{"id":3815,"date":"2023-07-15T09:08:37","date_gmt":"2023-07-15T09:08:37","guid":{"rendered":"https:\/\/statorials.org\/uk\/%d0%bd%d0%b0%d0%b8%d0%bc%d0%b5%d0%bd%d1%88-%d0%b7%d0%b2%d0%b0%d0%b6%d0%b5%d0%bd%d1%96-%d0%ba%d0%b2%d0%b0%d0%b4%d1%80%d0%b0%d1%82%d0%b8-%d0%b2-python\/"},"modified":"2023-07-15T09:08:37","modified_gmt":"2023-07-15T09:08:37","slug":"%d0%bd%d0%b0%d0%b8%d0%bc%d0%b5%d0%bd%d1%88-%d0%b7%d0%b2%d0%b0%d0%b6%d0%b5%d0%bd%d1%96-%d0%ba%d0%b2%d0%b0%d0%b4%d1%80%d0%b0%d1%82%d0%b8-%d0%b2-python","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/%d0%bd%d0%b0%d0%b8%d0%bc%d0%b5%d0%bd%d1%88-%d0%b7%d0%b2%d0%b0%d0%b6%d0%b5%d0%bd%d1%96-%d0%ba%d0%b2%d0%b0%d0%b4%d1%80%d0%b0%d1%82%d0%b8-%d0%b2-python\/","title":{"rendered":"\u042f\u043a \u0432\u0438\u043a\u043e\u043d\u0430\u0442\u0438 \u0437\u0432\u0430\u0436\u0435\u043d\u0443 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u044e \u043d\u0430\u0439\u043c\u0435\u043d\u0448\u0438\u0445 \u043a\u0432\u0430\u0434\u0440\u0430\u0442\u0456\u0432 \u0443 python"},"content":{"rendered":"

<\/p>\n

\n

\u041e\u0434\u043d\u0435 \u0437 \u043a\u043b\u044e\u0447\u043e\u0432\u0438\u0445 \u043f\u0440\u0438\u043f\u0443\u0449\u0435\u043d\u044c \u043b\u0456\u043d\u0456\u0439\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457<\/a> \u043f\u043e\u043b\u044f\u0433\u0430\u0454 \u0432 \u0442\u043e\u043c\u0443, \u0449\u043e \u0437\u0430\u043b\u0438\u0448\u043a\u0438<\/a> \u0440\u043e\u0437\u043f\u043e\u0434\u0456\u043b\u044f\u044e\u0442\u044c\u0441\u044f \u0437 \u0440\u0456\u0432\u043d\u043e\u044e \u0434\u0438\u0441\u043f\u0435\u0440\u0441\u0456\u0454\u044e \u043d\u0430 \u043a\u043e\u0436\u043d\u043e\u043c\u0443 \u0440\u0456\u0432\u043d\u0456 \u0437\u043c\u0456\u043d\u043d\u043e\u0457 \u043f\u0440\u0435\u0434\u0438\u043a\u0442\u043e\u0440\u0430. \u0426\u0435 \u043f\u0440\u0438\u043f\u0443\u0449\u0435\u043d\u043d\u044f \u0432\u0456\u0434\u043e\u043c\u0435 \u044f\u043a \u0433\u043e\u043c\u043e\u0441\u043a\u0435\u0434\u0430\u0441\u0442\u0438\u0447\u043d\u0456\u0441\u0442\u044c<\/strong> .<\/span><\/p>\n

\u042f\u043a\u0449\u043e \u0446\u0435 \u043f\u0440\u0438\u043f\u0443\u0449\u0435\u043d\u043d\u044f \u043d\u0435 \u0432\u0438\u043a\u043e\u043d\u0443\u0454\u0442\u044c\u0441\u044f, \u043a\u0430\u0436\u0443\u0442\u044c, \u0449\u043e \u0433\u0435\u0442\u0435\u0440\u043e\u0441\u043a\u0435\u0434\u0430\u0441\u0442\u0438\u0447\u043d\u0456\u0441\u0442\u044c<\/a> \u043f\u0440\u0438\u0441\u0443\u0442\u043d\u044f \u0432 \u0437\u0430\u043b\u0438\u0448\u043a\u0430\u0445. \u041a\u043e\u043b\u0438 \u0446\u0435 \u0432\u0456\u0434\u0431\u0443\u0432\u0430\u0454\u0442\u044c\u0441\u044f, \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0438 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457 \u0441\u0442\u0430\u044e\u0442\u044c \u043d\u0435\u043d\u0430\u0434\u0456\u0439\u043d\u0438\u043c\u0438.<\/span><\/p>\n

