{"id":3118,"date":"2023-07-19T03:04:16","date_gmt":"2023-07-19T03:04:16","guid":{"rendered":"https:\/\/statorials.org\/uk\/%d1%82%d0%b5%d1%81%d1%82-%d0%bf%d0%be%d1%96%d0%b7%d0%b4%d0%b0-%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8\/"},"modified":"2023-07-19T03:04:16","modified_gmt":"2023-07-19T03:04:16","slug":"%d1%82%d0%b5%d1%81%d1%82-%d0%bf%d0%be%d1%96%d0%b7%d0%b4%d0%b0-%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/%d1%82%d0%b5%d1%81%d1%82-%d0%bf%d0%be%d1%96%d0%b7%d0%b4%d0%b0-%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8\/","title":{"rendered":"\u042f\u043a \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0442\u0440\u0435\u043d\u0443\u0432\u0430\u043b\u044c\u043d\u0438\u0439 \u0456 \u0442\u0435\u0441\u0442\u043e\u0432\u0438\u0439 \u043d\u0430\u0431\u0456\u0440 \u0456\u0437 pandas dataframe"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u041f\u0440\u0438 \u043f\u0456\u0434\u0433\u043e\u043d\u0446\u0456 <a href=\"https:\/\/statorials.org\" target=\"_blank\" rel=\"noopener\">\u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043d\u0430\u0432\u0447\u0430\u043d\u043d\u044f<\/a> \u0434\u043e \u043d\u0430\u0431\u043e\u0440\u0456\u0432 \u0434\u0430\u043d\u0438\u0445 \u043c\u0438 \u0447\u0430\u0441\u0442\u043e \u0434\u0456\u043b\u0438\u043c\u043e \u043d\u0430\u0431\u0456\u0440 \u0434\u0430\u043d\u0438\u0445 \u043d\u0430 \u0434\u0432\u0430 \u043d\u0430\u0431\u043e\u0440\u0438:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1. \u041d\u0430\u0432\u0447\u0430\u043b\u044c\u043d\u0438\u0439 \u043d\u0430\u0431\u0456\u0440:<\/strong> \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0454\u0442\u044c\u0441\u044f \u0434\u043b\u044f \u043d\u0430\u0432\u0447\u0430\u043d\u043d\u044f \u043c\u043e\u0434\u0435\u043b\u0456 (70-80% \u0432\u0438\u0445\u0456\u0434\u043d\u043e\u0433\u043e \u043d\u0430\u0431\u043e\u0440\u0443 \u0434\u0430\u043d\u0438\u0445)<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>2. \u0422\u0435\u0441\u0442\u043e\u0432\u0438\u0439 \u043d\u0430\u0431\u0456\u0440:<\/strong> \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0454\u0442\u044c\u0441\u044f \u0434\u043b\u044f \u043e\u0442\u0440\u0438\u043c\u0430\u043d\u043d\u044f \u043d\u0435\u0443\u043f\u0435\u0440\u0435\u0434\u0436\u0435\u043d\u043e\u0457 \u043e\u0446\u0456\u043d\u043a\u0438 \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u0438\u0432\u043d\u043e\u0441\u0442\u0456 \u043c\u043e\u0434\u0435\u043b\u0456 (20-30% \u0432\u0438\u0445\u0456\u0434\u043d\u043e\u0433\u043e \u043d\u0430\u0431\u043e\u0440\u0443 \u0434\u0430\u043d\u0438\u0445)<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0423 Python \u0456\u0441\u043d\u0443\u0454 \u0434\u0432\u0430 \u043f\u043e\u0448\u0438\u0440\u0435\u043d\u0438\u0445 \u0441\u043f\u043e\u0441\u043e\u0431\u0438 \u0440\u043e\u0437\u0434\u0456\u043b\u0438\u0442\u0438 pandas DataFrame \u043d\u0430 \u043d\u0430\u0432\u0447\u0430\u043b\u044c\u043d\u0438\u0439 \u043d\u0430\u0431\u0456\u0440 \u0456 \u0442\u0435\u0441\u0442\u043e\u0432\u0438\u0439 \u043d\u0430\u0431\u0456\u0440:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\u0421\u043f\u043e\u0441\u0456\u0431 1: \u0412\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0439\u0442\u0435 sklearn train_test_split()<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> train_test_split\n\ntrain, test = train_test_split(df, test_size= <span style=\"color: #008000;\">0.2<\/span> , random_state= <span style=\"color: #008000;\">0<\/span> )<\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><strong>\u0421\u043f\u043e\u0441\u0456\u0431 2: \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0439\u0442\u0435 sample() \u0437 pandas<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>train = df. <span style=\"color: #3366ff;\">sample<\/span> (frac= <span style=\"color: #008000;\">0.8<\/span> , random_state= <span style=\"color: #008000;\">0<\/span> )\ntest = df. <span style=\"color: #3366ff;\">drop<\/span> ( <span style=\"color: #3366ff;\">train.index<\/span> )<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0423 \u043d\u0430\u0432\u0435\u0434\u0435\u043d\u0438\u0445 \u043d\u0438\u0436\u0447\u0435 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0430\u0445 \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u043e, \u044f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u043a\u043e\u0436\u0435\u043d \u043c\u0435\u0442\u043e\u0434 \u0456\u0437 \u0442\u0430\u043a\u0438\u043c\u0438 pandas DataFrame:<\/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> numpy <span style=\"color: #008000;\">as<\/span> np\n\n<span style=\"color: #008080;\">#make this example reproducible\n<\/span>n.p. <span style=\"color: #3366ff;\">random<\/span> . <span style=\"color: #3366ff;\">seeds<\/span> (1)\n\n<span style=\"color: #008080;\">#create DataFrame with 1,000 rows and 3 columns\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> <span style=\"color: #3366ff;\">(<\/span> {' <span style=\"color: #ff0000;\">x1<\/span> ': <span style=\"color: #3366ff;\">np.random.randint<\/span> (30,size=1000),\n                   ' <span style=\"color: #ff0000;\">x2<\/span> ': np. <span style=\"color: #3366ff;\">random<\/span> . <span style=\"color: #3366ff;\">randint<\/span> (12, size=1000),\n                   ' <span style=\"color: #ff0000;\">y<\/span> ': np. <span style=\"color: #3366ff;\">random<\/span> . <span style=\"color: #3366ff;\">randint<\/span> (2, size=1000)})\n\n<span style=\"color: #008080;\">#view first few rows of DataFrame<\/span>\ndf. <span style=\"color: #3366ff;\">head<\/span> ()\n\n        x1 x2 y\n0 5 1 1\n1 11 8 0\n2 12 4 1\n3 8 7 0\n4 9 0 0\n<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>\u041f\u0440\u0438\u043a\u043b\u0430\u0434 1: \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0439\u0442\u0435 train_test_split() \u0432\u0456\u0434 sklearn<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">\u041d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u043a\u043e\u0434 \u043f\u043e\u043a\u0430\u0437\u0443\u0454, \u044f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u044e <strong>train_test_split()<\/strong> <strong>sklearn<\/strong> , \u0449\u043e\u0431 \u0440\u043e\u0437\u0434\u0456\u043b\u0438\u0442\u0438 pandas DataFrame \u043d\u0430 \u043d\u0430\u0432\u0447\u0430\u043b\u044c\u043d\u0456 \u0442\u0430 \u0442\u0435\u0441\u0442\u043e\u0432\u0456 \u043d\u0430\u0431\u043e\u0440\u0438:<\/span><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> train_test_split\n\n<span style=\"color: #008080;\">#split original DataFrame into training and testing sets\n<\/span>train, test = train_test_split(df, test_size= <span style=\"color: #008000;\">0.2<\/span> , random_state= <span style=\"color: #008000;\">0<\/span> )\n\n<span style=\"color: #008080;\">#view first few rows of each set<\/span>\n<span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">train.head<\/span> ())\n\n     x1 x2 y\n687 16 2 0\n500 18 2 1\n332 4 10 1\n979 2 8 1\n817 11 1 0\n\n<span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">test.head<\/span> ())\n\n     x1 x2 y\n993 22 1 1\n859 27 6 0\n298 27 8 1\n553 20 6 0\n672 9 2 1\n\n<span style=\"color: #008080;\">#print size of each set<\/span>\n<span style=\"color: #008000;\">print<\/span> (train. <span style=\"color: #3366ff;\">shape<\/span> , test. <span style=\"color: #3366ff;\">shape<\/span> )\n\n(800, 3) (200, 3)\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 \u0431\u0443\u043b\u043e \u0441\u0442\u0432\u043e\u0440\u0435\u043d\u043e \u0434\u0432\u0430 \u043d\u0430\u0431\u043e\u0440\u0438:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\u041d\u0430\u0432\u0447\u0430\u043b\u044c\u043d\u0438\u0439 \u043d\u0430\u0431\u0456\u0440: 800 \u0440\u044f\u0434\u043a\u0456\u0432 \u0456 3 \u0441\u0442\u043e\u0432\u043f\u0446\u0456<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u041d\u0430\u0431\u0456\u0440 \u0442\u0435\u0441\u0442\u0456\u0432: 200 \u0440\u044f\u0434\u043a\u0456\u0432 \u0456 3 \u0441\u0442\u043e\u0432\u043f\u0446\u0456<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u0417\u0430\u0443\u0432\u0430\u0436\u0442\u0435, \u0449\u043e <strong>test_size<\/strong> \u043a\u043e\u043d\u0442\u0440\u043e\u043b\u044e\u0454 \u0432\u0456\u0434\u0441\u043e\u0442\u043e\u043a \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u044c \u0437 \u043e\u0440\u0438\u0433\u0456\u043d\u0430\u043b\u044c\u043d\u043e\u0433\u043e DataFrame, \u044f\u043a\u0438\u0439 \u043d\u0430\u043b\u0435\u0436\u0430\u0442\u0438\u043c\u0435 \u0434\u043e \u0442\u0435\u0441\u0442\u043e\u0432\u043e\u0433\u043e \u043d\u0430\u0431\u043e\u0440\u0443, \u0430 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f <strong>random_state<\/strong> \u0440\u043e\u0431\u0438\u0442\u044c \u0440\u043e\u0437\u0434\u0456\u043b\u0435\u043d\u043d\u044f \u0432\u0456\u0434\u0442\u0432\u043e\u0440\u044e\u0432\u0430\u043d\u0438\u043c.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u041f\u0440\u0438\u043a\u043b\u0430\u0434 2: \u0412\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0439\u0442\u0435 sample() \u0456\u0437 pandas<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u041d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u043a\u043e\u0434 \u043f\u043e\u043a\u0430\u0437\u0443\u0454, \u044f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u044e <b>pandas<\/b> <strong>sample()<\/strong> , \u0449\u043e\u0431 \u0440\u043e\u0437\u0434\u0456\u043b\u0438\u0442\u0438 pandas DataFrame \u043d\u0430 \u043d\u0430\u0432\u0447\u0430\u043b\u044c\u043d\u0456 \u0442\u0430 \u0442\u0435\u0441\u0442\u043e\u0432\u0456 \u043d\u0430\u0431\u043e\u0440\u0438:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#split original DataFrame into training and testing sets\n<\/span>train = df. <span style=\"color: #3366ff;\">sample<\/span> (frac= <span style=\"color: #008000;\">0.8<\/span> , random_state= <span style=\"color: #008000;\">0<\/span> )\ntest = df. <span style=\"color: #3366ff;\">drop<\/span> ( <span style=\"color: #3366ff;\">train.index<\/span> )\n\n<span style=\"color: #008080;\">#view first few rows of each set<\/span>\n<span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">train.head<\/span> ())\n\n     x1 x2 y\n993 22 1 1\n859 27 6 0\n298 27 8 1\n553 20 6 0\n672 9 2 1\n\n<span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">test.head<\/span> ())\n\n    x1 x2 y\n9 16 5 0\n11 12 10 0\n19 5 9 0\n23 28 1 1\n28 18 0 1\n\n<span style=\"color: #008080;\">#print size of each set<\/span>\n<span style=\"color: #008000;\">print<\/span> (train. <span style=\"color: #3366ff;\">shape<\/span> , test. <span style=\"color: #3366ff;\">shape<\/span> )\n\n(800, 3) (200, 3)\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 \u0431\u0443\u043b\u043e \u0441\u0442\u0432\u043e\u0440\u0435\u043d\u043e \u0434\u0432\u0430 \u043d\u0430\u0431\u043e\u0440\u0438:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\u041d\u0430\u0432\u0447\u0430\u043b\u044c\u043d\u0438\u0439 \u043d\u0430\u0431\u0456\u0440: 800 \u0440\u044f\u0434\u043a\u0456\u0432 \u0456 3 \u0441\u0442\u043e\u0432\u043f\u0446\u0456<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u041d\u0430\u0431\u0456\u0440 \u0442\u0435\u0441\u0442\u0456\u0432: 200 \u0440\u044f\u0434\u043a\u0456\u0432 \u0456 3 \u0441\u0442\u043e\u0432\u043f\u0446\u0456<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u0417\u0430\u0443\u0432\u0430\u0436\u0442\u0435, \u0449\u043e <b>frac<\/b> \u043a\u043e\u043d\u0442\u0440\u043e\u043b\u044e\u0454 \u0432\u0456\u0434\u0441\u043e\u0442\u043e\u043a \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u044c \u0437 \u0432\u0438\u0445\u0456\u0434\u043d\u043e\u0433\u043e DataFrame, \u044f\u043a\u0438\u0439 \u043d\u0430\u043b\u0435\u0436\u0430\u0442\u0438\u043c\u0435 \u0434\u043e \u043d\u0430\u0432\u0447\u0430\u043b\u044c\u043d\u043e\u0433\u043e \u043d\u0430\u0431\u043e\u0440\u0443, \u0430 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f <strong>random_state<\/strong> \u0440\u043e\u0431\u0438\u0442\u044c \u0440\u043e\u0437\u043f\u043e\u0434\u0456\u043b \u0432\u0456\u0434\u0442\u0432\u043e\u0440\u044e\u0432\u0430\u043d\u0438\u043c.<\/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> <span style=\"color: #000000;\">\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<p> <a href=\"https:\/\/statorials.org\/uk\/\u043b\u043e\u0433\u0456\u0441\u0442\u0438\u0447\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 \u043b\u043e\u0433\u0456\u0441\u0442\u0438\u0447\u043d\u0443 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u044e \u0432 Python<\/a><br \/> <a href=\"https:\/\/statorials.org\/uk\/\u043f\u043b\u0443\u0442\u0430\u043d\u0438\u043d\u0430-\u043c\u0430\u0442\u0440\u0438\u0446\u0456-python\/\" target=\"_blank\" rel=\"noopener\">\u042f\u043a \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u043c\u0430\u0442\u0440\u0438\u0446\u044e \u043f\u043b\u0443\u0442\u0430\u043d\u0438\u043d\u0438 \u0432 Python<\/a><br \/> <a href=\"https:\/\/statorials.org\/uk\/\u0437\u0431\u0430\u043b\u0430\u043d\u0441\u043e\u0432\u0430\u043d\u0430-\u0442\u043e\u0447\u043d\u0456\u0441\u0442\u044c-python-sklearn\/\">\u042f\u043a \u0440\u043e\u0437\u0440\u0430\u0445\u0443\u0432\u0430\u0442\u0438 \u0437\u0431\u0430\u043b\u0430\u043d\u0441\u043e\u0432\u0430\u043d\u0443 \u0442\u043e\u0447\u043d\u0456\u0441\u0442\u044c \u0443 Python<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u041f\u0440\u0438 \u043f\u0456\u0434\u0433\u043e\u043d\u0446\u0456 \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043d\u0430\u0432\u0447\u0430\u043d\u043d\u044f \u0434\u043e \u043d\u0430\u0431\u043e\u0440\u0456\u0432 \u0434\u0430\u043d\u0438\u0445 \u043c\u0438 \u0447\u0430\u0441\u0442\u043e \u0434\u0456\u043b\u0438\u043c\u043e \u043d\u0430\u0431\u0456\u0440 \u0434\u0430\u043d\u0438\u0445 \u043d\u0430 \u0434\u0432\u0430 \u043d\u0430\u0431\u043e\u0440\u0438: 1. \u041d\u0430\u0432\u0447\u0430\u043b\u044c\u043d\u0438\u0439 \u043d\u0430\u0431\u0456\u0440: \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0454\u0442\u044c\u0441\u044f \u0434\u043b\u044f \u043d\u0430\u0432\u0447\u0430\u043d\u043d\u044f \u043c\u043e\u0434\u0435\u043b\u0456 (70-80% \u0432\u0438\u0445\u0456\u0434\u043d\u043e\u0433\u043e \u043d\u0430\u0431\u043e\u0440\u0443 \u0434\u0430\u043d\u0438\u0445) 2. \u0422\u0435\u0441\u0442\u043e\u0432\u0438\u0439 \u043d\u0430\u0431\u0456\u0440: \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0454\u0442\u044c\u0441\u044f \u0434\u043b\u044f \u043e\u0442\u0440\u0438\u043c\u0430\u043d\u043d\u044f \u043d\u0435\u0443\u043f\u0435\u0440\u0435\u0434\u0436\u0435\u043d\u043e\u0457 \u043e\u0446\u0456\u043d\u043a\u0438 \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u0438\u0432\u043d\u043e\u0441\u0442\u0456 \u043c\u043e\u0434\u0435\u043b\u0456 (20-30% \u0432\u0438\u0445\u0456\u0434\u043d\u043e\u0433\u043e \u043d\u0430\u0431\u043e\u0440\u0443 \u0434\u0430\u043d\u0438\u0445) \u0423 Python \u0456\u0441\u043d\u0443\u0454 \u0434\u0432\u0430 \u043f\u043e\u0448\u0438\u0440\u0435\u043d\u0438\u0445 \u0441\u043f\u043e\u0441\u043e\u0431\u0438 \u0440\u043e\u0437\u0434\u0456\u043b\u0438\u0442\u0438 pandas DataFrame \u043d\u0430 \u043d\u0430\u0432\u0447\u0430\u043b\u044c\u043d\u0438\u0439 \u043d\u0430\u0431\u0456\u0440 \u0456 \u0442\u0435\u0441\u0442\u043e\u0432\u0438\u0439 [&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\/ 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