200.7,200.7,200.7,200.7,200.7,200.7,200.7,200.7,200.7,200.7,200.7,200。100.8,100.3,100.6,101.1,100,99.4,99.8,99.7,99.7,100.5,100.7,101.1,100,99.9,100.7,100.2,101.3,101]2.准备工作首先，你需要确定你的电脑是安装了Python，如果没有，可以看这篇文章：超级详细的Python安装指南然后，打开CMD（开始-运行-cmd）或终端（macOS）并输入以下命令来安装scipy和pandas。stats模块：importscipy.statsasstats然后调用函数计算：#计算广深二手房价格指数相关性print(stats.pearsonr(gz,sz))结果如下：F:push20191130>python1.py(0.4673289851643741,4.4100775485723706e-07)什么？！！广州和深圳二手房价格指数相关性仅为0.46？其他一线城市和深圳的对比呢？不过stats比较麻烦的地方是一次只能比较两个值，不能一次两两比较四个一线城市，但是有个模块可以。3.2Pandas一次性成对比较计算correlation首先介绍pandas：importpandasaspd创建一个DataFrame存储四个数据：df=pd.DataFrame()df['北京']=bjdf['上海']=shdf['广州']=gzdf['Shenzhen']=sz最后的相关性计算：print(df.corr())看一下结果：哇，看来深圳的二手房价格真的不一样，但是从下图，的确，深圳的二手房价格和北京的二手房价格出现了分化。我个人认为，这种偏离与近期的一系列政策和香港局势有关，但在目前严峻的金融形势下，不会持续太久。这是我们文章的结尾。如果你今天想要我们的Python教程，请继续关注我们。如果对您有帮助，请在下方点赞或观看。如果您有任何问题，可以在下方留言区留言。我们会耐心解答！Python实用书（pythondict.com）不只是一本书欢迎关注公众号：Python实用书原文来自Python实用书：Python计算一线城市二手房价格指数相关性
