{"id":1209,"date":"2023-07-27T06:54:42","date_gmt":"2023-07-27T06:54:42","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab%e1%84%8b%e1%85%b4-%e1%84%8e%e1%85%ac%e1%84%89%e1%85%a9-%e1%84%87%e1%85%ae%e1%84%87%e1%85%ae%e1%86%ab-%e1%84%80%e1%85%a1%e1%84%8c\/"},"modified":"2023-07-27T06:54:42","modified_gmt":"2023-07-27T06:54:42","slug":"%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab%e1%84%8b%e1%85%b4-%e1%84%8e%e1%85%ac%e1%84%89%e1%85%a9-%e1%84%87%e1%85%ae%e1%84%87%e1%85%ae%e1%86%ab-%e1%84%80%e1%85%a1%e1%84%8c","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab%e1%84%8b%e1%85%b4-%e1%84%8e%e1%85%ac%e1%84%89%e1%85%a9-%e1%84%87%e1%85%ae%e1%84%87%e1%85%ae%e1%86%ab-%e1%84%80%e1%85%a1%e1%84%8c\/","title":{"rendered":"Python\uc758 \ubd80\ubd84 \ucd5c\uc18c \uc81c\uacf1(\ub2e8\uacc4\ubcc4)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\uae30\uacc4 \ud559\uc2b5\uc5d0\uc11c \uc9c1\uba74\ud558\uac8c \ub418\ub294 \uac00\uc7a5 \uc77c\ubc18\uc801\uc778 \ubb38\uc81c \uc911 \ud558\ub098\ub294 <a href=\"https:\/\/statorials.org\/ko\/\u1103\u1161\u110c\u116e\u11bc\u1100\u1169\u11bc\u1109\u1165\u11ab\u1109\u1165\u11bc-\u1112\u116c\u1100\u1171\/\" target=\"_blank\" rel=\"noopener noreferrer\">\ub2e4\uc911 \uacf5\uc120\uc131<\/a> \uc785\ub2c8\ub2e4. \uc774\ub294 \ub370\uc774\ud130 \uc138\ud2b8\uc5d0\uc11c \ub450 \uac1c \uc774\uc0c1\uc758 \uc608\uce21 \ubcc0\uc218\uac00 \ub192\uc740 \uc0c1\uad00 \uad00\uacc4\ub97c \uac00\uc9c8 \ub54c \ubc1c\uc0dd\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc774\ub7f0 \uc77c\uc774 \ubc1c\uc0dd\ud558\uba74 \ubaa8\ub378\uc740 \ud6c8\ub828 \ub370\uc774\ud130 \uc138\ud2b8\uc5d0 \uc798 \ub9de\uc744 \uc218 \uc788\uc9c0\ub9cc, \ud6c8\ub828 \ub370\uc774\ud130 \uc138\ud2b8\uc5d0 \uacfc\uc801\ud569\ub418\uae30 \ub54c\ubb38\uc5d0 \ubcf8 \uc801\uc774 \uc5c6\ub294 \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130 \uc138\ud2b8\uc5d0\uc11c\ub294 \uc131\ub2a5\uc774 <a href=\"https:\/\/statorials.org\/ko\/\u1106\u1165\u1109\u1175\u11ab\u1105\u1165\u1102\u1175\u11bc-\u1100\u116a\u110c\u1165\u11a8\u1112\u1161\u11b8\/\" target=\"_blank\" rel=\"noopener noreferrer\">\uc800\ud558<\/a> \ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud2b8\ub808\uc774\ub2dd \uc138\ud2b8.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc774 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \ud55c \uac00\uc9c0 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc774 \uc791\ub3d9\ud558\ub294 <a href=\"https:\/\/statorials.org\/ko\/\u1107\u116e\u1107\u116e\u11ab-\u110e\u116c\u1109\u1169-\u110c\u1166\u1100\u1169\u11b8\/\" target=\"_blank\" rel=\"noopener noreferrer\">\ubd80\ubd84 \ucd5c\uc18c \uc81c\uacf1<\/a> \ubc95\uc744 \uc0ac\uc6a9\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\uc608\uce21 \ubcc0\uc218\uc640 \ubc18\uc751 \ubcc0\uc218\ub97c \ud45c\uc900\ud654\ud569\ub2c8\ub2e4.<\/span><\/li>\n<li> <span style=\"color: #000000;\">\ubc18\uc751 \ubcc0\uc218\uc640 \uc608\uce21 \ubcc0\uc218 \ubaa8\ub450\uc5d0\uc11c \uc0c1\ub2f9\ud55c \uc591\uc758 \ubcc0\ub3d9\uc744 \uc124\uba85\ud558\ub294<\/span> <em style=\"color: #000000;\">p\uac1c\uc758<\/em> \uc6d0\ub798 \uc608\uce21 \ubcc0\uc218 <span style=\"color: #000000;\">\uc758 <em>M\uac1c\uc758<\/em> \uc120\ud615 \uc870\ud569(&#8220;PLS \uad6c\uc131 \uc694\uc18c&#8221;\ub77c\uace0 \ud568)\uc744 \uacc4\uc0b0\ud569\ub2c8\ub2e4<\/span> .