{"id":1645,"date":"2023-07-25T12:50:55","date_gmt":"2023-07-25T12:50:55","guid":{"rendered":"https:\/\/statorials.org\/uk\/%d0%bf%d1%96%d0%b4%d0%b3%d0%be%d0%bd%d0%ba%d0%b0-%d0%ba%d1%80%d0%b8%d0%b2%d0%be%d1%96-python\/"},"modified":"2023-07-25T12:50:55","modified_gmt":"2023-07-25T12:50:55","slug":"%d0%bf%d1%96%d0%b4%d0%b3%d0%be%d0%bd%d0%ba%d0%b0-%d0%ba%d1%80%d0%b8%d0%b2%d0%be%d1%96-python","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/%d0%bf%d1%96%d0%b4%d0%b3%d0%be%d0%bd%d0%ba%d0%b0-%d0%ba%d1%80%d0%b8%d0%b2%d0%be%d1%96-python\/","title":{"rendered":"\u041f\u0456\u0434\u0433\u043e\u043d\u043a\u0430 \u043a\u0440\u0438\u0432\u043e\u0457 \u0432 python (\u0437 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0430\u043c\u0438)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u0427\u0430\u0441\u0442\u043e \u0432\u0430\u043c \u043c\u043e\u0436\u0435 \u0437\u043d\u0430\u0434\u043e\u0431\u0438\u0442\u0438\u0441\u044f \u043f\u0456\u0434\u0456\u0433\u043d\u0430\u0442\u0438 \u043a\u0440\u0438\u0432\u0443 \u0434\u043e \u043d\u0430\u0431\u043e\u0440\u0443 \u0434\u0430\u043d\u0438\u0445 \u0443 Python.<\/span> <\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-16261 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/courbepython3.png\" alt=\"\" width=\"392\" height=\"265\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">\u0423 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u043e\u043c\u0443 \u043f\u043e\u043a\u0440\u043e\u043a\u043e\u0432\u043e\u043c\u0443 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0456 \u043f\u043e\u044f\u0441\u043d\u044e\u0454\u0442\u044c\u0441\u044f, \u044f\u043a \u043f\u0456\u0434\u0456\u0433\u043d\u0430\u0442\u0438 \u043a\u0440\u0438\u0432\u0456 \u0434\u043e \u0434\u0430\u043d\u0438\u0445 \u0443 Python \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e \u0444\u0443\u043d\u043a\u0446\u0456\u0457 <a href=\"https:\/\/numpy.org\/doc\/stable\/reference\/generated\/numpy.polyfit.html\" target=\"_blank\" rel=\"noopener\">numpy.polyfit()<\/a> \u0456 \u044f\u043a \u0432\u0438\u0437\u043d\u0430\u0447\u0438\u0442\u0438, \u044f\u043a\u0430 \u043a\u0440\u0438\u0432\u0430 \u043d\u0430\u0439\u043a\u0440\u0430\u0449\u0435 \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0430\u0454 \u0434\u0430\u043d\u0438\u043c.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u041a\u0440\u043e\u043a 1: \u0421\u0442\u0432\u043e\u0440\u0435\u043d\u043d\u044f \u0442\u0430 \u0432\u0456\u0437\u0443\u0430\u043b\u0456\u0437\u0430\u0446\u0456\u044f \u0434\u0430\u043d\u0438\u0445<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0414\u0430\u0432\u0430\u0439\u0442\u0435 \u043f\u043e\u0447\u043d\u0435\u043c\u043e \u0437\u0456 \u0441\u0442\u0432\u043e\u0440\u0435\u043d\u043d\u044f \u043f\u0456\u0434\u0440\u043e\u0431\u043b\u0435\u043d\u043e\u0433\u043e \u043d\u0430\u0431\u043e\u0440\u0443 \u0434\u0430\u043d\u0438\u0445, \u0430 \u043f\u043e\u0442\u0456\u043c \u0441\u0442\u0432\u043e\u0440\u0438\u043c\u043e \u0434\u0456\u0430\u0433\u0440\u0430\u043c\u0443 \u0440\u043e\u0437\u0441\u0456\u044e\u0432\u0430\u043d\u043d\u044f \u0434\u043b\u044f \u0432\u0456\u0437\u0443\u0430\u043b\u0456\u0437\u0430\u0446\u0456\u0457 \u0434\u0430\u043d\u0438\u0445:<\/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> matplotlib. <span style=\"color: #3366ff;\">pyplot<\/span> <span style=\"color: #008000;\">as<\/span> plt\n\n<span style=\"color: #008080;\">#createDataFrame\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">x<\/span> ': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],\n                   ' <span style=\"color: #ff0000;\">y<\/span> ': [3, 14, 23, 25, 23, 15, 9, 5, 9, 13, 17, 24, 32, 36, 46]})\n\n<span style=\"color: #008080;\">#create scatterplot of x vs. y\n<\/span>plt. <span style=\"color: #3366ff;\">scatter<\/span> (df. <span style=\"color: #3366ff;\">x<\/span> , df. <span style=\"color: #3366ff;\">y<\/span> )<\/strong> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-16259 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/courbepython1.png\" alt=\"\" width=\"399\" height=\"269\" srcset=\"\" sizes=\"\"><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u041a\u0440\u043e\u043a 2: \u041d\u0430\u043b\u0430\u0448\u0442\u0443\u0439\u0442\u0435 \u043a\u0456\u043b\u044c\u043a\u0430 \u043a\u0440\u0438\u0432\u0438\u0445<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0422\u043e\u0434\u0456 \u0434\u0430\u0432\u0430\u0439\u0442\u0435 \u043f\u0456\u0434\u0431\u0435\u0440\u0435\u043c\u043e \u043a\u0456\u043b\u044c\u043a\u0430 \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u043f\u043e\u043b\u0456\u043d\u043e\u043c\u0456\u0430\u043b\u044c\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457 \u0434\u043e \u0434\u0430\u043d\u0438\u0445 \u0456 \u0432\u0456\u0437\u0443\u0430\u043b\u0456\u0437\u0443\u0454\u043c\u043e \u043a\u0440\u0438\u0432\u0443 \u043a\u043e\u0436\u043d\u043e\u0457 \u043c\u043e\u0434\u0435\u043b\u0456 \u043d\u0430 \u043e\u0434\u043d\u043e\u043c\u0443 \u0433\u0440\u0430\u0444\u0456\u043a\u0443:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n\n<span style=\"color: #008080;\">#fit polynomial models up to degree 5\n<\/span>model1 = np. <span style=\"color: #3366ff;\">poly1d<\/span> (np. <span style=\"color: #3366ff;\">polyfit<\/span> (df. <span style=\"color: #3366ff;\">x<\/span> , df. <span style=\"color: #3366ff;\">y<\/span> , 1))\nmodel2 = np. <span style=\"color: #3366ff;\">poly1d<\/span> (np. <span style=\"color: #3366ff;\">polyfit<\/span> (df. <span style=\"color: #3366ff;\">x<\/span> , df. <span style=\"color: #3366ff;\">y<\/span> , 2))\nmodel3 = np. <span style=\"color: #3366ff;\">poly1d<\/span> (np. <span style=\"color: #3366ff;\">polyfit<\/span> (df. <span style=\"color: #3366ff;\">x<\/span> , df. <span style=\"color: #3366ff;\">y<\/span> , 3))\nmodel4 = np. <span style=\"color: #3366ff;\">poly1d<\/span> (np. <span style=\"color: #3366ff;\">polyfit<\/span> (df. <span style=\"color: #3366ff;\">x<\/span> , df. <span style=\"color: #3366ff;\">y<\/span> , 4))\nmodel5 = np. <span style=\"color: #3366ff;\">poly1d<\/span> (np. <span style=\"color: #3366ff;\">polyfit<\/span> (df. <span style=\"color: #3366ff;\">x<\/span> , df. <span style=\"color: #3366ff;\">y<\/span> , 5))\n\n<span style=\"color: #008080;\">#create scatterplot\n<\/span>polyline = np. <span style=\"color: #3366ff;\">linspace<\/span> (1, 15, 50)\nplt. <span