{"id":1581,"date":"2023-07-25T18:37:40","date_gmt":"2023-07-25T18:37:40","guid":{"rendered":"https:\/\/statorials.org\/uk\/trace-roc-curve-python\/"},"modified":"2023-07-25T18:37:40","modified_gmt":"2023-07-25T18:37:40","slug":"trace-roc-curve-python","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/trace-roc-curve-python\/","title":{"rendered":"\u042f\u043a \u043d\u0430\u043c\u0430\u043b\u044e\u0432\u0430\u0442\u0438 roc-\u043a\u0440\u0438\u0432\u0443 \u0432 python (\u043a\u0440\u043e\u043a \u0437\u0430 \u043a\u0440\u043e\u043a\u043e\u043c)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/uk\/\u043b\u043e\u0433\u0456\u0441\u0442\u0438\u0447\u043d\u0430-\u0440\u0435\u0433\u0440\u0435\u0441\u0456\u044f-1\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u041b\u043e\u0433\u0456\u0441\u0442\u0438\u0447\u043d\u0430 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u044f<\/a> \u2013 \u0446\u0435 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u043d\u0438\u0439 \u043c\u0435\u0442\u043e\u0434, \u044f\u043a\u0438\u0439 \u043c\u0438 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0454\u043c\u043e \u0434\u043b\u044f \u043f\u0456\u0434\u0433\u043e\u043d\u043a\u0438 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0439\u043d\u043e\u0457 \u043c\u043e\u0434\u0435\u043b\u0456, \u043a\u043e\u043b\u0438 \u0437\u043c\u0456\u043d\u043d\u0430 \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0456 \u0454 \u0434\u0432\u0456\u0439\u043a\u043e\u0432\u043e\u044e. \u0429\u043e\u0431 \u043e\u0446\u0456\u043d\u0438\u0442\u0438, \u043d\u0430\u0441\u043a\u0456\u043b\u044c\u043a\u0438 \u043c\u043e\u0434\u0435\u043b\u044c \u043b\u043e\u0433\u0456\u0441\u0442\u0438\u0447\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457 \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0430\u0454 \u043d\u0430\u0431\u043e\u0440\u0443 \u0434\u0430\u043d\u0438\u0445, \u043c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0440\u043e\u0437\u0433\u043b\u044f\u043d\u0443\u0442\u0438 \u0442\u0430\u043a\u0456 \u0434\u0432\u0430 \u043f\u043e\u043a\u0430\u0437\u043d\u0438\u043a\u0438:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>\u0427\u0443\u0442\u043b\u0438\u0432\u0456\u0441\u0442\u044c:<\/strong> \u0439\u043c\u043e\u0432\u0456\u0440\u043d\u0456\u0441\u0442\u044c \u0442\u043e\u0433\u043e, \u0449\u043e \u043c\u043e\u0434\u0435\u043b\u044c \u043f\u0435\u0440\u0435\u0434\u0431\u0430\u0447\u0430\u0454 \u043f\u043e\u0437\u0438\u0442\u0438\u0432\u043d\u0438\u0439 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442 \u0434\u043b\u044f \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f, \u043a\u043e\u043b\u0438 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442 \u043d\u0430\u0441\u043f\u0440\u0430\u0432\u0434\u0456 \u043f\u043e\u0437\u0438\u0442\u0438\u0432\u043d\u0438\u0439. \u0426\u0435 \u0442\u0430\u043a\u043e\u0436 \u043d\u0430\u0437\u0438\u0432\u0430\u0454\u0442\u044c\u0441\u044f \u00ab\u0441\u043f\u0440\u0430\u0432\u0436\u043d\u0456\u0439 \u043f\u043e\u0437\u0438\u0442\u0438\u0432\u043d\u0438\u0439 \u043f\u043e\u043a\u0430\u0437\u043d\u0438\u043a\u00bb.