{"id":3575,"date":"2023-07-16T18:29:13","date_gmt":"2023-07-16T18:29:13","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e7%86%8a%e7%8c%ab%e7%99%be%e5%88%86%e4%bd%8d%e6%8e%92%e5%90%8d\/"},"modified":"2023-07-16T18:29:13","modified_gmt":"2023-07-16T18:29:13","slug":"%e7%86%8a%e7%8c%ab%e7%99%be%e5%88%86%e4%bd%8d%e6%8e%92%e5%90%8d","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e7%86%8a%e7%8c%ab%e7%99%be%e5%88%86%e4%bd%8d%e6%8e%92%e5%90%8d\/","title":{"rendered":"\u5982\u4f55\u8ba1\u7b97\u718a\u732b\u7684\u767e\u5206\u4f4d\u6392\u540d\uff08\u9644\u793a\u4f8b\uff09"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u503c\u7684<strong>\u767e\u5206\u4f4d\u6570\u6392\u540d<\/strong>\u544a\u8bc9\u6211\u4eec\u6570\u636e\u96c6\u4e2d\u6392\u540d\u7b49\u4e8e\u6216\u4f4e\u4e8e\u7ed9\u5b9a\u503c\u7684\u503c\u7684\u767e\u5206\u6bd4\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u65b9\u6cd5\u6765\u8ba1\u7b97 pandas \u4e2d\u7684\u767e\u5206\u4f4d\u6570\u6392\u540d\uff1a<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u65b9\u6cd5 1\uff1a\u8ba1\u7b97\u5217\u7684\u767e\u5206\u4f4d\u6570\u6392\u540d<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong>df[' <span style=\"color: #ff0000;\">percent_rank<\/span> '] = df[' <span style=\"color: #ff0000;\">some_column<\/span> ']. <span style=\"color: #3366ff;\">rank<\/span> (pct= <span style=\"color: #008000;\">True<\/span> )\n<\/strong><\/span><\/pre>\n<p><span style=\"color: #000000;\"><strong>\u65b9\u6cd52\uff1a\u6309\u7ec4\u8ba1\u7b97\u767e\u5206\u4f4d\u6392\u540d<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><b>df[' <span style=\"color: #ff0000;\">percent_rank<\/span> '] = df. <span style=\"color: #3366ff;\">groupby<\/span> (' <span style=\"color: #ff0000;\">group_var<\/span> ')[' <span style=\"color: #ff0000;\">value_var<\/span> ']. <span style=\"color: #3366ff;\">transform<\/span> (' <span style=\"color: #ff0000;\">rank<\/span> ', pct= <span style=\"color: #008000;\">True<\/span> )\n<\/b><\/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 \u7684\u6bcf\u79cd\u65b9\u6cd5\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><b><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;\">team<\/span> ': ['A', 'A', 'A', 'A', 'A', 'A', 'A',\n                            'B', 'B', 'B', 'B', 'B', 'B', 'B'],\n                   ' <span style=\"color: #ff0000;\">points<\/span> ': [2, 5, 5, 7, 9, 13, 15, 17, 22, 24, 30, 31, 38, 39]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n   team points\n0 to 2\n1 to 5\n2 to 5\n3 to 7\n4 to 9\n5 to 13\n6 to 15\n7 B 17\n8 B 22\n9 B 24\n10 B 30\n11 B 31\n12 B 38\n13 B 39\n<\/b><\/span><\/pre>\n<h2><strong><span style=\"color: #000000;\">\u793a\u4f8b 1\uff1a\u8ba1\u7b97\u5217\u7684\u767e\u5206\u4f4d\u6570\u6392\u540d<\/span><\/strong><\/h2>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u5982\u4f55\u8ba1\u7b97\u70b9\u5217\u4e2d\u6bcf\u4e2a\u503c\u7684\u767e\u5206\u4f4d\u6392\u540d\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#add new column that shows percentile rank of points\n<\/span>df[' <span style=\"color: #ff0000;\">percent_rank<\/span> '] = df[' <span style=\"color: #ff0000;\">points<\/span> ']. <span style=\"color: #3366ff;\">rank<\/span> (pct= <span style=\"color: #008000;\">True<\/span> )\n\n<span style=\"color: #008080;\">#view updated DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n   team points percent_rank\n0 A 2 0.071429\n1 to 5 0.178571\n2 to 5 0.178571\n3 to 7 0.285714\n4 to 9 0.357143\n5 A 13 0.428571\n6 A 15 0.500000\n7 B 17 0.571429\n8 B 22 0.642857\n9 B 24 0.714286\n10 B 30 0.785714\n11 B 31 0.857143\n12 B 38 0.928571\n13 B 39 