{"id":295,"date":"2023-05-16T00:46:53","date_gmt":"2022-12-04T06:39:18","guid":{"rendered":"https:\/\/www.silicloud.com\/ja\/blog\/index.php\/2023\/11\/30\/seaborn%e3%81%ae%e6%8a%98%e3%82%8c%e7%b7%9a%e3%82%b0%e3%83%a9%e3%83%95%e3%81%ab%e3%82%88%e3%82%8b%e3%83%87%e3%83%bc%e3%82%bf%e3%81%ae%e8%a6%96%e8%a6%9a%e5%8c%96\/"},"modified":"2025-07-31T22:25:13","modified_gmt":"2025-07-31T13:25:13","slug":"seaborn%e3%81%ae%e6%8a%98%e3%82%8c%e7%b7%9a%e3%82%b0%e3%83%a9%e3%83%95%e3%81%ab%e3%82%88%e3%82%8b%e3%83%87%e3%83%bc%e3%82%bf%e3%81%ae%e8%a6%96%e8%a6%9a%e5%8c%96","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/ja\/blog\/seaborn%e3%81%ae%e6%8a%98%e3%82%8c%e7%b7%9a%e3%82%b0%e3%83%a9%e3%83%95%e3%81%ab%e3%82%88%e3%82%8b%e3%83%87%e3%83%bc%e3%82%bf%e3%81%ae%e8%a6%96%e8%a6%9a%e5%8c%96\/","title":{"rendered":"Seaborn\u306e\u6298\u308c\u7dda\u30b0\u30e9\u30d5\u306b\u3088\u308b\u30c7\u30fc\u30bf\u306e\u8996\u899a\u5316"},"content":{"rendered":"

\u307f\u306a\u3055\u3093\u3001\u3053\u3093\u306b\u3061\u306f\uff01\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001Seaborn\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3092\u9032\u3081\u3066\u3044\u304d\u3001Seaborn\u306e\u6298\u308c\u7dda\u30b0\u30e9\u30d5\u306b\u3064\u3044\u3066\u7406\u89e3\u3057\u3066\u3044\u304d\u307e\u3059\u3002\u6700\u8fd1\u306fSeaborn\u306e\u30d2\u30fc\u30c8\u30de\u30c3\u30d7\u306b\u3064\u3044\u3066\u3082\u53d6\u308a\u4e0a\u3052\u307e\u3057\u305f\u306e\u3067\u3001\u30d2\u30fc\u30c8\u30de\u30c3\u30d7\u306b\u3064\u3044\u3066\u3082\u3063\u3068\u5b66\u3073\u305f\u3044\u65b9\u306f\u305c\u3072\u3054\u89a7\u304f\u3060\u3055\u3044\u3002<\/p>\n

\n

Line Plot\u3068\u306f\u4f55\u3067\u3059\u304b\uff1f<\/h2>\n

\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u4e0a\u3067\u69cb\u7bc9\u3055\u308c\u305f\u30e2\u30c7\u30eb\u306b\u3088\u3063\u3066\u4e88\u6e2c\u7d50\u679c\u3092\u5206\u6790\u3057\u3001\u30c7\u30fc\u30bf\u306e\u5909\u52d5\u3092\u8996\u899a\u5316\u3059\u308b\u305f\u3081\u306b\u3001Seaborn\u306f\u30e9\u30a4\u30d6\u30e9\u30ea\u3068\u3057\u3066\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002<\/p>\n

\u30b7\u30fc\u30dc\u30fc\u30f3\u306e\u6298\u308c\u7dda\u30b0\u30e9\u30d5\u306f\u3001\u9023\u7d9a\u5024\u3068\u30ab\u30c6\u30b4\u30ea\u5024\u306e\u95a2\u4fc2\u3092\u9023\u7d9a\u7684\u306a\u30c7\u30fc\u30bf\u30dd\u30a4\u30f3\u30c8\u5f62\u5f0f\u3067\u8868\u73fe\u3057\u307e\u3059\u3002<\/p>\n