style=\"color: #3366ff;\">scatter<\/span> (df. <span style=\"color: #3366ff;\">x<\/span> , df. <span style=\"color: #3366ff;\">y<\/span> )\n\n<span style=\"color: #008080;\">#add fitted polynomial lines to scatterplot \n<\/span>plt. <span style=\"color: #3366ff;\">plot<\/span> (polyline, model1(polyline), color=' <span style=\"color: #ff0000;\">green<\/span> ')\nplt. <span style=\"color: #3366ff;\">plot<\/span> (polyline, model2(polyline), color=' <span style=\"color: #ff0000;\">red<\/span> ')\nplt. <span style=\"color: #3366ff;\">plot<\/span> (polyline, model3(polyline), color=' <span style=\"color: #ff0000;\">purple<\/span> ')\nplt. <span style=\"color: #3366ff;\">plot<\/span> (polyline, model4(polyline), color=' <span style=\"color: #ff0000;\">blue<\/span> ')\nplt. <span style=\"color: #3366ff;\">plot<\/span> (polyline, model5(polyline), color=' <span style=\"color: #ff0000;\">orange<\/span> ')\nplt. <span style=\"color: #3366ff;\">show<\/span> ()\n<\/span><\/span><\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-16260 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/courbepython2.png\" alt=\"\" width=\"408\" height=\"279\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">\u0429\u043e\u0431 \u0432\u0438\u0437\u043d\u0430\u0447\u0438\u0442\u0438, \u044f\u043a\u0430 \u043a\u0440\u0438\u0432\u0430 \u043d\u0430\u0439\u043a\u0440\u0430\u0449\u0435 \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0430\u0454 \u0434\u0430\u043d\u0438\u043c, \u043c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u043f\u043e\u0434\u0438\u0432\u0438\u0442\u0438\u0441\u044f \u043d\u0430 <a href=\"https:\/\/statorials.org\/uk\/r-\u043a\u0432\u0430\u0434\u0440\u0430\u0442\u0456\u0432-\u0443-r-\u043f\u0456\u0434\u0445\u043e\u0434\u0438\u0442\u044c\/\" target=\"_blank\" rel=\"noopener\">\u0441\u043a\u043e\u0440\u0438\u0433\u043e\u0432\u0430\u043d\u0438\u0439 R-\u043a\u0432\u0430\u0434\u0440\u0430\u0442<\/a> \u043a\u043e\u0436\u043d\u043e\u0457 \u043c\u043e\u0434\u0435\u043b\u0456.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0426\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u043f\u043e\u0432\u0456\u0434\u043e\u043c\u043b\u044f\u0454 \u043d\u0430\u043c \u043f\u0440\u043e \u0432\u0456\u0434\u0441\u043e\u0442\u043e\u043a \u0432\u0430\u0440\u0456\u0430\u0446\u0456\u0457 \u0437\u043c\u0456\u043d\u043d\u043e\u0457 \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0456, \u044f\u043a\u0438\u0439 \u043c\u043e\u0436\u043d\u0430 \u043f\u043e\u044f\u0441\u043d\u0438\u0442\u0438 \u0437\u043c\u0456\u043d\u043d\u043e\u044e(\u044f\u043c\u0438) \u043f\u0440\u0435\u0434\u0438\u043a\u0442\u043e\u0440\u0430 \u0432 \u043c\u043e\u0434\u0435\u043b\u0456, \u0441\u043a\u043e\u0440\u0438\u0433\u043e\u0432\u0430\u043d\u043e\u044e \u043d\u0430 \u043a\u0456\u043b\u044c\u043a\u0456\u0441\u0442\u044c \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;\">#define function to calculate adjusted r-squared\n<span style=\"color: #000000;\"><span style=\"color: #008000;\">def<\/span> adjR(x, y, degree):\n    results = {}\n    coeffs = np. <span style=\"color: #3366ff;\">polyfit<\/span> (x, y, degree)\n    p = np. <span style=\"color: #3366ff;\">poly1d<\/span> (coeffs)\n    