{"id":4205,"date":"2023-07-12T20:21:46","date_gmt":"2023-07-12T20:21:46","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%8d%e3%82%b9%e3%83%88%e3%81%95%e3%82%8c%e3%81%9f%e3%83%86%e3%82%99%e3%83%bc%e3%82%bf%e3%83%95%e3%83%ac%e3%83%bc%e3%83%a0\/"},"modified":"2023-07-12T20:21:46","modified_gmt":"2023-07-12T20:21:46","slug":"%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%ae%e3%83%8d%e3%82%b9%e3%83%88%e3%81%95%e3%82%8c%e3%81%9f%e3%83%86%e3%82%99%e3%83%bc%e3%82%bf%e3%83%95%e3%83%ac%e3%83%bc%e3%83%a0","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%8d%e3%82%b9%e3%83%88%e3%81%95%e3%82%8c%e3%81%9f%e3%83%86%e3%82%99%e3%83%bc%e3%82%bf%e3%83%95%e3%83%ac%e3%83%bc%e3%83%a0\/","title":{"rendered":"Pandas \u3067\u30cd\u30b9\u30c8\u3055\u308c\u305f dataframe \u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5 (\u4f8b\u4ed8\u304d)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001\u8907\u6570\u306e\u30d1\u30f3\u30c0 \u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u5225\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u5185\u306b\u30cd\u30b9\u30c8\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df_all = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">idx<\/span> ':[1,2,3],' <span style=\"color: #ff0000;\">dfs<\/span> ':[df1,df2,df3]})\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u3053\u306e\u7279\u5b9a\u306e\u4f8b\u3067\u306f\u30013 \u3064\u306e DataFrame ( <strong>df1<\/strong> \u3001 <strong>df2<\/strong> \u3001 <strong>df3<\/strong> ) \u3092<strong>df_all<\/strong>\u3068\u3044\u3046\u3088\u308a\u5927\u304d\u306a DataFrame \u306b\u30cd\u30b9\u30c8\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306b\u3001\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001\u7279\u5b9a\u306e\u30cd\u30b9\u30c8\u3055\u308c\u305f DataFrame \u306e 1 \u3064\u306b\u30a2\u30af\u30bb\u30b9\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#display first nested DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df_all[' <span style=\"color: #ff0000;\">dfs<\/span> ']. <span style=\"color: #3366ff;\">iloc<\/span> [0])<\/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<h2><span style=\"color: #000000;\"><strong>\u4f8b: Pandas \u3067\u30cd\u30b9\u30c8\u3055\u308c\u305f DataFrame \u3092\u4f5c\u6210\u3059\u308b<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">3 \u3064\u306e\u30d1\u30f3\u30c0 DataFrame \u304c\u3042\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#create first DataFrame\n<\/span>df1 = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">item<\/span> ': ['A', 'B', 'C', 'D', 'E'],\n                    ' <span style=\"color: #ff0000;\">sales<\/span> ': [18, 22, 19, 14, 30]})\n\n<span style=\"color: #008000;\">print<\/span> (df1)\n\n  item sales\n0 to 18\n1 B 22\n2 C 19\n3 D 14\n4 E 30\n\n<span style=\"color: #008080;\">#create second DataFrame\n<\/span>df2 = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">item<\/span> ': ['F', 'G', 'H', 'I', 'J'],\n                    ' <span style=\"color: #ff0000;\">sales<\/span> ': [10, 12, 13, 13, 19]})\n\n<span style=\"color: #008000;\">print<\/span> (df2)\n\n  item sales\n0 F 10\n1 G 12\n2:13 a.m.\n3 I 13\n4 Day 19\n\n<span style=\"color: #008080;\">#create third DataFrame\n<\/span>df3 = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">item<\/span> ': ['K', 'L', 'M', 'N', 'O'],\n                    ' <span style=\"color: #ff0000;\">sales<\/span> ': [41, 22, 28, 25, 18]})\n\n<span style=\"color: #008000;\">print<\/span> (df3)\n\n  item sales\n0 K 41\n1 L 22\n2 M 28\n3 N 25\n4 O 18\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u3053\u3053\u3067\u3001\u3053\u308c\u3089 3 \u3064\u306e DataFrame \u3092\u4fdd\u6301\u3059\u308b\u305f\u3081\u306e\u5927\u304d\u306a DataFrame \u3092\u4f5c\u6210\u3059\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u308c\u3092\u884c\u3046\u306b\u306f\u3001\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df_all = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">idx<\/span> ':[1,2,3],' <span style=\"color: #ff0000;\">dfs<\/span> ':[df1,df2,df3]})\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6b21\u306b\u3001pandas <strong>iloc<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u7279\u5b9a\u306e\u30cd\u30b9\u30c8\u3055\u308c\u305f DataFrame \u306b\u30a2\u30af\u30bb\u30b9\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u305f\u3068\u3048\u3070\u3001\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001\u6700\u521d\u306e\u30cd\u30b9\u30c8\u3055\u308c\u305f DataFrame \u306b\u30a2\u30af\u30bb\u30b9\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#display first nested DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df_all[' <span style=\"color: #ff0000;\">dfs<\/span> ']. <span style=\"color: #3366ff;\">iloc<\/span> [0])\n\n  item sales\n0 to 18\n1 B 22\n2 C 19\n3 D 14\n4 E 30\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u307e\u305f\u306f\u3001\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066 2 \u756a\u76ee\u306e\u30cd\u30b9\u30c8\u3055\u308c\u305f DataFrame \u306b\u30a2\u30af\u30bb\u30b9\u3059\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;\">#display second nested DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df_all[' <span style=\"color: #ff0000;\">dfs<\/span> ']. <span style=\"color: #3366ff;\">iloc<\/span> [1])\n\n  item sales\n0 F 10\n1 G 12\n2:13 a.m.\n3 I 13\n4 Day 19<\/strong><\/pre>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\">\u7b49\u3005\u3002<\/span><\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h2>\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\u6a5f\u80fd\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\u306f\u30a4\u30f3\u30c6\u3099\u30c3\u30af\u30b9\u3092\u5217\u306b\u5909\u63db\u3057\u307e\u3059\/\" target=\"_blank\" rel=\"noopener\">Pandas\u3067\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u3092\u5217\u306b\u5909\u63db\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u30cf\u309a\u30f3\u30bf\u3099\u30a4\u30f3\u30c6\u3099\u30c3\u30af\u30b9\u306e\u540d\u524d\u3092\u5909\u66f4\u3059\u308b\/\" target=\"_blank\" rel=\"noopener\">Pandas\u3067\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u306e\u540d\u524d\u3092\u5909\u66f4\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u30cf\u309a\u30f3\u30bf\u3099\u306f\u5217\u3092\u30a4\u30f3\u30c6\u3099\u30c3\u30af\u30b9\u3068\u3057\u3066\u8a2d\u5b9a\u3057\u307e\u3059\/\" target=\"_blank\" rel=\"noopener\">Pandas\u3067\u5217\u3092\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u3068\u3057\u3066\u8a2d\u5b9a\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001\u8907\u6570\u306e\u30d1\u30f3\u30c0 \u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u5225\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u5185\u306b\u30cd\u30b9\u30c8\u3067\u304d\u307e\u3059\u3002 df_all = pd. DataFrame ({&#8216; idx &#8216;:[1,2,3],&#8217; dfs &#8216;:[df1,df2,df3]})  [&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-4205","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>Pandas \u3067\u30cd\u30b9\u30c8\u3055\u308c\u305f DataFrame \u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5 (\u4f8b\u4ed8\u304d) - Statorials<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001\u30d1\u30f3\u30c0\u3067\u30cd\u30b9\u30c8\u3055\u308c\u305f DataFrame \u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5\u3068\u3001\u305d\u308c\u306b\u542b\u307e\u308c\u308b DataFrame \u306b\u30a2\u30af\u30bb\u30b9\u3059\u308b\u65b9\u6cd5\u3092\u4f8b\u3092\u542b\u3081\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\u30cd\u30b9\u30c8\u3055\u308c\u305f\u30c6\u3099\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandas \u3067\u30cd\u30b9\u30c8\u3055\u308c\u305f DataFrame \u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5 (\u4f8b\u4ed8\u304d) - 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