{"id":829,"date":"2023-07-28T15:10:19","date_gmt":"2023-07-28T15:10:19","guid":{"rendered":"https:\/\/statorials.org\/cn\/python-%e7%9b%b8%e5%85%b3%e7%9f%a9%e9%98%b5\/"},"modified":"2023-07-28T15:10:19","modified_gmt":"2023-07-28T15:10:19","slug":"python-%e7%9b%b8%e5%85%b3%e7%9f%a9%e9%98%b5","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/python-%e7%9b%b8%e5%85%b3%e7%9f%a9%e9%98%b5\/","title":{"rendered":"\u5982\u4f55\u5728 python \u4e2d\u521b\u5efa\u76f8\u5173\u77e9\u9635"},"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><\/span><\/p>\n<p><span style=\"color: #000000;\">\u5b83\u7684\u503c\u4ecb\u4e8e -1 \u548c 1 \u4e4b\u95f4\uff0c\u5176\u4e2d\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">-1 \u8868\u793a\u5b8c\u5168\u8d1f\u7ebf\u6027\u76f8\u5173\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\">0 \u8868\u793a\u6ca1\u6709\u7ebf\u6027\u76f8\u5173\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\">1 \u8868\u793a\u5b8c\u5168\u6b63\u7ebf\u6027\u76f8\u5173\u3002<\/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;\">\u4f46\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u6211\u4eec\u60f3\u8981\u4e86\u89e3\u591a\u5bf9\u53d8\u91cf\u4e4b\u95f4\u7684\u76f8\u5173\u6027\u3002\u5728\u8fd9\u4e9b\u60c5\u51b5\u4e0b\uff0c\u6211\u4eec\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a<a href=\"https:\/\/statorials.org\/cn\/\u5982\u4f55\u8bfb\u53d6\u76f8\u5173\u77e9\u9635\/\" target=\"_blank\" rel=\"noopener\">\u76f8\u5173\u77e9\u9635<\/a>\uff0c\u5b83\u662f\u4e00\u4e2a\u65b9\u8868\uff0c\u663e\u793a\u53d8\u91cf\u7684\u591a\u4e2a\u6210\u5bf9\u7ec4\u5408\u4e4b\u95f4\u7684\u76f8\u5173\u7cfb\u6570\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u672c\u6559\u7a0b\u4ecb\u7ecd\u5982\u4f55\u5728 Python \u4e2d\u521b\u5efa\u548c\u89e3\u91ca\u76f8\u5173\u77e9\u9635\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u5982\u4f55\u5728 Python \u4e2d\u521b\u5efa\u76f8\u5173\u77e9\u9635<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u4f7f\u7528\u4ee5\u4e0b\u6b65\u9aa4\u5728 Python \u4e2d\u521b\u5efa\u76f8\u5173\u77e9\u9635\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u6b65\u9aa4 1\uff1a\u521b\u5efa\u6570\u636e\u96c6\u3002<\/strong><\/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 = {'assists': [4, 5, 5, 6, 7, 8, 8, 10],\n        'rebounds': [12, 14, 13, 7, 8, 8, 9, 13],\n        'points': [22, 24, 26, 26, 29, 32, 20, 14]\n        }\n\ndf = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> (data, columns=['assists','rebounds','points'])\ndf\n\n   assist rebound points\n0 4 12 22\n1 5 14 24\n2 5 13 26\n3 6 7 26\n4 7 8 29\n5 8 8 32\n6 8 9 20\n7 10 13 14\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\"><strong>\u6b65\u9aa4 2\uff1a\u521b\u5efa\u76f8\u5173\u77e9\u9635\u3002<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create correlation matrix<\/span>\ndf. <span style=\"color: #3366ff;\">corr<\/span> ()\n\n                assists rebound points\nassists 1.000000 -0.244861 -0.329573\nrebounds -0.244861 1.000000 -0.522092\npoints -0.329573 -0.522092 1.000000\n\n<span style=\"color: #008080;\">#create same correlation matrix with coefficients rounded to 3 decimals<\/span> \ndf. <span style=\"color: #3366ff;\">corr<\/span> (). <span style=\"color: #3366ff;\">round<\/span> (3)\n\t       assists rebound points\nassists 1.000 -0.245 -0.330\nrebounds -0.245 1.000 -0.522\npoints -0.330 -0.522 1.000\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\"><strong>\u6b65\u9aa4 3\uff1a\u89e3\u91ca\u76f8\u5173\u77e9\u9635\u3002<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u6cbf\u8868\u5bf9\u89d2\u7ebf\u7684\u76f8\u5173\u7cfb\u6570\u5747\u7b49\u4e8e 