{"id":4300,"date":"2023-07-12T04:44:09","date_gmt":"2023-07-12T04:44:09","guid":{"rendered":"https:\/\/statorials.org\/cn\/p%e5%80%bcpandas%e7%9b%b8%e5%85%b3%e6%80%a7\/"},"modified":"2023-07-12T04:44:09","modified_gmt":"2023-07-12T04:44:09","slug":"p%e5%80%bcpandas%e7%9b%b8%e5%85%b3%e6%80%a7","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/p%e5%80%bcpandas%e7%9b%b8%e5%85%b3%e6%80%a7\/","title":{"rendered":"\u5982\u4f55\u6c42pandas\u4e2d\u76f8\u5173\u7cfb\u6570\u7684p\u503c"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/cn\/\u76ae\u5c14\u900a\u76f8\u5173\u7cfb\u6570-1\/\" target=\"_blank\" rel=\"noopener\">\u76ae\u5c14\u900a\u76f8\u5173\u7cfb\u6570<\/a>\u53ef\u7528\u4e8e\u8861\u91cf\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u7ebf\u6027\u5173\u8054\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8be5\u76f8\u5173\u7cfb\u6570\u59cb\u7ec8\u53d6<strong>-1<\/strong>\u548c<strong>1<\/strong>\u4e4b\u95f4\u7684\u503c\uff0c\u5176\u4e2d\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>-1<\/strong> \uff1a\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u5b8c\u5168\u8d1f\u7ebf\u6027\u76f8\u5173\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>0<\/strong> \uff1a\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u6ca1\u6709\u7ebf\u6027\u76f8\u5173\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>1\uff1a<\/strong>\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u5b8c\u5168\u6b63\u7ebf\u6027\u76f8\u5173\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u8981\u786e\u5b9a\u76f8\u5173\u7cfb\u6570\u662f\u5426\u5177\u6709\u7edf\u8ba1\u663e\u7740\u6027\uff0c\u60a8\u53ef\u4ee5\u8ba1\u7b97\u76f8\u5e94\u7684 t \u5206\u6570\u548c p \u503c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u76f8\u5173\u7cfb\u6570 (r) \u7684 t \u5206\u6570\u8ba1\u7b97\u516c\u5f0f\u4e3a\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\">t = r\u221a <span style=\"border-top: 1px solid black;\">n-2<\/span> \/ \u221a <span style=\"border-top: 1px solid black;\">1-r <sup>2<\/sup><\/span><\/span><\/p>\n<p> <span style=\"color: #000000;\">p \u503c\u8ba1\u7b97\u4e3a\u5177\u6709 n-2 \u81ea\u7531\u5ea6\u7684 t \u5206\u5e03\u7684\u76f8\u5e94\u53cc\u5c3e p \u503c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8981\u8ba1\u7b97 pandas \u4e2d Pearson \u76f8\u5173\u7cfb\u6570\u7684 p \u503c\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528<strong>SciPy<\/strong>\u5e93\u4e2d\u7684<strong>pearsonr()<\/strong>\u51fd\u6570\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">from<\/span> scipy. <span style=\"color: #3366ff;\">stats<\/span> <span style=\"color: #008000;\">import<\/span> pearsonr\n\npearsonr(df[' <span style=\"color: #ff0000;\">column1<\/span> '], df[' <span style=\"color: #ff0000;\">column2<\/span> '])\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6b64\u51fd\u6570\u5c06\u8fd4\u56de\u5217<strong>1<\/strong>\u548c<strong>2<\/strong>\u4e4b\u95f4\u7684\u76ae\u5c14\u900a\u76f8\u5173\u7cfb\u6570\u4ee5\u53ca\u76f8\u5e94\u7684 p \u503c\uff0c\u8be5\u503c\u544a\u8bc9\u6211\u4eec\u76f8\u5173\u7cfb\u6570\u662f\u5426\u5177\u6709\u7edf\u8ba1\u663e\u7740\u6027\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5982\u679c\u8981\u8ba1\u7b97 DataFrame \u4e2d\u6bcf\u4e2a\u53ef\u80fd\u7684\u6210\u5bf9\u5217\u7ec4\u5408\u7684 Pearson \u76f8\u5173\u7cfb\u6570\u7684 p \u503c\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u81ea\u5b9a\u4e49\u51fd\u6570\u6765\u6267\u884c\u6b64\u64cd\u4f5c\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">def<\/span> r_pvalues(df):\n    cols = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> (columns= <span style=\"color: #3366ff;\">df.columns<\/span> )\n    p = cols. <span style=\"color: #3366ff;\">transpose<\/span> (). <span style=\"color: #3366ff;\">join<\/span> (cols, how=' <span style=\"color: #ff0000;\">outer<\/span> ')\n    <span style=\"color: #008000;\">for<\/span> r <span style=\"color: #008000;\">in<\/span> df. <span style=\"color: #3366ff;\">columns<\/span> :\n        <span style=\"color: #008000;\">for<\/span> c <span