{"id":866,"date":"2023-07-28T11:48:31","date_gmt":"2023-07-28T11:48:31","guid":{"rendered":"https:\/\/statorials.org\/uk\/%d0%b7%d0%b0%d0%bb%d0%b8%d1%88%d0%ba%d0%be%d0%b2%d0%b8%d0%b8-%d0%b3%d1%80%d0%b0%d1%84%d1%96%d0%ba-python\/"},"modified":"2023-07-28T11:48:31","modified_gmt":"2023-07-28T11:48:31","slug":"%d0%b7%d0%b0%d0%bb%d0%b8%d1%88%d0%ba%d0%be%d0%b2%d0%b8%d0%b8-%d0%b3%d1%80%d0%b0%d1%84%d1%96%d0%ba-python","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/%d0%b7%d0%b0%d0%bb%d0%b8%d1%88%d0%ba%d0%be%d0%b2%d0%b8%d0%b8-%d0%b3%d1%80%d0%b0%d1%84%d1%96%d0%ba-python\/","title":{"rendered":"\u042f\u043a \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0437\u0430\u043b\u0438\u0448\u043a\u043e\u0432\u0438\u0439 \u0433\u0440\u0430\u0444\u0456\u043a \u0443 python"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>\u0414\u0456\u0430\u0433\u0440\u0430\u043c\u0430 \u0437\u0430\u043b\u0438\u0448\u043a\u0456\u0432<\/strong> \u2013 \u0446\u0435 \u0442\u0438\u043f \u0433\u0440\u0430\u0444\u0456\u043a\u0430, \u044f\u043a\u0438\u0439 \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0436\u0430\u0454 \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u043d\u0456 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0432\u0456\u0434\u043d\u043e\u0441\u043d\u043e \u0437\u0430\u043b\u0438\u0448\u043a\u0456\u0432 <a href=\"https:\/\/statorials.org\/uk\/\u043b\u0456\u043d\u0456\u0438\u043d\u0430-\u0440\u0435\u0433\u0440\u0435\u0441\u0456\u044f-python\/\" target=\"_blank\" rel=\"noopener\">\u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0439\u043d\u043e\u0457 \u043c\u043e\u0434\u0435\u043b\u0456<\/a> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0426\u0435\u0439 \u0442\u0438\u043f \u0433\u0440\u0430\u0444\u0456\u043a\u0430 \u0447\u0430\u0441\u0442\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0454\u0442\u044c\u0441\u044f \u0434\u043b\u044f \u043e\u0446\u0456\u043d\u043a\u0438 \u0442\u043e\u0433\u043e, \u0447\u0438 \u043c\u043e\u0434\u0435\u043b\u044c \u043b\u0456\u043d\u0456\u0439\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457 \u043f\u0456\u0434\u0445\u043e\u0434\u0438\u0442\u044c \u0434\u043b\u044f \u0434\u0430\u043d\u043e\u0433\u043e \u043d\u0430\u0431\u043e\u0440\u0443 \u0434\u0430\u043d\u0438\u0445, \u0456 \u0434\u043b\u044f \u043f\u0435\u0440\u0435\u0432\u0456\u0440\u043a\u0438 \u0437\u0430\u043b\u0438\u0448\u043a\u0456\u0432 \u043d\u0430 <a href=\"https:\/\/statorials.org\/uk\/\u0440\u0435\u0433\u0440\u0435\u0441\u0456\u044f-\u0433\u0435\u0442\u0435\u0440\u043e\u0441\u043a\u0435\u0434\u0430\u0441\u0442\u0438\u0447\u043d\u043e\u0441\u0442\u0456\/\" target=\"_blank\" rel=\"noopener\">\u0433\u0435\u0442\u0435\u0440\u043e\u0441\u043a\u0435\u0434\u0430\u0441\u0442\u0438\u0447\u043d\u0456\u0441\u0442\u044c<\/a> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0423 \u0446\u044c\u043e\u043c\u0443 \u043f\u043e\u0441\u0456\u0431\u043d\u0438\u043a\u0443 \u043f\u043e\u044f\u0441\u043d\u044e\u0454\u0442\u044c\u0441\u044f, \u044f\u043a \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0433\u0440\u0430\u0444\u0456\u043a \u0437\u0430\u043b\u0438\u0448\u043a\u0456\u0432 \u0434\u043b\u044f \u043c\u043e\u0434\u0435\u043b\u0456 \u043b\u0456\u043d\u0456\u0439\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457 \u0432 Python.