\u4ee5\u4e0b\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3057\u3066\u3001\u30c7\u30fc\u30bf\u3092\u64cd\u4f5c\u3057\u30e9\u30a4\u30f3\u30d7\u30ed\u30c3\u30c8\u3092\u5f62\u6210\u3057\u3066\u3044\u304f\u305f\u3081\u306b\u3001\u3053\u306e\u8a18\u4e8b\u3067\u306f\u7d39\u4ecb\u3057\u307e\u3059\u3002\u5148\u306b\u9032\u3080\u524d\u306b\u3001\u4ee5\u4e0b\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30b9\u30ca\u30c3\u30d7\u30b7\u30e7\u30c3\u30c8\u3092\u3054\u89a7\u304f\u3060\u3055\u3044\u3002<\/p>\n

\u4ee5\u4e0b\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3067\u306f\u3001\u30c7\u30fc\u30bf\u5909\u6570\u300ccyl\u300d\u300cvs\u300d\u300cam\u300d\u300cgear\u300d\u300ccarb\u300d\u306f\u30ab\u30c6\u30b4\u30ea\u5909\u6570\u3067\u3042\u308a\u3001\u3059\u3079\u3066\u306e\u30c7\u30fc\u30bf\u5024\u304c\u7279\u5b9a\u306e\u30ab\u30c6\u30b4\u30ea\u3084\u5024\u306e\u7bc4\u56f2\u306b\u542b\u307e\u308c\u3066\u3044\u308b\u305f\u3081\u3067\u3059\u3002<\/p>\n

\u6b8b\u308a\u306e\u30c7\u30fc\u30bf\u5217\u306f\u3001\u96e2\u6563\u7684\u306a\u6574\u6570\u5024\u3092\u6301\u3063\u3066\u3044\u308b\u305f\u3081\u3001\u6574\u6570\/\u9023\u7d9a\u5909\u6570\u306e\u7bc4\u7587\u306b\u5f53\u3066\u306f\u307e\u308a\u307e\u3059\u3002<\/p>\n

\u5165\u529b\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8:<\/p>\n

\"MTCARS<\/div>\n
\n

\u6700\u521d\u306eSeaborn\u6298\u308c\u7dda\u30b0\u30e9\u30d5\u306e\u30d7\u30ed\u30c3\u30c8\u3092\u8b1b\u7fa9\u3057\u307e\u3059\u3002<\/h2>\n

\u300c\u30e9\u30a4\u30f3\u30d7\u30ed\u30c3\u30c8\u3092\u59cb\u3081\u308b\u305f\u3081\u306b\u306f\u3001\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u3092\u4f7f\u7528\u3057\u3066\u3001Seaborn\u30e9\u30a4\u30d6\u30e9\u30ea\u3092Python\u74b0\u5883\u306b\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u3001\u30a4\u30f3\u30dd\u30fc\u30c8\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u300d<\/p>\n

\u6587\u6cd5\uff1a<\/p>\n

pip install seaborn\r\n<\/code><\/pre>\n
\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u304c\u5b8c\u4e86\u3057\u305f\u3089\u3001\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u73fe\u5728\u306e\u4f5c\u696d\u74b0\u5883\u306b\u30a4\u30f3\u30dd\u30fc\u30c8\u3057\u3066\u3001\u6a5f\u80fd\u3092\u4f7f\u7528\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n
\u6587\u6cd5:<\/p>\n
import<\/span> seaborn\r\n<\/code><\/pre>\n
\u300c\u30b7\u30fc\u30dc\u30fc\u30f3\u300d\u30b7\u30ea\u30fc\u30ba\u5168\u4f53\u3067\u3001\u30c7\u30fc\u30bf\u3092\u30d7\u30ed\u30c3\u30c8\u3057\u3066\u9069\u5207\u306a\u53ef\u8996\u5316\u65b9\u6cd5\u3067\u8868\u793a\u3059\u308b\u305f\u3081\u306b\u3001Matplotlib\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/p>\n
\n
Seaborn\u3092\u4f7f\u7528\u3057\u3066\u5358\u4e00\u306e\u30e9\u30a4\u30f3\u30d7\u30ed\u30c3\u30c8\u3092\u4f5c\u6210\u3059\u308b\u3002<\/h3>\n
