{"id":2154,"date":"2023-07-23T11:19:27","date_gmt":"2023-07-23T11:19:27","guid":{"rendered":"https:\/\/statorials.org\/ru\/%d1%81%d1%80%d0%b5%d0%b4%d0%bd%d0%b8%d0%b8-%d1%80%d0%b0%d0%b7%d0%bc%d0%b5%d1%80-%d0%b2-python\/"},"modified":"2023-07-23T11:19:27","modified_gmt":"2023-07-23T11:19:27","slug":"%d1%81%d1%80%d0%b5%d0%b4%d0%bd%d0%b8%d0%b8-%d1%80%d0%b0%d0%b7%d0%bc%d0%b5%d1%80-%d0%b2-python","status":"publish","type":"post","link":"https:\/\/statorials.org\/ru\/%d1%81%d1%80%d0%b5%d0%b4%d0%bd%d0%b8%d0%b8-%d1%80%d0%b0%d0%b7%d0%bc%d0%b5%d1%80-%d0%b2-python\/","title":{"rendered":"\u041a\u0430\u043a \u0432\u044b\u0447\u0438\u0441\u043b\u0438\u0442\u044c \u0443\u0441\u0435\u0447\u0435\u043d\u043d\u043e\u0435 \u0441\u0440\u0435\u0434\u043d\u0435\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0432 python (\u0441 \u043f\u0440\u0438\u043c\u0435\u0440\u0430\u043c\u0438)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>\u041e\u0431\u0440\u0435\u0437\u0430\u043d\u043d\u043e\u0435 \u0441\u0440\u0435\u0434\u043d\u0435\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435<\/strong> \u2014 \u044d\u0442\u043e \u0441\u0440\u0435\u0434\u043d\u0435\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u043d\u0430\u0431\u043e\u0440\u0430 \u0434\u0430\u043d\u043d\u044b\u0445, \u043a\u043e\u0442\u043e\u0440\u043e\u0435 \u0431\u044b\u043b\u043e \u0440\u0430\u0441\u0441\u0447\u0438\u0442\u0430\u043d\u043e \u043f\u043e\u0441\u043b\u0435 \u0443\u0434\u0430\u043b\u0435\u043d\u0438\u044f \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u043d\u043e\u0433\u043e \u043f\u0440\u043e\u0446\u0435\u043d\u0442\u0430 \u043d\u0430\u0438\u043c\u0435\u043d\u044c\u0448\u0438\u0445 \u0438 \u043d\u0430\u0438\u0431\u043e\u043b\u044c\u0448\u0438\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0432 \u043d\u0430\u0431\u043e\u0440\u0435 \u0434\u0430\u043d\u043d\u044b\u0445.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0421\u0430\u043c\u044b\u0439 \u043f\u0440\u043e\u0441\u0442\u043e\u0439 \u0441\u043f\u043e\u0441\u043e\u0431 \u0432\u044b\u0447\u0438\u0441\u043b\u0438\u0442\u044c \u0443\u0441\u0435\u0447\u0435\u043d\u043d\u043e\u0435 \u0441\u0440\u0435\u0434\u043d\u0435\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0432 Python \u2014 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0444\u0443\u043d\u043a\u0446\u0438\u044e <strong>Trim_mean()<\/strong> \u0438\u0437 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438 SciPy.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u042d\u0442\u0430 \u0444\u0443\u043d\u043a\u0446\u0438\u044f \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u0431\u0430\u0437\u043e\u0432\u044b\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">from<\/span> scipy <span style=\"color: #008000;\">import<\/span> stats\n\n<span style=\"color: #008080;\">#calculate 10% trimmed mean\n<\/span>stats. <span style=\"color: #3366ff;\">trim_mean<\/span> (data, <span style=\"color: #008000;\">0.1<\/span> )\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0421\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0435 \u043f\u0440\u0438\u043c\u0435\u0440\u044b \u043f\u043e\u043a\u0430\u0437\u044b\u0432\u0430\u044e\u0442, \u043a\u0430\u043a \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u044d\u0442\u0443 \u0444\u0443\u043d\u043a\u0446\u0438\u044e \u0434\u043b\u044f \u0440\u0430\u0441\u0447\u0435\u0442\u0430 \u0443\u0441\u0435\u0447\u0435\u043d\u043d\u043e\u0433\u043e \u0441\u0440\u0435\u0434\u043d\u0435\u0433\u043e \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u043a\u0435.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u041f\u0440\u0438\u043c\u0435\u0440 1. \u0412\u044b\u0447\u0438\u0441\u043b\u0435\u043d\u0438\u0435 \u0443\u0441\u0435\u0447\u0435\u043d\u043d\u043e\u0433\u043e \u0441\u0440\u0435\u0434\u043d\u0435\u0433\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0442\u0430\u0431\u043b\u0438\u0446\u044b.