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u041f\u0440\u0438\u043a\u043b\u0430\u0434: \u0434\u0456\u0430\u0433\u0440\u0430\u043c\u0430 \u0437\u0430\u043b\u0438\u0448\u043a\u0456\u0432 \u0443 Python<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0414\u043b\u044f \u0446\u044c\u043e\u0433\u043e \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0443 \u043c\u0438 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u0454\u043c\u043e \u043d\u0430\u0431\u0456\u0440 \u0434\u0430\u043d\u0438\u0445, \u044f\u043a\u0438\u0439 \u043e\u043f\u0438\u0441\u0443\u0454 \u0430\u0442\u0440\u0438\u0431\u0443\u0442\u0438 10 \u0431\u0430\u0441\u043a\u0435\u0442\u0431\u043e\u043b\u0456\u0441\u0442\u0456\u0432:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">import<\/span> numpy <span style=\"color: #107d3f;\">as<\/span> np\n<span style=\"color: #107d3f;\">import<\/span> pandas <span style=\"color: #107d3f;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#create dataset<\/span>\ndf = pd.DataFrame({'rating': [90, 85, 82, 88, 94, 90, 76, 75, 87, 86],\n                   'points': [25, 20, 14, 16, 27, 20, 12, 15, 14, 19],\n                   'assists': [5, 7, 7, 8, 5, 7, 6, 9, 9, 5],\n                   'rebounds': [11, 8, 10, 6, 6, 9, 6, 10, 10, 7]})\n\n<span style=\"color: #008080;\">#view dataset\n<\/span>df\n\n\trating points assists rebounds\n0 90 25 5 11\n1 85 20 7 8\n2 82 14 7 10\n3 88 16 8 6\n4 94 27 5 6\n5 90 20 7 9\n6 76 12 6 6\n7 75 15 9 10\n8 87 14 9 10\n9 86 19 5 7<\/strong><\/pre>\n<h3> <strong><span style=\"color: #000000;\">\u0414\u0456\u0430\u0433\u0440\u0430\u043c\u0430 \u0437\u0430\u043b\u0438\u0448\u043a\u0443 \u0434\u043b\u044f \u043f\u0440\u043e\u0441\u0442\u043e\u0457 \u043b\u0456\u043d\u0456\u0439\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457<\/span><\/strong><\/h3>\n<p> <span style=\"color: #000000;\">\u041f\u0440\u0438\u043f\u0443\u0441\u0442\u0456\u043c\u043e, \u0449\u043e \u043c\u0438 \u043f\u0456\u0434\u0431\u0438\u0440\u0430\u0454\u043c\u043e \u043f\u0440\u043e\u0441\u0442\u0443 \u043c\u043e\u0434\u0435\u043b\u044c \u043b\u0456\u043d\u0456\u0439\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457, \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u044e\u0447\u0438 <em>\u0431\u0430\u043b\u0438<\/em> \u044f\u043a \u0437\u043c\u0456\u043d\u043d\u0443 \u043f\u0440\u0435\u0434\u0438\u043a\u0442\u043e\u0440\u0430 \u0442\u0430 <em>\u043e\u0446\u0456\u043d\u043a\u0443<\/em> \u044f\u043a \u0437\u043c\u0456\u043d\u043d\u0443 \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0456:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#import necessary libraries<\/span>\n<span style=\"color: #107d3f;\">import<\/span> matplotlib.pyplot <span style=\"color: #107d3f;\">as<\/span> plt\n<span style=\"color: #107d3f;\">import<\/span> statsmodels.api <span style=\"color: #107d3f;\">as<\/span> sm\n<span style=\"color: #107d3f;\">from<\/span> statsmodels.formula.api <span style=\"color: #107d3f;\">import<\/span> ols\n\n<span style=\"color: #008080;\">#fit simple linear regression model\n<\/span>model = ols('rating ~ points', data=df). <span style=\"color: #3366ff;\">fit<\/span> ()\n\n<span style=\"color: #008080;\">#view model summary\n<\/span>print(model.summary())\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u041c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0437\u0430\u043b\u0438\u0448\u043a\u043e\u0432\u0438\u0439 \u0430\u0431\u043e \u043f\u0456\u0434\u0456\u0433\u043d\u0430\u043d\u0438\u0439 \u0433\u0440\u0430\u0444\u0456\u043a \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e <a href=\"https:\/\/www.statsmodels.org\/stable\/generated\/statsmodels.graphics.regressionplots.plot_regress_exog.html\" target=\"_blank\" rel=\"noopener\">\u0444\u0443\u043d\u043a\u0446\u0456\u0457 plot_regress_exog()<\/a> \u0456\u0437 \u0431\u0456\u0431\u043b\u0456\u043e\u0442\u0435\u043a\u0438 statsmodels:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define figure size\n<\/span>fig = plt.figure(figsize=(12,8))\n\n<span style=\"color: #008080;\">#produce regression plots<\/span>\nfig = sm.graphics.plot_regress_exog(model, ' <span style=\"color: #008000;\">points<\/span> ', fig=fig)\n<\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-9430 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/residplotpython1.png\" alt=\"\u0414\u0456\u0430\u0433\u0440\u0430\u043c\u0430 \u0437\u0430\u043b\u0438\u0448\u043a\u0443 \u0432 Python\" width=\"624\" height=\"415\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">\u0412\u0438\u0440\u043e\u0431\u043b\u044f\u0454\u0442\u044c\u0441\u044f \u0447\u043e\u0442\u0438\u0440\u0438 \u0434\u0456\u043b\u044f\u043d\u043a\u0438. \u0423 \u0432\u0435\u0440\u0445\u043d\u044c\u043e\u043c\u0443 \u043f\u0440\u0430\u0432\u043e\u043c\u0443 \u043a\u0443\u0442\u0456 \u2014 \u0446\u0435 \u0437\u0430\u043b\u0438\u0448\u043a\u043e\u0432\u0438\u0439 \u0433\u0440\u0430\u0444\u0456\u043a \u043f\u043e\u0440\u0456\u0432\u043d\u044f\u043d\u043e \u0437\u0456 \u0441\u043a\u043e\u0440\u0438\u0433\u043e\u0432\u0430\u043d\u0438\u043c \u0433\u0440\u0430\u0444\u0456\u043a\u043e\u043c. \u0412\u0456\u0441\u044c \u0430\u0431\u0441\u0446\u0438\u0441 \u043d\u0430 \u0446\u044c\u043e\u043c\u0443 \u0433\u0440\u0430\u0444\u0456\u043a\u0443 \u043f\u043e\u043a\u0430\u0437\u0443\u0454 \u0444\u0430\u043a\u0442\u0438\u0447\u043d\u0456 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f <em>\u0442\u043e\u0447\u043e\u043a<\/em> \u0437\u043c\u0456\u043d\u043d\u043e\u0457 \u043f\u0440\u0435\u0434\u0438\u043a\u0442\u043e\u0440\u0430, \u0430 \u0432\u0456\u0441\u044c \u043e\u0440\u0434\u0438\u043d\u0430\u0442 \u043f\u043e\u043a\u0430\u0437\u0443\u0454 \u0437\u0430\u043b\u0438\u0448\u043a\u043e\u0432\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0434\u043b\u044f \u0446\u044c\u043e\u0433\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u041e\u0441\u043a\u0456\u043b\u044c\u043a\u0438 \u0437\u0430\u043b\u0438\u0448\u043a\u0438 \u0437\u0434\u0430\u044e\u0442\u044c\u0441\u044f \u0432\u0438\u043f\u0430\u0434\u043a\u043e\u0432\u0438\u043c \u0447\u0438\u043d\u043e\u043c \u0440\u043e\u0437\u043a\u0438\u0434\u0430\u043d\u0438\u043c\u0438 \u043d\u0430\u0432\u043a\u043e\u043b\u043e \u043d\u0443\u043b\u044f, \u0446\u0435 \u0432\u043a\u0430\u0437\u0443\u0454 \u043d\u0430 \u0442\u0435, \u0449\u043e \u0433\u0435\u0442\u0435\u0440\u043e\u0441\u043a\u0435\u0434\u0430\u0441\u0442\u0438\u0447\u043d\u0456\u0441\u0442\u044c \u043d\u0435 \u0454 \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u043e\u044e \u0434\u043b\u044f \u043f\u0440\u0435\u0434\u0438\u043a\u0442\u043e\u0440\u043d\u043e\u0457 \u0437\u043c\u0456\u043d\u043d\u043e\u0457.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u0413\u0440\u0430\u0444\u0456\u043a\u0438 \u0437\u0430\u043b\u0438\u0448\u043a\u0456\u0432 \u0434\u043b\u044f \u043c\u043d\u043e\u0436\u0438\u043d\u043d\u043e\u0457 \u043b\u0456\u043d\u0456\u0439\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u041f\u0440\u0438\u043f\u0443\u0441\u0442\u0456\u043c\u043e, \u0449\u043e \u043d\u0430\u0442\u043e\u043c\u0456\u0441\u0442\u044c \u043c\u0438 \u043f\u0456\u0434\u0445\u043e\u0434\u0438\u043c\u043e \u0434\u043e \u043c\u043e\u0434\u0435\u043b\u0456 \u043c\u043d\u043e\u0436\u0438\u043d\u043d\u043e\u0457 \u043b\u0456\u043d\u0456\u0439\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457, \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u044e\u0447\u0438 <em>\u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0438\u0432\u043d\u0456 \u043f\u0435\u0440\u0435\u0434\u0430\u0447\u0456<\/em> \u0442\u0430 <em>\u043f\u0456\u0434\u0431\u0438\u0440\u0430\u043d\u043d\u044f<\/em> \u044f\u043a \u0437\u043c\u0456\u043d\u043d\u0443 \u043f\u0440\u043e\u0433\u043d\u043e\u0441\u0442\u0438\u043a\u0443 \u0442\u0430 <em>\u0440\u0435\u0439\u0442\u0438\u043d\u0433<\/em> \u044f\u043a \u0437\u043c\u0456\u043d\u043d\u0443 \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0456:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit multiple linear regression model\n<\/span>model = ols('rating ~ assists + rebounds', data=df). <span style=\"color: #3366ff;\">fit<\/span> ()\n\n<span style=\"color: #008080;\">#view model summary\n<\/span>print(model.summary())\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0417\u043d\u043e\u0432\u0443 \u0436 \u0442\u0430\u043a\u0438, \u043c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0433\u0440\u0430\u0444\u0456\u043a \u0437\u0430\u043b\u0435\u0436\u043d\u043e\u0441\u0442\u0456 \u0437\u0430\u043b\u0438\u0448\u043a\u0456\u0432 \u0432\u0456\u0434 \u043f\u0440\u0435\u0434\u0438\u043a\u0442\u043e\u0440\u0456\u0432 \u0434\u043b\u044f \u043a\u043e\u0436\u043d\u043e\u0433\u043e \u043e\u043a\u0440\u0435\u043c\u043e\u0433\u043e \u043f\u0440\u0435\u0434\u0438\u043a\u0442\u043e\u0440\u0430 \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e <a href=\"https:\/\/www.statsmodels.org\/stable\/generated\/statsmodels.graphics.regressionplots.plot_regress_exog.html\" target=\"_blank\" rel=\"noopener\">\u0444\u0443\u043d\u043a\u0446\u0456\u0457 plot_regress_exog()<\/a> \u0456\u0437 \u0431\u0456\u0431\u043b\u0456\u043e\u0442\u0435\u043a\u0438 statsmodels.