{"id":3119,"date":"2023-07-19T03:04:16","date_gmt":"2023-07-19T03:04:16","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%80%e1%85%b5%e1%84%8e%e1%85%a1-%e1%84%90%e1%85%a6%e1%84%89%e1%85%b3%e1%84%90%e1%85%b3\/"},"modified":"2023-07-19T03:04:16","modified_gmt":"2023-07-19T03:04:16","slug":"%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%80%e1%85%b5%e1%84%8e%e1%85%a1-%e1%84%90%e1%85%a6%e1%84%89%e1%85%b3%e1%84%90%e1%85%b3","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%80%e1%85%b5%e1%84%8e%e1%85%a1-%e1%84%90%e1%85%a6%e1%84%89%e1%85%b3%e1%84%90%e1%85%b3\/","title":{"rendered":"Pandas dataframe\uc5d0\uc11c \ud559\uc2b5 \ubc0f \ud14c\uc2a4\ud2b8 \uc138\ud2b8\ub97c \ub9cc\ub4dc\ub294 \ubc29\ubc95"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/ko\/\u1110\u1169\u11bc\u1100\u1168\u1112\u1161\u11a8\u110b\u1173\u11ab-\u1100\u1161\u11ab\u1103\u1161\u11ab\u1112\u1161\u1100\u1169-\u110c\u1175\u11a8\u110c\u1165\u11b8\u110c\u1165\u11a8\u110b\u1175\u11ab-\u1107\u1161\u11bc\u1107\u1165\u11b8\u110b\u1173\u1105\u1169-\u1100\u1162\u1102\u1167\u11b7\u110b\u1173\u11af-\u1109\u1165\u11af\u1106\u1167\u11bc\u1112\u1161\u1106\u1173\u1105\u1169-\u1110\u1169\u11bc\u1100\u1168\u1105\u1173\u11af-\u1103\u1165-\u1109\u1171\u11b8\u1100\u1166-\u1107\u1162\u110b\u116e\u11af-\u1109\u116e-\u110b\u1175\u11bb\u1109\u1173\u11b8\u1102\u1175\u1103\u1161.\/\" target=\"_blank\" rel=\"noopener\">\uae30\uacc4 \ud559\uc2b5 \ubaa8\ub378\uc744<\/a> \ub370\uc774\ud130 \uc138\ud2b8\uc5d0 \ub9de\ucd9c \ub54c \ub370\uc774\ud130 \uc138\ud2b8\ub97c \ub450 \uc138\ud2b8\ub85c \ub098\ub204\ub294 \uacbd\uc6b0\uac00 \ub9ce\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1. \ud6c8\ub828 \uc138\ud2b8:<\/strong> \ubaa8\ub378\uc744 \ud6c8\ub828\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4(\uc6d0\ub798 \ub370\uc774\ud130 \uc138\ud2b8\uc758 70-80%)<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>2. \ud14c\uc2a4\ud2b8 \uc138\ud2b8:<\/strong> \ubaa8\ub378 \uc131\ub2a5\uc758 \ud3b8\uacac \uc5c6\ub294 \ucd94\uc815\uce58\ub97c \uc5bb\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4(\uc6d0\ub798 \ub370\uc774\ud130 \uc138\ud2b8\uc758 20-30%)<\/span><\/p>\n<p> <span style=\"color: #000000;\">Python\uc5d0\ub294 Pandas DataFrame\uc744 \ud6c8\ub828 \uc138\ud2b8\uc640 \ud14c\uc2a4\ud2b8 \uc138\ud2b8\ub85c \ubd84\ud560\ud558\ub294 \ub450 \uac00\uc9c0 \uc77c\ubc18\uc801\uc778 \ubc29\ubc95\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\ubc29\ubc95 1: sklearn\uc758 train_test_split() \uc0ac\uc6a9<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> train_test_split\n\ntrain, test = train_test_split(df, test_size= <span style=\"color: #008000;\">0.2<\/span> , random_state= <span style=\"color: #008000;\">0<\/span> )<\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><strong>\ubc29\ubc95 2: pandas\uc758 \uc0d8\ud50c() \uc0ac\uc6a9<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>train = df. <span style=\"color: #3366ff;\">sample<\/span> (frac= <span style=\"color: #008000;\">0.8<\/span> , random_state= <span style=\"color: #008000;\">0<\/span> )\ntest = df. <span style=\"color: #3366ff;\">drop<\/span> ( <span style=\"color: #3366ff;\">train.index<\/span> )<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \ub2e4\uc74c Pandas DataFrame\uc5d0\uc11c \uac01 \uba54\uc11c\ub4dc\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/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: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n\n<span style=\"color: #008080;\">#make this example reproducible\n<\/span>n.p. <span style=\"color: #3366ff;\">random<\/span> . <span style=\"color: #3366ff;\">seeds<\/span> (1)\n\n<span style=\"color: #008080;\">#create DataFrame with 1,000 rows and 3 columns\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> <span style=\"color: #3366ff;\">(<\/span> {' <span style=\"color: #ff0000;\">x1<\/span> ': <span style=\"color: #3366ff;\">np.random.randint<\/span> (30,size=1000),\n                   ' <span style=\"color: #ff0000;\">x2<\/span> ': np. <span style=\"color: #3366ff;\">random<\/span> . <span style=\"color: #3366ff;\">randint<\/span> (12, size=1000),\n                   ' <span style=\"color: #ff0000;\">y<\/span> ': np. <span style=\"color: #3366ff;\">random<\/span> . <span style=\"color: #3366ff;\">randint<\/span> (2, size=1000)})\n\n<span style=\"color: #008080;\">#view first few rows of DataFrame<\/span>\ndf. <span style=\"color: #3366ff;\">head<\/span> ()\n\n        x1 x2 y\n0 5 1 1\n1 11 8 0\n2 12 4 1\n3 8 7 0\n4 9 0 0\n<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>\uc608 1: sklearn\uc758 train_test_split() \uc0ac\uc6a9<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">\ub2e4\uc74c \ucf54\ub4dc\ub294 <strong>sklearn<\/strong> \uc758 <strong>train_test_split()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec Pandas DataFrame\uc744 \ud6c8\ub828 \ubc0f \ud14c\uc2a4\ud2b8 \uc138\ud2b8\ub85c \ubd84\ud560\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> train_test_split\n\n<span style=\"color: #008080;\">#split original DataFrame into training and testing sets\n<\/span>train, test = train_test_split(df, test_size= <span style=\"color: #008000;\">0.2<\/span> , random_state= <span style=\"color: #008000;\">0<\/span> )\n\n<span style=\"color: #008080;\">#view first few rows of each set<\/span>\n<span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">train.head<\/span> ())\n\n     x1 x2 y\n687 16 2 0\n500 18 2 1\n332 4 10 1\n979 2 8 1\n817 11 1 0\n\n<span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">test.head<\/span> ())\n\n     x1 x2 y\n993 22 1 1\n859 27 6 0\n298 27 8 1\n553 20 6 0\n672 9 2 1\n\n<span style=\"color: #008080;\">#print size of each set<\/span>\n<span style=\"color: #008000;\">print<\/span> (train. <span style=\"color: #3366ff;\">shape<\/span> , test. <span style=\"color: #3366ff;\">shape<\/span> )\n\n(800, 3) (200, 3)\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\uacb0\uacfc\uc5d0\uc11c \ub450 \uac1c\uc758 \uc138\ud2b8\uac00 \uc0dd\uc131\ub418\uc5c8\uc74c\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\ud6c8\ub828 \uc138\ud2b8: \ud589 800\uac1c\uc640 \uc5f4 3\uac1c<\/span><\/li>\n<li> <span style=\"color: #000000;\">\ud14c\uc2a4\ud2b8 \uc138\ud2b8: \ud589 200\uac1c\uc640 \uc5f4 3\uac1c<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><strong>test_size<\/strong> \ub294 \ud14c\uc2a4\ud2b8 \uc138\ud2b8\uc5d0 \uc18d\ud560 \uc6d0\ubcf8 DataFrame\uc758 \uad00\uce21\uce58 \ube44\uc728\uc744 \uc81c\uc5b4\ud558\uace0 <strong>random_state<\/strong> \uac12\uc740 \ubd84\ud560\uc744 \uc7ac\ud604 \uac00\ub2a5\ud558\uac8c \ub9cc\ub4ed\ub2c8\ub2e4.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\uc608 2: Pandas\uc758 Sample() \uc0ac\uc6a9<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ucf54\ub4dc\ub294 <b>pandas<\/b> <strong>\uc0d8\ud50c()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec pandas DataFrame\uc744 \ud6c8\ub828 \uc138\ud2b8\uc640 \ud14c\uc2a4\ud2b8 \uc138\ud2b8\ub85c \ubd84\ud560\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#split original DataFrame into training and testing sets\n<\/span>train = df. <span style=\"color: #3366ff;\">sample<\/span> (frac= <span style=\"color: #008000;\">0.8<\/span> , random_state= <span style=\"color: #008000;\">0<\/span> )\ntest = df. <span style=\"color: #3366ff;\">drop<\/span> ( <span style=\"color: #3366ff;\">train.index<\/span> )\n\n<span style=\"color: #008080;\">#view first few rows of each set<\/span>\n<span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">train.head<\/span> ())\n\n     x1 x2 y\n993 22 1 1\n859 27 6 0\n298 27 8 1\n553 20 6 0\n672 9 2 1\n\n<span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">test.head<\/span> ())\n\n    