{"id":984,"date":"2023-07-28T02:09:42","date_gmt":"2023-07-28T02:09:42","guid":{"rendered":"https:\/\/statorials.org\/uk\/%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8-%d1%81%d1%82%d1%80%d0%b0%d1%82%d0%b8%d1%84%d1%96%d0%ba%d0%be%d0%b2%d0%b0%d0%bd%d0%b0-%d0%b2%d0%b8%d0%b1%d1%96%d1%80%d0%ba%d0%b0\/"},"modified":"2023-07-28T02:09:42","modified_gmt":"2023-07-28T02:09:42","slug":"%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8-%d1%81%d1%82%d1%80%d0%b0%d1%82%d0%b8%d1%84%d1%96%d0%ba%d0%be%d0%b2%d0%b0%d0%bd%d0%b0-%d0%b2%d0%b8%d0%b1%d1%96%d1%80%d0%ba%d0%b0","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8-%d1%81%d1%82%d1%80%d0%b0%d1%82%d0%b8%d1%84%d1%96%d0%ba%d0%be%d0%b2%d0%b0%d0%bd%d0%b0-%d0%b2%d0%b8%d0%b1%d1%96%d1%80%d0%ba%d0%b0\/","title":{"rendered":"\u0421\u0442\u0440\u0430\u0442\u0438\u0444\u0456\u043a\u043e\u0432\u0430\u043d\u0430 \u0432\u0438\u0431\u0456\u0440\u043a\u0430 \u0443 \u043f\u0430\u043d\u0434 (\u0437 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0430\u043c\u0438)"},"content":{"rendered":"

<\/p>\n

\n

\u0414\u043e\u0441\u043b\u0456\u0434\u043d\u0438\u043a\u0438 \u0447\u0430\u0441\u0442\u043e \u0431\u0435\u0440\u0443\u0442\u044c \u0437\u0440\u0430\u0437\u043a\u0438<\/a> \u0437 \u043f\u043e\u043f\u0443\u043b\u044f\u0446\u0456\u0457 \u0442\u0430 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u044e\u0442\u044c \u0434\u0430\u043d\u0456 \u0437 \u0432\u0438\u0431\u0456\u0440\u043a\u0438, \u0449\u043e\u0431 \u0437\u0440\u043e\u0431\u0438\u0442\u0438 \u0432\u0438\u0441\u043d\u043e\u0432\u043a\u0438 \u043f\u0440\u043e \u043f\u043e\u043f\u0443\u043b\u044f\u0446\u0456\u044e \u0432 \u0446\u0456\u043b\u043e\u043c\u0443.<\/span><\/p>\n

\u0417\u0430\u0437\u0432\u0438\u0447\u0430\u0439 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u043d\u0438\u043c \u043c\u0435\u0442\u043e\u0434\u043e\u043c \u0432\u0438\u0431\u0456\u0440\u043a\u0438 \u0454 \u0441\u0442\u0440\u0430\u0442\u0438\u0444\u0456\u043a\u043e\u0432\u0430\u043d\u0430 \u0432\u0438\u043f\u0430\u0434\u043a\u043e\u0432\u0430 \u0432\u0438\u0431\u0456\u0440\u043a\u0430<\/strong> , \u0437\u0430 \u044f\u043a\u043e\u0457 \u0433\u0435\u043d\u0435\u0440\u0430\u043b\u044c\u043d\u0443 \u0441\u0443\u043a\u0443\u043f\u043d\u0456\u0441\u0442\u044c \u043f\u043e\u0434\u0456\u043b\u044f\u044e\u0442\u044c \u043d\u0430 \u0433\u0440\u0443\u043f\u0438, \u0456 \u043f\u0435\u0432\u043d\u0443 \u043a\u0456\u043b\u044c\u043a\u0456\u0441\u0442\u044c \u0447\u043b\u0435\u043d\u0456\u0432 \u043a\u043e\u0436\u043d\u043e\u0457 \u0433\u0440\u0443\u043f\u0438 \u0432\u0438\u043f\u0430\u0434\u043a\u043e\u0432\u0438\u043c \u0447\u0438\u043d\u043e\u043c \u0432\u0456\u0434\u0431\u0438\u0440\u0430\u044e\u0442\u044c \u0434\u043b\u044f \u0432\u043a\u043b\u044e\u0447\u0435\u043d\u043d\u044f \u0443 \u0432\u0438\u0431\u0456\u0440\u043a\u0443.<\/span><\/p>\n

