{"id":827,"date":"2023-07-28T15:21:12","date_gmt":"2023-07-28T15:21:12","guid":{"rendered":"https:\/\/statorials.org\/cn\/python-%e4%b8%ad%e7%9a%84%e7%9b%b8%e5%85%b3%e6%80%a7\/"},"modified":"2023-07-28T15:21:12","modified_gmt":"2023-07-28T15:21:12","slug":"python-%e4%b8%ad%e7%9a%84%e7%9b%b8%e5%85%b3%e6%80%a7","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/python-%e4%b8%ad%e7%9a%84%e7%9b%b8%e5%85%b3%e6%80%a7\/","title":{"rendered":"\u5982\u4f55\u5728python\u4e2d\u8ba1\u7b97\u76f8\u5173\u6027"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u91cf\u5316\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u5173\u7cfb\u7684\u4e00\u79cd\u65b9\u6cd5\u662f\u4f7f\u7528<a href=\"https:\/\/statorials.org\/cn\/\u76ae\u5c14\u900a\u76f8\u5173\u7cfb\u6570-1\/\" target=\"_blank\" rel=\"noopener\">Pearson \u76f8\u5173\u7cfb\u6570<\/a>\uff0c\u5b83\u662f\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7ebf\u6027\u5173\u8054\u7684\u5ea6\u91cf<em>\u3002<\/em>\u5b83\u59cb\u7ec8\u91c7\u7528 -1 \u548c 1 \u4e4b\u95f4\u7684\u503c\uff0c\u5176\u4e2d\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">-1 \u8868\u793a\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u5b8c\u5168\u8d1f\u7ebf\u6027\u76f8\u5173<\/span><\/li>\n<li><span style=\"color: #000000;\">0 \u8868\u793a\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u4e0d\u5b58\u5728\u7ebf\u6027\u76f8\u5173<\/span><\/li>\n<li><span style=\"color: #000000;\">1 \u8868\u793a\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u5b58\u5728\u5b8c\u5168\u6b63\u7ebf\u6027\u76f8\u5173<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u76f8\u5173\u7cfb\u6570\u79bb\u96f6\u8d8a\u8fdc\uff0c\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u76f8\u5173\u6027\u8d8a\u5f3a\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u672c\u6559\u7a0b\u4ecb\u7ecd\u5982\u4f55\u5728 Python \u4e2d\u8ba1\u7b97\u53d8\u91cf\u4e4b\u95f4\u7684\u76f8\u5173\u6027\u3002<\/span><\/p>\n<h3><strong><span style=\"color: #000000;\">\u5982\u4f55\u5728Python\u4e2d\u8ba1\u7b97\u76f8\u5173\u6027<\/span><\/strong><\/h3>\n<p><span style=\"color: #000000;\">\u8981\u5728Python\u4e2d\u8ba1\u7b97\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u76f8\u5173\u6027\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Numpy <strong>corrcoef()<\/strong>\u51fd\u6570\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">import<\/span> numpy <span style=\"color: #107d3f;\">as<\/span> np\n\nnp.random.seed(100)\n\n<span style=\"color: #008080;\">#create array of 50 random integers between 0 and 10<\/span>\nvar1 = np.random.randint(0, 10, 50)\n\n<span style=\"color: #008080;\">#create a positively correlated array with some random noise\n<\/span>var2 = var1 + np.random.normal(0, 10, 50)\n\n<span style=\"color: #008080;\">#calculate the correlation between the two arrays\n<\/span>np.corrcoef(var1, var2)\n\n[[ 1. 0.335]\n[ 0.335 1. ]]\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u53ef\u4ee5\u770b\u51fa\uff0c\u8fd9\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u76f8\u5173\u7cfb\u6570\u4e3a<strong>0.335<\/strong> \uff0c\u5448\u6b63\u76f8\u5173\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff0c\u8be5\u51fd\u6570\u751f\u6210\u76f8\u5173\u7cfb\u6570\u77e9\u9635\u3002\u5982\u679c\u6211\u4eec\u53ea\u60f3\u8fd4\u56de\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u76f8\u5173\u7cfb\u6570\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>np.corrcoef(var1, var2)[0,1]\n\n0.335\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4e3a\u4e86\u6d4b\u8bd5\u8fd9\u79cd\u76f8\u5173\u6027\u662f\u5426\u5177\u6709\u7edf\u8ba1\u663e\u7740\u6027\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 