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u041d\u0430\u043f\u0440\u0438\u043a\u043b\u0430\u0434, \u043e\u0441\u044c \u044f\u043a \u0432\u0438\u0433\u043b\u044f\u0434\u0430\u0454 \u0433\u0440\u0430\u0444\u0456\u043a \u0437\u0430\u043b\u0438\u0448\u043a\u0456\u0432\/\u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0456\u0432 \u0434\u043b\u044f <em>\u0434\u043e\u043f\u043e\u043c\u0456\u0436\u043d\u0438\u0445<\/em> \u0437\u043c\u0456\u043d\u043d\u0438\u0445 \u043f\u0440\u0435\u0434\u0438\u043a\u0442\u043e\u0440\u0430:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create residual vs. predictor plot for 'assists'\n<\/span>fig = plt.figure(figsize=(12,8))\nfig = sm.graphics.plot_regress_exog(model, ' <span style=\"color: #008000;\">assists<\/span> ', fig=fig)\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-9431 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/residplotpython2.png\" alt=\"\u0417\u0430\u043b\u0438\u0448\u043a\u043e\u0432\u0430 \u0430\u0431\u043e \u0441\u043a\u043e\u0440\u0438\u0433\u043e\u0432\u0430\u043d\u0430 \u0437\u0435\u043c\u043b\u044f\" width=\"625\" height=\"409\" srcset=\"\" sizes=\"\"><\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0406 \u043e\u0441\u044c \u044f\u043a \u0432\u0438\u0433\u043b\u044f\u0434\u0430\u0454 \u0433\u0440\u0430\u0444\u0456\u043a \u0437\u0430\u043b\u0438\u0448\u043a\u0443\/\u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0443\u0432\u0430\u043d\u043d\u044f \u0434\u043b\u044f <em>\u0432\u0456\u0434\u0441\u043a\u043e\u043a\u0456\u0432<\/em> \u0437\u043c\u0456\u043d\u043d\u043e\u0457 \u043f\u0440\u0435\u0434\u0438\u043a\u0442\u043e\u0440\u0430:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create residual vs. predictor plot for 'assists'\n<\/span>fig = plt.figure(figsize=(12,8))\nfig = sm.graphics.plot_regress_exog(model, ' <span style=\"color: #008000;\">rebounds<\/span> ', fig=fig)\n<\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-9432 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/residplotpython3.png\" alt=\"\u0417\u0430\u043b\u0438\u0448\u043a\u043e\u0432\u0438\u0439 \u0430\u0431\u043e \u0441\u043a\u043e\u0440\u0438\u0433\u043e\u0432\u0430\u043d\u0438\u0439 \u0433\u0440\u0430\u0444\u0456\u043a \u0443 Python\" width=\"624\" height=\"408\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">\u041d\u0430 \u043e\u0431\u043e\u0445 \u0433\u0440\u0430\u0444\u0456\u043a\u0430\u0445 \u0437\u0430\u043b\u0438\u0448\u043a\u0438 \u0432\u0438\u043f\u0430\u0434\u043a\u043e\u0432\u043e \u0440\u043e\u0437\u043a\u0438\u0434\u0430\u043d\u0456 \u043d\u0430\u0432\u043a\u043e\u043b\u043e \u043d\u0443\u043b\u044f, \u0449\u043e \u0432\u043a\u0430\u0437\u0443\u0454 \u043d\u0430 \u0442\u0435, \u0449\u043e \u0433\u0435\u0442\u0435\u0440\u043e\u0441\u043a\u0435\u0434\u0430\u0441\u0442\u0438\u0447\u043d\u0456\u0441\u0442\u044c \u043d\u0435 \u0454 \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u043e\u044e \u0434\u043b\u044f \u0436\u043e\u0434\u043d\u043e\u0457 \u0437\u0456 \u0437\u043c\u0456\u043d\u043d\u0438\u0445 \u043f\u0440\u0435\u0434\u0438\u043a\u0442\u043e\u0440\u0430 \u0432 \u043c\u043e\u0434\u0435\u043b\u0456.