{"id":911,"date":"2023-07-28T08:30:34","date_gmt":"2023-07-28T08:30:34","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e5%9f%ba%e4%ba%8e%e5%88%97%e5%88%9b%e5%bb%ba-pandas\/"},"modified":"2023-07-28T08:30:34","modified_gmt":"2023-07-28T08:30:34","slug":"%e5%9f%ba%e4%ba%8e%e5%88%97%e5%88%9b%e5%bb%ba-pandas","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e5%9f%ba%e4%ba%8e%e5%88%97%e5%88%9b%e5%bb%ba-pandas\/","title":{"rendered":"\u5982\u4f55\u6839\u636e pandas \u4e2d\u7684\u6761\u4ef6\u521b\u5efa\u65b0\u5217"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u901a\u5e38\uff0c\u60a8\u53ef\u80fd\u5e0c\u671b\u6839\u636e\u67d0\u4e9b\u6761\u4ef6\u5728 pandas DataFrame \u4e2d\u521b\u5efa\u65b0\u5217\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u672c\u6559\u7a0b\u63d0\u4f9b\u4e86\u51e0\u4e2a\u793a\u4f8b\uff0c\u8bf4\u660e\u5982\u4f55\u4f7f\u7528\u4ee5\u4e0b DataFrame \u6267\u884c\u6b64\u64cd\u4f5c\uff1a<\/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<span style=\"color: #107d3f;\">import<\/span> numpy <span style=\"color: #107d3f;\">as<\/span> np\n\n<span style=\"color: #008080;\">#createDataFrame<\/span>\ndf = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({'rating': [90, 85, 82, 88, 94, 90, 76, 75, 87, 86],\n                   'points': [25, 20, 14, 16, 27, 20, 12, 15, 14, 19],\n                   'assists': [5, 7, 7, 8, 5, 7, 6, 9, 9, 5],\n                   'rebounds': [11, 8, 10, 6, 6, 9, 6, 10, 10, 7]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<span style=\"color: #000000;\">df\n<\/span>\n<\/span>rating points assists rebounds\n0 90 25 5 11\n1 85 20 7 8\n2 82 14 7 10\n3 88 16 8 6\n4 94 27 5 6\n5 90 20 7 9\n6 76 12 6 6\n7 75 15 9 10\n8 87 14 9 10\n9 86 19 5 7\n<\/strong><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u793a\u4f8b 1\uff1a\u521b\u5efa\u4e00\u4e2a\u5305\u542b\u4e8c\u8fdb\u5236\u503c\u7684\u65b0\u5217<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u5982\u4f55\u521b\u5efa\u4e00\u4e2a\u540d\u4e3a\u201cGood\u201d\u7684\u65b0\u5217\uff0c\u5982\u679c\u7ed9\u5b9a\u884c\u4e2d\u7684\u70b9\u5927\u4e8e 20\uff0c\u5219\u503c\u4e3a\u201cyes\u201d\uff0c\u5426\u5219\u503c\u4e3a\u201cno\u201d\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create new column titled 'Good'<\/span>\ndf['Good'] = np. <span style=\"color: #3366ff;\">where<\/span> (df['points']&gt;20, ' <span style=\"color: #008000;\">yes<\/span> ', ' <span style=\"color: #008000;\">no<\/span> ')\n\n<span style=\"color: #008080;\">#view DataFrame<\/span> \ndf\n\n        rating points assists rebounds Good\n0 90 25 5 11 yes\n1 85 20 7 8 no\n2 82 14 7 10 no\n3 88 16 8 6 no\n4 94 27 5 6 yes\n5 90 20 7 9 no\n6 76 12 6 6 no\n7 75 15 9 10 no\n8 87 14 9 10 no\n9 86 19 5 7 no\n<\/strong><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u793a\u4f8b 2\uff1a\u521b\u5efa\u5305\u542b\u591a\u4e2a\u503c\u7684\u65b0\u5217<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u5982\u4f55\u521b\u5efa\u4e00\u4e2a\u540d\u4e3a\u201cGood\u201d\u7684\u65b0\u5217\uff0c\u5176\u503c\u4e3a\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u5982\u679c\u5206\u6570 \u2265 25\uff0c\u5219\u201c\u662f\u201d<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5982\u679c 15 \u2264 \u5206 &lt; 25\uff0c\u5219\u201c\u4e5f\u8bb8\u201d<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5982\u679c\u5206\u6570 &lt; 15\uff0c\u5219\u201c\u5426\u201d<\/span><\/li>\n<\/ul>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define