{"id":3950,"date":"2023-07-14T13:59:17","date_gmt":"2023-07-14T13:59:17","guid":{"rendered":"https:\/\/statorials.org\/cn\/pandas-value_counts-%e7%99%be%e5%88%86%e6%af%94\/"},"modified":"2023-07-14T13:59:17","modified_gmt":"2023-07-14T13:59:17","slug":"pandas-value_counts-%e7%99%be%e5%88%86%e6%af%94","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/pandas-value_counts-%e7%99%be%e5%88%86%e6%af%94\/","title":{"rendered":"Pandas\uff1a\u5982\u4f55\u5c06 value_counts \u8868\u793a\u4e3a\u767e\u5206\u6bd4"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u60a8\u53ef\u4ee5\u4f7f\u7528 pandas \u4e2d\u7684<strong>value_counts()<\/strong>\u51fd\u6570\u6765\u8ba1\u7b97 DataFrame \u7ed9\u5b9a\u5217\u4e2d\u503c\u7684\u51fa\u73b0\u6b21\u6570\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8981\u5c06\u503c\u8868\u793a\u4e3a\u767e\u5206\u6bd4\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u65b9\u6cd5\u4e4b\u4e00\uff1a<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u65b9\u6cd51\uff1a\u5c06\u503c\u8868\u793a\u4e3a\u767e\u5206\u6bd4\uff08\u683c\u5f0f\u4e3a\u5c0f\u6570\uff09<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df. <span style=\"color: #3366ff;\">my_col<\/span> . <span style=\"color: #3366ff;\">value_counts<\/span> (normalize= <span style=\"color: #008000;\">True<\/span> )\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\"><strong>\u65b9\u6cd52\uff1a\u5c06\u503c\u8868\u793a\u4e3a\u767e\u5206\u6bd4\uff08\u7528\u767e\u5206\u53f7\u7b26\u53f7\u683c\u5f0f\u5316\uff09<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df. <span style=\"color: #3366ff;\">my_col<\/span> . <span style=\"color: #3366ff;\">value_counts<\/span> (normalize= <span style=\"color: #008000;\">True<\/span> ). <span style=\"color: #3366ff;\">mul<\/span> ( <span style=\"color: #008000;\">100<\/span> ). <span style=\"color: #3366ff;\">round<\/span> ( <span style=\"color: #008000;\">1<\/span> ). <span style=\"color: #3366ff;\">astype<\/span> (str) + ' <span style=\"color: #ff0000;\">%<\/span> '<\/strong><\/pre>\n<p><span style=\"color: #000000;\"><strong>\u65b9\u6cd5 3\uff1a\u5c06\u503c\u8ba1\u6570\u8868\u793a\u4e3a\u767e\u5206\u6bd4\uff08\u5e26\u8ba1\u6570\uff09<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>counts = df. <span style=\"color: #3366ff;\">my_col<\/span> . <span style=\"color: #3366ff;\">value_counts<\/span> ()<\/strong>\n<strong>percs = df. <span style=\"color: #3366ff;\">my_col<\/span> . <span style=\"color: #3366ff;\">value_counts<\/span> (normalize= <span style=\"color: #008000;\">True<\/span> )<\/strong>\n<strong>p.d. <span style=\"color: #3366ff;\">concat<\/span> ([counts, percs], axis= <span style=\"color: #008000;\">1<\/span> , keys=[' <span style=\"color: #ff0000;\">count<\/span> ', ' <span style=\"color: #ff0000;\">percentage<\/span> '])<\/strong><\/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;\"> <strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#createDataFrame<span style=\"color: #000000;\">\ndf = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">team<\/span> ': ['A', 'A', 'B', 'B', 'B', 'B', 'B', 'C'],\n                   ' <span style=\"color: #ff0000;\">points<\/span> ': [15, 12, 18, 20, 22, 28, 35, 40]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n  team points\n0 to 15\n1 to 12\n2 B 18\n3 B 20\n4 B 22\n5 B 28\n6 B 35\n7 C 40\n<\/span><\/span><\/strong><\/pre>\n<h2><span style=\"color: #000000;\"><strong>\u793a\u4f8b 1\uff1a\u5c06\u503c\u8868\u793a\u4e3a\u767e\u5206\u6bd4\uff08\u683c\u5f0f\u4e3a\u5c0f\u6570\uff09<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u5982\u4f55\u8ba1\u7b97<strong>\u56e2\u961f<\/strong>\u5217\u4e2d\u6bcf\u4e2a\u503c\u7684\u51fa\u73b0\u6b21\u6570\uff0c\u5e76\u5c06\u51fa\u73b0\u6b21\u6570\u8868\u793a\u4e3a\u603b\u6570\u7684\u767e\u5206\u6bd4\uff08\u91c7\u7528\u5c0f\u6570\u683c\u5f0f\uff09\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#count occurrence of each value in 'team' column as percentage of total\n<\/span>df. <span style=\"color: #3366ff;\">team<\/span> . <span style=\"color: #3366ff;\">value_counts<\/span> (normalize= <span style=\"color: #008000;\">True<\/span> )\n\nB 0.625\nAt 0.250\nC 0.125\nName: team, dtype: float64\n<\/strong><\/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;\">\u503c<strong>B<\/strong>\u4ee3\u8868\u56e2\u961f\u5217\u4e2d\u51fa\u73b0\u6b21\u6570\u7684 62.5%\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u503c<strong>A<\/strong>\u4ee3\u8868\u56e2\u961f\u5217\u4e2d 