yhat = p(x)\n    ybar = np. <span style=\"color: #3366ff;\">sum<\/span> (y)\/len(y)\n    ssreg = np. <span style=\"color: #3366ff;\">sum<\/span> ((yhat-ybar)**2)\n    sstot = np. <span style=\"color: #3366ff;\">sum<\/span> ((y - ybar)**2)\n    results[' <span style=\"color: #ff0000;\">r_squared<\/span> '] = 1- (((1-(ssreg\/sstot))*(len(y)-1))\/(len(y)-degree-1))\n\n    <span style=\"color: #008000;\">return<\/span> results<\/span>\n\n#calculated adjusted R-squared of each model\n<\/span>adjR(df. <span style=\"color: #3366ff;\">x<\/span> , df. <span style=\"color: #3366ff;\">y<\/span> , 1)\nadjR(df. <span style=\"color: #3366ff;\">x<\/span> , df. <span style=\"color: #3366ff;\">y<\/span> , 2)\nadjR(df. <span style=\"color: #3366ff;\">x<\/span> , df. <span style=\"color: #3366ff;\">y<\/span> , 3)\nadjR(df. <span style=\"color: #3366ff;\">x<\/span> , df. <span style=\"color: #3366ff;\">y<\/span> , 4)\nadjR(df. <span style=\"color: #3366ff;\">x<\/span> , df. <span style=\"color: #3366ff;\">y<\/span> , 5)\n\n{'r_squared': 0.3144819}\n{'r_squared': 0.5186706}\n{'r_squared': 0.7842864}\n{'r_squared': 0.9590276}\n{'r_squared': 0.9549709}\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0417 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0443 \u043c\u0438 \u0431\u0430\u0447\u0438\u043c\u043e, \u0449\u043e \u043c\u043e\u0434\u0435\u043b\u044c \u0437 \u043d\u0430\u0439\u0432\u0438\u0449\u0438\u043c \u0441\u043a\u043e\u0440\u0438\u0433\u043e\u0432\u0430\u043d\u0438\u043c R-\u043a\u0432\u0430\u0434\u0440\u0430\u0442\u043e\u043c \u0454 \u043f\u043e\u043b\u0456\u043d\u043e\u043c\u043e\u043c \u0447\u0435\u0442\u0432\u0435\u0440\u0442\u043e\u0433\u043e \u0441\u0442\u0443\u043f\u0435\u043d\u044f, \u044f\u043a\u0438\u0439 \u043c\u0430\u0454 \u0441\u043a\u043e\u0440\u0438\u0433\u043e\u0432\u0430\u043d\u0438\u0439 R-\u043a\u0432\u0430\u0434\u0440\u0430\u0442 <strong>0,959<\/strong> .<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u041a\u0440\u043e\u043a 3: \u0412\u0456\u0437\u0443\u0430\u043b\u0456\u0437\u0443\u0439\u0442\u0435 \u043e\u0441\u0442\u0430\u0442\u043e\u0447\u043d\u0443 \u043a\u0440\u0438\u0432\u0443<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u041d\u0430\u0440\u0435\u0448\u0442\u0456, \u043c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0434\u0456\u0430\u0433\u0440\u0430\u043c\u0443 \u0440\u043e\u0437\u0441\u0456\u044e\u0432\u0430\u043d\u043d\u044f \u0437 \u043a\u0440\u0438\u0432\u043e\u044e \u043f\u043e\u043b\u0456\u043d\u043e\u043c\u0456\u0430\u043b\u044c\u043d\u043e\u0457 \u043c\u043e\u0434\u0435\u043b\u0456 \u0447\u0435\u0442\u0432\u0435\u0440\u0442\u043e\u0433\u043e \u0441\u0442\u0443\u043f\u0435\u043d\u044f:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit fourth-degree polynomial\n<\/span>model4 = np. <span style=\"color: #3366ff;\">poly1d<\/span> (np. <span style=\"color: #3366ff;\">polyfit<\/span> (df. <span style=\"color: #3366ff;\">x<\/span> , df. <span style=\"color: #3366ff;\">y<\/span> , 4))\n\n<span style=\"color: #008080;\">#define scatterplot\n<\/span>polyline = np. <span style=\"color: #3366ff;\">linspace<\/span> (1, 15, 50)\nplt. <span style=\"color: #3366ff;\">scatter<\/span> (df. <span