<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0421\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u043a\u043e\u0434 \u043f\u043e\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u0442, \u043a\u0430\u043a \u0432\u044b\u0447\u0438\u0441\u043b\u0438\u0442\u044c \u0443\u0441\u0435\u0447\u0435\u043d\u043d\u043e\u0435 \u0441\u0440\u0435\u0434\u043d\u0435\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u043d\u0430 10&nbsp;% \u0434\u043b\u044f \u0442\u0430\u0431\u043b\u0438\u0446\u044b \u0434\u0430\u043d\u043d\u044b\u0445:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">from<\/span> scipy <span style=\"color: #008000;\">import<\/span> stats\n\n<span style=\"color: #008080;\">#define data\n<\/span>data = [22, 25, 29, 11, 14, 18, 13, 13, 17, 11, 8, 8, 7, 12, 15, 6, 8, 7, 9, 12]\n\n<span style=\"color: #008080;\">#calculate 10% trimmed mean<\/span>\nstats. <span style=\"color: #3366ff;\">trim_mean<\/span> (data, <span style=\"color: #008000;\">0.1<\/span> )\n\n12,375\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0421\u0440\u0435\u0434\u043d\u0435\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435, \u0443\u0441\u0435\u0447\u0435\u043d\u043d\u043e\u0435 \u043d\u0430 10&nbsp;%, \u0441\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0442 <strong>12,375<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u042d\u0442\u043e \u0441\u0440\u0435\u0434\u043d\u0435\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u043d\u0430\u0431\u043e\u0440\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 \u043f\u043e\u0441\u043b\u0435 \u0442\u043e\u0433\u043e, \u043a\u0430\u043a \u0438\u0437 \u043d\u0430\u0431\u043e\u0440\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 \u0431\u044b\u043b\u0438 \u0443\u0434\u0430\u043b\u0435\u043d\u044b \u043d\u0430\u0438\u043c\u0435\u043d\u044c\u0448\u0438\u0435 10% \u0438 \u0441\u0430\u043c\u044b\u0435 \u0431\u043e\u043b\u044c\u0448\u0438\u0435 10% \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u041f\u0440\u0438\u043c\u0435\u0440 2. \u0412\u044b\u0447\u0438\u0441\u043b\u0435\u043d\u0438\u0435 \u0443\u0441\u0435\u0447\u0435\u043d\u043d\u043e\u0433\u043e \u0441\u0440\u0435\u0434\u043d\u0435\u0433\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0441\u0442\u043e\u043b\u0431\u0446\u0430 \u0432 Pandas<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0412 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0435\u043c \u043a\u043e\u0434\u0435 \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u043e, \u043a\u0430\u043a \u0432\u044b\u0447\u0438\u0441\u043b\u0438\u0442\u044c \u0443\u0441\u0435\u0447\u0435\u043d\u043d\u043e\u0435 \u0441\u0440\u0435\u0434\u043d\u0435\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u043d\u0430 5&nbsp;% \u0434\u043b\u044f \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u043d\u043e\u0433\u043e \u0441\u0442\u043e\u043b\u0431\u0446\u0430 \u0432 DataFrame pandas:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">from<\/span> scipy <span style=\"color: #008000;\">import<\/span> stats\n<span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#define DataFrame\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">points<\/span> ': [25, 12, 15, 14, 19, 23, 25, 29],\n                   ' <span style=\"color: #ff0000;\">assists<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' <span style=\"color: #ff0000;\">rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12]})\n\n\n<span style=\"color: #008080;\">#calculate 5% trimmed mean of points<\/span>\nstats. <span style=\"color: #3366ff;\">trim_mean<\/span> (df. <span style=\"color: #3366ff;\">points<\/span> , <span style=\"color: #008000;\">0.05<\/span> ) \n\n20.25<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">5%-\u043d\u043e\u0435 \u0443\u0441\u0435\u0447\u0435\u043d\u043d\u043e\u0435 \u0441\u0440\u0435\u0434\u043d\u0435\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0432 \u0441\u0442\u043e\u043b\u0431\u0446\u0435 \u00ab\u043f\u0443\u043d\u043a\u0442\u044b\u00bb \u0441\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0442 <strong>20,25<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u042d\u0442\u043e \u0441\u0440\u0435\u0434\u043d\u0435\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0441\u0442\u043e\u043b\u0431\u0446\u0430 \u00ab\u0442\u043e\u0447\u0435\u043a\u00bb \u043f\u043e\u0441\u043b\u0435 \u0443\u0434\u0430\u043b\u0435\u043d\u0438\u044f 5&nbsp;% \u043d\u0430\u0438\u043c\u0435\u043d\u044c\u0448\u0435\u0433\u043e \u0438 5&nbsp;% \u043d\u0430\u0438\u0431\u043e\u043b\u044c\u0448\u0435\u0433\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u041f\u0440\u0438\u043c\u0435\u0440 3. \u0412\u044b\u0447\u0438\u0441\u043b\u0435\u043d\u0438\u0435 \u0443\u0441\u0435\u0447\u0435\u043d\u043d\u043e\u0433\u043e \u0441\u0440\u0435\u0434\u043d\u0435\u0433\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u0445 \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0412 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0435\u043c \u043a\u043e\u0434\u0435 \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u043e, \u043a\u0430\u043a \u0432\u044b\u0447\u0438\u0441\u043b\u0438\u0442\u044c \u0443\u0441\u0435\u0447\u0435\u043d\u043d\u043e\u0435 \u0441\u0440\u0435\u0434\u043d\u0435\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u043d\u0430 5&nbsp;% \u0434\u043b\u044f \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u0445 \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 \u0432 DataFrame pandas:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">from<\/span> scipy <span style=\"color: #008000;\">import<\/span> stats\n<span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#define DataFrame\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">points<\/span> ': [25, 12, 15, 14, 19, 23, 25, 29],\n                   ' <span style=\"color: #ff0000;\">assists<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' <span style=\"color: #ff0000;\">rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12]})\n\n\n<span style=\"color: #008080;\">#calculate 5% trimmed mean of 'points' and 'assists' columns<\/span>\nstats. <span style=\"color: #3366ff;\">trim_mean<\/span> (df[[' <span style=\"color: #ff0000;\">points<\/span> ', ' <span style=\"color: #ff0000;\">assists<\/span> ']], <span style=\"color: #008000;\">0.05<\/span> )\n\narray([20.25, 7.75])\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u041f\u043e \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0443 \u043c\u044b \u0432\u0438\u0434\u0438\u043c:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\u0423\u0441\u0435\u0447\u0435\u043d\u043d\u043e\u0435 \u043d\u0430 5% \u0441\u0440\u0435\u0434\u043d\u0435\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0441\u0442\u043e\u043b\u0431\u0446\u0430 \u00ab\u043f\u0443\u043d\u043a\u0442\u044b\u00bb \u0441\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0442 <strong>20,25<\/strong> .<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u0423\u0441\u0435\u0447\u0435\u043d\u043d\u043e\u0435 5% \u0441\u0440\u0435\u0434\u043d\u0435\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0441\u0442\u043e\u043b\u0431\u0446\u0430 \u00ab\u043f\u0435\u0440\u0435\u0434\u0430\u0447\u0438\u00bb \u0441\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0442 <strong>7,75<\/strong> .