1.000000\n<\/strong><\/span><\/pre>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u662f\u5982\u4f55\u89e3\u91ca<strong>percent_rank<\/strong>\u5217\u4e2d\u7684\u503c\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>7.14%<\/strong>\u7684\u70b9\u503c\u7b49\u4e8e\u6216\u5c0f\u4e8e2\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>17.86%<\/strong>\u7684\u70b9\u503c\u7b49\u4e8e\u6216\u5c0f\u4e8e5\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>28.57%<\/strong>\u7684\u5206\u503c\u7b49\u4e8e\u6216\u5c0f\u4e8e7\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u7b49\u7b49\u3002<\/span><\/p>\n<h2><strong><span style=\"color: #000000;\">\u793a\u4f8b 2\uff1a\u6309\u7ec4\u8ba1\u7b97\u767e\u5206\u4f4d\u6392\u540d<\/span><\/strong><\/h2>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u5982\u4f55\u8ba1\u7b97\u5206\u6570\u5217\u4e2d\u6bcf\u4e2a\u503c\u7684\u767e\u5206\u4f4d\u6570\u6392\u540d\uff08\u6309\u56e2\u961f\u5206\u7ec4\uff09\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#add new column that shows percentile rank of points, grouped by team\n<\/span>df[' <span style=\"color: #ff0000;\">percent_rank<\/span> '] = df. <span style=\"color: #3366ff;\">groupby<\/span> (' <span style=\"color: #ff0000;\">team<\/span> ')[' <span style=\"color: #ff0000;\">points<\/span> ']. <span style=\"color: #3366ff;\">transform<\/span> (' <span style=\"color: #ff0000;\">rank<\/span> ', pct= <span style=\"color: #008000;\">True<\/span> )\n\n<span style=\"color: #008080;\">#view updated DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n   team points percent_rank\n0 A 2 0.142857\n1 A 5 0.357143\n2 A 5 0.357143\n3 to 7 0.571429\n4 to 9 0.714286\n5 A 13 0.857143\n6 to 15 1.000000\n7 B 17 0.142857\n8 B 22 0.285714\n9 B 24 0.428571\n10 B 30 0.571429\n11 B 31 0.714286\n12 B 38 0.857143\n13 B 39 1.000000\n<\/strong><\/span><\/pre>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u662f\u5982\u4f55\u89e3\u91ca<strong>percent_rank<\/strong>\u5217\u4e2d\u7684\u503c\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">A\u961f<strong>14.3%<\/strong>\u7684\u5206\u503c\u7b49\u4e8e\u6216\u5c0f\u4e8e2\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\">A\u961f<strong>35.7%<\/strong>\u7684\u5206\u503c\u7b49\u4e8e\u6216\u5c0f\u4e8e5\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\">A\u961f<strong>57.1%<\/strong>\u7684\u5206\u503c\u7b49\u4e8e\u6216\u5c0f\u4e8e7\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u7b49\u7b49\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\u5728 pandas \u4e2d\u6267\u884c\u5176\u4ed6\u5e38\u89c1\u4efb\u52a1\uff1a<\/span><\/p>\n<p><a href=\"https:\/\/statorials.org\/cn\/\u5927\u718a\u732b\u7684\u767e\u5206\u6bd4\u53d8\u5316\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u8ba1\u7b97\u718a\u732b\u7684\u767e\u5206\u6bd4\u53d8\u5316<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/pandas-\u7d2f\u8ba1\u767e\u5206\u6bd4\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u8ba1\u7b97 pandas \u7684\u7d2f\u79ef\u767e\u5206\u6bd4<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u718a\u732b\u7fa4\u4f53\u767e\u5206\u6bd4\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u8ba1\u7b97 pandas \u4e2d\u4e00\u7ec4\u5185\u603b\u6570\u7684\u767e\u5206\u6bd4<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u503c\u7684\u767e\u5206\u4f4d\u6570\u6392\u540d\u544a\u8bc9\u6211\u4eec\u6570\u636e\u96c6\u4e2d\u6392\u540d\u7b49\u4e8e\u6216\u4f4e\u4e8e\u7ed9\u5b9a\u503c\u7684\u503c\u7684\u767e\u5206\u6bd4\u3002 \u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u65b9\u6cd5\u6765\u8ba1\u7b97 pandas \u4e2d [&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-3575","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - 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