\u30b7\u30fc\u30dc\u30fc\u30f3\u306e\u6298\u308c\u7dda\u30b0\u30e9\u30d5\u3092\u4f5c\u6210\u3059\u308b\u305f\u3081\u306b\u3001\u96e2\u6563\u5024\u3092\u63d0\u4f9b\u3059\u308b\u304b\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
\u6587\u6cd5: \u6587\u306e\u69cb\u9020\u53ca\u3073\u8a00\u8a9e\u306e\u30eb\u30fc\u30eb<\/p>\n
seaborn.<\/span>lineplot(<\/span>x,<\/span> y,<\/span> data)<\/span>\r\n<\/code><\/pre>\n
    \n
  • x: Data variable for the x-axis<\/li>\n
  • y: The data variable for the y-axis<\/li>\n
  • data: The object pointing to the entire data set or data values<\/li>\n<\/ul>\n
    \u4f8b1\uff1a\u30e9\u30f3\u30c0\u30e0\u30c7\u30fc\u30bf\u3092\u4f7f\u7528\u3057\u3066\u3001Seaborn\u306e\u6298\u308c\u7dda\u30b0\u30e9\u30d5\u3092\u4f5c\u6210\u3059\u308b\u3002<\/p>\n
    import<\/span> pandas as<\/span> pd\r\nimport<\/span> seaborn as<\/span> sns\r\nimport<\/span> matplotlib.<\/span>pyplot as<\/span> plt\r\n\r\nYear =<\/span> [<\/span>2012<\/span>,<\/span> 2014<\/span>,<\/span> 2016<\/span>,<\/span> 2020<\/span>,<\/span> 2021<\/span>,<\/span> 2022<\/span>,<\/span> 2018<\/span>]<\/span>\r\nProfit =<\/span> [<\/span>80<\/span>,<\/span> 75.8<\/span>,<\/span> 74<\/span>,<\/span> 65<\/span>,<\/span> 99.5<\/span>,<\/span> 19<\/span>,<\/span> 33.6<\/span>]<\/span>\r\n\r\ndata_plot =<\/span> pd.<\/span>DataFrame(<\/span>{<\/span>\"Year\"<\/span>:<\/span>Year,<\/span> \"Profit\"<\/span>:<\/span>Profit}<\/span>)<\/span>\r\n\r\nsns.<\/span>lineplot(<\/span>x =<\/span> \"Year\"<\/span>,<\/span> y =<\/span> \"Profit\"<\/span>,<\/span> data=<\/span>data_plot)<\/span>\r\nplt.<\/span>show(<\/span>)<\/span>\r\n<\/code><\/pre>\n
    \u4ee5\u4e0b\u306e\u6298\u308c\u7dda\u30b0\u30e9\u30d5\u3067\u306f\u3001\u300c\u5e74\u300d\u3068\u300c\u5229\u76ca\u300d\u3068\u3044\u30462\u3064\u306e\u30c7\u30fc\u30bf\u5909\u6570\u306e\u9593\u306e\u76f4\u7dda\u7684\u306a\u95a2\u4fc2\u304c\u89b3\u5bdf\u3067\u304d\u307e\u3059\u3002<\/p>\n
    \u51fa\u529b\uff1a<\/p>\n
    \"LinePlot<\/div>\n
    \u4f8b2\uff1a\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3057\u3066\u3001\u30c7\u30fc\u30bf\u5217\u9593\u306e\u95a2\u4fc2\u3092\u793a\u3059\u305f\u3081\u306b\u6298\u308c\u7dda\u30b0\u30e9\u30d5\u3092\u4f5c\u6210\u3059\u308b\u3002<\/p>\n
    import<\/span> pandas as<\/span> pd\r\nimport<\/span> seaborn as<\/span> sns\r\nimport<\/span> matplotlib.<\/span>pyplot as<\/span> plt\r\n\r\ndata =<\/span> pd.<\/span>read_csv(<\/span>\"C:\/mtcars.csv\"<\/span>)<\/span>\r\ninfo =<\/span> data.<\/span>iloc[<\/span>1<\/span>:<\/span>20<\/span>,<\/span>:<\/span>5<\/span>]<\/span>\r\nsns.<\/span>lineplot(<\/span>x =<\/span> \"drat\"<\/span>,<\/span> y =<\/span> \"mpg\"<\/span>,<\/span>data=<\/span>info)<\/span>\r\nsns.<\/span>set<\/span>(<\/span>style=<\/span>'dark'<\/span>,<\/span>)<\/span>\r\nplt.<\/span>show(<\/span>)<\/span>\r\n<\/code><\/pre>\n