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\u0421\u043f\u0435\u0446\u0438\u0444\u0456\u0447\u043d\u0456\u0441\u0442\u044c:<\/strong> \u0439\u043c\u043e\u0432\u0456\u0440\u043d\u0456\u0441\u0442\u044c \u0442\u043e\u0433\u043e, \u0449\u043e \u043c\u043e\u0434\u0435\u043b\u044c \u043f\u0435\u0440\u0435\u0434\u0431\u0430\u0447\u0430\u0454 \u043d\u0435\u0433\u0430\u0442\u0438\u0432\u043d\u0438\u0439 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442 \u0434\u043b\u044f \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f, \u043a\u043e\u043b\u0438 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442 \u043d\u0430\u0441\u043f\u0440\u0430\u0432\u0434\u0456 \u043d\u0435\u0433\u0430\u0442\u0438\u0432\u043d\u0438\u0439. \u0426\u0435 \u0442\u0430\u043a\u043e\u0436 \u043d\u0430\u0437\u0438\u0432\u0430\u0454\u0442\u044c\u0441\u044f \u00ab\u0441\u043f\u0440\u0430\u0432\u0436\u043d\u0456\u0439 \u043d\u0435\u0433\u0430\u0442\u0438\u0432\u043d\u0438\u0439 \u043f\u043e\u043a\u0430\u0437\u043d\u0438\u043a\u00bb.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u041e\u0434\u043d\u0438\u043c \u0456\u0437 \u0441\u043f\u043e\u0441\u043e\u0431\u0456\u0432 \u0432\u0456\u0437\u0443\u0430\u043b\u0456\u0437\u0430\u0446\u0456\u0457 \u0446\u0438\u0445 \u0434\u0432\u043e\u0445 \u0432\u0438\u043c\u0456\u0440\u044e\u0432\u0430\u043d\u044c \u0454 \u0441\u0442\u0432\u043e\u0440\u0435\u043d\u043d\u044f <strong>\u043a\u0440\u0438\u0432\u043e\u0457 ROC<\/strong> , \u0449\u043e \u043e\u0437\u043d\u0430\u0447\u0430\u0454 \u043a\u0440\u0438\u0432\u0443 \u00ab\u0440\u043e\u0431\u043e\u0447\u0430 \u0445\u0430\u0440\u0430\u043a\u0442\u0435\u0440\u0438\u0441\u0442\u0438\u043a\u0430 \u043f\u0440\u0438\u0439\u043c\u0430\u0447\u0430\u00bb. \u0426\u0435 \u0433\u0440\u0430\u0444\u0456\u043a, \u044f\u043a\u0438\u0439 \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0436\u0430\u0454 \u0447\u0443\u0442\u043b\u0438\u0432\u0456\u0441\u0442\u044c \u0456 \u0441\u043f\u0435\u0446\u0438\u0444\u0456\u0447\u043d\u0456\u0441\u0442\u044c \u043c\u043e\u0434\u0435\u043b\u0456 \u043b\u043e\u0433\u0456\u0441\u0442\u0438\u0447\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u041d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u043f\u043e\u043a\u0440\u043e\u043a\u043e\u0432\u0438\u0439 \u043f\u0440\u0438\u043a\u043b\u0430\u0434 \u043f\u043e\u043a\u0430\u0437\u0443\u0454, \u044f\u043a \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0442\u0430 \u0456\u043d\u0442\u0435\u0440\u043f\u0440\u0435\u0442\u0443\u0432\u0430\u0442\u0438 \u043a\u0440\u0438\u0432\u0443 ROC \u0443 Python.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong><span style=\"color: #000000;\">\u041a\u0440\u043e\u043a 1. \u0406\u043c\u043f\u043e\u0440\u0442\u0443\u0439\u0442\u0435 \u043d\u0435\u043e\u0431\u0445\u0456\u0434\u043d\u0456 \u043f\u0430\u043a\u0435\u0442\u0438<\/span><\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0421\u043f\u043e\u0447\u0430\u0442\u043a\u0443 \u043c\u0438 \u0456\u043c\u043f\u043e\u0440\u0442\u0443\u0454\u043c\u043e \u043d\u0435\u043e\u0431\u0445\u0456\u0434\u043d\u0456 \u043f\u0430\u043a\u0435\u0442\u0438 \u0434\u043b\u044f \u0432\u0438\u043a\u043e\u043d\u0430\u043d\u043d\u044f \u043b\u043e\u0433\u0456\u0441\u0442\u0438\u0447\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457 \u0432 Python:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">import<\/span> pandas <span style=\"color: #107d3f;\">as<\/span> pd\n<span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\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> sklearn. <span style=\"color: #3366ff;\">linear_model<\/span> <span style=\"color: #008000;\">import<\/span> LogisticRegression\n<span style=\"color: #008000;\">from<\/span> sklearn <span style=\"color: #008000;\">import<\/span> metrics\n<span style=\"color: #008000;\">import<\/span> matplotlib. <span style=\"color: #3366ff;\">pyplot<\/span> <span style=\"color: #008000;\">as<\/span> plt\n<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>\u041a\u0440\u043e\u043a 2: \u041f\u0456\u0434\u0433\u043e\u043d\u043a\u0430 \u043c\u043e\u0434\u0435\u043b\u0456 \u043b\u043e\u0433\u0456\u0441\u0442\u0438\u0447\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0414\u0430\u043b\u0456 \u043c\u0438 \u0456\u043c\u043f\u043e\u0440\u0442\u0443\u0454\u043c\u043e \u043d\u0430\u0431\u0456\u0440 \u0434\u0430\u043d\u0438\u0445 \u0456 \u043f\u0456\u0434\u0431\u0435\u0440\u0435\u043c\u043e \u0434\u043e \u043d\u044c\u043e\u0433\u043e \u043c\u043e\u0434\u0435\u043b\u044c \u043b\u043e\u0433\u0456\u0441\u0442\u0438\u0447\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#import dataset from CSV file on Github\n<\/span>url = \"https:\/\/raw.githubusercontent.com\/Statology\/Python-Guides\/main\/default.csv\"\ndata = pd. <span style=\"color: #3366ff;\">read_csv<\/span> (url)\n\n<span style=\"color: #008080;\">#define the predictor variables and the response variable\n<\/span>X = data[[' <span style=\"color: #ff0000;\">student<\/span> ',' <span style=\"color: #ff0000;\">balance<\/span> ',' <span style=\"color: #ff0000;\">income<\/span> ']]\ny = data[' <span style=\"color: #ff0000;\">default<\/span> ']\n\n<span style=\"color: #008080;\">#split the dataset into training (70%) and testing (30%) sets\n<\/span>X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state=0) \n\n<span style=\"color: #008080;\">#instantiate the model\n<\/span>log_regression = LogisticRegression()\n\n<span style=\"color: #008080;\">#fit the model using the training data\n<\/span>log_regression. <span style=\"color: #3366ff;\">fit<\/span> (X_train,y_train)<\/strong><\/span><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>\u041a\u0440\u043e\u043a 3: \u041d\u0430\u043c\u0430\u043b\u044e\u0439\u0442\u0435 \u043a\u0440\u0438\u0432\u0443 ROC<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0414\u0430\u043b\u0456 \u043c\u0438 \u0440\u043e\u0437\u0440\u0430\u0445\u0443\u0454\u043c\u043e \u0456\u0441\u0442\u0438\u043d\u043d\u0438\u0439 \u0456 \u0445\u0438\u0431\u043d\u043e-\u043f\u043e\u0437\u0438\u0442\u0438\u0432\u043d\u0438\u0439 \u0440\u0456\u0432\u0435\u043d\u044c \u0456 \u0441\u0442\u0432\u043e\u0440\u0438\u043c\u043e ROC-\u043a\u0440\u0438\u0432\u0443 \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e \u043f\u0430\u043a\u0435\u0442\u0430 \u0432\u0456\u0437\u0443\u0430\u043b\u0456\u0437\u0430\u0446\u0456\u0457 \u0434\u0430\u043d\u0438\u0445 