{"id":4000,"date":"2023-07-14T05:45:40","date_gmt":"2023-07-14T05:45:40","guid":{"rendered":"https:\/\/statorials.org\/ja\/seaborn-%e3%83%95%e3%82%9a%e3%83%ad%e3%83%83%e3%83%88%e6%95%b0%e8%a1%8c\/"},"modified":"2023-07-14T05:45:40","modified_gmt":"2023-07-14T05:45:40","slug":"seaborn-%e3%83%95%e3%82%9a%e3%83%ad%e3%83%83%e3%83%88%e6%95%b0%e8%a1%8c","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/seaborn-%e3%83%95%e3%82%9a%e3%83%ad%e3%83%83%e3%83%88%e6%95%b0%e8%a1%8c\/","title":{"rendered":"Seaborn \u3067\u8907\u6570\u306e\u7dda\u3092\u63cf\u304f\u65b9\u6cd5 (\u4f8b\u4ed8\u304d)"},"content":{"rendered":"

<\/p>\n

\n

Python \u3067 seaborn \u3092\u4f7f\u7528\u3059\u308b\u3068\u3001\u6b21\u306e\u57fa\u672c\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001\u540c\u3058\u30d7\u30ed\u30c3\u30c8\u4e0a\u306b\u8907\u6570\u306e\u7dda\u3092\u63cf\u753b\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n

