{"id":4069,"date":"2023-07-13T19:59:50","date_gmt":"2023-07-13T19:59:50","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e7%ae%b1%e7%ba%bf%e5%9b%be%e5%88%a0%e9%99%a4%e5%bc%82%e5%b8%b8%e5%80%bc\/"},"modified":"2023-07-13T19:59:50","modified_gmt":"2023-07-13T19:59:50","slug":"%e7%ae%b1%e7%ba%bf%e5%9b%be%e5%88%a0%e9%99%a4%e5%bc%82%e5%b8%b8%e5%80%bc","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e7%ae%b1%e7%ba%bf%e5%9b%be%e5%88%a0%e9%99%a4%e5%bc%82%e5%b8%b8%e5%80%bc\/","title":{"rendered":"\u5982\u4f55\u4ece seaborn \u7bb1\u7ebf\u56fe\u4e2d\u5220\u9664\u5f02\u5e38\u503c"},"content":{"rendered":"

<\/p>\n

\n

\u5728 Seaborn \u4e2d\u521b\u5efa\u7bb1\u7ebf\u56fe\u65f6\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528showfliers=False<\/strong>\u53c2\u6570\u4ece\u56fe\u4e2d\u5220\u9664\u5f02\u5e38\u503c\uff1a<\/span><\/p>\n