Matplotlib:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define metrics\n<\/span>y_pred_proba = log_regression. <span style=\"color: #3366ff;\">predict_proba<\/span> (X_test)[::,1]\nfpr, tpr, _ = metrics. <span style=\"color: #3366ff;\">roc_curve<\/span> (y_test, y_pred_proba)\n\n<span style=\"color: #008080;\">#create ROC curve\n<\/span>plt. <span style=\"color: #3366ff;\">plot<\/span> (fpr,tpr)\nplt. <span style=\"color: #3366ff;\">ylabel<\/span> (' <span style=\"color: #ff0000;\">True Positive Rate<\/span> ')\nplt. <span style=\"color: #3366ff;\">xlabel<\/span> (' <span style=\"color: #ff0000;\">False Positive Rate<\/span> ')\nplt. <span style=\"color: #3366ff;\">show<\/span> ()<\/strong><\/span> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-15772 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/rocpython1.png\" alt=\"\" width=\"399\" height=\"267\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">\u0427\u0438\u043c \u0431\u043b\u0438\u0436\u0447\u0435 \u043a\u0440\u0438\u0432\u0430 \u043f\u0456\u0434\u0445\u043e\u0434\u0438\u0442\u044c \u0434\u043e \u0432\u0435\u0440\u0445\u043d\u044c\u043e\u0433\u043e \u043b\u0456\u0432\u043e\u0433\u043e \u043a\u0443\u0442\u0430 \u0433\u0440\u0430\u0444\u0456\u043a\u0430, \u0442\u0438\u043c \u043a\u0440\u0430\u0449\u0435 \u043c\u043e\u0434\u0435\u043b\u044c \u043c\u043e\u0436\u0435 \u043a\u043b\u0430\u0441\u0438\u0444\u0456\u043a\u0443\u0432\u0430\u0442\u0438 \u0434\u0430\u043d\u0456 \u0437\u0430 \u043a\u0430\u0442\u0435\u0433\u043e\u0440\u0456\u044f\u043c\u0438.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u042f\u043a \u043c\u0438 \u0431\u0430\u0447\u0438\u043c\u043e \u0437 \u0433\u0440\u0430\u0444\u0456\u043a\u0430 \u0432\u0438\u0449\u0435, \u0446\u044f \u043c\u043e\u0434\u0435\u043b\u044c \u043b\u043e\u0433\u0456\u0441\u0442\u0438\u0447\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457 \u0434\u043e\u0441\u0438\u0442\u044c \u043f\u043e\u0433\u0430\u043d\u043e \u0441\u043f\u0440\u0430\u0432\u043b\u044f\u0454\u0442\u044c\u0441\u044f \u0437 \u0441\u043e\u0440\u0442\u0443\u0432\u0430\u043d\u043d\u044f\u043c \u0434\u0430\u043d\u0438\u0445 \u0437\u0430 \u043a\u0430\u0442\u0435\u0433\u043e\u0440\u0456\u044f\u043c\u0438.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0429\u043e\u0431 \u0432\u0438\u0437\u043d\u0430\u0447\u0438\u0442\u0438 \u0446\u0435 \u043a\u0456\u043b\u044c\u043a\u0456\u0441\u043d\u043e, \u043c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u043e\u0431\u0447\u0438\u0441\u043b\u0438\u0442\u0438 AUC \u2013 \u043f\u043b\u043e\u0449\u0443 \u043f\u0456\u0434 \u043a\u0440\u0438\u0432\u043e\u044e \u2013 \u044f\u043a\u0430 \u043f\u043e\u0432\u0456\u0434\u043e\u043c\u043b\u044f\u0454 \u043d\u0430\u043c, \u044f\u043a\u0430 \u0447\u0430\u0441\u0442\u0438\u043d\u0430 \u0434\u0456\u043b\u044f\u043d\u043a\u0438 \u0437\u043d\u0430\u0445\u043e\u0434\u0438\u0442\u044c\u0441\u044f \u043f\u0456\u0434 \u043a\u0440\u0438\u0432\u043e\u044e.