    \u5165\u529b\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8: \u5165\u529b\u30c7\u30fc\u30bf\u306e\u96c6\u5408<\/p>\n
    \"Input<\/div>\n
    \u51fa\u529b:<\/p>\n
    \"LinePlot<\/div>\n
    \n
    \u8907\u6570\u306e\u30b7\u30fc\u30dc\u30fc\u30f3\u306e\u6298\u308c\u7dda\u30b0\u30e9\u30d5\u3092\u8868\u793a\u3057\u307e\u3059\u3002 (Fukus\u016b no sh\u012bb\u014dn no ore-sen gurafu o hy\u014dji shimasu.)<\/h2>\n
    \u540c\u3058\u30b9\u30da\u30fc\u30b9\u3084\u30d7\u30ed\u30c3\u30c8\u5185\u3067\u30c7\u30fc\u30bf\u3092\u53ef\u8996\u5316\u3059\u308b\u305f\u3081\u306b\u3001\u8907\u6570\u306e\u7dda\u3092\u4f5c\u6210\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u540c\u3058\u30c7\u30fc\u30bf\u306e\u5217\u307e\u305f\u306f\u8907\u6570\u306e\u30c7\u30fc\u30bf\u5909\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u305d\u308c\u3089\u306e\u95a2\u4fc2\u3092\u4e00\u7dd2\u306b\u8868\u73fe\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
    \u8907\u6570\u306e\u30c7\u30fc\u30bf\u30dd\u30a4\u30f3\u30c8\u306b\u5bfe\u3057\u3066\u8272\u306e\u8272\u76f8\u3092\u4f5c\u6210\u3059\u308b\u305f\u3081\u306b\u3001\u8272\u76f8\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u4f7f\u7528\u3059\u308b\u3002<\/h3>\n
    \u30d1\u30e9\u30e1\u30fc\u30bf\u300c\u8272\u76f8\uff08hue\uff09\u300d\u306f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u7570\u306a\u308b\u5909\u6570\u3092\u30b0\u30eb\u30fc\u30d7\u5316\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u3001x\u8ef8\u3068y\u8ef8\u306e\u30c7\u30fc\u30bf\u5217\u3068\u3001\u30d1\u30e9\u30e1\u30fc\u30bf\u306b\u6e21\u3055\u308c\u305f\u5217\u3068\u306e\u95a2\u4fc2\u3092\u793a\u3059\u306e\u306b\u5f79\u7acb\u3061\u307e\u3059\u3002<\/p>\n
    \u6587\u6cd5\uff1a<\/p>\n
    seaborn.<\/span>lineplot(<\/span>x,<\/span>y,<\/span>data,<\/span>hue)<\/span>\r\n<\/code><\/pre>\n
    \u3068\u3066\u3082\u5bd2\u3044\u65e5\u3067\u3059\u306d\u3002<\/p>\n
    import<\/span> pandas as<\/span> pd\r\nimport<\/span> seaborn as<\/span> sns\r\nimport<\/span> matplotlib.<\/span>pyplot as<\/span> plt\r\n\r\ndata =<\/span> pd.<\/span>read_csv(<\/span>\"C:\/mtcars.csv\"<\/span>)<\/span>\r\ninfo =<\/span> data.<\/span>iloc[<\/span>1<\/span>:<\/span>20<\/span>,<\/span>:<\/span>5<\/span>]<\/span>\r\nsns.<\/span>lineplot(<\/span>x =<\/span> \"drat\"<\/span>,<\/span> y =<\/span> \"mpg\"<\/span>,<\/span> data=<\/span>info,<\/span> hue=<\/span>\"cyl\"<\/span>)<\/span>\r\nplt.<\/span>show(<\/span>)<\/span>\r\n<\/code><\/pre>\n