<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><strong>\u041f\u0440\u0438\u043c\u0435\u0447\u0430\u043d\u0438\u0435<\/strong> . \u041f\u043e\u043b\u043d\u0443\u044e \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u0430\u0446\u0438\u044e \u043f\u043e \u0444\u0443\u043d\u043a\u0446\u0438\u0438 <strong>Trim_mean()<\/strong> \u043c\u043e\u0436\u043d\u043e \u043d\u0430\u0439\u0442\u0438 <a href=\"https:\/\/docs.scipy.org\/doc\/scipy\/reference\/generated\/scipy.stats.trim_mean.html\" target=\"_blank\" rel=\"noopener\">\u0437\u0434\u0435\u0441\u044c<\/a> .<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u0414\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0435 \u0440\u0435\u0441\u0443\u0440\u0441\u044b<\/strong><\/span><\/h3>\n<p> <a href=\"https:\/\/statorials.org\/ru\/\u0440\u0430\u0441\u0441\u0447\u0438\u0442\u0430\u0442\u044c-\u0441\u043a\u043e\u0440\u0440\u0435\u043a\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u043e\u0435-\u0441\u0440\u0435\u0434\u043d\u0435\u0435-\u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435\/\" target=\"_blank\" rel=\"noopener\">\u041a\u0430\u043a \u0432\u0440\u0443\u0447\u043d\u0443\u044e \u0432\u044b\u0447\u0438\u0441\u043b\u0438\u0442\u044c \u0443\u0441\u0435\u0447\u0435\u043d\u043d\u043e\u0435 \u0441\u0440\u0435\u0434\u043d\u0435\u0435<\/a><br \/> \u041a\u0430\u043b\u044c\u043a\u0443\u043b\u044f\u0442\u043e\u0440 \u0443\u0441\u0435\u0447\u0435\u043d\u043d\u043e\u0433\u043e \u0441\u0440\u0435\u0434\u043d\u0435\u0433\u043e<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u041e\u0431\u0440\u0435\u0437\u0430\u043d\u043d\u043e\u0435 \u0441\u0440\u0435\u0434\u043d\u0435\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u2014 \u044d\u0442\u043e \u0441\u0440\u0435\u0434\u043d\u0435\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u043d\u0430\u0431\u043e\u0440\u0430 \u0434\u0430\u043d\u043d\u044b\u0445, \u043a\u043e\u0442\u043e\u0440\u043e\u0435 \u0431\u044b\u043b\u043e \u0440\u0430\u0441\u0441\u0447\u0438\u0442\u0430\u043d\u043e \u043f\u043e\u0441\u043b\u0435 \u0443\u0434\u0430\u043b\u0435\u043d\u0438\u044f \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u043d\u043e\u0433\u043e \u043f\u0440\u043e\u0446\u0435\u043d\u0442\u0430 \u043d\u0430\u0438\u043c\u0435\u043d\u044c\u0448\u0438\u0445 \u0438 \u043d\u0430\u0438\u0431\u043e\u043b\u044c\u0448\u0438\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0432 \u043d\u0430\u0431\u043e\u0440\u0435 \u0434\u0430\u043d\u043d\u044b\u0445. \u0421\u0430\u043c\u044b\u0439 \u043f\u0440\u043e\u0441\u0442\u043e\u0439 \u0441\u043f\u043e\u0441\u043e\u0431 \u0432\u044b\u0447\u0438\u0441\u043b\u0438\u0442\u044c \u0443\u0441\u0435\u0447\u0435\u043d\u043d\u043e\u0435 \u0441\u0440\u0435\u0434\u043d\u0435\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0432 Python \u2014 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0444\u0443\u043d\u043a\u0446\u0438\u044e Trim_mean() \u0438\u0437 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438 SciPy. \u042d\u0442\u0430 \u0444\u0443\u043d\u043a\u0446\u0438\u044f \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u0431\u0430\u0437\u043e\u0432\u044b\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441: from scipy import stats #calculate 10% trimmed mean stats. trim_mean [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-2154","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u041a\u0430\u043a \u0440\u0430\u0441\u0441\u0447\u0438\u0442\u0430\u0442\u044c \u0443\u0441\u0435\u0447\u0435\u043d\u043d\u043e\u0435 \u0441\u0440\u0435\u0434\u043d\u0435\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0432 Python (\u0441 \u043f\u0440\u0438\u043c\u0435\u0440\u0430\u043c\u0438)<\/title>\n<meta name=\"description\" content=\"\u0412 \u044d\u0442\u043e\u043c \u0440\u0443\u043a\u043e\u0432\u043e\u0434\u0441\u0442\u0432\u0435 \u043e\u0431\u044a\u044f\u0441\u043d\u044f\u0435\u0442\u0441\u044f, \u043a\u0430\u043a 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