{"id":3927,"date":"2023-07-14T16:46:14","date_gmt":"2023-07-14T16:46:14","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e4%b8%b8%e6%9f%b1%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99\/"},"modified":"2023-07-14T16:46:14","modified_gmt":"2023-07-14T16:46:14","slug":"%e4%b8%b8%e6%9f%b1%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/%e4%b8%b8%e6%9f%b1%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99\/","title":{"rendered":"Pandas dataframe \u3067\u5358\u4e00\u306e\u5217\u3092\u4e38\u3081\u308b\u65b9\u6cd5"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u57fa\u672c\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001pandas DataFrame \u306e\u5358\u4e00\u5217\u306e\u5024\u3092\u4e38\u3081\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df. <span style=\"color: #3366ff;\">my_column<\/span> = df. <span style=\"color: #3366ff;\">my_column<\/span> . <span style=\"color: #3366ff;\">round<\/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<h2><span style=\"color: #000000;\"><strong>\u4f8b: Pandas DataFrame \u306e\u5358\u4e00\u5217\u306e\u4e38\u3081<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u3055\u307e\u3056\u307e\u306a\u30a2\u30b9\u30ea\u30fc\u30c8\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<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;\">#createDataFrame\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">athlete<\/span> ': ['A', 'B', 'C', 'D', 'E', 'F'],\n                   ' <span style=\"color: #ff0000;\">time<\/span> ': [12.443, 15.8, 16.009, 5.06, 11.075, 12.9546],\n                   ' <span style=\"color: #ff0000;\">points<\/span> ': [5, 7, 7, 9, 12, 9]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n  athlete time points\n0 A 12.4430 5\n1 B 15.8000 7\n2 C 16.0090 7\n3 D 5.0600 9\n4 E 11.0750 12\n5 F 12.9546 9<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u3092\u4f7f\u7528\u3057\u3066\u3001<strong>\u6642\u9593<\/strong>\u5217\u306e\u5404\u5024\u3092\u6700\u3082\u8fd1\u3044\u6574\u6570\u306b\u4e38\u3081\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#round values in 'time' column of DataFrame\n<\/span>df. <span style=\"color: #3366ff;\">time<\/span> = df. <span style=\"color: #3366ff;\">time<\/span> . <span style=\"color: #3366ff;\">round<\/span> ()\n\n<span style=\"color: #008080;\">#view updated DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n  athlete time points\n0 A 12.0 5\n1 B 16.0 7\n2 C 16.0 7\n3D 5.0 9\n4 E 11.0 12\n5 F 13.0 9\n<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\"><strong>\u6642\u9593<\/strong>\u5217\u306e\u5404\u5024\u306f\u6700\u3082\u8fd1\u3044\u6574\u6570\u306b\u4e38\u3081\u3089\u308c\u3066\u3044\u307e\u3059\u3002<\/span><\/span><\/p>\n<p><span style=\"color: #000000;\">\u4f8b\u3048\u3070\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>12,443 \u306f<\/strong><strong>12<\/strong>\u306b\u56db\u6368\u4e94\u5165\u3055\u308c\u307e\u3057\u305f\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>15.8 \u306f<\/strong><strong>16<\/strong>\u306b\u56db\u6368\u4e94\u5165\u3055\u308c\u307e\u3057\u305f\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>16.009 \u306f<\/strong><strong>16<\/strong>\u306b\u56db\u6368\u4e94\u5165\u3055\u308c\u307e\u3057\u305f\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u7b49\u3005\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5217\u306e\u5024\u3092\u7279\u5b9a\u306e\u5c0f\u6570\u70b9\u4ee5\u4e0b\u306e\u6841\u6570\u306b\u4e38\u3081\u308b\u306b\u306f\u3001 <strong>round()<\/strong>\u95a2\u6570\u3067\u305d\u306e\u5024\u3092\u6307\u5b9a\u3059\u308b\u3060\u3051\u3067\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u305f\u3068\u3048\u3070\u3001\u6b21\u306e\u30b3\u30fc\u30c9\u3092\u4f7f\u7528\u3057\u3066\u3001 <strong>time<\/strong>\u5217\u306e\u5404\u5024\u3092\u5c0f\u6570\u70b9\u7b2c 2 \u4f4d\u306b\u56db\u6368\u4e94\u5165\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#round values in 'time' column to two decimal places\n<\/span>df. <span style=\"color: #3366ff;\">time<\/span> = df. <span style=\"color: #3366ff;\">time<\/span> . <span style=\"color: #3366ff;\">round<\/span> ( <span style=\"color: #008000;\">2<\/span> )\n\n<span style=\"color: #008080;\">#view updated DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n  athlete time points\n0 A 12.44 5\n1 B 15.80 7\n2 C 16.01 7\n3 D 5.06 9\n4 E 11.08 12\n5 F 12.95 9<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\"><strong>\u6642\u9593<\/strong>\u5217\u306e\u5404\u5024\u306f\u5c0f\u6570\u70b9\u7b2c 2 \u4f4d\u306b\u56db\u6368\u4e94\u5165\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4f8b\u3048\u3070\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>12.443 \u306f<\/strong><strong>12.44<\/strong>\u306b\u56db\u6368\u4e94\u5165\u3055\u308c\u307e\u3057\u305f\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>15.8 \u306f<\/strong><strong>15.80<\/strong>\u306b\u56db\u6368\u4e94\u5165\u3055\u308c\u307e\u3057\u305f\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>16.009 \u306f<\/strong><strong>1601<\/strong>\u306b\u56db\u6368\u4e94\u5165\u3055\u308c\u307e\u3057\u305f\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u7b49\u3005\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u307e\u305f\u3001\u4ed6\u306e\u6570\u5024\u5217\u306e\u5024\u3001 <strong>Points<\/strong>\u304c\u5909\u66f4\u3055\u308c\u3066\u3044\u306a\u3044\u3053\u3068\u306b\u3082\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/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\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\/\u30a4\u30f3\u30c6\u3099\u30c3\u30af\u30b9\u306e\u306a\u3044\u30cf\u309a\u30f3\u30bf\u3099\/\" target=\"_blank\" rel=\"noopener\">\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u306a\u3057\u3067 Pandas DataFrame \u3092\u5370\u5237\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u30cf\u309a\u30f3\u30bf\u3099\u306f\u3059\u3078\u3099\u3066\u306e\u884c\u3092\u8868\u793a\u3057\u307e\u3059\/\" target=\"_blank\" rel=\"noopener\">Pandas DataFrame \u306e\u3059\u3079\u3066\u306e\u884c\u3092\u8868\u793a\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u30cf\u309a\u30f3\u30bf\u3099\u306f\u3059\u3078\u3099\u3066\u306e\u5217\u306e-dtype-\u3092\u30c1\u30a7\u30c3\u30af\u3057\u307e\u3059\/\" target=\"_blank\" rel=\"noopener\">Pandas DataFrame \u306e\u3059\u3079\u3066\u306e\u5217\u306e\u578b\u3092\u78ba\u8a8d\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u6b21\u306e\u57fa\u672c\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001pandas DataFrame \u306e\u5358\u4e00\u5217\u306e\u5024\u3092\u4e38\u3081\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002 df. my_column = df. my_column . round () \u6b21\u306e\u4f8b\u306f\u3001\u3053\u306e\u69cb\u6587\u3092\u5b9f\u969b\u306b\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u793a [&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-3927","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 DataFrame \u3067\u5358\u4e00\u306e\u5217\u3092\u4e38\u3081\u308b\u65b9\u6cd5 \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001pandas DataFrame \u306e\u5217\u306e\u5024\u3092\u4e38\u3081\u308b\u65b9\u6cd5\u3092\u4f8b\u3092\u6319\u3052\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\" 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