\u041e\u0434\u043d\u0438\u043c \u0456\u0437 \u0441\u043f\u043e\u0441\u043e\u0431\u0456\u0432 \u0432\u0438\u0440\u0456\u0448\u0435\u043d\u043d\u044f \u0446\u0456\u0454\u0457 \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u0438 \u0454 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u043d\u044f \u0437\u0432\u0430\u0436\u0435\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457 \u043d\u0430\u0439\u043c\u0435\u043d\u0448\u0438\u0445 \u043a\u0432\u0430\u0434\u0440\u0430\u0442\u0456\u0432<\/strong> , \u044f\u043a\u0430 \u043f\u0440\u0438\u0437\u043d\u0430\u0447\u0430\u0454 \u0432\u0430\u0433\u0438 \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f\u043c<\/a> \u0442\u0430\u043a\u0438\u043c \u0447\u0438\u043d\u043e\u043c, \u0449\u043e \u0442\u0456 \u0437 \u043d\u0438\u0437\u044c\u043a\u043e\u044e \u0434\u0438\u0441\u043f\u0435\u0440\u0441\u0456\u0454\u044e \u043f\u043e\u043c\u0438\u043b\u043e\u043a \u043e\u0442\u0440\u0438\u043c\u0443\u044e\u0442\u044c \u0431\u0456\u043b\u044c\u0448\u0443 \u0432\u0430\u0433\u0443, \u043e\u0441\u043a\u0456\u043b\u044c\u043a\u0438 \u043c\u0456\u0441\u0442\u044f\u0442\u044c \u0431\u0456\u043b\u044c\u0448\u0435 \u0456\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0456\u0457 \u043f\u043e\u0440\u0456\u0432\u043d\u044f\u043d\u043e \u0437\u0456 \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f\u043c\u0438 \u0437 \u0431\u0456\u043b\u044c\u0448\u043e\u044e \u0434\u0438\u0441\u043f\u0435\u0440\u0441\u0456\u0454\u044e \u043f\u043e\u043c\u0438\u043b\u043e\u043a.<\/span><\/p>\n

\u0426\u0435\u0439 \u043f\u0456\u0434\u0440\u0443\u0447\u043d\u0438\u043a \u043d\u0430\u0434\u0430\u0454 \u043f\u043e\u043a\u0440\u043e\u043a\u043e\u0432\u0438\u0439 \u043f\u0440\u0438\u043a\u043b\u0430\u0434 \u0442\u043e\u0433\u043e, \u044f\u043a \u0432\u0438\u043a\u043e\u043d\u0443\u0432\u0430\u0442\u0438 \u0437\u0432\u0430\u0436\u0435\u043d\u0443 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u044e \u043d\u0430\u0439\u043c\u0435\u043d\u0448\u0438\u0445 \u043a\u0432\u0430\u0434\u0440\u0430\u0442\u0456\u0432 \u0443 Python.<\/span><\/p>\n

\u041a\u0440\u043e\u043a 1: \u0421\u0442\u0432\u043e\u0440\u0456\u0442\u044c \u0434\u0430\u043d\u0456<\/strong><\/span><\/h2>\n

\u0421\u043f\u043e\u0447\u0430\u0442\u043a\u0443 \u0434\u0430\u0432\u0430\u0439\u0442\u0435 \u0441\u0442\u0432\u043e\u0440\u0438\u043c\u043e \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 pandas DataFrame, \u044f\u043a\u0438\u0439 \u043c\u0456\u0441\u0442\u0438\u0442\u044c \u0456\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0456\u044e \u043f\u0440\u043e \u043a\u0456\u043b\u044c\u043a\u0456\u0441\u0442\u044c \u0432\u0438\u0432\u0447\u0435\u043d\u0438\u0445 \u0433\u043e\u0434\u0438\u043d \u0456 \u043f\u0456\u0434\u0441\u0443\u043c\u043a\u043e\u0432\u0443 \u043e\u0446\u0456\u043d\u043a\u0443 \u0456\u0441\u043f\u0438\u0442\u0443 \u0434\u043b\u044f 16 \u0441\u0442\u0443\u0434\u0435\u043d\u0442\u0456\u0432 \u0443 \u043a\u043b\u0430\u0441\u0456:<\/span><\/p>\n

 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' hours<\/span> ': [1, 1, 2, 2, 2, 3, 4, 4, 4, 5, 5, 5, 6, 6, 7, 8],\n                   ' score<\/span> ': [48, 78, 72, 70, 66, 92, 93, 75, 75, 80, 95, 97,\n                             90, 96, 99, 99]})\n\n#view first five rows of DataFrame\n<\/span>print<\/span> ( df.head<\/span> ())\n\n   hours score\n0 1 48\n1 1 78\n2 2 72\n3 2 70\n4 2 66<\/strong><\/pre>\n
 \u041a\u0440\u043e\u043a 2. \u041f\u0456\u0434\u0431\u0435\u0440\u0456\u0442\u044c \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<\/strong><\/span><\/h2>\n