<\/li>\n<li> <span style=\"color: #000000;\">PLS \uad6c\uc131\uc694\uc18c\ub97c \uc608\uce21 \ubcc0\uc218\ub85c \uc0ac\uc6a9\ud558\uc5ec \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \ub9de\ucd94\ub824\uba74 \ucd5c\uc18c \uc81c\uacf1\ubc95\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/ko\/k-\u1100\u1167\u11b8-\u1100\u116d\u110e\u1161-\u1100\u1165\u11b7\u110c\u1173\u11bc\/\" target=\"_blank\" rel=\"noopener noreferrer\">k-\uacb9 \uad50\ucc28 \uac80\uc99d\uc744<\/a> \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc5d0 \uc720\uc9c0\ud560 \ucd5c\uc801\uc758 PLS \uad6c\uc131 \uc694\uc18c \uc218\ub97c \ucc3e\uc2b5\ub2c8\ub2e4.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Python\uc5d0\uc11c \ubd80\ubd84 \ucd5c\uc18c \uc81c\uacf1\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud55c \ub2e8\uacc4\ubcc4 \uc608\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>1\ub2e8\uacc4: \ud544\uc694\ud55c \ud328\ud0a4\uc9c0 \uac00\uc838\uc624\uae30<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\uba3c\uc800 Python\uc5d0\uc11c \ubd80\ubd84 \ucd5c\uc18c \uc81c\uacf1\uc744 \uc218\ud589\ud558\ub294 \ub370 \ud544\uc694\ud55c \ud328\ud0a4\uc9c0\ub97c \uac00\uc838\uc635\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n<span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n<span style=\"color: #008000;\">import<\/span> matplotlib. <span style=\"color: #3366ff;\">pyplot<\/span> <span style=\"color: #008000;\">as<\/span> plt\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">preprocessing<\/span> <span style=\"color: #008000;\">import<\/span> scale \n<span style=\"color: #008000;\">from<\/span> sklearn <span style=\"color: #008000;\">import<\/span> model_selection\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> RepeatedKFold\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> train_test_split\n<span style=\"color: #008000;\">from <span style=\"color: #000000;\">sklearn. <span style=\"color: #3366ff;\">cross_decomposition<\/span> <span style=\"color: #008000;\">import<\/span> PLSRegression<\/span>\n<span style=\"color: #008000;\">from<\/span> <span style=\"color: #000000;\">sklearn<\/span> . <span style=\"color: #3366ff;\">metrics<\/span> <span style=\"color: #008000;\">import<\/span> <span style=\"color: #000000;\">mean_squared_error\n<\/span><\/span><\/strong><\/span><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>2\ub2e8\uacc4: \ub370\uc774\ud130 \ub85c\ub4dc<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\uc774 \uc608\uc5d0\uc11c\ub294 33\uac1c\uc758 \uc11c\ub85c \ub2e4\ub978 \uc790\ub3d9\ucc28\uc5d0 \ub300\ud55c \uc815\ubcf4\uac00 \ud3ec\ud568\ub41c <strong>mtcars<\/strong> \ub77c\ub294 \ub370\uc774\ud130 \uc138\ud2b8\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4. <strong>hp\ub97c<\/strong> \ubc18\uc751 \ubcc0\uc218\ub85c \uc0ac\uc6a9\ud558\uace0 \ub2e4\uc74c \ubcc0\uc218\ub97c \uc608\uce21 \ubcc0\uc218\ub85c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">mpg<\/span><\/li>\n<li> <span style=\"color: #000000;\">\ud45c\uc2dc\ud558\ub2e4<\/span><\/li>\n<li> <span style=\"color: #000000;\">\ub625<\/span><\/li>\n<li> <span style=\"color: #000000;\">\ubb34\uac8c<\/span><\/li>\n<li> <span style=\"color: #000000;\">q\ucd08<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ucf54\ub4dc\ub294 \uc774 \ub370\uc774\ud130\uc138\ud2b8\ub97c \ub85c\ub4dc\ud558\uace0 \ud45c\uc2dc\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define URL where data is located\n<\/span>url = \"https:\/\/raw.githubusercontent.com\/Statorials\/Python-Guides\/main\/mtcars.csv\"\n\n<span style=\"color: #008080;\">#read in data\n<\/span>data_full = pd. <span style=\"color: #3366ff;\">read_csv<\/span> (url)\n\n<span style=\"color: #008080;\">#select subset of data\n<\/span>data = data_full[[\"mpg\", \"disp\", \"drat\", \"wt\", \"qsec\", \"hp\"]]\n\n<span style=\"color: #008080;\">#view first six rows of data\n<\/span>data[0:6]\n\n\n        mpg disp drat wt qsec hp\n0 21.0 160.0 3.90 2.620 16.46 110\n1 21.0 160.0 3.90 2.875 17.02 110\n2 22.8 108.0 3.85 2.320 18.61 93\n3 21.4 258.0 3.08 3.215 19.44 110\n4 18.7 360.0 3.15 3.440 17.02 175\n5 18.1 225.0 2.76 3.460 20.22 105<\/strong><\/span><\/pre>\n<h3> <strong>3\ub2e8\uacc4: \ubd80\ubd84 \ucd5c\uc18c \uc81c\uacf1 \ubaa8\ub378 \ud53c\ud305<\/strong><\/h3>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ucf54\ub4dc\ub294 PLS \ubaa8\ub378\uc744 \uc774 \ub370\uc774\ud130\uc5d0 \ub9de\ucd94\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>cv = RepeatedKFold()\ub294<\/strong> Python\uc5d0 <a href=\"https:\/\/statorials.org\/ko\/k-\u1100\u1167\u11b8-\u1100\u116d\u110e\u1161-\u1100\u1165\u11b7\u110c\u1173\u11bc\/\" target=\"_blank\" rel=\"noopener noreferrer\">k-\uacb9 \uad50\ucc28 \uac80\uc99d\uc744<\/a> \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\ub3c4\ub85d \uc9c0\uc2dc\ud569\ub2c8\ub2e4<\/span> <span style=\"color: #000000;\">.<\/span> <span style=\"color: #000000;\">\uc774 \uc608\uc5d0\uc11c\ub294 k = 10 \uc811\uae30\ub97c \uc120\ud0dd\ud558\uace0 3\ubc88 \ubc18\ubcf5\ud569\ub2c8\ub2e4.<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define predictor and response variables\n<\/span>X = data[[\"mpg\", \"disp\", \"drat\", \"wt\", \"qsec\"]]\ny = data[[\"hp\"]]\n\n<span style=\"color: #008080;\">#define cross-validation method\n<span style=\"color: #000000;\">cv = RepeatedKFold(n_splits= <span style=\"color: #008000;\">10<\/span> , n_repeats= <span style=\"color: #008000;\">3<\/span> , random_state= <span style=\"color: #008000;\">1<\/span> )\n\nmse = []\nn = <span style=\"color: #3366ff;\">len<\/span> (X)<\/span>\n\n# Calculate MSE with only the intercept\n<span style=\"color: #000000;\">score = -1*model_selection. <span style=\"color: #3366ff;\">cross_val_score<\/span> (PLSRegression(n_components=1),<\/span>\n<span style=\"color: #000000;\">n.p. <span style=\"color: #3366ff;\">ones<\/span> ((n,1)), y, cv=cv, scoring=' <span style=\"color: #008000;\">neg_mean_squared_error<\/span> '). <span style=\"color: #3366ff;\">mean<\/span> ()    \nmse. <span style=\"color: #3366ff;\">append<\/span> (score)<\/span>\n\n# Calculate MSE using cross-validation, adding one component at a time\n<span style=\"color: #000000;\"><span style=\"color: #008000;\">for<\/span> i <span style=\"color: #008000;\">in<\/span> np. <span style=\"color: #3366ff;\">arange<\/span> (1, 6):\n    pls = PLSRegression(n_components=i)\n    score = -1*model_selection. <span style=\"color: #3366ff;\">cross_val_score<\/span> (pls, scale(X), y, cv=cv,\n               scoring=' <span style=\"color: #008000;\">neg_mean_squared_error<\/span> '). <span style=\"color: #3366ff;\">mean<\/span> ()\n    mse. <span style=\"color: #3366ff;\">append<\/span> (score)<\/span>\n\n#plot test MSE vs. number of components\n<span style=\"color: #000000;\">plt. <span style=\"color: #3366ff;\">plot<\/span> (mse)\nplt. <span style=\"color: #3366ff;\">xlabel<\/span> (' <span style=\"color: #008000;\">Number of PLS Components<\/span> ')\nplt. <span style=\"color: #3366ff;\">ylabel<\/span> (' <span style=\"color: #008000;\">MSE<\/span> ')\nplt. <span style=\"color: #3366ff;\">title<\/span> (' <span style=\"color: #008000;\">hp<\/span> ')<\/span>\n<\/span><\/strong><\/span><\/pre>\n<h3><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-11985 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/svppython1.png\" alt=\"Python \uad50\ucc28 \uac80\uc99d \ud50c\ub86f\uc758 \ubd80\ubd84 \ucd5c\uc18c \uc81c\uacf1\" width=\"405\" height=\"284\" srcset=\"\" sizes=\"auto, \"><\/h3>\n<p> <span style=\"color: #000000;\">\ud50c\ub86f\uc740 x\ucd95\uc744 \ub530\ub77c PLS \uc131\ubd84\uc758 \uc218\ub97c \ud45c\uc2dc\ud558\uace0 y\ucd95\uc744 \ub530\ub77c MSE(\ud3c9\uade0 \uc81c\uacf1 \uc624\ucc28) \ud14c\uc2a4\ud2b8\ub97c \ud45c\uc2dc\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uadf8\ub798\ud504\uc5d0\uc11c \ub450 \uac1c\uc758 PLS \uad6c\uc131\uc694\uc18c\ub97c \ucd94\uac00\ud558\uba74 \ud14c\uc2a4\ud2b8\uc758 MSE\uac00 \uac10\uc18c\ud558\uc9c0\ub9cc, \ub450 \uac1c \uc774\uc0c1\uc758 PLS \uad6c\uc131\uc694\uc18c\ub97c \ucd94\uac00\ud558\uba74 \uc99d\uac00\ud558\uae30 \uc2dc\uc791\ud558\ub294 \uac83\uc744 \ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\ub530\ub77c\uc11c \ucd5c\uc801\uc758 \ubaa8\ub378\uc5d0\ub294 \ucc98\uc74c \ub450 \uac1c\uc758 PLS \uad6c\uc131\uc694\uc18c\ub9cc \ud3ec\ud568\ub429\ub2c8\ub2e4.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>4\ub2e8\uacc4: \ucd5c\uc885 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc608\uce21<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\ub450 \uac1c\uc758 PLS \uad6c\uc131\uc694\uc18c\uac00 \ud3ec\ud568\ub41c \ucd5c\uc885 PLS \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc0c8\ub85c\uc6b4 \uad00\uce21\uac12\uc5d0 \ub300\ud574 \uc608\uce21\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ucf54\ub4dc\ub294 \uc6d0\ubcf8 \ub370\uc774\ud130 \uc138\ud2b8\ub97c \ud6c8\ub828 \uc138\ud2b8\uc640 \ud14c\uc2a4\ud2b8 \uc138\ud2b8\ub85c \ubd84\ud560\ud558\uace0 \ub450 \uac1c\uc758 PLS \uad6c\uc131\uc694\uc18c\uac00 \uc788\ub294 PLS \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud14c\uc2a4\ud2b8 \uc138\ud2b8\uc5d0 \ub300\ud574 \uc608\uce21\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#split