\u0426\u0435\u0439 \u043f\u043e\u0441\u0456\u0431\u043d\u0438\u043a \u043f\u043e\u044f\u0441\u043d\u044e\u0454 \u0434\u0432\u0430 \u043c\u0435\u0442\u043e\u0434\u0438 \u0432\u0438\u043a\u043e\u043d\u0430\u043d\u043d\u044f \u0441\u0442\u0440\u0430\u0442\u0438\u0444\u0456\u043a\u043e\u0432\u0430\u043d\u043e\u0457 \u0432\u0438\u043f\u0430\u0434\u043a\u043e\u0432\u043e\u0457 \u0432\u0438\u0431\u0456\u0440\u043a\u0438 \u0432 Python.<\/span><\/p>\n

\u041f\u0440\u0438\u043a\u043b\u0430\u0434 1: \u0441\u0442\u0440\u0430\u0442\u0438\u0444\u0456\u043a\u043e\u0432\u0430\u043d\u0430 \u0432\u0438\u0431\u0456\u0440\u043a\u0430 \u0437 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u043d\u044f\u043c \u043f\u0456\u0434\u0440\u0430\u0445\u0443\u043d\u043a\u0456\u0432<\/strong><\/h3>\n

\u041f\u0440\u0438\u043f\u0443\u0441\u0442\u0456\u043c\u043e, \u0449\u043e \u0443 \u043d\u0430\u0441 \u0454 \u0442\u0430\u043a\u0438\u0439 \u0444\u0440\u0435\u0439\u043c \u0434\u0430\u043d\u0438\u0445 pandas, \u044f\u043a\u0438\u0439 \u043c\u0456\u0441\u0442\u0438\u0442\u044c \u0434\u0430\u043d\u0456 \u043f\u0440\u043e 8 \u0431\u0430\u0441\u043a\u0435\u0442\u0431\u043e\u043b\u0456\u0441\u0442\u0456\u0432 \u0437 2 \u0440\u0456\u0437\u043d\u0438\u0445 \u043a\u043e\u043c\u0430\u043d\u0434:<\/span><\/p>\n