 \u0414\u0430\u043b\u0456 \u043c\u0438 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u0454\u043c\u043e \u0444\u0443\u043d\u043a\u0446\u0456\u0457 \u0432 \u043c\u043e\u0434\u0443\u043b\u0456 statsmodels<\/strong> , \u0449\u043e\u0431 \u043f\u0456\u0434\u0456\u0431\u0440\u0430\u0442\u0438 \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 \u0433\u043e\u0434\u0438\u043d\u0438<\/strong> \u044f\u043a \u0437\u043c\u0456\u043d\u043d\u0443 \u043f\u0440\u043e\u0433\u043d\u043e\u0441\u0442\u0438\u043a\u0443 \u0442\u0430 \u043e\u0446\u0456\u043d\u043a\u0443<\/strong> \u044f\u043a \u0437\u043c\u0456\u043d\u043d\u0443 \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0456:<\/span><\/p>\n
 import<\/span> statsmodels.api as<\/span> sm\n\n#define predictor and response variables\n<\/span>y = df[' score<\/span> ']\nX = df[' hours<\/span> ']\n\n#add constant to predictor variables\n<\/span>X = sm. add_constant<\/span> (x)\n\n#fit linear regression model\n<\/span>fit = sm. OLS<\/span> (y,x). fit<\/span> ()\n\n#view model summary\n<\/span>print<\/span> ( fit.summary<\/span> ())\n\n                            OLS Regression Results                            \n==================================================== ============================\nDept. Variable: R-squared score: 0.630\nModel: OLS Adj. R-squared: 0.603\nMethod: Least Squares F-statistic: 23.80\nDate: Mon, 31 Oct 2022 Prob (F-statistic): 0.000244\nTime: 11:19:54 Log-Likelihood: -57.184\nNo. Observations: 16 AIC: 118.4\nDf Residuals: 14 BIC: 119.9\nModel: 1                                         \nCovariance Type: non-robust                                         \n==================================================== ============================\n                 coef std err t P>|t| [0.025 0.975]\n-------------------------------------------------- ----------------------------\nconst 60.4669 5.128 11.791 0.000 49.468 71.465\nhours 5.5005 1.127 4.879 0.000 3.082 7.919\n==================================================== ============================\nOmnibus: 0.041 Durbin-Watson: 1.910\nProb(Omnibus): 0.980 Jarque-Bera (JB): 0.268\nSkew: -0.010 Prob(JB): 0.875\nKurtosis: 2.366 Cond. No. 10.5<\/strong><\/pre>\n
 \u0417\u0456 \u0437\u0432\u0435\u0434\u0435\u043d\u043d\u044f \u043c\u043e\u0434\u0435\u043b\u0456 \u043c\u0438 \u0431\u0430\u0447\u0438\u043c\u043e, \u0449\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f R-\u043a\u0432\u0430\u0434\u0440\u0430\u0442 \u043c\u043e\u0434\u0435\u043b\u0456 \u0441\u0442\u0430\u043d\u043e\u0432\u0438\u0442\u044c 0,630<\/strong> .<\/span><\/p>\n
 \u041a\u0440\u043e\u043a 3. \u041f\u0456\u0434\u0431\u0435\u0440\u0456\u0442\u044c \u0437\u0432\u0430\u0436\u0435\u043d\u0443 \u043c\u043e\u0434\u0435\u043b\u044c \u043d\u0430\u0439\u043c\u0435\u043d\u0448\u0438\u0445 \u043a\u0432\u0430\u0434\u0440\u0430\u0442\u0456\u0432<\/strong><\/span><\/h2>\n