25% \u7684\u51fa\u73b0\u6b21\u6570\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>C<\/strong>\u503c\u4ee3\u8868\u56e2\u961f\u680f\u4e2d\u51fa\u73b0\u768412.5%\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u8bf7\u6ce8\u610f\uff0c\u767e\u5206\u6bd4\u683c\u5f0f\u4e3a\u5c0f\u6570\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u793a\u4f8b 2\uff1a\u5c06\u503c\u8868\u793a\u4e3a\u767e\u5206\u6bd4\uff08\u4f7f\u7528\u767e\u5206\u6bd4\u7b26\u53f7\u683c\u5f0f\u5316\uff09<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u5982\u4f55\u8ba1\u7b97<strong>\u56e2\u961f<\/strong>\u5217\u4e2d\u6bcf\u4e2a\u503c\u7684\u51fa\u73b0\u6b21\u6570\uff0c\u5e76\u5c06\u51fa\u73b0\u6b21\u6570\u8868\u793a\u4e3a\u603b\u6570\u7684\u767e\u5206\u6bd4\uff0c\u5e76\u4f7f\u7528\u767e\u5206\u6bd4\u7b26\u53f7\u8fdb\u884c\u683c\u5f0f\u5316\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#count occurrence of each value in 'team' column as percentage of total\n<\/span>df. <span style=\"color: #3366ff;\">team<\/span> . <span style=\"color: #3366ff;\">value_counts<\/span> (normalize= <span style=\"color: #008000;\">True<\/span> ). <span style=\"color: #3366ff;\">mul<\/span> ( <span style=\"color: #008000;\">100<\/span> ). <span style=\"color: #3366ff;\">round<\/span> ( <span style=\"color: #008000;\">1<\/span> ). <span style=\"color: #3366ff;\">astype<\/span> (str) + ' <span style=\"color: #ff0000;\">%<\/span> '\n\nB 62.5%\nAt 25.0%\nC 12.5%\nName: team, dtype: object\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u8bf7\u6ce8\u610f\uff0c\u767e\u5206\u6bd4\u88ab\u683c\u5f0f\u5316\u4e3a\u5e26\u6709\u767e\u5206\u53f7\u7b26\u53f7\u7684\u5b57\u7b26\u4e32\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u793a\u4f8b 3\uff1a\u5c06\u503c\u7684\u6570\u91cf\u8868\u793a\u4e3a\u767e\u5206\u6bd4\uff08\u5e26\u6570\u5b57\uff09<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u5982\u4f55\u8ba1\u7b97<strong>team<\/strong>\u5217\u4e2d\u6bcf\u4e2a\u503c\u7684\u51fa\u73b0\u6b21\u6570\uff0c\u5e76\u5c06\u51fa\u73b0\u6b21\u6570\u8868\u793a\u4e3a\u6570\u5b57\u548c\u767e\u5206\u6bd4\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#count occurrence of each value in 'team' column\n<\/span>counts = df. <span style=\"color: #3366ff;\">team<\/span> . <span style=\"color: #3366ff;\">value_counts<\/span> ()\n\n<span style=\"color: #008080;\">#count occurrence of each value in 'team' column as percentage of total<\/span>\npercs = df. <span style=\"color: #3366ff;\">team<\/span> . <span style=\"color: #3366ff;\">value_counts<\/span> (normalize= <span style=\"color: #008000;\">True<\/span> )\n\n<span style=\"color: #008080;\">#concatenate results into one DataFrame\n<\/span>p.d. <span style=\"color: #3366ff;\">concat<\/span> ([counts, percs], axis= <span style=\"color: #008000;\">1<\/span> , keys=[' <span style=\"color: #ff0000;\">count<\/span> ', ' <span style=\"color: #ff0000;\">percentage<\/span> '])\n\n        count percentage\nB 5 0.625\nAt 2 0.250\nC 1 0.125<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u8bf7\u6ce8\u610f\uff0c<strong>\u8ba1\u6570<\/strong>\u5217\u663e\u793a\u56e2\u961f\u5217\u4e2d\u6bcf\u4e2a\u552f\u4e00\u503c\u7684\u8ba1\u6570\uff0c\u800c<strong>\u767e\u5206\u6bd4<\/strong>\u5217\u5c06\u6bcf\u4e2a\u552f\u4e00\u503c\u663e\u793a\u4e3a\u51fa\u73b0\u603b\u6570\u7684\u767e\u5206\u6bd4\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\/pandas-\u7ed8\u56fe\u503c\u8ba1\u6570\/\" target=\"_blank\" rel=\"noopener\">Pandas\uff1a\u5982\u4f55\u7ed8\u5236\u4ef7\u503c\u8d26\u6237<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/pandas-groupby-\u503c\u8ba1\u6570\/\" target=\"_blank\" rel=\"noopener\">Pandas\uff1a\u5982\u4f55\u4f7f\u7528 GroupBy \u548c\u503c\u8ba1\u6570<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u6309\u7ec4\u5212\u5206\u7684-pandas-\u76f4\u65b9\u56fe\/\" target=\"_blank\" rel=\"noopener\">Pandas\uff1a\u5982\u4f55\u6309\u7ec4\u7ed8\u5236\u76f4\u65b9\u56fe<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u60a8\u53ef\u4ee5\u4f7f\u7528 pandas \u4e2d\u7684value_counts()\u51fd\u6570\u6765\u8ba1\u7b97 DataFrame \u7ed9\u5b9a\u5217\u4e2d\u503c\u7684\u51fa\u73b0\u6b21\u6570 [&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-3950","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>Pandas\uff1a\u5982\u4f55\u5c06 value_counts \u8868\u793a\u4e3a\u767e\u5206\u6bd4 - Statorials<\/title>\n<meta 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