1\uff0c\u56e0\u4e3a\u6bcf\u4e2a\u53d8\u91cf\u4e0e\u5176\u81ea\u8eab\u5b8c\u5168\u76f8\u5173\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6240\u6709\u5176\u4ed6\u76f8\u5173\u7cfb\u6570\u8868\u793a\u53d8\u91cf\u7684\u4e0d\u540c\u6210\u5bf9\u7ec4\u5408\u4e4b\u95f4\u7684\u76f8\u5173\u6027\u3002\u4f8b\u5982\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u52a9\u653b\u548c\u7bee\u677f\u4e4b\u95f4\u7684\u76f8\u5173\u7cfb\u6570\u4e3a<strong>-0.245<\/strong> \u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u52a9\u653b\u6570\u4e0e\u5f97\u5206\u4e4b\u95f4\u7684\u76f8\u5173\u7cfb\u6570\u4e3a<strong>-0.330<\/strong> \u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u7bee\u677f\u6570\u4e0e\u5f97\u5206\u4e4b\u95f4\u7684\u76f8\u5173\u7cfb\u6570\u4e3a<strong>-0.522<\/strong> \u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\"><strong>\u6b65\u9aa4 4\uff1a\u53ef\u89c6\u5316\u76f8\u5173\u77e9\u9635\uff08\u53ef\u9009\uff09\u3002<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u60a8\u53ef\u4ee5\u4f7f\u7528 pandas \u4e2d\u63d0\u4f9b\u7684<a href=\"https:\/\/pandas.pydata.org\/pandas-docs\/stable\/user_guide\/style.html\" target=\"_blank\" rel=\"noopener\">\u6837\u5f0f\u9009\u9879<\/a>\u53ef\u89c6\u5316\u76f8\u5173\u77e9\u9635\uff1a<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>corr = df. <span style=\"color: #3366ff;\">corr<\/span> ()\ncorr. <span style=\"color: #3366ff;\">style<\/span> . <span style=\"color: #3366ff;\">background_gradient<\/span> (cmap='coolwarm')\n<\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-9176 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/correlationmatrixpython1.png\" alt=\"Python \u4e2d\u7684\u76f8\u5173\u77e9\u9635\" width=\"309\" height=\"135\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p><span style=\"color: #000000;\">\u60a8\u8fd8\u53ef\u4ee5\u4fee\u6539<strong>cmap<\/strong>\u53c2\u6570\u4ee5\u751f\u6210\u5177\u6709\u4e0d\u540c\u989c\u8272\u7684\u76f8\u5173\u77e9\u9635\u3002<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>corr = df. <span style=\"color: #3366ff;\">corr<\/span> ()\ncorr. <span style=\"color: #3366ff;\">style<\/span> . <span style=\"color: #3366ff;\">background_gradient<\/span> (cmap=' <span style=\"color: #800080;\">RdYlGn<\/span> ')<\/strong> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-9177 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/correlationmatrixpython2.png\" alt=\"Python \u4e2d\u4f7f\u7528 matplotlib \u7684\u76f8\u5173\u77e9\u9635\" width=\"297\" height=\"125\" srcset=\"\" sizes=\"auto, \"><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>corr = df. <span style=\"color: #3366ff;\">corr<\/span> ()\ncorr. <span style=\"color: #3366ff;\">style<\/span> . <span style=\"color: #3366ff;\">background_gradient<\/span> (cmap=' <span style=\"color: #800080;\">bwr<\/span> ')<\/strong> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-9179 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/correlationmatrixpython3.png\" alt=\"\u4f7f\u7528 Pandas \u7684\u76f8\u5173\u77e9\u9635\" width=\"309\" height=\"125\" srcset=\"\" sizes=\"auto, \"><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>corr = df. <span style=\"color: #3366ff;\">corr<\/span> ()\ncorr. <span style=\"color: #3366ff;\">style<\/span> . <span style=\"color: #3366ff;\">background_gradient<\/span> (cmap=' <span style=\"color: #800080;\">PuOr<\/span> ')<\/strong> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-9180 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/correlationmatrixpython4.png\" alt=\"Python \u4e2d\u7684\u76f8\u5173\u77e9\u9635\u793a\u4f8b\" width=\"306\" height=\"125\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p><span style=\"color: #000000;\"><strong>\u6ce8\u610f<\/strong>\uff1a\u6709\u5173<strong>cmap<\/strong>\u53c2\u6570\u7684\u5b8c\u6574\u5217\u8868\uff0c\u8bf7\u53c2\u9605<a href=\"https:\/\/matplotlib.org\/stable\/tutorials\/colors\/colormaps.html\" target=\"_blank\" rel=\"noopener\">matplotlib \u6587\u6863<\/a>\u3002<\/span><\/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\u7684\u503c\u4ecb\u4e8e &#8211; [&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-829","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\u5728 Python \u4e2d\u521b\u5efa\u76f8\u5173\u77e9\u9635 - Statorials<\/title>\n<meta name=\"description\" content=\"\u7b80\u5355\u89e3\u91ca\u5982\u4f55\u5728 Python 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