    \u4e0b\u306e\u30d7\u30ed\u30c3\u30c8\u3067\u8868\u793a\u3055\u308c\u3066\u3044\u308b\u3068\u304a\u308a\u30013\u3064\u306e\u7570\u306a\u308b\u8272\u8abf\u3067\u8868\u3055\u308c\u305f3\u672c\u306e\u7dda\u306f\u3001\u300cdrat\u300d\u3001\u300cmpg\u300d\u3001\u300ccyl\u300d\u306e\u95a2\u4fc2\u3092\u63cf\u5199\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n
    \u7d50\u679c\uff1a<\/p>\n
    \"Multiple<\/div>\n
    \n
    2. \u7570\u306a\u308b\u7a2e\u985e\u306e\u7dda\u3092\u63cf\u753b\u3059\u308b\u305f\u3081\u306b\u30b9\u30bf\u30a4\u30eb\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u4f7f\u7528\u3059\u308b\u3002<\/h3>\n
    \u79c1\u305f\u3061\u306f\u3001x\u8ef8\u3068y\u8ef8\u3068\u4e00\u7dd2\u306b\u8868\u793a\u3057\u305f\u3044\u5024\u3068\u3001\u3055\u307e\u3056\u307e\u306a\u7dda\u306e\u30b9\u30bf\u30a4\u30eb\uff08\u7834\u7dda\u3001\u70b9\u7dda\uff08\u30de\u30fc\u30ab\u30fc\uff09\u3001\u306a\u3069\uff09\u3092\u6307\u5b9a\u3059\u308b\u305f\u3081\u306b\u3001\u30b9\u30bf\u30a4\u30eb\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u8a2d\u5b9a\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
    \u6587\u6cd5\uff1a<\/p>\n
    seaborn.<\/span>lineplot(<\/span>x,<\/span> y,<\/span> data,<\/span> style)<\/span>\r\n<\/code><\/pre>\n
    \u4f8b2\uff1a<\/p>\n
    import<\/span> pandas as<\/span> pd\r\nimport<\/span> seaborn as<\/span> sns\r\nimport<\/span> matplotlib.<\/span>pyplot as<\/span> plt\r\n\r\ndata =<\/span> pd.<\/span>read_csv(<\/span>\"C:\/mtcars.csv\"<\/span>)<\/span>\r\ninfo =<\/span> data.<\/span>iloc[<\/span>1<\/span>:<\/span>20<\/span>,<\/span>:<\/span>5<\/span>]<\/span>\r\nsns.<\/span>lineplot(<\/span>x =<\/span> \"drat\"<\/span>,<\/span> y =<\/span> \"mpg\"<\/span>,<\/span> data=<\/span>info,<\/span> hue=<\/span>\"cyl\"<\/span>,<\/span> style=<\/span>\"cyl\"<\/span>)<\/span>\r\nplt.<\/span>show(<\/span>)<\/span>\r\n<\/code><\/pre>\n
    \u660e\u3089\u304b\u306b\u898b\u308b\u3068\u3001\u30d7\u30ed\u30c3\u30c8\u306f\u7570\u306a\u308b\u7dda\u306e\u69cb\u9020\u3001\u3064\u307e\u308a\u5b9f\u7dda\u3001\u7834\u7dda\u3001\u30de\u30fc\u30ab\u30fc\u3092\u7528\u3044\u3066\u3001’mpg’\u3068’drat’\u3068\u306e\u95a2\u4fc2\u3067’cyl’\u306e\u5024\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n
    \u51fa\u529b\uff1a<\/p>\n
    \"Line<\/div>\n
    \n
    3. \u30b5\u30a4\u30ba\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u4f7f\u7528\u3057\u3066\u3001Seaborn\u3067\u8907\u6570\u306e\u6298\u308c\u7dda\u30b0\u30e9\u30d5\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u3002<\/h3>\n