Scipy <strong>pearsonr()<\/strong>\u51fd\u6570\u8ba1\u7b97\u4e0e Pearson \u76f8\u5173\u7cfb\u6570\u76f8\u5173\u7684 p \u503c\uff0c\u8be5\u51fd\u6570\u8fd4\u56de Pearson \u76f8\u5173\u7cfb\u6570\u4ee5\u53ca\u53cc\u5c3e p \u503c\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">from<\/span> scipy.stats.stats <span style=\"color: #107d3f;\">import<\/span> pearsonr\n\npearsonr(var1, var2)\n\n(0.335, 0.017398)\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u76f8\u5173\u7cfb\u6570\u4e3a<strong>0.335<\/strong> \uff0c\u53cc\u5c3e p \u503c\u4e3a<strong>0.017<\/strong> \u3002\u7531\u4e8e\u8be5 p \u503c\u5c0f\u4e8e 0.05\uff0c\u6211\u4eec\u53ef\u4ee5\u5f97\u51fa\u7ed3\u8bba\uff0c\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u5b58\u5728\u7edf\u8ba1\u4e0a\u663e\u7740\u7684\u76f8\u5173\u6027\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5982\u679c\u4f60\u60f3\u8ba1\u7b97 Pandas DataFrame \u4e2d\u591a\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u76f8\u5173\u6027\uff0c\u4f60\u53ef\u4ee5\u7b80\u5355\u5730\u4f7f\u7528<strong>.corr()<\/strong>\u51fd\u6570\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">import<\/span> pandas <span style=\"color: #107d3f;\">as<\/span> pd\n\ndata = pd.DataFrame(np.random.randint(0, 10, size=(5, 3)), columns=['A', 'B', 'C'])\ndata\n\n  ABC\n0 8 0 9\n1 4 0 7\n2 9 6 8\n3 1 8 1\n4 8 0 8\n\n<span style=\"color: #008080;\">#calculate correlation coefficients for all pairwise combinations\n<\/span>data.corr()\n\n          ABC\nA 1.000000 -0.775567 -0.493769\nB -0.775567 1.000000 0.000000\nC -0.493769 0.000000 1.000000\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u5982\u679c\u4f60\u53ea\u60f3\u8ba1\u7b97DataFrame\u4e2d\u4e24\u4e2a\u7279\u5b9a\u53d8\u91cf\u4e4b\u95f4\u7684\u76f8\u5173\u6027\uff0c\u4f60\u53ef\u4ee5\u6307\u5b9a\u53d8\u91cf\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>data['A'].corr(data['B'])\n\n-0.775567\n<\/strong><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u5176\u4ed6\u5e38\u89c1\u4efb\u52a1\uff1a<\/span><\/p>\n<p><a href=\"https:\/\/statorials.org\/cn\/python-\u76f8\u5173\u77e9\u9635\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 Python \u4e2d\u521b\u5efa\u76f8\u5173\u77e9\u9635<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u76f8\u5173\u6027-\u77db\u66fc\u87d2\u86c7\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 Python \u4e2d\u8ba1\u7b97\u65af\u76ae\u5c14\u66fc\u7b49\u7ea7\u76f8\u5173\u6027<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u81ea\u76f8\u5173-python\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u7528 Python \u8ba1\u7b97\u81ea\u76f8\u5173<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u91cf\u5316\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u5173\u7cfb\u7684\u4e00\u79cd\u65b9\u6cd5\u662f\u4f7f\u7528Pearson \u76f8\u5173\u7cfb\u6570\uff0c\u5b83\u662f\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7ebf\u6027\u5173\u8054\u7684\u5ea6\u91cf\u3002\u5b83\u59cb\u7ec8\u91c7\u7528 -1 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-827","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u5982\u4f55\u7528 Python \u8ba1\u7b97\u76f8\u5173\u6027 - \u7edf\u8ba1\u5b66<\/title>\n<meta name=\"description\" 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