{"id":2912,"date":"2023-07-20T02:53:55","date_gmt":"2023-07-20T02:53:55","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%ae%e3%83%a9%e3%83%b3%e3%82%af%e5%88%a5%e3%82%af%e3%82%99%e3%83%ab%e3%83%bc%e3%83%95%e3%82%9a%e5%8c%96\/"},"modified":"2023-07-20T02:53:55","modified_gmt":"2023-07-20T02:53:55","slug":"%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%ae%e3%83%a9%e3%83%b3%e3%82%af%e5%88%a5%e3%82%af%e3%82%99%e3%83%ab%e3%83%bc%e3%83%95%e3%82%9a%e5%8c%96","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%ae%e3%83%a9%e3%83%b3%e3%82%af%e5%88%a5%e3%82%af%e3%82%99%e3%83%ab%e3%83%bc%e3%83%95%e3%82%9a%e5%8c%96\/","title":{"rendered":"Pandas: groupby \u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306e\u30e9\u30f3\u30ad\u30f3\u30b0\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001\u30d1\u30f3\u30c0\u306e GroupBy \u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u5185\u306e\u5024\u306e\u9806\u5e8f\u3092\u8a08\u7b97\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df[' <span style=\"color: #ff0000;\">rank<\/span> '] = df. <span style=\"color: #3366ff;\">groupby<\/span> ([' <span style=\"color: #ff0000;\">group_var<\/span> '])[' <span style=\"color: #ff0000;\">value_var<\/span> ']. <span style=\"color: #3366ff;\">rank<\/span> ()\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\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<h3><span style=\"color: #000000;\"><strong>\u4f8b: GroupBy \u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3067\u306e\u30e9\u30f3\u30ad\u30f3\u30b0\u306e\u8a08\u7b97<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u3055\u307e\u3056\u307e\u306a\u30c1\u30fc\u30e0\u306e\u30d0\u30b9\u30b1\u30c3\u30c8\u30dc\u30fc\u30eb\u9078\u624b\u304c\u7372\u5f97\u3057\u305f\u30dd\u30a4\u30f3\u30c8\u3092\u793a\u3059\u6b21\u306e\u30d1\u30f3\u30c0 \u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u304c\u3042\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#createDataFrame\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">team<\/span> ': ['A', 'A', 'A', 'A', 'B', 'B', 'C', 'C', 'C'],\n                   ' <span style=\"color: #ff0000;\">points<\/span> ': [10, 10, 12, 15, 19, 23, 20, 20, 26]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n  team points\n0 to 10\n1 to 10\n2 to 12\n3 to 15\n4 B 19\n5 B 23\n6 C 20\n7 C 20\n8 C 26<\/strong><\/span><\/pre>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001\u5404\u30c1\u30fc\u30e0\u306e\u30dd\u30a4\u30f3\u30c8\u5024\u306e\u30e9\u30f3\u30ad\u30f3\u30b0\u3092\u8a08\u7b97\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#add ranking column to data frame\n<span style=\"color: #000000;\">df[' <span style=\"color: #ff0000;\">points_rank<\/span> '] = df. <span style=\"color: #3366ff;\">groupby<\/span> ([' <span style=\"color: #ff0000;\">team<\/span> '])[' <span style=\"color: #ff0000;\">points<\/span> ']. <span style=\"color: #3366ff;\">rank<\/span> ()\n\n<span style=\"color: #008080;\">#view updated DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n  team points points_rank\n0 to 10 1.5\n1 to 10 1.5\n2 to 12 3.0\n3 to 15 4.0\n4 B 19 1.0\n5 B 23 2.0\n6 C 20 1.5\n7 C 20 1.5\n8 C 26 3.0<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u30c7\u30d5\u30a9\u30eb\u30c8\u3067\u306f\u3001 <strong>Rank()<\/strong>\u95a2\u6570\u306f\u6607\u9806\u3067\u30e9\u30f3\u30af\u5024\u3092\u5272\u308a\u5f53\u3066\u3001\u540c\u70b9\u306e\u5834\u5408\u306f\u5e73\u5747\u30e9\u30f3\u30af\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u305f\u3060\u3057\u3001<strong>\u30dc\u30c8\u30e0\u30a2\u30c3\u30d7<\/strong><strong>\u30e1\u30bd\u30c3\u30c9<\/strong>\u3068\u5f15\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u5225\u306e\u65b9\u6cd5\u3067\u5024\u3092\u4e26\u3079\u66ff\u3048\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#add