{"id":4367,"date":"2023-07-11T15:20:37","date_gmt":"2023-07-11T15:20:37","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%af%e8%a4%87%e6%95%b0%e3%81%ae%e5%88%97%e3%81%ab%e3%81%82%e3%82%8a%e3%81%be%e3%81%99\/"},"modified":"2023-07-11T15:20:37","modified_gmt":"2023-07-11T15:20:37","slug":"%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%af%e8%a4%87%e6%95%b0%e3%81%ae%e5%88%97%e3%81%ab%e3%81%82%e3%82%8a%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%e8%a4%87%e6%95%b0%e3%81%ae%e5%88%97%e3%81%ab%e3%81%82%e3%82%8a%e3%81%be%e3%81%99\/","title":{"rendered":"\u30d1\u30f3\u30c0: \u8907\u6570\u306e\u5217\u306b isin \u3092\u4f7f\u7528\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">pandas <strong>isin()<\/strong>\u95a2\u6570\u3067\u6b21\u306e\u30e1\u30bd\u30c3\u30c9\u3092\u4f7f\u7528\u3059\u308b\u3068\u3001pandas DataFrame \u306e\u8907\u6570\u306e\u5217\u306b\u57fa\u3065\u3044\u3066\u30d5\u30a3\u30eb\u30bf\u30ea\u30f3\u30b0\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u65b9\u6cd5 1: \u8907\u6570\u306e\u5217\u304c\u7279\u5b9a\u306e\u5024\u3068\u7b49\u3057\u3044\u5834\u5408\u306b\u30d5\u30a3\u30eb\u30bf\u30fc\u3059\u308b<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df = df[df[[' <span style=\"color: #ff0000;\">team<\/span> ', ' <span style=\"color: #ff0000;\">position<\/span> ']]. <span style=\"color: #3366ff;\">isin<\/span> ([' <span style=\"color: #ff0000;\">A<\/span> ',' <span style=\"color: #ff0000;\">Guard<\/span> ']). <span style=\"color: #3366ff;\">all<\/span> (axis= <span style=\"color: #008000;\">1<\/span> )]\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u3053\u306e\u7279\u5b9a\u306e\u4f8b\u3067\u306f\u3001<strong>\u30c1\u30fc\u30e0<\/strong>\u5217\u304c\u300cA\u300d\u306b\u7b49\u3057\u304f<b>\u3001<\/b><strong>\u30dd\u30b8\u30b7\u30e7\u30f3<\/strong>\u5217\u304c\u300c\u30ac\u30fc\u30c9\u300d\u306b\u7b49\u3057\u3044\u884c\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u30d5\u30a3\u30eb\u30bf\u30fc\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u65b9\u6cd5 2: \u5c11\u306a\u304f\u3068\u3082 1 \u3064\u306e\u5217\u304c\u7279\u5b9a\u306e\u5024\u3068\u7b49\u3057\u3044\u30d5\u30a3\u30eb\u30bf\u30fc<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df = df[df[[' <span style=\"color: #ff0000;\">team<\/span> ', ' <span style=\"color: #ff0000;\">position<\/span> ']]. <span style=\"color: #3366ff;\">isin<\/span> ([' <span style=\"color: #ff0000;\">A<\/span> ',' <span style=\"color: #ff0000;\">Guard<\/span> ']). <span style=\"color: #3366ff;\">any<\/span> (axis= <span style=\"color: #008000;\">1<\/span> )]<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u3053\u306e\u7279\u5b9a\u306e\u4f8b\u3067\u306f\u3001<strong>\u30c1\u30fc\u30e0<\/strong>\u5217\u304c\u300cA\u300d<b>\u307e\u305f\u306f<\/b><strong>\u30dd\u30b8\u30b7\u30e7\u30f3<\/strong>\u5217\u304c\u300cGuard\u300d\u306b\u7b49\u3057\u3044\u884c\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u30d5\u30a3\u30eb\u30bf\u30fc\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u4f8b\u306f\u3001\u6b21\u306e pandas DataFrame \u3067\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>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">team<\/span> ': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],\n                   ' <span style=\"color: #ff0000;\">position<\/span> ': ['Guard', 'Guard', 'Forward', 'Forward',\n                                'Guard', 'Guard', 'Forward', 'Forward'],\n                   ' <span style=\"color: #ff0000;\">points<\/span> ': [11, 18, 10, 22, 26, 35, 19, 12]})\n                   \n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n  team position points\n0 A Guard 11\n1 A Guard 18\n2 A Forward 10\n3 A Forward 22\n4 B Guard 26\n5 B Guard 35\n6 B Forward 19\n7 B Forward 12<\/strong><\/pre>\n<h2><span style=\"color: #000000;\"><strong>\u4f8b 1: \u8907\u6570\u306e\u5217\u304c\u7279\u5b9a\u306e\u5024\u3068\u7b49\u3057\u3044\u5834\u5408\u306e\u30d5\u30a3\u30eb\u30bf\u30fc<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u30d5\u30a3\u30eb\u30bf\u30ea\u30f3\u30b0\u3057\u3066\u3001<strong>\u30c1\u30fc\u30e0<\/strong>\u5217\u304c\u300cA\u300d\u306b\u7b49\u3057\u304f<b>\u3001<\/b><strong>\u30dd\u30b8\u30b7\u30e7\u30f3<\/strong>\u5217\u304c\u300cGuard\u300d\u306b\u7b49\u3057\u3044\u884c\u306e\u307f\u3092\u542b\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;\">#filter rows where team column is 'A' and position column is 'Guard'<\/span>\ndf = df[df[[' <span style=\"color: #ff0000;\">team<\/span> ', ' <span style=\"color: #ff0000;\">position<\/span> ']]. <span style=\"color: #3366ff;\">isin<\/span> ([' <span style=\"color: #ff0000;\">A<\/span> ',' <span style=\"color: #ff0000;\">Guard<\/span> ']). <span style=\"color: #3366ff;\">all<\/span> (axis= <span style=\"color: #008000;\">1<\/span> )]\n\n<span style=\"color: #008080;\">#view filtered DataFrame<\/span>\n<span style=\"color: #008000;\">print<\/span> (df)\n\n  team position points\n0 A Guard 11\n1 A Guard 18\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u30d5\u30a3\u30eb\u30bf\u30fc\u3055\u308c\u305f\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u306f\u3001<strong>\u30c1\u30fc\u30e0<\/strong>\u5217\u304c\u300cA\u300d\u306b\u7b49\u3057\u304f<b>\u3001<\/b><strong>\u30dd\u30b8\u30b7\u30e7\u30f3<\/strong>\u5217\u304c\u300cGuard\u300d\u306b\u7b49\u3057\u3044\u884c\u306e\u307f\u304c\u6b8b\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u4f8b 2: \u5c11\u306a\u304f\u3068\u3082 1 \u3064\u306e\u5217\u304c\u7279\u5b9a\u306e\u5024\u3068\u7b49\u3057\u3044\u30d5\u30a3\u30eb\u30bf\u30fc<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001<strong>\u30c1\u30fc\u30e0<\/strong>\u5217\u304c\u300cA\u300d\u306b\u7b49\u3057\u3044\u884c<b>\u3001\u307e\u305f\u306f<\/b><strong>\u30dd\u30b8\u30b7\u30e7\u30f3<\/strong>\u5217\u304c\u300c\u30ac\u30fc\u30c9\u300d\u306b\u7b49\u3057\u3044\u884c\u306e\u307f\u3092\u542b\u3080\u3088\u3046\u306b\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u30d5\u30a3\u30eb\u30bf\u30ea\u30f3\u30b0\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#filter rows where team column is 'A' or position column is 'Guard'<\/span>\ndf = df[df[[' <span style=\"color: #ff0000;\">team<\/span> ', ' <span style=\"color: #ff0000;\">position<\/span> ']]. <span style=\"color: #3366ff;\">isin<\/span> ([' <span style=\"color: #ff0000;\">A<\/span> ',' <span style=\"color: #ff0000;\">Guard<\/span> ']). <span style=\"color: #3366ff;\">any<\/span> (axis= <span style=\"color: #008000;\">1<\/span> )]\n\n<span style=\"color: #008080;\">#view filtered DataFrame<\/span>\n<span style=\"color: #008000;\">print<\/span> (df)\n\n  team position points\n0 A Guard 11\n1 A Guard 18\n2 A Forward 10\n3 A Forward 22\n4 B Guard 26\n5 B Guard 35\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u30d5\u30a3\u30eb\u30bf\u30fc\u3055\u308c\u305f\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u306f\u3001<strong>\u30c1\u30fc\u30e0<\/strong>\u5217\u304c\u300cA\u300d\u306b\u7b49\u3057\u3044\u884c<b>\u3001\u307e\u305f\u306f<\/b><strong>\u30dd\u30b8\u30b7\u30e7\u30f3<\/strong>\u5217\u304c\u300c\u30ac\u30fc\u30c9\u300d\u306b\u7b49\u3057\u3044\u884c\u306e\u307f\u304c\u6b8b\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u6ce8<\/strong>: pandas <strong>isin()<\/strong>\u95a2\u6570\u306e\u5b8c\u5168\u306a\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u306f<a href=\"https:\/\/pandas.pydata.org\/pandas-docs\/stable\/reference\/api\/pandas.DataFrame.isin.html\" target=\"_blank\" rel=\"noopener\">\u3053\u3053\u3067<\/a>\u898b\u3064\u3051\u308b\u3053\u3068\u304c\u3067\u304d\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\u30bf\u30b9\u30af\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\u30d2\u309a\u30db\u3099\u30c3\u30c8\u30c6\u30fc\u30d5\u3099\u30eb\u30d5\u30a3\u30eb\u30bf\u30fc\/\" target=\"_blank\" rel=\"noopener\">\u30d1\u30f3\u30c0: \u30d4\u30dc\u30c3\u30c8 \u30c6\u30fc\u30d6\u30eb\u306b\u30d5\u30a3\u30eb\u30bf\u30fc\u3092\u8ffd\u52a0\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u30cf\u309a\u30f3\u30bf\u3099\u306b\u306f\u542b\u307e\u308c\u3066\u3044\u307e\u305b\u3093\/\" target=\"_blank\" rel=\"noopener\">\u30d1\u30f3\u30c0: \u300c\u6b21\u306e\u5185\u5bb9\u3092\u542b\u307e\u306a\u3044\u300d\u3092\u30d5\u30a3\u30eb\u30bf\u30ea\u30f3\u30b0\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/pandas\u306f\u6587\u5b57\u5217\u3092\u542b\u3080\u884c\u3092\u30d5\u30a3\u30eb\u30bf\u30ea\u30f3\u30af\u3099\u3057\u307e\u3059\/\" target=\"_blank\" rel=\"noopener\">\u30d1\u30f3\u30c0: \u7279\u5b9a\u306e\u6587\u5b57\u5217\u3092\u542b\u3080\u884c\u3092\u30d5\u30a3\u30eb\u30bf\u30fc\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>pandas isin()\u95a2\u6570\u3067\u6b21\u306e\u30e1\u30bd\u30c3\u30c9\u3092\u4f7f\u7528\u3059\u308b\u3068\u3001pandas DataFrame \u306e\u8907\u6570\u306e\u5217\u306b\u57fa\u3065\u3044\u3066\u30d5\u30a3\u30eb\u30bf\u30ea\u30f3\u30b0\u3067\u304d\u307e\u3059\u3002 \u65b9\u6cd5 1: \u8907\u6570\u306e\u5217\u304c\u7279\u5b9a\u306e\u5024\u3068\u7b49\u3057\u3044\u5834\u5408\u306b\u30d5\u30a3\u30eb\u30bf\u30fc\u3059\u308b df = df[df[ [&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-4367","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: \u8907\u6570\u306e\u5217\u306b isin \u3092\u4f7f\u7528\u3059\u308b\u65b9\u6cd5 - Statology<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001pandas DataFrame \u306e\u8907\u6570\u306e\u5217\u3067 isin() \u95a2\u6570\u3092\u4f7f\u7528\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\u306f\u8907\u6570\u306e\u5217\u306b\u3042\u308a\u307e\u3059\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u30d1\u30f3\u30c0: \u8907\u6570\u306e\u5217\u306b isin \u3092\u4f7f\u7528\u3059\u308b\u65b9\u6cd5 - Statology\" \/>\n<meta property=\"og:description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001pandas DataFrame \u306e\u8907\u6570\u306e\u5217\u3067 isin() \u95a2\u6570\u3092\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u4f8b\u3092\u542b\u3081\u3066\u8aac\u660e\u3057\u307e\u3059\u3002\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/ja\/\u30cf\u309a\u30f3\u30bf\u3099\u306f\u8907\u6570\u306e\u5217\u306b\u3042\u308a\u307e\u3059\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-11T15:20:37+00:00\" \/>\n<meta name=\"author\" content=\"\u30d9\u30f3\u30b8\u30e3\u30df\u30f3\u30fb\u30a2\u30f3\u30c0\u30fc\u30bd\u30f3\u535a\u58eb\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u57f7\u7b46\u8005\" \/>\n\t<meta name=\"twitter:data1\" content=\"\u30d9\u30f3\u30b8\u30e3\u30df\u30f3\u30fb\u30a2\u30f3\u30c0\u30fc\u30bd\u30f3\u535a\u58eb\" \/>\n\t<meta name=\"twitter:label2\" content=\"\u63a8\u5b9a\u8aad\u307f\u53d6\u308a\u6642\u9593\" \/>\n\t<meta name=\"twitter:data2\" content=\"1\u5206\" 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