 import<\/span> seaborn as<\/span> sns\n\nsns. lineplot<\/span> (data=df[[' col1<\/span> ', ' col2<\/span> ', ' col3<\/span> ']]\n<\/b><\/pre>\n
\u3053\u306e\u7279\u5b9a\u306e\u4f8b\u3067\u306f\u30013 \u3064\u306e\u7570\u306a\u308b\u7dda\u304b\u3089\u306a\u308b\u30d1\u30b9\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u6b21\u306e\u4f8b\u306f\u3001\u3053\u306e\u69cb\u6587\u3092\u5b9f\u969b\u306b\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
\u4f8b: Seaborn \u3067\u8907\u6570\u306e\u7dda\u3092\u63cf\u753b\u3059\u308b<\/strong><\/span><\/h2>\n
4 \u3064\u306e\u7570\u306a\u308b\u5c0f\u58f2\u5e97 (A\u3001B\u3001C\u3001D) \u306b\u3088\u308b 8 \u5e74\u9593\u9023\u7d9a\u306e\u58f2\u4e0a\u306b\u95a2\u3059\u308b\u60c5\u5831\u3092\u542b\u3080\u6b21\u306e\u30d1\u30f3\u30c0 \u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u304c\u3042\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' year<\/span> ': [1, 2, 3, 4, 5, 6, 7, 8],\n                   ' A<\/span> ': [10, 12, 14, 15, 15, 14, 13, 18],\n                   ' B<\/span> ': [18, 18, 19, 14, 14, 11, 20, 28],\n                   ' C<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' D<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n   year ABCD\n0 1 10 18 5 11\n1 2 12 18 7 8\n2 3 14 19 7 10\n3 4 15 14 9 6\n4 5 15 14 12 6\n5 6 14 11 9 5\n6 7 13 20 9 9\n7 8 18 28 4 12\n<\/span><\/span><\/strong><\/pre>\n
 Seaborn \u306elineplot()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u5404\u5e74\u306e\u5404\u5e97\u8217\u306e\u58f2\u4e0a\u3092\u8868\u3059 4 \u672c\u306e\u7dda\u3092\u8868\u793a\u3059\u308b\u30d7\u30ed\u30c3\u30c8\u3092\u4f5c\u6210\u3067\u304d\u307e\u3059\u3002<\/span> <\/p>\n
 import<\/span> seaborn as<\/span> sns\n\n#plot sales of each store as a line\n<\/span>sns. lineplot<\/span> (data=df[[' A<\/span> ',' B<\/span> ',' C<\/span> ',' D<\/span> ']])\n<\/strong><\/pre>\n
\"\u8907\u6570\u884c\u306e\u6d77\u6d0b\u30d7\u30ed\u30c3\u30c8\"<\/p>\n
\u5404\u884c\u306f\u30014 \u3064\u306e\u30b9\u30c8\u30a2\u306e\u3046\u3061 1 \u3064\u306e\u5024\u3092\u8868\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u5de6\u4e0a\u9685\u306e\u51e1\u4f8b\u306f\u3001\u3069\u306e\u8272\u304c\u3069\u306e\u30b9\u30c8\u30a2\u306b\u5bfe\u5fdc\u3059\u308b\u304b\u3092\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u30d1\u30ec\u30c3\u30c8<\/strong>\u5f15\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u30d7\u30ed\u30c3\u30c8\u3067\u4f7f\u7528\u3059\u308b\u72ec\u81ea\u306e\u8272\u3092\u6307\u5b9a\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span> <\/p>\n
 import<\/span> seaborn as<\/span> sns\n\n#plot sales of each store with custom colors\n<\/span>sns. lineplot<\/span> (data=df[[' A<\/span> ', ' B<\/span> ', ' C<\/span> ', ' D<\/span> ']], palette=[' red<\/span> ', ' blue<\/span> ', ' purple<\/span> ', ' pink<\/span> '])<\/strong> <\/pre>\n
\"\"<\/p>\n
\u884c\u306e\u8272\u304c\u3001\u30d1\u30ec\u30c3\u30c8<\/strong>\u5f15\u6570\u3067\u6307\u5b9a\u3057\u305f 4 \u3064\u306e\u8272\u3068\u4e00\u81f4\u3059\u308b\u3053\u3068\u306b\u6ce8\u76ee\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span><\/p>\n
\u5404\u7dda\u3092\u5b9f\u7dda\u306b\u3057\u305f\u3044\u5834\u5408\u306f\u3001pandas Melt()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066 DataFrame \u3092\u9577\u3044\u5f62\u5f0f<\/a>\u306b\u6eb6\u304b\u3057\u3001\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u7dda\u3092\u63cf\u753b\u3057\u307e\u3059\u3002<\/span> <\/p>\n
 import<\/span> seaborn as<\/span> sns\n\n#plot sales of each store with custom colors\n<\/span>sns. lineplot<\/span> (x=' year<\/span> ', y=' value<\/span> ', hue=' variable<\/span> ', \n             data=pd. melt<\/span> (df,[' year<\/span> ']),\n             palette=[' red<\/span> ', ' blue<\/span> ', ' purple<\/span> ', ' pink<\/span> '])\n<\/strong><\/pre>\n
\"\"<\/p>\n
\u5404\u7dda\u306f\u3001\u72ec\u81ea\u306e\u7dda\u30b9\u30bf\u30a4\u30eb\u3092\u6301\u3064\u306e\u3067\u306f\u306a\u304f\u3001\u5b9f\u7dda\u306b\u306a\u308a\u307e\u3057\u305f\u3002<\/span><\/p>\n
\u6ce8<\/strong>: Seaborn \u3092 Jupyter \u30ce\u30fc\u30c8\u30d6\u30c3\u30af\u306b\u30a4\u30f3\u30dd\u30fc\u30c8\u3059\u308b\u306e\u304c\u96e3\u3057\u3044\u5834\u5408\u306f\u3001\u6700\u521d\u306b%pip install seaborn<\/strong>\u30b3\u30de\u30f3\u30c9\u3092\u5b9f\u884c\u3059\u308b\u5fc5\u8981\u304c\u3042\u308b\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n
\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h2>\n
\u6b21\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Seaborn \u3067\u4ed6\u306e\u4e00\u822c\u7684\u306a\u30bf\u30b9\u30af\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n
 Seaborn \u30d7\u30ed\u30c3\u30c8\u306b\u30bf\u30a4\u30c8\u30eb\u3092\u8ffd\u52a0\u3059\u308b\u65b9\u6cd5<\/a>
Seaborn \u30d7\u30ed\u30c3\u30c8\u306e\u30d5\u30a9\u30f3\u30c8 \u30b5\u30a4\u30ba\u3092\u5909\u66f4\u3059\u308b\u65b9\u6cd5<\/a>
Seaborn \u30d7\u30ed\u30c3\u30c8\u306e\u56f3\u306e\u30b5\u30a4\u30ba\u3092\u8abf\u6574\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
Python \u3067 seaborn \u3092\u4f7f\u7528\u3059\u308b\u3068\u3001\u6b21\u306e\u57fa\u672c\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001\u540c\u3058\u30d7\u30ed\u30c3\u30c8\u4e0a\u306b\u8907\u6570\u306e\u7dda\u3092\u63cf\u753b\u3067\u304d\u307e\u3059\u3002 import seaborn as sns sns. lineplot (data=df[[‘ col1  […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[],"class_list":["post-4000","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"\nSeaborn \u3067\u8907\u6570\u306e\u7dda\u3092\u63cf\u304f\u65b9\u6cd5 (\u4f8b\u4ed8\u304d) - \u8a18\u4e8b<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Seaborn \u3067 1 \u3064\u306e\u30d1\u30b9\u306b\u8907\u6570\u306e\u7dda\u3092\u63cf\u753b\u3059\u308b\u65b9\u6cd5\u3092\u4f8b\u3092\u793a\u3057\u3066\u8aac\u660e\u3057\u307e\u3059\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\/ja\/seaborn-\u30d5\u309a\u30ed\u30c3\u30c8\u6570\u884c\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Seaborn \u3067\u8907\u6570\u306e\u7dda\u3092\u63cf\u304f\u65b9\u6cd5 (\u4f8b\u4ed8\u304d) - 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