    seaborn.lineplot() \u95a2\u6570\u306e size \u30d1\u30e9\u30e1\u30fc\u30bf\u30fc\u3092\u4f7f\u3048\u3070\u3001\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u7dda\u306e\u5927\u304d\u3055\u3092\u5909\u3048\u308b\u3053\u3068\u3067\u3001\u8907\u6570\u306e\u30c7\u30fc\u30bf\u5909\u6570\u306e\u95a2\u4fc2\u6027\u3092\u8868\u73fe\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u305d\u306e\u305f\u3081\u3001\u30c7\u30fc\u30bf\u306e\u5927\u304d\u3055\u306b\u5fdc\u3058\u3066\u7570\u306a\u308b\u5927\u304d\u3055\/\u5e45\u306e\u30b0\u30eb\u30fc\u30d7\u5206\u3051\u5909\u6570\u3068\u3057\u3066\u6a5f\u80fd\u3057\u307e\u3059\u3002<\/p>\n
    \u6587\u6cd5\uff1a<\/p>\n
    seaborn.<\/span>lineplot(<\/span>x,<\/span> y,<\/span> data,<\/span> size)<\/span>\r\n<\/code><\/pre>\n
    3\u3064\u76ee\u306e\u4f8b\uff1a<\/p>\n
    import<\/span> pandas as<\/span> pd\r\nimport<\/span> seaborn as<\/span> sns\r\nimport<\/span> matplotlib.<\/span>pyplot as<\/span> plt\r\n\r\ndata =<\/span> pd.<\/span>read_csv(<\/span>\"C:\/mtcars.csv\"<\/span>)<\/span>\r\ninfo =<\/span> data.<\/span>iloc[<\/span>1<\/span>:<\/span>20<\/span>,<\/span>]<\/span>\r\nsns.<\/span>lineplot(<\/span>x =<\/span> \"drat\"<\/span>,<\/span> y =<\/span> \"mpg\"<\/span>,<\/span> data=<\/span>info,<\/span> hue=<\/span>\"gear\"<\/span>,<\/span>style=<\/span>\"gear\"<\/span>,<\/span>size=<\/span>\"gear\"<\/span>)<\/span>\r\nplt.<\/span>show(<\/span>)<\/span>\r\n<\/code><\/pre>\n
    \u5165\u529b\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8:<\/p>\n
    \"Dataset<\/div>\n
    \u51fa\u529b:<\/p>\n
    \"Line<\/div>\n
    \n
    \u30e9\u30a4\u30f3\u30d7\u30ed\u30c3\u30c8\u306b\u7570\u306a\u308b\u30ab\u30e9\u30fc\u30d1\u30ec\u30c3\u30c8\u3092\u4f7f\u7528\u3059\u308b<\/h2>\n
    Seaborn\u306e\u30ab\u30e9\u30fc\u30de\u30c3\u30d7\u3068\u30d1\u30ec\u30c3\u30c8\u306f\u3001\u53ef\u8996\u5316\u30e2\u30c7\u30eb\u306e\u8272\u7bc4\u56f2\u3092\u5b9a\u7fa9\u3057\u307e\u3059\u3002\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u30d1\u30ec\u30c3\u30c8\u3068hue\u3092\u4f7f\u7528\u3059\u308b\u3053\u3068\u3067\u3001\u30c7\u30fc\u30bf\u5909\u6570\u306b\u57fa\u3065\u3044\u305f\u8272\u306e\u30a8\u30f3\u30b3\u30fc\u30c7\u30a3\u30f3\u30b0\u30b9\u30ad\u30fc\u30e0\u3092\u6c7a\u5b9a\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
    \u3082\u3063\u3068\u30ab\u30e9\u30fc\u30d1\u30ec\u30c3\u30c8\u304c\u6b32\u3057\u3044\u5834\u5408\u306f\u3001\u3053\u3061\u3089\u306e\u30ea\u30f3\u30af\u3092\u53c2\u7167\u3057\u3066\u304f\u3060\u3055\u3044\uff1a\u30ab\u30e9\u30fc\u30d1\u30ec\u30c3\u30c8<\/p>\n
    \u6587\u6cd5:<\/p>\n