x1 x2 y\n9 16 5 0\n11 12 10 0\n19 5 9 0\n23 28 1 1\n28 18 0 1\n\n<span style=\"color: #008080;\">#print size of each set<\/span>\n<span style=\"color: #008000;\">print<\/span> (train. <span style=\"color: #3366ff;\">shape<\/span> , test. <span style=\"color: #3366ff;\">shape<\/span> )\n\n(800, 3) (200, 3)\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\uacb0\uacfc\uc5d0\uc11c \ub450 \uac1c\uc758 \uc138\ud2b8\uac00 \uc0dd\uc131\ub418\uc5c8\uc74c\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\ud6c8\ub828 \uc138\ud2b8: \ud589 800\uac1c\uc640 \uc5f4 3\uac1c<\/span><\/li>\n<li> <span style=\"color: #000000;\">\ud14c\uc2a4\ud2b8 \uc138\ud2b8: \ud589 200\uac1c\uc640 \uc5f4 3\uac1c<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><b>frac\uc740<\/b> \ud6c8\ub828 \uc138\ud2b8\uc5d0 \uc18d\ud558\uac8c \ub420 \uc6d0\ubcf8 DataFrame\uc758 \uad00\uce21\uce58 \ube44\uc728\uc744 \uc81c\uc5b4\ud558\uace0 <strong>Random_state<\/strong> \uac12\uc740 \ubd84\ud560\uc744 \uc7ac\ud604 \uac00\ub2a5\ud558\uac8c \ub9cc\ub4ed\ub2c8\ub2e4.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Python\uc5d0\uc11c \ub2e4\ub978 \uc77c\ubc18\uc801\uc778 \uc791\uc5c5\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/ko\/\u1105\u1169\u110c\u1175\u1109\u1173\u1110\u1175\u11a8-\u1112\u116c\u1100\u1171-\u1111\u1161\u110b\u1175\u110a\u1165\u11ab\/\" target=\"_blank\" rel=\"noopener\">Python\uc5d0\uc11c \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a><br \/> <a href=\"https:\/\/statorials.org\/ko\/\u1111\u1161\u110b\u1175\u110a\u1165\u11ab-\u1106\u1162\u1110\u1173\u1105\u1175\u11a8\u1109\u1173-\u1112\u1169\u11ab\u1105\u1161\u11ab\/\" target=\"_blank\" rel=\"noopener\">Python\uc5d0\uc11c \ud63c\ub3d9 \ud589\ub82c\uc744 \ub9cc\ub4dc\ub294 \ubc29\ubc95<\/a><br \/> <a href=\"https:\/\/statorials.org\/ko\/\u1100\u1172\u11ab\u1112\u1167\u11bc-\u110c\u1161\u11b8\u1112\u1175\u11ab-\u110c\u1165\u11bc\u1106\u1175\u11af\u1103\u1169-python-sklearn\/\">Python\uc5d0\uc11c \uade0\ud615 \uc7a1\ud78c \uc815\ubc00\ub3c4\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\uae30\uacc4 \ud559\uc2b5 \ubaa8\ub378\uc744 \ub370\uc774\ud130 \uc138\ud2b8\uc5d0 \ub9de\ucd9c \ub54c \ub370\uc774\ud130 \uc138\ud2b8\ub97c \ub450 \uc138\ud2b8\ub85c \ub098\ub204\ub294 \uacbd\uc6b0\uac00 \ub9ce\uc2b5\ub2c8\ub2e4. 1. \ud6c8\ub828 \uc138\ud2b8: \ubaa8\ub378\uc744 \ud6c8\ub828\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4(\uc6d0\ub798 \ub370\uc774\ud130 \uc138\ud2b8\uc758 70-80%) 2. \ud14c\uc2a4\ud2b8 \uc138\ud2b8: \ubaa8\ub378 \uc131\ub2a5\uc758 \ud3b8\uacac \uc5c6\ub294 \ucd94\uc815\uce58\ub97c \uc5bb\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4(\uc6d0\ub798 \ub370\uc774\ud130 \uc138\ud2b8\uc758 20-30%) Python\uc5d0\ub294 Pandas DataFrame\uc744 \ud6c8\ub828 \uc138\ud2b8\uc640 \ud14c\uc2a4\ud2b8 \uc138\ud2b8\ub85c \ubd84\ud560\ud558\ub294 \ub450 \uac00\uc9c0 \uc77c\ubc18\uc801\uc778 \ubc29\ubc95\uc774 \uc788\uc2b5\ub2c8\ub2e4. \ubc29\ubc95 1: sklearn\uc758 train_test_split() [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-3119","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Pandas DataFrame\uc5d0\uc11c \ud6c8\ub828 \ubc0f \ud14c\uc2a4\ud2b8 \uc138\ud2b8\ub97c \uc0dd\uc131\ud558\ub294 \ubc29\ubc95 - Statorials<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \ub2e8\uc77c Pandas DataFrame\uc5d0\uc11c \ud6c8\ub828 \ubc0f \ud14c\uc2a4\ud2b8 \uc138\ud2b8\ub97c \uc0dd\uc131\ud558\ub294 \ub370 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \uc5ec\ub7ec \uac00\uc9c0 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.\" \/>\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\/ko\/\u1111\u1162\u11ab\u1103\u1165-\u1100\u1175\u110e\u1161-\u1110\u1166\u1109\u1173\u1110\u1173\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta 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