 sns. boxplot<\/span> (x=' variable<\/span> ', y=' value<\/span> ', data=df, showfliers= False<\/span> )\n<\/strong><\/pre>\n
\u5982\u679c\u60a8\u53ea\u60f3\u66f4\u6539\u79bb\u7fa4\u503c\u6807\u8bb0\u7684\u5927\u5c0f\uff0c\u53ef\u4ee5\u4f7f\u7528fliersize<\/strong>\u53c2\u6570\uff1a<\/span><\/span><\/p>\n
 sns. boxplot<\/span> (x=' variable<\/span> ', y=' value<\/span> ', data=df, fliersize= 3<\/span> )<\/strong><\/pre>\n
\u8bf7\u6ce8\u610f\uff0c fliersize<\/strong>\u7684\u9ed8\u8ba4\u5927\u5c0f\u662f5<\/strong> \u3002<\/span><\/p>\n
\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5b9e\u8df5\u4e2d\u4f7f\u7528\u8fd9\u4e9b\u53c2\u6570\u3002<\/span><\/p>\n
\u793a\u4f8b\uff1a\u4ece Seaborn \u7bb1\u7ebf\u56fe\u4e2d\u5220\u9664\u5f02\u5e38\u503c<\/strong><\/span><\/h2>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4ee5\u4e0b pandas DataFrame\uff0c\u663e\u793a\u4e09\u4e2a\u4e0d\u540c\u7bee\u7403\u961f\u7403\u5458\u7684\u5f97\u5206\uff1a<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\ndf = pd. DataFrame<\/span> ({' A<\/span> ': [5, 7, 7, 9, 12, 78],\n                   ' B<\/span> ': [8, 8, 9, 13, 15, 17],\n                   ' C<\/span> ': [1, 2, 2, 4, 5, 60]})\n\n#melt data frame into long format\n<\/span>df_melted = pd. melt<\/span> (df)\n\n#view head of DataFrame\n<\/span>print<\/span> ( df_melted.head<\/span> ())\n\n  variable value\n0 to 5\n1 to 7\n2 to 7\n3 to 19\n4 to 22<\/span><\/span><\/strong><\/pre>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u521b\u5efa\u4e09\u4e2a\u7bb1\u7ebf\u56fe\uff0c\u663e\u793a\u6bcf\u4e2a\u56e2\u961f\u5f97\u5206\u7684\u5206\u5e03\uff1a<\/span> <\/p>\n
 import<\/span> seaborn as<\/span> sns\n\n#create boxplot to visualize distribution of points by team\n<\/span>sns. boxplot<\/span> (x=' variable<\/span> ', y=' value<\/span> ', data=df_melted)<\/span><\/span><\/strong> <\/pre>\n
\"\"<\/p>\n
\u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff0cseaborn \u4f7f\u7528\u83f1\u5f62\u6807\u8bb0\u6765\u663e\u793a\u5f02\u5e38\u503c\u3002<\/span><\/p>\n
\u8981\u4ece\u56fe\u4e2d\u5220\u9664\u8fd9\u4e9b\u5f02\u5e38\u503c\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528showfliers=False<\/strong>\u53c2\u6570\uff1a<\/span> <\/p>\n
 import<\/span> seaborn as<\/span> sns\n\n#create boxplots and remove outliers\n<\/span>sns. boxplot<\/span> (x=' variable<\/span> ', y=' value<\/span> ', data=df_melted, showfliers= False<\/span> )<\/strong><\/span> <\/pre>\n
\"\"<\/p>\n
\u8bf7\u6ce8\u610f\uff0c\u5f02\u5e38\u503c\u5df2\u4ece\u56fe\u4e2d\u5b8c\u5168\u5220\u9664\u3002<\/span><\/p>\n
\u8981\u7b80\u5355\u5730\u66f4\u6539\u79bb\u7fa4\u503c\u6807\u8bb0\u7684\u5927\u5c0f\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528fliersize<\/strong>\u53c2\u6570\uff1a<\/span> <\/p>\n
 import<\/span> seaborn as<\/span> sns\n\n#create boxplots and adjust markers for outliers to be smaller\n<\/span>sns. boxplot<\/span> (x=' variable<\/span> ', y=' value<\/span> ', data=df_melted, fliersize= 3<\/span> )<\/strong><\/span> <\/pre>\n
\"\"<\/p>\n
\u8bf7\u6ce8\u610f\uff0c\u5f02\u5e38\u503c\u7684\u6807\u8bb0\u5c3a\u5bf8\u8981\u5c0f\u5f97\u591a\u3002<\/span><\/p>\n
\u60a8\u53ef\u4ee5\u968f\u610f\u8c03\u6574\u4f20\u5355<\/strong>\u5c3a\u5bf8\u503c\uff0c\u4f7f\u6807\u8bb0\u5c3a\u5bf8\u6839\u636e\u60a8\u7684\u9700\u8981\u53d8\u5c0f\u6216\u53d8\u5927\u3002<\/span><\/p>\n
\u6ce8\u610f<\/strong>\uff1a\u60a8\u53ef\u4ee5 \u5728\u6b64\u5904<\/a>\u627e\u5230seaborn.boxplot()<\/strong>\u51fd\u6570\u7684\u5b8c\u6574\u6587\u6863\u3002<\/span><\/p>\n
\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h2>\n
\u4ee5\u4e0b\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u5728seaborn<\/a>\u4e2d\u521b\u5efa\u5176\u4ed6\u5e38\u89c1\u7684\u53ef\u89c6\u5316\uff1a<\/span><\/p>\n
\u5982\u4f55\u5728 Seaborn \u4e2d\u521b\u5efa\u997c\u56fe<\/a>
\u5982\u4f55\u5728 Seaborn \u4e2d\u521b\u5efa\u9762\u79ef\u56fe<\/a>
\u5982\u4f55\u5728 Seaborn \u4e2d\u521b\u5efa\u65f6\u95f4\u5e8f\u5217\u56fe<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u5728 Seaborn \u4e2d\u521b\u5efa\u7bb1\u7ebf\u56fe\u65f6\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528showfliers=False\u53c2\u6570\u4ece\u56fe\u4e2d\u5220\u9664\u5f02\u5e38\u503c\uff1a sns. […]<\/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-4069","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\n\u5982\u4f55\u4ece Seaborn \u7bb1\u7ebf\u56fe\u4e2d\u5220\u9664\u5f02\u5e38\u503c - Statorials<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u901a\u8fc7\u4e00\u4e2a\u793a\u4f8b\u89e3\u91ca\u4e86\u5982\u4f55\u4ece Seaborn \u7684\u7bb1\u7ebf\u56fe\u4e2d\u5220\u9664\u5f02\u5e38\u503c\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\/\u7bb1\u7ebf\u56fe\u5220\u9664\u5f02\u5e38\u503c\/\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u5982\u4f55\u4ece Seaborn \u7bb1\u7ebf\u56fe\u4e2d\u5220\u9664\u5f02\u5e38\u503c - 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