ranking column to data frame\n<span style=\"color: #000000;\">df[' <span style=\"color: #ff0000;\">points_rank<\/span> '] = df. <span style=\"color: #3366ff;\">groupby<\/span> ([' <span style=\"color: #ff0000;\">team<\/span> '])[' <span style=\"color: #ff0000;\">points<\/span> ']. <span style=\"color: #3366ff;\">rank<\/span> (' <span style=\"color: #ff0000;\">dense<\/span> ', ascending= <span style=\"color: #008000;\">False<\/span> )\n\n<span style=\"color: #008080;\">#view updated DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n  team points points_rank\n0 to 10 3.0\n1 to 10 3.0\n2 to 12 2.0\n3 to 15 1.0\n4 B 19 2.0\n5 B 23 1.0\n6 C 20 2.0\n7 C 20 2.0\n8 C 26 1.0\n<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u3053\u306e\u30e1\u30bd\u30c3\u30c9\u306f\u3001\u5404\u30b0\u30eb\u30fc\u30d7\u306e\u6700\u5927\u5024\u306b\u5024 1 \u3092\u5272\u308a\u5f53\u3066\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>Rank()<\/strong>\u95a2\u6570\u3067\u4f7f\u7528\u3067\u304d\u308b\u30e9\u30f3\u30ad\u30f3\u30b0\u65b9\u6cd5\u306e\u5b8c\u5168\u306a\u30ea\u30b9\u30c8\u306f<a href=\"https:\/\/pandas.pydata.org\/pandas-docs\/version\/0.17.0\/generated\/pandas.core.groupby.DataFrameGroupBy.rank.html\" target=\"_blank\" rel=\"noopener\">\u3001\u3053\u3053\u3067<\/a>\u898b\u3064\u3051\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u6ce8<\/strong>: pandas \u3067\u306e GroupBy \u64cd\u4f5c\u306e\u5b8c\u5168\u306a\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u306f<a href=\"https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.groupby.html\" target=\"_blank\" rel=\"noopener\">\u3053\u3053\u3067<\/a>\u898b\u3064\u3051\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001\u30d1\u30f3\u30c0\u3067\u4ed6\u306e\u4e00\u822c\u7684\u306a\u64cd\u4f5c\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/ja\/\u30cf\u309a\u30f3\u30bf\u3099\u306e\u30af\u3099\u30eb\u30fc\u30d5\u309a\u3053\u3099\u3068\u306e\u7d2f\u7a4d\u5408\u8a08\/\" target=\"_blank\" rel=\"noopener\">Pandas: \u30b0\u30eb\u30fc\u30d7\u3054\u3068\u306e\u7d2f\u7a4d\u5408\u8a08\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/pandas-groupby-\u30ab\u30a6\u30f3\u30c8\u306f\u4e00\u610f\u3066\u3099\u3059\/\" target=\"_blank\" rel=\"noopener\">\u30d1\u30f3\u30c0: \u30b0\u30eb\u30fc\u30d7\u3054\u3068\u306b\u4e00\u610f\u306e\u5024\u3092\u6570\u3048\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u30cf\u309a\u30f3\u30bf\u3099\u306egroupby\u76f8\u95a2\/\" target=\"_blank\" rel=\"noopener\">\u30d1\u30f3\u30c0: \u30b0\u30eb\u30fc\u30d7\u3054\u3068\u306e\u76f8\u95a2\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001\u30d1\u30f3\u30c0\u306e GroupBy \u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u5185\u306e\u5024\u306e\u9806\u5e8f\u3092\u8a08\u7b97\u3067\u304d\u307e\u3059\u3002 df[&#8216; rank &#8216;] = df. groupby ([&#8216; group_var &#8216;])[&#8216; value_var &#8216;]. rank  [&hellip;]<\/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-2912","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u30d1\u30f3\u30c0: GroupBy \u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306e\u30e9\u30f3\u30ad\u30f3\u30b0\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5 \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001pandas \u306e GroupBy \u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306e\u30e9\u30f3\u30af\u3092\u8a08\u7b97\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\/\u30cf\u309a\u30f3\u30bf\u3099\u306e\u30e9\u30f3\u30af\u5225\u30af\u3099\u30eb\u30fc\u30d5\u309a\u5316\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u30d1\u30f3\u30c0: GroupBy 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