{"id":4152,"date":"2023-07-13T05:34:42","date_gmt":"2023-07-13T05:34:42","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%af%e6%97%a5%e4%bb%98%e3%82%92%e6%af%94%e8%bc%83%e3%81%97%e3%81%be%e3%81%99\/"},"modified":"2023-07-13T05:34:42","modified_gmt":"2023-07-13T05:34:42","slug":"%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%af%e6%97%a5%e4%bb%98%e3%82%92%e6%af%94%e8%bc%83%e3%81%97%e3%81%be%e3%81%99","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%af%e6%97%a5%e4%bb%98%e3%82%92%e6%af%94%e8%bc%83%e3%81%97%e3%81%be%e3%81%99\/","title":{"rendered":"\u30d1\u30f3\u30c0\u3067\u65e5\u4ed8\u3092\u6bd4\u8f03\u3059\u308b\u65b9\u6cd5: \u4f8b\u4ed8\u304d"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30e1\u30bd\u30c3\u30c9\u3092\u4f7f\u7528\u3057\u3066\u3001pandas DataFrame \u306e 2 \u3064\u306e\u5217\u9593\u306e\u65e5\u4ed8\u3092\u6bd4\u8f03\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u65b9\u6cd5 1: \u65e5\u4ed8\u6bd4\u8f03\u3092\u8868\u793a\u3059\u308b\u65b0\u3057\u3044\u5217\u3092 DataFrame \u306b\u8ffd\u52a0\u3057\u307e\u3059\u3002<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df[' <span style=\"color: #ff0000;\">met_due_date<\/span> '] = df[' <span style=\"color: #ff0000;\">comp_date<\/span> '] <span style=\"color: #800080;\">&lt;<\/span> df[' <span style=\"color: #ff0000;\">due_date<\/span> ']\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u3053\u306e\u7279\u5b9a\u306e\u4f8b\u3067\u306f\u3001 <strong>met_due_date<\/strong>\u3068\u3044\u3046\u65b0\u3057\u3044\u5217\u3092\u8ffd\u52a0\u3057\u307e\u3059\u3002\u3053\u306e\u5217\u306f\u3001 <strong>comp_date<\/strong>\u5217\u306e\u65e5\u4ed8\u304c<strong>due_date<\/strong>\u5217\u306e\u65e5\u4ed8\u3088\u308a\u524d\u304b\u3069\u3046\u304b\u306b\u5fdc\u3058\u3066 True \u307e\u305f\u306f False \u3092\u8fd4\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u65b9\u6cd5 2: \u65e5\u4ed8\u6bd4\u8f03\u306b\u57fa\u3065\u3044\u3066 DataFrame \u3092\u30d5\u30a3\u30eb\u30bf\u30fc\u3059\u308b<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df_met_due_date = df[df[' <span style=\"color: #ff0000;\">comp_date<\/span> '] <span style=\"color: #800080;\">&lt;<\/span> df[' <span style=\"color: #ff0000;\">due_date<\/span> ']]<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u3053\u306e\u7279\u5b9a\u306e\u4f8b\u3067\u306f\u3001DataFrame \u3092\u30d5\u30a3\u30eb\u30bf\u30fc\u51e6\u7406\u3057\u3066\u3001 <strong>comp_date<\/strong>\u5217\u306e\u65e5\u4ed8\u304c<strong>due_date<\/strong>\u5217\u306e\u65e5\u4ed8\u3088\u308a\u524d\u306e\u884c\u306e\u307f\u3092\u4fdd\u6301\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u4f8b\u306f\u3001\u6b21\u306e pandas DataFrame \u3067\u3053\u308c\u3089\u306e\u5404\u30e1\u30bd\u30c3\u30c9\u3092\u5b9f\u969b\u306b\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\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 style=\"color: #000000;\">df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">task<\/span> ': ['A', 'B', 'C', 'D'],\n                   ' <span style=\"color: #ff0000;\">due_date<\/span> ': ['4-15-2022', '5-19-2022', '6-14-2022', '10-24-2022'],\n                   ' <span style=\"color: #ff0000;\">comp_date<\/span> ': ['4-14-2022', '5-23-2022', '6-24-2022', '10-7-2022']})\n\n<span style=\"color: #008080;\">#convert due_date and comp_date columns to datetime format\n<\/span>df[[' <span style=\"color: #ff0000;\">due_date<\/span> ', ' <span style=\"color: #ff0000;\">comp_date<\/span> ']] = df[[' <span style=\"color: #ff0000;\">due_date<\/span> ', ' <span style=\"color: #ff0000;\">comp_date<\/span> ']]. <span style=\"color: #3366ff;\">apply<\/span> (pd. <span style=\"color: #3366ff;\">to_datetime<\/span> )\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n  task due_date comp_date\n0 A 2022-04-15 2022-04-14\n1 B 2022-05-19 2022-05-23\n2 C 2022-06-14 2022-06-24\n3 D 2022-10-24 2022-10-07<\/span><\/span><\/strong><\/pre>\n<h2><span style=\"color: #000000;\"><strong>\u4f8b 1: \u65e5\u4ed8\u6bd4\u8f03\u3092\u8868\u793a\u3059\u308b\u65b0\u3057\u3044\u5217\u3092 DataFrame \u306b\u8ffd\u52a0\u3057\u307e\u3059\u3002<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001 <strong>comp_date<\/strong>\u5217\u306e\u65e5\u4ed8\u304c<strong>due_date<\/strong>\u5217\u306e\u65e5\u4ed8\u3088\u308a\u524d\u3067\u3042\u308b\u304b\u3069\u3046\u304b\u306b\u5fdc\u3058\u3066 True \u307e\u305f\u306f False \u3092\u8fd4\u3059<strong>met_due_date<\/strong>\u3068\u3044\u3046\u65b0\u3057\u3044\u5217\u3092\u8ffd\u52a0\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\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;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#create new column that shows if completion date is before due date\n<\/span>df[' <span style=\"color: #ff0000;\">met_due_date<\/span> '] = df[' <span style=\"color: #ff0000;\">comp_date<\/span> '] <span style=\"color: #800080;\">&lt;<\/span> df[' <span style=\"color: #ff0000;\">due_date<\/span> ']\n\n<span style=\"color: #008080;\">#view updated DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n  task due_date comp_date met_due_date\n0 A 2022-04-15 2022-04-14 True\n1 B 2022-05-19 2022-05-23 False\n2 C 2022-06-14 2022-06-24 False\n3 D 2022-10-24 2022-10-07 True\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">DataFrame \u306e\u5404\u884c\u306b\u3064\u3044\u3066\u3001\u65b0\u3057\u3044<strong>met_due_date<\/strong>\u5217\u306f\u3001 <strong>comp_date<\/strong>\u5217\u306e\u65e5\u4ed8\u304c<strong>due_date<\/strong>\u5217\u306e\u65e5\u4ed8\u3088\u308a\u524d\u3067\u3042\u308b\u304b\u3069\u3046\u304b\u3092\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u305f\u3068\u3048\u3070\u3001\u30bf\u30b9\u30af A \u306e\u671f\u9650\u306f 2022 \u5e74 4 \u6708 15 \u65e5\u3001\u5b8c\u4e86\u65e5\u306f 2022 \u5e74 4 \u6708 14 \u65e5\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5b8c\u4e86\u65e5\u304c\u671f\u9650\u524d\u3067\u3042\u308b\u305f\u3081\u3001 <strong>met_due_date<\/strong>\u5217\u306e\u5024\u306f True \u306b\u306a\u308a\u307e\u3059\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u4f8b 2: \u65e5\u4ed8\u6bd4\u8f03\u306b\u57fa\u3065\u3044\u3066 DataFrame \u3092\u30d5\u30a3\u30eb\u30bf\u30ea\u30f3\u30b0\u3059\u308b<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001 <strong>comp_date<\/strong>\u5217\u306e\u65e5\u4ed8\u304c<strong>due_date<\/strong>\u5217\u306e\u65e5\u4ed8\u3088\u308a\u524d\u306e\u884c\u306e\u307f\u3092\u542b\u3080\u3088\u3046\u306b DataFrame \u3092\u30d5\u30a3\u30eb\u30bf\u30fc\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\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<span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">\n#filter for rows where completion date is before due date\n<\/span>df_met_due_date = df[df[' <span style=\"color: #ff0000;\">comp_date<\/span> '] <span style=\"color: #800080;\">&lt;<\/span> df[' <span style=\"color: #ff0000;\">due_date<\/span> ']]\n\n<span style=\"color: #008080;\">#view results\n<\/span><span style=\"color: #008000;\">print<\/span> (df_met_due_date)\n\n  task due_date comp_date\n0 A 2022-04-15 2022-04-14\n3 D 2022-10-24 2022-10-07<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u65b0\u3057\u3044\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306f\u3001 <strong>comp_date<\/strong>\u5217\u306e\u65e5\u4ed8\u304c<strong>due_date<\/strong>\u5217\u306e\u65e5\u4ed8\u3088\u308a\u524d\u306e\u884c\u306e\u307f\u3092\u542b\u3080\u3088\u3046\u306b\u30d5\u30a3\u30eb\u30bf\u30fc\u51e6\u7406\u3055\u308c\u3066\u3044\u307e\u3059\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\/\u30cf\u309a\u30f3\u30bf\u3099\u306e\u65e5\u4ed8\u7bc4\u56f2\/\" target=\"_blank\" rel=\"noopener\">Pandas \u3067\u65e5\u4ed8\u7bc4\u56f2\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u30cf\u309a\u30f3\u30bf\u3099\u306e\u30bf\u30a4\u30e0\u30b9\u30bf\u30f3\u30d5\u309a\u306e\u65e5\u4ed8\u6642\u523b\/\" target=\"_blank\" rel=\"noopener\">Pandas\u3067\u30bf\u30a4\u30e0\u30b9\u30bf\u30f3\u30d7\u3092\u65e5\u4ed8\/\u6642\u523b\u306b\u5909\u63db\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u30cf\u309a\u30f3\u30bf\u3099\u306e\u65e5\u4ed8\u306e\u9055\u3044\/\" target=\"_blank\" rel=\"noopener\">\u30d1\u30f3\u30c0\u3067 2 \u3064\u306e\u65e5\u4ed8\u306e\u5dee\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u6b21\u306e\u30e1\u30bd\u30c3\u30c9\u3092\u4f7f\u7528\u3057\u3066\u3001pandas DataFrame \u306e 2 \u3064\u306e\u5217\u9593\u306e\u65e5\u4ed8\u3092\u6bd4\u8f03\u3067\u304d\u307e\u3059\u3002 \u65b9\u6cd5 1: \u65e5\u4ed8\u6bd4\u8f03\u3092\u8868\u793a\u3059\u308b\u65b0\u3057\u3044\u5217\u3092 DataFrame \u306b\u8ffd\u52a0\u3057\u307e\u3059\u3002 df[&#8216; met_due_date &#8216;] = [&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-4152","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\u65e5\u4ed8\u3092\u6bd4\u8f03\u3059\u308b\u65b9\u6cd5 (\u4f8b\u4ed8\u304d) - \u7d71\u8a08<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001pandas DataFrame \u3067\u65e5\u4ed8\u3092\u6bd4\u8f03\u3059\u308b\u65b9\u6cd5\u3092\u3044\u304f\u3064\u304b\u306e\u4f8b\u3068\u3068\u3082\u306b\u8aac\u660e\u3057\u307e\u3059\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" 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