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u0414\u0456\u0430\u0433\u0440\u0430\u043c\u0430 \u0437\u0430\u043b\u0438\u0448\u043a\u0456\u0432 \u2013 \u0446\u0435 \u0442\u0438\u043f \u0433\u0440\u0430\u0444\u0456\u043a\u0430, \u044f\u043a\u0438\u0439 \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0436\u0430\u0454 \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u043d\u0456 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0432\u0456\u0434\u043d\u043e\u0441\u043d\u043e \u0437\u0430\u043b\u0438\u0448\u043a\u0456\u0432 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0439\u043d\u043e\u0457 \u043c\u043e\u0434\u0435\u043b\u0456 . \u0426\u0435\u0439 \u0442\u0438\u043f \u0433\u0440\u0430\u0444\u0456\u043a\u0430 \u0447\u0430\u0441\u0442\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0454\u0442\u044c\u0441\u044f \u0434\u043b\u044f \u043e\u0446\u0456\u043d\u043a\u0438 \u0442\u043e\u0433\u043e, \u0447\u0438 \u043c\u043e\u0434\u0435\u043b\u044c \u043b\u0456\u043d\u0456\u0439\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457 \u043f\u0456\u0434\u0445\u043e\u0434\u0438\u0442\u044c \u0434\u043b\u044f \u0434\u0430\u043d\u043e\u0433\u043e \u043d\u0430\u0431\u043e\u0440\u0443 \u0434\u0430\u043d\u0438\u0445, \u0456 \u0434\u043b\u044f \u043f\u0435\u0440\u0435\u0432\u0456\u0440\u043a\u0438 \u0437\u0430\u043b\u0438\u0448\u043a\u0456\u0432 \u043d\u0430 \u0433\u0435\u0442\u0435\u0440\u043e\u0441\u043a\u0435\u0434\u0430\u0441\u0442\u0438\u0447\u043d\u0456\u0441\u0442\u044c . \u0423 \u0446\u044c\u043e\u043c\u0443 \u043f\u043e\u0441\u0456\u0431\u043d\u0438\u043a\u0443 \u043f\u043e\u044f\u0441\u043d\u044e\u0454\u0442\u044c\u0441\u044f, \u044f\u043a \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0433\u0440\u0430\u0444\u0456\u043a \u0437\u0430\u043b\u0438\u0448\u043a\u0456\u0432 \u0434\u043b\u044f \u043c\u043e\u0434\u0435\u043b\u0456 \u043b\u0456\u043d\u0456\u0439\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457 \u0432 Python. \u041f\u0440\u0438\u043a\u043b\u0430\u0434: \u0434\u0456\u0430\u0433\u0440\u0430\u043c\u0430 [&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 \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0437\u0430\u043b\u0438\u0448\u043a\u043e\u0432\u0438\u0439 \u0433\u0440\u0430\u0444\u0456\u043a \u0443 Python - Statorials<\/title>\n<meta name=\"description\" content=\"\u041f\u0440\u043e\u0441\u0442\u0435 \u043f\u043e\u044f\u0441\u043d\u0435\u043d\u043d\u044f \u0442\u043e\u0433\u043e, \u044f\u043a \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0434\u0456\u0430\u0433\u0440\u0430\u043c\u0443 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