the dataset into training (70%) and testing (30%) sets\n<\/span><span style=\"color: #3366ff;\">X_train<\/span> <span style=\"color: #008000;\">,<\/span> <span style=\"color: #008000;\">_<\/span><span style=\"color: #008080;\">\n\n#calculate RMSE\n<span style=\"color: #000000;\">pls = PLSRegression(n_components=2)\npls. <span style=\"color: #3366ff;\">fit<\/span> (scale(X_train), y_train)<\/span>\n\n<span style=\"color: #000000;\">n.p. <span style=\"color: #3366ff;\">sqrt<\/span> (mean_squared_error(y_test, pls. <span style=\"color: #3366ff;\">predict<\/span> (scale(X_test))))\n<\/span>\n<span style=\"color: #000000;\">29.9094\n<\/span><\/span><\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">\ud14c\uc2a4\ud2b8\uc758 RMSE\ub294 <strong>29.9094<\/strong> \ub85c \ub098\ud0c0\ub0ac\uc2b5\ub2c8\ub2e4. \uc774\ub294 \ud14c\uc2a4\ud2b8 \uc138\ud2b8 \uad00\ucc30\uc5d0 \ub300\ud574 \uc608\uce21\ub41c <em>HP<\/em> \uac12\uacfc \uad00\ucc30\ub41c <em>HP<\/em> \uac12 \uc0ac\uc774\uc758 \ud3c9\uade0 \ud3b8\ucc28\uc785\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc774 \uc608\uc81c\uc5d0 \uc0ac\uc6a9\ub41c \uc804\uccb4 Python \ucf54\ub4dc\ub294 <a href=\"https:\/\/github.com\/Statorials\/Python-Guides\/blob\/main\/partial_least_squares.py\" target=\"_blank\" rel=\"noopener noreferrer\">\uc5ec\uae30\uc5d0\uc11c<\/a> \ucc3e\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\uae30\uacc4 \ud559\uc2b5\uc5d0\uc11c \uc9c1\uba74\ud558\uac8c \ub418\ub294 \uac00\uc7a5 \uc77c\ubc18\uc801\uc778 \ubb38\uc81c \uc911 \ud558\ub098\ub294 \ub2e4\uc911 \uacf5\uc120\uc131 \uc785\ub2c8\ub2e4. \uc774\ub294 \ub370\uc774\ud130 \uc138\ud2b8\uc5d0\uc11c \ub450 \uac1c \uc774\uc0c1\uc758 \uc608\uce21 \ubcc0\uc218\uac00 \ub192\uc740 \uc0c1\uad00 \uad00\uacc4\ub97c \uac00\uc9c8 \ub54c \ubc1c\uc0dd\ud569\ub2c8\ub2e4. \uc774\ub7f0 \uc77c\uc774 \ubc1c\uc0dd\ud558\uba74 \ubaa8\ub378\uc740 \ud6c8\ub828 \ub370\uc774\ud130 \uc138\ud2b8\uc5d0 \uc798 \ub9de\uc744 \uc218 \uc788\uc9c0\ub9cc, \ud6c8\ub828 \ub370\uc774\ud130 \uc138\ud2b8\uc5d0 \uacfc\uc801\ud569\ub418\uae30 \ub54c\ubb38\uc5d0 \ubcf8 \uc801\uc774 \uc5c6\ub294 \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130 \uc138\ud2b8\uc5d0\uc11c\ub294 \uc131\ub2a5\uc774 \uc800\ud558 \ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud2b8\ub808\uc774\ub2dd \uc138\ud2b8. [&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-1209","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>Python\uc758 \ubd80\ubd84 \ucd5c\uc18c \uc81c\uacf1\ubc95(\ub2e8\uacc4\ubcc4) - \ud1b5\uacc4\ud559<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \ub2e8\uacc4\ubcc4 \uc608\uc81c\ub97c \ud3ec\ud568\ud558\uc5ec Python\uc5d0\uc11c \ubd80\ubd84 \ucd5c\uc18c \uc81c\uacf1\uc744 \uc218\ud589\ud558\ub294 \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\u1161\u110b\u1175\u110a\u1165\u11ab\u110b\u1174-\u110e\u116c\u1109\u1169-\u1107\u116e\u1107\u116e\u11ab-\u1100\u1161\u110c\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python\uc758 \ubd80\ubd84 \ucd5c\uc18c \uc81c\uacf1\ubc95(\ub2e8\uacc4\ubcc4) - 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