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0427\u0438\u043c \u0431\u043b\u0438\u0436\u0447\u0435 AUC \u0434\u043e 1, \u0442\u0438\u043c \u043a\u0440\u0430\u0449\u0430 \u043c\u043e\u0434\u0435\u043b\u044c. \u041c\u043e\u0434\u0435\u043b\u044c \u0437 AUC, \u0449\u043e \u0434\u043e\u0440\u0456\u0432\u043d\u044e\u0454 0,5, \u043d\u0435 \u043a\u0440\u0430\u0449\u0430 \u0437\u0430 \u043c\u043e\u0434\u0435\u043b\u044c, \u044f\u043a\u0430 \u0432\u0438\u043a\u043e\u043d\u0443\u0454 \u0432\u0438\u043f\u0430\u0434\u043a\u043e\u0432\u0443 \u043a\u043b\u0430\u0441\u0438\u0444\u0456\u043a\u0430\u0446\u0456\u044e.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u041a\u0440\u043e\u043a 4: \u041e\u0431\u0447\u0438\u0441\u043b\u0456\u0442\u044c AUC<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u041c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u0442\u0438 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u043a\u043e\u0434, \u0449\u043e\u0431 \u043e\u0431\u0447\u0438\u0441\u043b\u0438\u0442\u0438 AUC \u043c\u043e\u0434\u0435\u043b\u0456 \u0442\u0430 \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0437\u0438\u0442\u0438 \u0439\u043e\u0433\u043e \u0432 \u043d\u0438\u0436\u043d\u044c\u043e\u043c\u0443 \u043f\u0440\u0430\u0432\u043e\u043c\u0443 \u043a\u0443\u0442\u0456 \u0433\u0440\u0430\u0444\u0456\u043a\u0430 ROC:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define metrics\n<\/span>y_pred_proba = log_regression. <span style=\"color: #3366ff;\">predict_proba<\/span> (X_test)[::,1]\nfpr, tpr, _ = metrics. <span style=\"color: #3366ff;\">roc_curve<\/span> (y_test, y_pred_proba)\nauc = metrics. <span style=\"color: #3366ff;\">roc_auc_score<\/span> (y_test, y_pred_proba)\n\n<span style=\"color: #008080;\">#create ROC curve\n<\/span>plt. <span style=\"color: #3366ff;\">plot<\/span> (fpr,tpr,label=\" <span style=\"color: #ff0000;\">AUC=<\/span> \"+str(auc))\nplt. <span style=\"color: #3366ff;\">ylabel<\/span> (' <span style=\"color: #ff0000;\">True Positive Rate<\/span> ')\nplt. <span style=\"color: #3366ff;\">xlabel<\/span> (' <span style=\"color: #ff0000;\">False Positive Rate<\/span> ')\nplt. <span style=\"color: #3366ff;\">legend<\/span> (loc=4)\nplt. <span style=\"color: #3366ff;\">show<\/span> ()<\/strong><\/span> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-15773 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/rocpython2.png\" alt=\"\" width=\"404\" height=\"275\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">AUC \u0446\u0456\u0454\u0457 \u043c\u043e\u0434\u0435\u043b\u0456 \u043b\u043e\u0433\u0456\u0441\u0442\u0438\u0447\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457 \u0434\u043e\u0440\u0456\u0432\u043d\u044e\u0454 <strong>0,5602<\/strong> . \u041e\u0441\u043a\u0456\u043b\u044c\u043a\u0438 \u0446\u0435\u0439 \u043f\u043e\u043a\u0430\u0437\u043d\u0438\u043a \u0431\u043b\u0438\u0437\u044c\u043a\u0438\u0439 \u0434\u043e 0,5, \u0446\u0435 \u043f\u0456\u0434\u0442\u0432\u0435\u0440\u0434\u0436\u0443\u0454, \u0449\u043e \u043c\u043e\u0434\u0435\u043b\u044c \u043f\u043e\u0433\u0430\u043d\u043e \u043a\u043b\u0430\u0441\u0438\u0444\u0456\u043a\u0443\u0454 \u0434\u0430\u043d\u0456.