function for classifying players based on points<\/span>\n<span style=\"color: #008000;\">def<\/span> f(row):\n    <span style=\"color: #008000;\">if<\/span> row['points'] &lt; 15:\n        val = 'no'\n    <span style=\"color: #008000;\">elif<\/span> row['points'] &lt; 25:\n        val = 'maybe'\n    <span style=\"color: #008000;\">else<\/span> :\n        val = 'yes'\n    <span style=\"color: #008000;\">return<\/span> val\n\n<span style=\"color: #008080;\">#create new column 'Good' using the function above<\/span>\ndf['Good'] = df. <span style=\"color: #3366ff;\">apply<\/span> (f, axis=1)\n\n<span style=\"color: #008080;\">#view DataFrame<\/span> \ndf\n\n        rating points assists rebounds Good\n0 90 25 5 11 yes\n1 85 20 7 8 maybe\n2 82 14 7 10 no\n3 88 16 8 6 maybe\n4 94 27 5 6 yes\n5 90 20 7 9 maybe\n6 76 12 6 6 no\n7 75 15 9 10 maybe\n8 87 14 9 10 no\n9 86 19 5 7 maybe\n<\/strong><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u793a\u4f8b 3\uff1a\u6839\u636e\u4e0e\u73b0\u6709\u5217\u7684\u6bd4\u8f83\u521b\u5efa\u65b0\u5217<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u5982\u4f55\u521b\u5efa\u4e00\u4e2a\u540d\u4e3a\u201cassist_more\u201d\u7684\u65b0\u5217\uff0c\u5176\u503c\u4e3a\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u5982\u679c\u52a9\u653b &gt; \u7bee\u677f\uff0c\u5219\u201c\u662f\u201d\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5426\u5219\u5c31\u8bf4\u201c\u4e0d\u201d\u3002<\/span><\/li>\n<\/ul>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create new column titled 'assist_more'<\/span>\n<span style=\"color: #000000;\">df['assist_more']<\/span> = np. <span style=\"color: #3366ff;\">where<\/span> (df['assists']&gt;df['rebounds'], ' <span style=\"color: #008000;\">yes<\/span> ', ' <span style=\"color: #008000;\">no<\/span> ')\n\n<span style=\"color: #008080;\">#view DataFrame<\/span> \ndf\n\n        rating points assists rebounds assist_more\n0 90 25 5 11 no\n1 85 20 7 8 no\n2 82 14 7 10 no\n3 88 16 8 6 yes\n4 94 27 5 6 no\n5 90 20 7 9 no\n6 76 12 6 6 no\n7 75 15 9 10 no\n8 87 14 9 10 no\n9 86 19 5 7 no<\/strong><\/pre>\n<p><span style=\"color: #000000;\"><em><span style=\"color: #000000;\">\u60a8\u53ef\u4ee5<a href=\"https:\/\/statorials.org\/cn\/\u7edf\u8ba1\u5b66\u4ee5\u7b80\u5355\u76f4\u63a5\u7684\u65b9\u5f0f\u89e3\u91ca\u6982\u5ff5\uff0c\u6211\u4eec\u4f7f\u5b66\u4e60\u7edf\u8ba1\u53d8\u5f97\u66f4\u5bb9\u6613\/\" target=\"_blank\" rel=\"noopener\">\u5728\u6b64\u5904<\/a>\u627e\u5230\u66f4\u591a Python \u6559\u7a0b\u3002<\/span><\/em><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u901a\u5e38\uff0c\u60a8\u53ef\u80fd\u5e0c\u671b\u6839\u636e\u67d0\u4e9b\u6761\u4ef6\u5728 pandas DataFrame \u4e2d\u521b\u5efa\u65b0\u5217\u3002 \u672c\u6559\u7a0b\u63d0\u4f9b\u4e86\u51e0\u4e2a\u793a\u4f8b\uff0c\u8bf4\u660e\u5982\u4f55 [&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-911","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\u6839\u636e Pandas \u4e2d\u7684\u6761\u4ef6\u521b\u5efa\u65b0\u5217<\/title>\n<meta name=\"description\" content=\"\u5173\u4e8e\u5982\u4f55\u6839\u636e\u7279\u5b9a\u6761\u4ef6\u5728 pandas DataFrame \u4e2d\u521b\u5efa\u65b0\u5217\u7684\u7b80\u5355\u8bf4\u660e\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/cn\/\u57fa\u4e8e\u5217\u521b\u5efa-pandas\/\" \/>\n<meta property=\"og:locale\" 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