 import<\/span> pandas as<\/span> pd\n\n#createDataFrame<\/span>\ndf = pd.DataFrame({'team': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],\n                   'position': ['G', 'G', 'F', 'G', 'F', 'F', 'C', 'C'],\n                   'assists': [5, 7, 7, 8, 5, 7, 6, 9],\n                   'rebounds': [11, 8, 10, 6, 6, 9, 6, 10]})\n\n#view DataFrame\n<\/span>df\n\n        team position assists rebounds\n0 A G 5 11\n1 A G 7 8\n2 A F 7 10\n3 A G 8 6\n4 B F 5 6\n5 B F 7 9\n6 B C 6 6\n7 B C 9 10\n<\/strong><\/pre>\n
 \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\u043d\u0430\u0442\u0438 \u0441\u0442\u0440\u0430\u0442\u0438\u0444\u0456\u043a\u043e\u0432\u0430\u043d\u0443 \u0432\u0438\u043f\u0430\u0434\u043a\u043e\u0432\u0443 \u0432\u0438\u0431\u0456\u0440\u043a\u0443 \u0448\u043b\u044f\u0445\u043e\u043c \u0432\u0438\u043f\u0430\u0434\u043a\u043e\u0432\u043e\u0433\u043e \u0432\u0438\u0431\u043e\u0440\u0443 2 \u0433\u0440\u0430\u0432\u0446\u0456\u0432 \u0456\u0437 \u043a\u043e\u0436\u043d\u043e\u0457 \u043a\u043e\u043c\u0430\u043d\u0434\u0438 \u0434\u043b\u044f \u0432\u043a\u043b\u044e\u0447\u0435\u043d\u043d\u044f \u0443 \u0432\u0438\u0431\u0456\u0440\u043a\u0443:<\/span><\/p>\n
 df. groupby<\/span> ('team', group_keys= False<\/span> ). apply<\/span> ( lambda<\/span> x: x.sample<\/span> (2))\n\n        team position assists rebounds\n0 A G 5 11\n3 A G 8 6\n6 B C 6 6\n5 B F 7 9\n<\/strong><\/pre>\n
 \u0417\u0430\u0443\u0432\u0430\u0436\u0442\u0435, \u0449\u043e \u043f\u043e \u0434\u0432\u0430 \u0433\u0440\u0430\u0432\u0446\u0456 \u0437 \u043a\u043e\u0436\u043d\u043e\u0457 \u043a\u043e\u043c\u0430\u043d\u0434\u0438 \u0432\u043a\u043b\u044e\u0447\u0435\u043d\u0456 \u0434\u043e \u0441\u0442\u0440\u0430\u0442\u0438\u0444\u0456\u043a\u043e\u0432\u0430\u043d\u043e\u0457 \u0432\u0438\u0431\u0456\u0440\u043a\u0438.<\/span><\/p>\n
 \u041f\u0440\u0438\u043a\u043b\u0430\u0434 2: \u0421\u0442\u0440\u0430\u0442\u0438\u0444\u0456\u043a\u043e\u0432\u0430\u043d\u0430 \u0432\u0438\u0431\u0456\u0440\u043a\u0430 \u0437 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u043d\u044f\u043c \u043f\u0440\u043e\u043f\u043e\u0440\u0446\u0456\u0439<\/strong><\/h3>\n
 \u0417\u043d\u043e\u0432\u0443 \u043f\u0440\u0438\u043f\u0443\u0441\u0442\u0456\u043c\u043e, \u0449\u043e \u0443 \u043d\u0430\u0441 \u0454 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 DataFrame pandas, \u044f\u043a\u0438\u0439 \u043c\u0456\u0441\u0442\u0438\u0442\u044c \u0434\u0430\u043d\u0456 \u043f\u0440\u043e 8 \u0431\u0430\u0441\u043a\u0435\u0442\u0431\u043e\u043b\u0456\u0441\u0442\u0456\u0432 \u0437 2 \u0440\u0456\u0437\u043d\u0438\u0445 \u043a\u043e\u043c\u0430\u043d\u0434:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame<\/span>\ndf = pd.DataFrame({'team': ['A', 'A', 'B', 'B', 'B', 'B', 'B', 'B'],\n                   'position': ['G', 'G', 'F', 'G', 'F', 'F', 'C', 'C'],\n                   'assists': [5, 7, 7, 8, 5, 7, 6, 9],\n                   'rebounds': [11, 8, 10, 6, 6, 9, 6, 10]})\n\n#view DataFrame\n<\/span>df\n\n        team position assists rebounds\n0 A G 5 11\n1 A G 7 8\n2 A F 7 10\n3 A G 8 6\n4 B F 5 6\n5 B F 7 9\n6 B C 6 6\n7 B C 9 10\n<\/strong><\/pre>\n
 \u0417\u0430\u0443\u0432\u0430\u0436\u0442\u0435, \u0449\u043e 6 \u0456\u0437 8 \u0433\u0440\u0430\u0432\u0446\u0456\u0432 (75%) \u0443 DataFrame \u0432\u0445\u043e\u0434\u044f\u0442\u044c \u0434\u043e \u043a\u043e\u043c\u0430\u043d\u0434\u0438 A, \u0430 2 \u0456\u0437 8 \u0433\u0440\u0430\u0432\u0446\u0456\u0432 (25%) \u2014 \u0434\u043e \u043a\u043e\u043c\u0430\u043d\u0434\u0438 B.<\/span><\/p>\n