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\u041f\u043e\u0432\u2019\u044f\u0437\u0430\u043d\u0435:<\/strong> <a href=\"https:\/\/statorials.org\/uk\/\u043c\u0430\u043b\u044e\u0432\u0430\u0442\u0438-\u043a\u0456\u043b\u044c\u043a\u0430-\u043a\u0440\u0438\u0432\u0438\u0445-roc-python\/\">\u042f\u043a \u043f\u043e\u0431\u0443\u0434\u0443\u0432\u0430\u0442\u0438 \u043a\u0456\u043b\u044c\u043a\u0430 \u043a\u0440\u0438\u0432\u0438\u0445 ROC \u0443 Python<\/a><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u041b\u043e\u0433\u0456\u0441\u0442\u0438\u0447\u043d\u0430 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u044f \u2013 \u0446\u0435 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u043d\u0438\u0439 \u043c\u0435\u0442\u043e\u0434, \u044f\u043a\u0438\u0439 \u043c\u0438 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0454\u043c\u043e \u0434\u043b\u044f \u043f\u0456\u0434\u0433\u043e\u043d\u043a\u0438 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0439\u043d\u043e\u0457 \u043c\u043e\u0434\u0435\u043b\u0456, \u043a\u043e\u043b\u0438 \u0437\u043c\u0456\u043d\u043d\u0430 \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0456 \u0454 \u0434\u0432\u0456\u0439\u043a\u043e\u0432\u043e\u044e. \u0429\u043e\u0431 \u043e\u0446\u0456\u043d\u0438\u0442\u0438, \u043d\u0430\u0441\u043a\u0456\u043b\u044c\u043a\u0438 \u043c\u043e\u0434\u0435\u043b\u044c \u043b\u043e\u0433\u0456\u0441\u0442\u0438\u0447\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457 \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0430\u0454 \u043d\u0430\u0431\u043e\u0440\u0443 \u0434\u0430\u043d\u0438\u0445, \u043c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0440\u043e\u0437\u0433\u043b\u044f\u043d\u0443\u0442\u0438 \u0442\u0430\u043a\u0456 \u0434\u0432\u0430 \u043f\u043e\u043a\u0430\u0437\u043d\u0438\u043a\u0438: \u0427\u0443\u0442\u043b\u0438\u0432\u0456\u0441\u0442\u044c: \u0439\u043c\u043e\u0432\u0456\u0440\u043d\u0456\u0441\u0442\u044c \u0442\u043e\u0433\u043e, \u0449\u043e \u043c\u043e\u0434\u0435\u043b\u044c \u043f\u0435\u0440\u0435\u0434\u0431\u0430\u0447\u0430\u0454 \u043f\u043e\u0437\u0438\u0442\u0438\u0432\u043d\u0438\u0439 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442 \u0434\u043b\u044f \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f, \u043a\u043e\u043b\u0438 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442 \u043d\u0430\u0441\u043f\u0440\u0430\u0432\u0434\u0456 \u043f\u043e\u0437\u0438\u0442\u0438\u0432\u043d\u0438\u0439. \u0426\u0435 \u0442\u0430\u043a\u043e\u0436 \u043d\u0430\u0437\u0438\u0432\u0430\u0454\u0442\u044c\u0441\u044f \u00ab\u0441\u043f\u0440\u0430\u0432\u0436\u043d\u0456\u0439 \u043f\u043e\u0437\u0438\u0442\u0438\u0432\u043d\u0438\u0439 \u043f\u043e\u043a\u0430\u0437\u043d\u0438\u043a\u00bb. \u0421\u043f\u0435\u0446\u0438\u0444\u0456\u0447\u043d\u0456\u0441\u0442\u044c: \u0439\u043c\u043e\u0432\u0456\u0440\u043d\u0456\u0441\u0442\u044c [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u042f\u043a \u043d\u0430\u043c\u0430\u043b\u044e\u0432\u0430\u0442\u0438 ROC-\u043a\u0440\u0438\u0432\u0443 \u0432 Python (\u043a\u0440\u043e\u043a \u0437\u0430 \u043a\u0440\u043e\u043a\u043e\u043c) - Statorials<\/title>\n<meta name=\"description\" content=\"\u0426\u0435\u0439 \u043f\u0456\u0434\u0440\u0443\u0447\u043d\u0438\u043a \u043f\u043e\u044f\u0441\u043d\u044e\u0454, \u044f\u043a 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