style=\"color: #3366ff;\">x<\/span> , df. <span style=\"color: #3366ff;\">y<\/span> )\n\n<span style=\"color: #008080;\">#add fitted polynomial curve to scatterplot\n<\/span>plt. <span style=\"color: #3366ff;\">plot<\/span> (polyline, model4(polyline), ' <span style=\"color: #ff0000;\">--<\/span> ', color=' <span style=\"color: #ff0000;\">red<\/span> ')\nplt. <span style=\"color: #3366ff;\">show<\/span> ()\n<\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-16261 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/courbepython3.png\" alt=\"\" width=\"392\" height=\"265\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">\u041c\u0438 \u0442\u0430\u043a\u043e\u0436 \u043c\u043e\u0436\u0435\u043c\u043e \u043e\u0442\u0440\u0438\u043c\u0430\u0442\u0438 \u0440\u0456\u0432\u043d\u044f\u043d\u043d\u044f \u0434\u043b\u044f \u0446\u044c\u043e\u0433\u043e \u0440\u044f\u0434\u043a\u0430 \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e \u0444\u0443\u043d\u043a\u0446\u0456\u0457 <strong>print()<\/strong> :<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #993300;\">print<\/span> (model4)\n\n          4 3 2\n-0.01924x + 0.7081x - 8.365x + 35.82x - 26.52\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0420\u0456\u0432\u043d\u044f\u043d\u043d\u044f \u043a\u0440\u0438\u0432\u043e\u0457 \u0432\u0438\u0433\u043b\u044f\u0434\u0430\u0454 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u043c \u0447\u0438\u043d\u043e\u043c:<\/span><\/p>\n<p> <span style=\"color: #000000;\">y = -0,01924x <sup>4<\/sup> + 0,7081x <sup>3<\/sup> \u2013 8,365x <sup>2<\/sup> + 35,82x \u2013 26,52<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u041c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0446\u0435 \u0440\u0456\u0432\u043d\u044f\u043d\u043d\u044f, \u0449\u043e\u0431 \u043f\u0435\u0440\u0435\u0434\u0431\u0430\u0447\u0438\u0442\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f <a href=\"https:\/\/statorials.org\/uk\/\u0437\u043c\u0456\u043d\u043d\u0456-\u043f\u043e\u044f\u0441\u043d\u044e\u0432\u0430\u043b\u044c\u043d\u0456-\u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0456\/\" target=\"_blank\" rel=\"noopener\">\u0437\u043c\u0456\u043d\u043d\u043e\u0457 \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0456<\/a> \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\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. \u041d\u0430\u043f\u0440\u0438\u043a\u043b\u0430\u0434, \u044f\u043a\u0449\u043e <em>x<\/em> = 4, \u043c\u0438 \u043f\u0435\u0440\u0435\u0434\u0431\u0430\u0447\u0438\u043c\u043e, \u0449\u043e <em>y<\/em> = <strong>23,32<\/strong> :<\/span><\/p>\n<p> <span style=\"color: #000000;\">y = -0,0192(4) <sup>4<\/sup> + 0,7081(4) <sup>3<\/sup> \u2013 8,365(4) <sup>2<\/sup> + 35,82(4) \u2013 26,52 = 23,32<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u0414\u043e\u0434\u0430\u0442\u043a\u043e\u0432\u0456 \u0440\u0435\u0441\u0443\u0440\u0441\u0438<\/strong><\/span><\/h3>\n<p> <a href=\"https:\/\/statorials.org\/uk\/\u043f\u043e\u043b\u0456\u043d\u043e\u043c\u0456\u0430\u043b\u044c\u043d\u0430-\u0440\u0435\u0433\u0440\u0435\u0441\u0456\u044f-1\/\" target=\"_blank\" rel=\"noopener\">\u0412\u0441\u0442\u0443\u043f \u0434\u043e \u043f\u043e\u043b\u0456\u043d\u043e\u043c\u0456\u0430\u043b\u044c\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457<br \/><\/a> <a href=\"https:\/\/statorials.org\/uk\/\u043f\u043e\u043b\u0456\u043d\u043e\u043c\u0456\u0430\u043b\u044c\u043d\u0430-\u0440\u0435\u0433\u0440\u0435\u0441\u0456\u044f-python\/\" target=\"_blank\" rel=\"noopener\">\u042f\u043a \u0432\u0438\u043a\u043e\u043d\u0430\u0442\u0438 \u043f\u043e\u043b\u0456\u043d\u043e\u043c\u0456\u0430\u043b\u044c\u043d\u0443 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u044e \u0432 Python<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u0427\u0430\u0441\u0442\u043e \u0432\u0430\u043c \u043c\u043e\u0436\u0435 \u0437\u043d\u0430\u0434\u043e\u0431\u0438\u0442\u0438\u0441\u044f \u043f\u0456\u0434\u0456\u0433\u043d\u0430\u0442\u0438 \u043a\u0440\u0438\u0432\u0443 \u0434\u043e \u043d\u0430\u0431\u043e\u0440\u0443 \u0434\u0430\u043d\u0438\u0445 \u0443 Python. \u0423 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u043e\u043c\u0443 \u043f\u043e\u043a\u0440\u043e\u043a\u043e\u0432\u043e\u043c\u0443 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0456 \u043f\u043e\u044f\u0441\u043d\u044e\u0454\u0442\u044c\u0441\u044f, \u044f\u043a \u043f\u0456\u0434\u0456\u0433\u043d\u0430\u0442\u0438 \u043a\u0440\u0438\u0432\u0456 \u0434\u043e \u0434\u0430\u043d\u0438\u0445 \u0443 Python \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e \u0444\u0443\u043d\u043a\u0446\u0456\u0457 numpy.polyfit() \u0456 \u044f\u043a \u0432\u0438\u0437\u043d\u0430\u0447\u0438\u0442\u0438, \u044f\u043a\u0430 \u043a\u0440\u0438\u0432\u0430 \u043d\u0430\u0439\u043a\u0440\u0430\u0449\u0435 \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0430\u0454 \u0434\u0430\u043d\u0438\u043c. \u041a\u0440\u043e\u043a 1: \u0421\u0442\u0432\u043e\u0440\u0435\u043d\u043d\u044f \u0442\u0430 \u0432\u0456\u0437\u0443\u0430\u043b\u0456\u0437\u0430\u0446\u0456\u044f \u0434\u0430\u043d\u0438\u0445 \u0414\u0430\u0432\u0430\u0439\u0442\u0435 \u043f\u043e\u0447\u043d\u0435\u043c\u043e \u0437\u0456 \u0441\u0442\u0432\u043e\u0440\u0435\u043d\u043d\u044f \u043f\u0456\u0434\u0440\u043e\u0431\u043b\u0435\u043d\u043e\u0433\u043e \u043d\u0430\u0431\u043e\u0440\u0443 \u0434\u0430\u043d\u0438\u0445, \u0430 \u043f\u043e\u0442\u0456\u043c \u0441\u0442\u0432\u043e\u0440\u0438\u043c\u043e \u0434\u0456\u0430\u0433\u0440\u0430\u043c\u0443 \u0440\u043e\u0437\u0441\u0456\u044e\u0432\u0430\u043d\u043d\u044f \u0434\u043b\u044f \u0432\u0456\u0437\u0443\u0430\u043b\u0456\u0437\u0430\u0446\u0456\u0457 [&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>\u041f\u0456\u0434\u0433\u043e\u043d\u043a\u0430 \u043a\u0440\u0438\u0432\u043e\u0457 \u0432 Python (\u0437 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0430\u043c\u0438) - \u0421\u0442\u0430\u0442\u043e\u043b\u043e\u0433\u0456\u044f<\/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 \u043f\u0456\u0434\u0433\u0430\u043d\u044f\u0442\u0438 \u043a\u0440\u0438\u0432\u0456 \u0432 Python, \u0437 \u043a\u0456\u043b\u044c\u043a\u043e\u043c\u0430 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0430\u043c\u0438.\" \/>\n<meta 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