style=\"color: #008000;\">in<\/span> df. <span style=\"color: #3366ff;\">columns<\/span> :\n            tmp = df[df[r]. <span style=\"color: #3366ff;\">notnull<\/span> () &amp; df[c]. <span style=\"color: #3366ff;\">notnull<\/span> ()]\n            p[r][c] = round(pearsonr(tmp[r], tmp[c])[1], 4)\n    <span style=\"color: #008000;\">return<\/span> p\n<\/strong><\/span><\/pre>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5b9e\u8df5\u4e2d\u4f7f\u7528\u4ee5\u4e0b pandas DataFrame \u8ba1\u7b97\u76f8\u5173\u7cfb\u6570\u7684 p \u503c\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#createDataFrame\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">x<\/span> ': [4, 5, 5, 7, 8, 10, 12, 13, 14, 15],\n                   ' <span style=\"color: #ff0000;\">y<\/span> ': [10, 12, 14, 18, np.nan, 19, 13, 20, 14, np.nan],\n                   ' <span style=\"color: #ff0000;\">z<\/span> ': [20, 24, 24, 23, 19, 15, 18, 14, 10, 12]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n    X Y Z\n0 4 10.0 20\n1 5 12.0 24\n2 5 14.0 24\n3 7 18.0 23\n4 8 NaN 19\n5 10 19.0 15\n6 12 13.0 18\n7 13 20.0 14\n8 14 14.0 10\n9 15 NaN 12\n<\/strong><\/span><\/pre>\n<h2><span style=\"color: #000000;\"><strong>\u793a\u4f8b 1\uff1a\u8ba1\u7b97 Pandas \u4e2d\u4e24\u5217\u4e4b\u95f4\u7684\u76f8\u5173\u7cfb\u6570\u7684 P \u503c<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u5982\u4f55\u8ba1\u7b97 DataFrame \u7684<strong>x<\/strong>\u548c<strong>y<\/strong>\u5217\u7684 Pearson \u76f8\u5173\u7cfb\u6570\u548c\u76f8\u5e94\u7684 p \u503c\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">from<\/span> scipy. <span style=\"color: #3366ff;\">stats<\/span> <span style=\"color: #008000;\">import<\/span> pearsonr\n\n<span style=\"color: #008080;\">#drop all rows with NaN values\n<\/span>df_new = df. <span style=\"color: #3366ff;\">dropna<\/span> ()\n\n<span style=\"color: #008080;\">#calculation correlation coefficient and p-value between x and y\n<\/span>pearsonr(df_new[' <span style=\"color: #ff0000;\">x<\/span> '], df_new[' <span style=\"color: #ff0000;\">y<\/span> '])\n\nPearsonRResult(statistic=0.4791621985883838, pvalue=0.22961622926360523)\n<\/strong><\/span><\/pre>\n<p><span style=\"color: #000000;\">\u4ece\u7ed3\u679c\u6211\u4eec\u53ef\u4ee5\u770b\u51fa\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u76ae\u5c14\u900a\u76f8\u5173\u7cfb\u6570\u4e3a<strong>0.4792<\/strong> \u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u76f8\u5e94\u7684 p \u503c\u4e3a<strong>0.2296<\/strong> \u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u7531\u4e8e\u76f8\u5173\u7cfb\u6570\u4e3a\u6b63\uff0c\u8fd9\u8868\u660e\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u5b58\u5728\u6b63\u7ebf\u6027\u5173\u7cfb\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u7136\u800c\uff0c\u7531\u4e8e\u76f8\u5173\u7cfb\u6570\u7684 p \u503c\u4e0d\u5c0f\u4e8e 0.05\uff0c\u56e0\u6b64\u76f8\u5173\u6027\u5728\u7edf\u8ba1\u4e0a\u4e0d\u663e\u7740\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8bf7\u6ce8\u610f\uff0c\u6211\u4eec\u8fd8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u4ece\u76f8\u5173\u7cfb\u6570\u4e2d\u63d0\u53d6 p \u503c\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#extract p-value of correlation coefficient\n<\/span>pearsonr(df_new[' <span style=\"color: #ff0000;\">x<\/span> '], df_new[' <span style=\"color: #ff0000;\">y<\/span> '])[1]\n\n0.22961622926360523\n<\/strong><\/span><\/pre>\n<p><span style=\"color: #000000;\">\u76f8\u5173\u7cfb\u6570\u7684 p \u503c\u4e3a<strong>0.2296<\/strong> \u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8fd9\u4e0e\u5148\u524d\u8f93\u51fa\u7684 p \u503c\u76f8\u5339\u914d\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u793a\u4f8b2\uff1a\u8ba1\u7b97Pandas\u4e2d\u6240\u6709\u5217\u4e4b\u95f4\u7684\u76f8\u5173\u7cfb\u6570\u7684P\u503c<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u4e86\u5982\u4f55\u8ba1\u7b97 pandas DataFrame \u4e2d\u6bcf\u4e2a\u6210\u5bf9\u5217\u7ec4\u5408\u7684 Pearson \u76f8\u5173\u7cfb\u6570\u548c\u76f8\u5e94\u7684 p \u503c\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\"><span style=\"color: #008080;\">#create function to calculate p-values for each pairwise correlation coefficient<\/span>\ndef<\/span> r_pvalues(df):\n    cols = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> (columns= <span style=\"color: #3366ff;\">df.columns<\/span> )\n    p = cols. <span style=\"color: #3366ff;\">transpose<\/span> (). <span style=\"color: #3366ff;\">join<\/span> (cols, how=' <span style=\"color: #ff0000;\">outer<\/span> ')\n    <span style=\"color: #008000;\">for<\/span> r <span style=\"color: #008000;\">in<\/span> df. <span style=\"color: #3366ff;\">columns<\/span> :\n        <span style=\"color: #008000;\">for<\/span> c <span style=\"color: #008000;\">in<\/span> df. <span style=\"color: #3366ff;\">columns<\/span> :\n            tmp = df[df[r]. <span style=\"color: #3366ff;\">notnull<\/span> () &amp; df[c]. <span style=\"color: #3366ff;\">notnull<\/span> ()]\n            p[r][c] = round(pearsonr(tmp[r], tmp[c])[1], 4)\n    <span style=\"color: #008000;\">return<\/span> p\n\n<span style=\"color: #008080;\">#use custom function to calculate p-values\n<\/span>r_pvalues(df)\n\n             X Y Z\nx 0.0 0.2296 0.0005\ny 0.2296 0.0 0.4238\nz 0.0005 0.4238 0.0<\/strong><\/span><\/pre>\n<p><span style=\"color: #000000;\">\u4ece\u7ed3\u679c\u6211\u4eec\u53ef\u4ee5\u770b\u51fa\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">x \u548c y \u4e4b\u95f4\u7684\u76f8\u5173\u7cfb\u6570\u7684 p \u503c\u4e3a<strong>0.2296<\/strong> \u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\">x \u548c z \u4e4b\u95f4\u7684\u76f8\u5173\u7cfb\u6570\u7684 p \u503c\u4e3a<strong>0.0005<\/strong> \u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\">y \u548c z \u4e4b\u95f4\u7684\u76f8\u5173\u7cfb\u6570\u7684 p \u503c\u4e3a<strong>0.4238<\/strong> \u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u8bf7\u6ce8\u610f\uff0c\u6211\u4eec\u5728\u81ea\u5b9a\u4e49\u51fd\u6570\u4e2d\u5c06 p \u503c\u56db\u820d\u4e94\u5165\u5230\u5c0f\u6570\u70b9\u540e\u56db\u4f4d\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8bf7\u968f\u610f\u5c06\u51fd\u6570\u6700\u540e\u4e00\u884c\u4e2d\u7684<strong>4<\/strong>\u66ff\u6362\u4e3a\u4e0d\u540c\u7684\u6570\u5b57\uff0c\u4ee5\u56db\u820d\u4e94\u5165\u5230\u4e0d\u540c\u7684\u5c0f\u6570\u4f4d\u6570\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u6ce8\u610f<\/strong>\uff1a\u60a8\u53ef\u4ee5<a href=\"https:\/\/docs.scipy.org\/doc\/scipy\/reference\/generated\/scipy.stats.pearsonr.html\" target=\"_blank\" rel=\"noopener\">\u5728\u6b64\u5904<\/a>\u627e\u5230 SciPy <strong>pearsonr()<\/strong>\u51fd\u6570\u7684\u5b8c\u6574\u6587\u6863\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u6267\u884c\u5176\u4ed6\u5e38\u89c1\u7684 panda \u4efb\u52a1\uff1a<\/span><\/p>\n<p><a href=\"https:\/\/statorials.org\/cn\/pandas-\u5206\u7ec4\u76f8\u5173\u6027\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 Pandas \u4e2d\u6309\u7ec4\u8ba1\u7b97\u76f8\u5173\u6027<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/pandas\u76f8\u5173\u8f74\u627f\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u8ba1\u7b97pandas\u4e2d\u7684\u6ed1\u52a8\u76f8\u5173\u6027<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u76f8\u5173\u6027-\u77db\u66fc\u87d2\u86c7\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u8ba1\u7b97 pandas \u4e2d\u7684\u65af\u76ae\u5c14\u66fc\u7b49\u7ea7\u76f8\u5173\u6027<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u76ae\u5c14\u900a\u76f8\u5173\u7cfb\u6570\u53ef\u7528\u4e8e\u8861\u91cf\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u7ebf\u6027\u5173\u8054\u3002 \u8be5\u76f8\u5173\u7cfb\u6570\u59cb\u7ec8\u53d6-1\u548c1\u4e4b\u95f4\u7684\u503c\uff0c\u5176\u4e2d\uff1a -1 \uff1a\u4e24\u4e2a\u53d8\u91cf\u4e4b [&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-4300","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\u627e\u5230 pandas \u4e2d\u76f8\u5173\u7cfb\u6570\u7684 P \u503c - Statorials<\/title>\n<meta name=\"description\" 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