{"id":2131,"date":"2023-07-23T13:49:44","date_gmt":"2023-07-23T13:49:44","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e6%b5%b7%e6%b4%8b%e5%8c%ba%e5%9f%9f%e5%9c%b0%e5%9b%be\/"},"modified":"2023-07-23T13:49:44","modified_gmt":"2023-07-23T13:49:44","slug":"%e6%b5%b7%e6%b4%8b%e5%8c%ba%e5%9f%9f%e5%9c%b0%e5%9b%be","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e6%b5%b7%e6%b4%8b%e5%8c%ba%e5%9f%9f%e5%9c%b0%e5%9b%be\/","title":{"rendered":"\u5982\u4f55\u5728 seaborn \u4e2d\u521b\u5efa\u9762\u79ef\u56fe\uff08\u5e26\u6709\u793a\u4f8b\uff09"},"content":{"rendered":"

<\/p>\n

\n

\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u57fa\u672c\u8bed\u6cd5\u5728seaborn<\/a>\u4e2d\u521b\u5efa\u9762\u79ef\u56fe\uff1a<\/span><\/p>\n

 import<\/span> matplotlib. pyplot<\/span> as<\/span> plt\nimport<\/span> seaborn as<\/span> sns\n\n#set seaborn style\n<\/span>sns. set_theme<\/span> ()\n\n#create seaborn area chart<\/span>\nplt. stackplot<\/span> ( df.x<\/span> , df.y1<\/span> , df.y2<\/span> , df.y3<\/span> )\n<\/strong><\/pre>\n
\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5b9e\u8df5\u4e2d\u4f7f\u7528\u6b64\u8bed\u6cd5\u3002<\/span><\/p>\n
\u793a\u4f8b 1\uff1a\u5728 Seaborn \u4e2d\u521b\u5efa\u57fa\u672c\u9762\u79ef\u56fe<\/strong><\/span><\/h3>\n
\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u5728 Seaborn \u4e2d\u521b\u5efa\u57fa\u672c\u9762\u79ef\u56fe\uff1a<\/span> <\/p>\n
 import<\/span> pandas as<\/span> pd\nimport<\/span> matplotlib. pyplot<\/span> as<\/span> plt\nimport<\/span> seaborn as<\/span> sns\n\n#set seaborn style\n<\/span>sns. set_theme<\/span> ()\n \n#define DataFrame\n<\/span>df = pd. DataFrame<\/span> ({' period<\/span> ': [1, 2, 3, 4, 5, 6, 7, 8],\n                   ' team_A<\/span> ': [20, 12, 15, 14, 19, 23, 25, 29],\n                   ' team_B<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' team_C<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12]})\n\n#create area chart\n<\/span>plt. stackplot<\/span> (df. period<\/span> , df. team_A<\/span> , df. team_B<\/span> , df. team_C<\/span> )\n<\/strong><\/pre>\n
\"\"<\/p>\n
 x \u8f74\u663e\u793a\u5468\u671f\u53d8\u91cf\uff0cy \u8f74\u663e\u793a\u4e09\u652f\u7403\u961f\u968f\u65f6\u95f4\u53d8\u5316\u7684\u503c\u3002<\/span><\/p>\n
\u793a\u4f8b 2\uff1a\u5728 Seaborn \u4e2d\u521b\u5efa\u81ea\u5b9a\u4e49\u9762\u79ef\u56fe<\/strong><\/span><\/h3>\n
\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u5982\u4f55\u66f4\u6539\u9762\u79ef\u56fe\u7684\u989c\u8272\u5e76\u6dfb\u52a0\u5e26\u6709\u7279\u5b9a\u6807\u7b7e\u7684\u56fe\u4f8b\uff1a<\/span> <\/p>\n
 import<\/span> pandas as<\/span> pd\nimport<\/span> matplotlib. pyplot<\/span> as<\/span> plt\nimport<\/span> seaborn as<\/span> sns\n\n#set seaborn style\n<\/span>sns. set_theme<\/span> ()\n \n#define DataFrame\n<\/span>df = pd. DataFrame<\/span> ({' period<\/span> ': [1, 2, 3, 4, 5, 6, 7, 8],\n                   ' team_A<\/span> ': [20, 12, 15, 14, 19, 23, 25, 29],\n                   ' team_B<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' team_C<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12]})\n\n#define colors to use in chart\n<\/span>color_map = [' red<\/span> ', ' steelblue<\/span> ', ' pink<\/span> ']\n    \n#create area chart\n<\/span>plt. stackplot<\/span> (df. period<\/span> , df. team_A<\/span> , df. team_B<\/span> , df. team_C<\/span> ,\n              labels=[' Team A<\/span> ', ' Team B<\/span> ', ' Team C<\/span> '],\n              colors=color_map)\n\n#add legend\n<\/span>plt. legend<\/span> (loc=' upper left<\/span> ')\n\n#add axis labels\n<\/span>plt. xlabel<\/span> (' Period<\/span> ')\nplt. ylabel<\/span> (' Points Scored<\/span> ')\n\n#display area chart\n<\/span>plt. show<\/span> ()<\/strong> <\/pre>\n
\"\"<\/p>\n
\u8bf7\u6ce8\u610f\uff0c\u989c\u8272<\/strong>\u53c2\u6570\u63a5\u53d7\u989c\u8272\u540d\u79f0\u4ee5\u53ca\u5341\u516d\u8fdb\u5236\u989c\u8272\u4ee3\u7801\u3002<\/span><\/p>\n
\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h3>\n
\u4ee5\u4e0b\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u5728 Seaborn \u4e2d\u521b\u5efa\u5176\u4ed6\u5e38\u89c1\u7ed8\u56fe\uff1a<\/span><\/p>\n
\u5982\u4f55\u5728 Seaborn \u4e2d\u521b\u5efa\u65f6\u95f4\u5e8f\u5217\u56fe<\/a>
\u5982\u4f55\u5728 Seaborn \u4e2d\u521b\u5efa\u997c\u56fe<\/a>
\u5982\u4f55\u5728 Seaborn \u4e2d\u521b\u5efa\u6761\u5f62\u56fe<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u57fa\u672c\u8bed\u6cd5\u5728seaborn\u4e2d\u521b\u5efa\u9762\u79ef\u56fe\uff1a import matplotlib. pyplot as […]<\/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-2131","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\n\u5982\u4f55\u5728 Seaborn \u4e2d\u521b\u5efa\u9762\u79ef\u56fe\uff08\u5e26\u6709\u793a\u4f8b\uff09 \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u901a\u8fc7\u51e0\u4e2a\u793a\u4f8b\u89e3\u91ca\u4e86\u5982\u4f55\u5728 Seaborn \u4e2d\u521b\u5efa\u533a\u57df\u5730\u56fe\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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