    seaborn.<\/span>lineplot(<\/span>x,<\/span>y,<\/span>data,<\/span>hue,<\/span>palette)<\/span>\r\n<\/code><\/pre>\n
    \u4f8b\uff1a
    \n– Can you help me with my homework?<\/p>\n
    import<\/span> pandas as<\/span> pd\r\nimport<\/span> seaborn as<\/span> sns\r\nimport<\/span> matplotlib.<\/span>pyplot as<\/span> plt\r\n \r\ndata =<\/span> pd.<\/span>read_csv(<\/span>\"C:\/mtcars.csv\"<\/span>)<\/span>\r\ninfo =<\/span> data.<\/span>iloc[<\/span>1<\/span>:<\/span>20<\/span>,<\/span>]<\/span>\r\nsns.<\/span>lineplot(<\/span>x =<\/span> \"drat\"<\/span>,<\/span> y =<\/span> \"mpg\"<\/span>,<\/span> data=<\/span>info,<\/span> hue=<\/span>\"gear\"<\/span>,<\/span> palette =<\/span> \"Set1\"<\/span>)<\/span>\r\nplt.<\/span>show(<\/span>)<\/span>\r\n<\/code><\/pre>\n
    \u51fa\u529b\uff1a<\/p>\n
    \"Line<\/div>\n
    \n
    \u6298\u308c\u7dda\u30b0\u30e9\u30d5\u306b\u30a8\u30e9\u30fc\u30d0\u30fc\u3092\u8ffd\u52a0\u3059\u308b\u3002<\/h2>\n
    \u30e9\u30a4\u30f3\u30d7\u30ed\u30c3\u30c8\u3067\u306f\u3001err_style\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u4f7f\u7528\u3057\u3066\u30d7\u30ed\u30c3\u30c8\u5185\u306e\u4fe1\u983c\u30ec\u30d9\u30eb\/\u533a\u9593\u3092\u5b9a\u7fa9\u3057\u3001\u30a8\u30e9\u30fc\u7387\u3092\u8868\u793a\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
    \u6587\u6cd5:<\/p>\n
    seaborn.<\/span>lineplot(<\/span>x,<\/span>y,<\/span>data,<\/span>err_style=<\/span>\"bars\"<\/span>)<\/span>\r\n<\/code><\/pre>\n
    There are many benefits to exercising regularly, such as improving physical health, boosting mood, and increasing energy levels. Additionally, regular exercise can help prevent chronic diseases and maintain a healthy weight.<\/p>\n
    import<\/span> pandas as<\/span> pd\r\nimport<\/span> seaborn as<\/span> sns\r\nimport<\/span> matplotlib.<\/span>pyplot as<\/span> plt\r\n\r\ndata =<\/span> pd.<\/span>read_csv(<\/span>\"C:\/mtcars.csv\"<\/span>)<\/span>\r\ninfo =<\/span> data.<\/span>iloc[<\/span>1<\/span>:<\/span>20<\/span>,<\/span>]<\/span>\r\nsns.<\/span>lineplot(<\/span>x =<\/span> \"cyl\"<\/span>,<\/span> y =<\/span> \"mpg\"<\/span>,<\/span>data=<\/span>info,<\/span> err_style=<\/span>\"bars\"<\/span>)<\/span>\r\nplt.<\/span>show(<\/span>)<\/span>\r\n<\/code><\/pre>\n
    \u51fa\u529b:<\/p>\n
    \"Line<\/div>\n
    \n
    seaborn.set() \u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u7570\u306a\u308b\u30b9\u30bf\u30a4\u30eb\u3092\u8a2d\u5b9a\u3059\u308b<\/h2>\n
    Python\u306eseaborn.set()\u95a2\u6570\u3092\u4f7f\u3046\u3068\u3001\u30d7\u30ed\u30c3\u30c8\u3092\u7570\u306a\u308b\u80cc\u666f\u30b9\u30bf\u30a4\u30eb\u3067\u8868\u793a\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