 \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\u043d\u0430\u0442\u0438 \u0441\u0442\u0440\u0430\u0442\u0438\u0444\u0456\u043a\u043e\u0432\u0430\u043d\u0443 \u0432\u0438\u043f\u0430\u0434\u043a\u043e\u0432\u0443 \u0432\u0438\u0431\u0456\u0440\u043a\u0443 \u0442\u0430\u043a, \u0449\u043e\u0431 \u0447\u0430\u0441\u0442\u043a\u0430 \u0433\u0440\u0430\u0432\u0446\u0456\u0432 \u0443 \u0432\u0438\u0431\u0456\u0440\u0446\u0456 \u0432\u0456\u0434 \u043a\u043e\u0436\u043d\u043e\u0457 \u043a\u043e\u043c\u0430\u043d\u0434\u0438 \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0430\u043b\u0430 \u0447\u0430\u0441\u0442\u0446\u0456 \u0433\u0440\u0430\u0432\u0446\u0456\u0432 \u0432\u0456\u0434 \u043a\u043e\u0436\u043d\u043e\u0457 \u043a\u043e\u043c\u0430\u043d\u0434\u0438 \u0443 \u0431\u0456\u043b\u044c\u0448\u043e\u043c\u0443 DataFrame:<\/span><\/p>\n
 import<\/span> numpy as np\n\n<\/span>#define total sample size desired<\/span>\nN = 4\n\n#perform stratified random sampling<\/span>\ndf. groupby<\/span> ('team', group_keys= False<\/span> ). apply<\/span> ( lambda<\/span> x: x. sample<\/span> (int(np. rint<\/span> (N* len<\/span> (x)\/ len<\/span> (df))))). sample<\/span> (frac=1). reset_index<\/span> (drop= True<\/span> )\n\n        team position assists rebounds\n0 B F 7 9\n1 B G 8 6\n2 B C 6 6\n3 A G 7 8\n<\/strong><\/pre>\n
 \u0417\u0430\u0443\u0432\u0430\u0436\u0442\u0435, \u0449\u043e \u0447\u0430\u0441\u0442\u043a\u0430 \u0433\u0440\u0430\u0432\u0446\u0456\u0432 \u043a\u043e\u043c\u0430\u043d\u0434\u0438 A \u0443 \u0441\u0442\u0440\u0430\u0442\u0438\u0444\u0456\u043a\u043e\u0432\u0430\u043d\u0456\u0439 \u0432\u0438\u0431\u0456\u0440\u0446\u0456 (25%) \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0430\u0454 \u0447\u0430\u0441\u0442\u0446\u0456 \u0433\u0440\u0430\u0432\u0446\u0456\u0432 \u043a\u043e\u043c\u0430\u043d\u0434\u0438 A \u0443 \u0431\u0456\u043b\u044c\u0448\u043e\u043c\u0443 DataFrame.<\/span><\/p>\n
 \u041f\u043e\u0434\u0456\u0431\u043d\u0438\u043c \u0447\u0438\u043d\u043e\u043c \u0447\u0430\u0441\u0442\u043a\u0430 \u0433\u0440\u0430\u0432\u0446\u0456\u0432 \u043a\u043e\u043c\u0430\u043d\u0434\u0438 B \u0443 \u0441\u0442\u0440\u0430\u0442\u0438\u0444\u0456\u043a\u043e\u0432\u0430\u043d\u0456\u0439 \u0432\u0438\u0431\u0456\u0440\u0446\u0456 (75%) \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0430\u0454 \u0447\u0430\u0441\u0442\u0446\u0456 \u0433\u0440\u0430\u0432\u0446\u0456\u0432 \u043a\u043e\u043c\u0430\u043d\u0434\u0438 B \u0443 \u0431\u0456\u043b\u044c\u0448\u043e\u043c\u0443 DataFrame.<\/span><\/p>\n
 \u0414\u043e\u0434\u0430\u0442\u043a\u043e\u0432\u0456 \u0440\u0435\u0441\u0443\u0440\u0441\u0438<\/strong><\/span><\/h3>\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\u0431\u0440\u0430\u0442\u0438 \u0456\u043d\u0448\u0456 \u0442\u0438\u043f\u0438 \u0437\u0440\u0430\u0437\u043a\u0456\u0432 \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e \u043f\u0430\u043d\u0434:<\/span><\/p>\n
 \u042f\u043a \u0432\u0438\u043a\u043e\u043d\u0430\u0442\u0438 \u043a\u043b\u0430\u0441\u0442\u0435\u0440\u043d\u0443 \u0432\u0438\u0431\u0456\u0440\u043a\u0443 \u0432 Pandas<\/a>
 \u042f\u043a \u043f\u0440\u043e\u0432\u043e\u0434\u0438\u0442\u0438 \u0441\u0438\u0441\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u043d\u0438\u0439 \u0432\u0456\u0434\u0431\u0456\u0440 \u043f\u0440\u043e\u0431 \u0441\u0435\u0440\u0435\u0434 \u043f\u0430\u043d\u0434<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u0414\u043e\u0441\u043b\u0456\u0434\u043d\u0438\u043a\u0438 \u0447\u0430\u0441\u0442\u043e \u0431\u0435\u0440\u0443\u0442\u044c \u0437\u0440\u0430\u0437\u043a\u0438 \u0437 \u043f\u043e\u043f\u0443\u043b\u044f\u0446\u0456\u0457 \u0442\u0430 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u044e\u0442\u044c \u0434\u0430\u043d\u0456 \u0437 \u0432\u0438\u0431\u0456\u0440\u043a\u0438, \u0449\u043e\u0431 \u0437\u0440\u043e\u0431\u0438\u0442\u0438 \u0432\u0438\u0441\u043d\u043e\u0432\u043a\u0438 \u043f\u0440\u043e \u043f\u043e\u043f\u0443\u043b\u044f\u0446\u0456\u044e \u0432 \u0446\u0456\u043b\u043e\u043c\u0443. \u0417\u0430\u0437\u0432\u0438\u0447\u0430\u0439 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u043d\u0438\u043c \u043c\u0435\u0442\u043e\u0434\u043e\u043c \u0432\u0438\u0431\u0456\u0440\u043a\u0438 \u0454 \u0441\u0442\u0440\u0430\u0442\u0438\u0444\u0456\u043a\u043e\u0432\u0430\u043d\u0430 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