 \u0414\u0430\u043b\u0456 \u043c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u044e statsmodels<\/strong> WLS()<\/strong> \u0434\u043b\u044f \u0432\u0438\u043a\u043e\u043d\u0430\u043d\u043d\u044f \u0437\u0432\u0430\u0436\u0435\u043d\u0438\u0445 \u043c\u0435\u0442\u043e\u0434\u0456\u0432 \u043d\u0430\u0439\u043c\u0435\u043d\u0448\u0438\u0445 \u043a\u0432\u0430\u0434\u0440\u0430\u0442\u0456\u0432, \u0432\u0441\u0442\u0430\u043d\u043e\u0432\u043b\u044e\u044e\u0447\u0438 \u0432\u0430\u0433\u0438 \u0442\u0430\u043a, \u0449\u043e\u0431 \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f \u0437 \u043c\u0435\u043d\u0448\u043e\u044e \u0434\u0438\u0441\u043f\u0435\u0440\u0441\u0456\u0454\u044e \u043e\u0442\u0440\u0438\u043c\u0443\u0432\u0430\u043b\u0438 \u0431\u0456\u043b\u044c\u0448\u0443 \u0432\u0430\u0433\u0443:<\/span><\/p>\n
 #define weights to use\n<\/span>wt = 1\/smf. ols<\/span> (' fit.resid.abs() ~ fit.fittedvalues<\/span> ', data=df). fit<\/span> (). fitted values<\/span> **2\n\n#fit weighted least squares regression model\n<\/span>fit_wls = sm. WLS<\/span> (y, X, weights=wt). fit<\/span> ()\n\n#view summary of weighted least squares regression model\n<\/span>print<\/span> ( fit_wls.summary<\/span> ())\n\n                            WLS Regression Results                            \n==================================================== ============================\nDept. Variable: R-squared score: 0.676\nModel: WLS Adj. R-squared: 0.653\nMethod: Least Squares F-statistic: 29.24\nDate: Mon, 31 Oct 2022 Prob (F-statistic): 9.24e-05\nTime: 11:20:10 Log-Likelihood: -55.074\nNo. Comments: 16 AIC: 114.1\nDf Residuals: 14 BIC: 115.7\nModel: 1                                         \nCovariance Type: non-robust                                         \n==================================================== ============================\n                 coef std err t P>|t| [0.025 0.975]\n-------------------------------------------------- ----------------------------\nconst 63.9689 5.159 12.400 0.000 52.905 75.033\nhours 4.7091 0.871 5.407 0.000 2.841 6.577\n==================================================== ============================\nOmnibus: 2,482 Durbin-Watson: 1,786\nProb(Omnibus): 0.289 Jarque-Bera (JB): 1.058\nSkew: 0.029 Prob(JB): 0.589\nKurtosis: 1.742 Cond. No. 17.6\n==================================================== ============================<\/strong><\/span><\/pre>\n
 \u0417 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0443 \u043c\u0438 \u0431\u0430\u0447\u0438\u043c\u043e, \u0449\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f R-\u043a\u0432\u0430\u0434\u0440\u0430\u0442 \u0434\u043b\u044f \u0446\u0456\u0454\u0457 \u0437\u0432\u0430\u0436\u0435\u043d\u043e\u0457 \u043c\u043e\u0434\u0435\u043b\u0456 \u043d\u0430\u0439\u043c\u0435\u043d\u0448\u0438\u0445 \u043a\u0432\u0430\u0434\u0440\u0430\u0442\u0456\u0432 \u0437\u0440\u043e\u0441\u043b\u043e \u0434\u043e 0,676<\/strong> .<\/span><\/p>\n
 \u0426\u0435 \u0432\u043a\u0430\u0437\u0443\u0454 \u043d\u0430 \u0442\u0435, \u0449\u043e \u0437\u0432\u0430\u0436\u0435\u043d\u0430 \u043c\u043e\u0434\u0435\u043b\u044c \u043d\u0430\u0439\u043c\u0435\u043d\u0448\u0438\u0445 \u043a\u0432\u0430\u0434\u0440\u0430\u0442\u0456\u0432 \u0437\u0434\u0430\u0442\u043d\u0430 \u043f\u043e\u044f\u0441\u043d\u0438\u0442\u0438 \u0431\u0456\u043b\u044c\u0448\u0435 \u0440\u043e\u0437\u0431\u0456\u0436\u043d\u043e\u0441\u0442\u0435\u0439 \u0432 \u0456\u0441\u043f\u0438\u0442\u043e\u0432\u0438\u0445 \u0431\u0430\u043b\u0430\u0445, \u043d\u0456\u0436 \u043f\u0440\u043e\u0441\u0442\u0430 \u043c\u043e\u0434\u0435\u043b\u044c \u043b\u0456\u043d\u0456\u0439\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457.<\/span><\/p>\n