    \u6587\u6cd5\uff1a<\/p>\n
    seaborn.<\/span>set<\/span>(<\/span>style)<\/span>\r\n<\/code><\/pre>\n
    \u4f8b\u3048\u3070\uff1a<\/p>\n
    import<\/span> pandas as<\/span> pd\r\nimport<\/span> seaborn as<\/span> sns\r\nimport<\/span> matplotlib.<\/span>pyplot as<\/span> plt\r\n\r\ndata =<\/span> pd.<\/span>read_csv(<\/span>\"C:\/mtcars.csv\"<\/span>)<\/span>\r\ninfo =<\/span> data.<\/span>iloc[<\/span>1<\/span>:<\/span>20<\/span>,<\/span>]<\/span>\r\nsns.<\/span>lineplot(<\/span>x =<\/span> \"cyl\"<\/span>,<\/span> y =<\/span> \"mpg\"<\/span>,<\/span>data=<\/span>info,<\/span>hue=<\/span>\"gear\"<\/span>)<\/span>\r\nsns.<\/span>set<\/span>(<\/span>style=<\/span>'dark'<\/span>,<\/span>)<\/span>\r\nplt.<\/span>show(<\/span>)<\/span>\r\n<\/code><\/pre>\n
    \u51fa\u529b:<\/p>\n
    \"Line<\/div>\n
    \n
    \u7d50\u8ad6<\/h2>\n
    \u3057\u305f\u304c\u3063\u3066\u3001\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001\u30e9\u30a4\u30f3\u30d7\u30ed\u30c3\u30c8\u3068\u305d\u308c\u306b\u95a2\u9023\u3059\u308b\u30d0\u30ea\u30a8\u30fc\u30b7\u30e7\u30f3\u3092\u7406\u89e3\u3057\u307e\u3057\u305f\u3002<\/p>\n
    Python Matplotlib\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3092\u8aad\u3080\u3053\u3068\u3092\u8aad\u8005\u306b\u5f37\u304f\u304a\u3059\u3059\u3081\u3057\u307e\u3059\u3002\u305d\u3046\u3059\u308c\u3070\u3001\u3088\u308a\u826f\u3044\u65b9\u6cd5\u3067\u6298\u308c\u7dda\u30b0\u30e9\u30d5\u3092\u7406\u89e3\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
    \n
    \u53c2\u8003\u6587\u732e<\/h2>\n
      \n
    • Seaborn Line Plot \u2013 Official Documentation<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"
      \u307f\u306a\u3055\u3093\u3001\u3053\u3093\u306b\u3061\u306f\uff01\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001Seaborn\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3092\u9032\u3081\u3066\u3044\u304d\u3001Seaborn\u306e\u6298\u308c\u7dda\u30b0\u30e9\u30d5\u306b\u3064\u3044\u3066\u7406\u89e3\u3057\u3066\u3044\u304d\u307e\u3059\u3002\u6700\u8fd1\u306fSeaborn\u306e\u30d2\u30fc\u30c8\u30de\u30c3\u30d7\u306b\u3064\u3044\u3066\u3082\u53d6\u308a\u4e0a\u3052\u307e\u3057\u305f\u306e\u3067\u3001\u30d2\u30fc\u30c8\u30de\u30c3\u30d7\u306b\u3064\u3044\u3066\u3082 […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[26,61],"class_list":["post-295","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-26","tag-61"],"yoast_head":"\nSeaborn\u306e\u6298\u308c\u7dda\u30b0\u30e9\u30d5\u306b\u3088\u308b\u30c7\u30fc\u30bf\u306e\u8996\u899a\u5316 - 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