 \u0426\u0435 \u0433\u043e\u0432\u043e\u0440\u0438\u0442\u044c \u043d\u0430\u043c \u043f\u0440\u043e \u0442\u0435, \u0449\u043e \u0437\u0432\u0430\u0436\u0435\u043d\u0430 \u043c\u043e\u0434\u0435\u043b\u044c \u043d\u0430\u0439\u043c\u0435\u043d\u0448\u0438\u0445 \u043a\u0432\u0430\u0434\u0440\u0430\u0442\u0456\u0432 \u0437\u0430\u0431\u0435\u0437\u043f\u0435\u0447\u0443\u0454 \u043a\u0440\u0430\u0449\u0443 \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u043d\u0456\u0441\u0442\u044c \u0434\u0430\u043d\u0438\u0445 \u043f\u043e\u0440\u0456\u0432\u043d\u044f\u043d\u043e \u0437 \u043c\u043e\u0434\u0435\u043b\u043b\u044e \u043f\u0440\u043e\u0441\u0442\u043e\u0457 \u043b\u0456\u043d\u0456\u0439\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457.<\/span><\/p>\n
 \u0414\u043e\u0434\u0430\u0442\u043a\u043e\u0432\u0456 \u0440\u0435\u0441\u0443\u0440\u0441\u0438<\/strong><\/span><\/h2>\n
 \u0423 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0445 \u043f\u043e\u0441\u0456\u0431\u043d\u0438\u043a\u0430\u0445 \u043f\u043e\u044f\u0441\u043d\u044e\u0454\u0442\u044c\u0441\u044f, \u044f\u043a \u0432\u0438\u043a\u043e\u043d\u0443\u0432\u0430\u0442\u0438 \u0456\u043d\u0448\u0456 \u0442\u0438\u043f\u043e\u0432\u0456 \u0437\u0430\u0432\u0434\u0430\u043d\u043d\u044f \u0432 Python:<\/span><\/p>\n
 \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<\/a>
 \u042f\u043a \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0433\u0440\u0430\u0444\u0456\u043a QQ \u0443 Python<\/a>
 \u042f\u043a \u043f\u0435\u0440\u0435\u0432\u0456\u0440\u0438\u0442\u0438 \u043c\u0443\u043b\u044c\u0442\u0438\u043a\u043e\u043b\u0456\u043d\u0435\u0430\u0440\u043d\u0456\u0441\u0442\u044c \u0443 Python<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u041e\u0434\u043d\u0435 \u0437 \u043a\u043b\u044e\u0447\u043e\u0432\u0438\u0445 \u043f\u0440\u0438\u043f\u0443\u0449\u0435\u043d\u044c \u043b\u0456\u043d\u0456\u0439\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457 \u043f\u043e\u043b\u044f\u0433\u0430\u0454 \u0432 \u0442\u043e\u043c\u0443, \u0449\u043e \u0437\u0430\u043b\u0438\u0448\u043a\u0438 \u0440\u043e\u0437\u043f\u043e\u0434\u0456\u043b\u044f\u044e\u0442\u044c\u0441\u044f \u0437 \u0440\u0456\u0432\u043d\u043e\u044e \u0434\u0438\u0441\u043f\u0435\u0440\u0441\u0456\u0454\u044e \u043d\u0430 \u043a\u043e\u0436\u043d\u043e\u043c\u0443 \u0440\u0456\u0432\u043d\u0456 \u0437\u043c\u0456\u043d\u043d\u043e\u0457 \u043f\u0440\u0435\u0434\u0438\u043a\u0442\u043e\u0440\u0430. \u0426\u0435 \u043f\u0440\u0438\u043f\u0443\u0449\u0435\u043d\u043d\u044f \u0432\u0456\u0434\u043e\u043c\u0435 \u044f\u043a \u0433\u043e\u043c\u043e\u0441\u043a\u0435\u0434\u0430\u0441\u0442\u0438\u0447\u043d\u0456\u0441\u0442\u044c . \u042f\u043a\u0449\u043e \u0446\u0435 \u043f\u0440\u0438\u043f\u0443\u0449\u0435\u043d\u043d\u044f \u043d\u0435 \u0432\u0438\u043a\u043e\u043d\u0443\u0454\u0442\u044c\u0441\u044f, \u043a\u0430\u0436\u0443\u0442\u044c, \u0449\u043e \u0433\u0435\u0442\u0435\u0440\u043e\u0441\u043a\u0435\u0434\u0430\u0441\u0442\u0438\u0447\u043d\u0456\u0441\u0442\u044c \u043f\u0440\u0438\u0441\u0443\u0442\u043d\u044f \u0432 \u0437\u0430\u043b\u0438\u0448\u043a\u0430\u0445. \u041a\u043e\u043b\u0438 \u0446\u0435 \u0432\u0456\u0434\u0431\u0443\u0432\u0430\u0454\u0442\u044c\u0441\u044f, \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0438 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457 \u0441\u0442\u0430\u044e\u0442\u044c \u043d\u0435\u043d\u0430\u0434\u0456\u0439\u043d\u0438\u043c\u0438. \u041e\u0434\u043d\u0438\u043c \u0456\u0437 \u0441\u043f\u043e\u0441\u043e\u0431\u0456\u0432 \u0432\u0438\u0440\u0456\u0448\u0435\u043d\u043d\u044f \u0446\u0456\u0454\u0457 \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u0438 \u0454 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u043d\u044f \u0437\u0432\u0430\u0436\u0435\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457 \u043d\u0430\u0439\u043c\u0435\u043d\u0448\u0438\u0445 […]<\/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":"\n\u042f\u043a \u0432\u0438\u043a\u043e\u043d\u0430\u0442\u0438 \u0437\u0432\u0430\u0436\u0435\u043d\u0443 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u044e \u043d\u0430\u0439\u043c\u0435\u043d\u0448\u0438\u0445 \u043a\u0432\u0430\u0434\u0440\u0430\u0442\u0456\u0432 \u0443 Python - \u0421\u0442\u0430\u0442\u043e\u043b\u043e\u0433\u0456\u044f<\/title>\n<meta name=\"description\" content=\"\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 \u0432\u0438\u043a\u043e\u043d\u0443\u0432\u0430\u0442\u0438 \u0437\u0432\u0430\u0436\u0435\u043d\u0443 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u044e \u043d\u0430\u0439\u043c\u0435\u043d\u0448\u0438\u0445 \u043a\u0432\u0430\u0434\u0440\u0430\u0442\u0456\u0432 \u0443 Python, \u0432\u043a\u043b\u044e\u0447\u0430\u044e\u0447\u0438 \u043f\u043e\u043a\u0440\u043e\u043a\u043e\u0432\u0438\u0439 \u043f\u0440\u0438\u043a\u043b\u0430\u0434.\" \/>\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\/uk\/\u043d\u0430\u0438\u043c\u0435\u043d\u0448-\u0437\u0432\u0430\u0436\u0435\u043d\u0456-\u043a\u0432\u0430\u0434\u0440\u0430\u0442\u0438-\u0432-python\/\" \/>\n<meta property=\"og:locale\" content=\"uk_UA\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u042f\u043a \u0432\u0438\u043a\u043e\u043d\u0430\u0442\u0438 \u0437\u0432\u0430\u0436\u0435\u043d\u0443 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u044e \u043d\u0430\u0439\u043c\u0435\u043d\u0448\u0438\u0445 \u043a\u0432\u0430\u0434\u0440\u0430\u0442\u0456\u0432 \u0443 Python - \u0421\u0442\u0430\u0442\u043e\u043b\u043e\u0433\u0456\u044f\" \/>\n<meta property=\"og:description\" content=\"\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 \u0432\u0438\u043a\u043e\u043d\u0443\u0432\u0430\u0442\u0438 \u0437\u0432\u0430\u0436\u0435\u043d\u0443 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u044e \u043d\u0430\u0439\u043c\u0435\u043d\u0448\u0438\u0445 \u043a\u0432\u0430\u0434\u0440\u0430\u0442\u0456\u0432 \u0443 Python, \u0432\u043a\u043b\u044e\u0447\u0430\u044e\u0447\u0438 \u043f\u043e\u043a\u0440\u043e\u043a\u043e\u0432\u0438\u0439 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