{"id":888,"date":"2023-07-28T10:02:57","date_gmt":"2023-07-28T10:02:57","guid":{"rendered":"https:\/\/statorials.org\/uk\/%d1%81%d0%b5%d1%80%d0%b5%d0%b4%d0%bd%d1%96%d0%b8-%d1%81%d1%82%d0%be%d0%b2%d0%bf%d1%87%d0%b8%d0%ba-%d0%bf%d0%b0%d0%bd%d0%b4\/"},"modified":"2023-07-28T10:02:57","modified_gmt":"2023-07-28T10:02:57","slug":"%d1%81%d0%b5%d1%80%d0%b5%d0%b4%d0%bd%d1%96%d0%b8-%d1%81%d1%82%d0%be%d0%b2%d0%bf%d1%87%d0%b8%d0%ba-%d0%bf%d0%b0%d0%bd%d0%b4","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/%d1%81%d0%b5%d1%80%d0%b5%d0%b4%d0%bd%d1%96%d0%b8-%d1%81%d1%82%d0%be%d0%b2%d0%bf%d1%87%d0%b8%d0%ba-%d0%bf%d0%b0%d0%bd%d0%b4\/","title":{"rendered":"\u042f\u043a \u043e\u0431\u0447\u0438\u0441\u043b\u0438\u0442\u0438 \u0441\u0435\u0440\u0435\u0434\u043d\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 pandas"},"content":{"rendered":"

<\/p>\n

\n

\u0427\u0430\u0441\u0442\u043e \u0432\u0430\u043c \u043c\u043e\u0436\u0435 \u0431\u0443\u0442\u0438 \u0446\u0456\u043a\u0430\u0432\u043e \u043e\u0431\u0447\u0438\u0441\u043b\u0438\u0442\u0438 \u0441\u0435\u0440\u0435\u0434\u043d\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u043e\u0434\u043d\u043e\u0433\u043e \u0430\u0431\u043e \u043a\u0456\u043b\u044c\u043a\u043e\u0445 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 pandas DataFrame. \u041d\u0430 \u0449\u0430\u0441\u0442\u044f, \u0432\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u043b\u0435\u0433\u043a\u043e \u0437\u0440\u043e\u0431\u0438\u0442\u0438 \u0446\u0435 \u0432 pandas \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e \u0444\u0443\u043d\u043a\u0446\u0456\u0457 Mean()<\/a> .<\/span><\/p>\n

\u0426\u0435\u0439 \u043f\u0456\u0434\u0440\u0443\u0447\u043d\u0438\u043a \u043f\u043e\u043a\u0430\u0437\u0443\u0454 \u043a\u0456\u043b\u044c\u043a\u0430 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0456\u0432 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u043d\u044f \u0446\u0456\u0454\u0457 \u0444\u0443\u043d\u043a\u0446\u0456\u0457.<\/span><\/p>\n

\u041f\u0440\u0438\u043a\u043b\u0430\u0434 1: \u0417\u043d\u0430\u0439\u0434\u0456\u0442\u044c \u0441\u0435\u0440\u0435\u0434\u043d\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u043e\u0434\u043d\u043e\u0433\u043e \u0441\u0442\u043e\u0432\u043f\u0446\u044f<\/strong><\/span><\/h3>\n

\u041f\u0440\u0438\u043f\u0443\u0441\u0442\u0456\u043c\u043e, \u0449\u043e \u0443 \u043d\u0430\u0441 \u0454 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0456 pandas DataFrame:<\/span><\/p>\n

 import<\/span> pandas as<\/span> pd\nimport<\/span> numpy as<\/span> np\n\n#createDataFrame<\/span>\ndf = pd.DataFrame({'player': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J '],\n                   'points': [25, 20, 14, 16, 27, 20, 12, 15, 14, 19],\n                   'assists': [5, 7, 7, 8, 5, 7, 6, 9, 9, 5],\n                   'rebounds': [np.nan, 8, 10, 6, 6, 9, 6, 10, 10, 7]})\n\n#view DataFrame<\/span> \ndf\n\n        player points assists rebounds\n0 A 25 5 NaN\n1 B 20 7 8.0\n2 C 14 7 10.0\n3 D 16 8 6.0\n4 E 27 5 6.0\n5 F 20 7 9.0\n6 G 12 6 6.0\n7:15 9 10.0\n8 I 14 9 10.0\n9 D 19 5 7.0\n<\/strong><\/pre>\n
 \u041c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0437\u043d\u0430\u0439\u0442\u0438 \u0441\u0435\u0440\u0435\u0434\u043d\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0441\u0442\u043e\u0432\u043f\u0446\u044f \u0437 \u043f\u043e\u0437\u043d\u0430\u0447\u043a\u043e\u044e \u00ab\u0431\u0430\u043b\u0438\u00bb, \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u044e\u0447\u0438 \u0442\u0430\u043a\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441:<\/span><\/p>\n
 df['points']. mean<\/span> ()\n\n18.2<\/strong><\/pre>\n
 \u0424\u0443\u043d\u043a\u0446\u0456\u044f Mean() \u0442\u0430\u043a\u043e\u0436 \u0432\u0438\u043a\u043b\u044e\u0447\u0430\u0454 NA \u0437\u0430 \u0437\u0430\u043c\u043e\u0432\u0447\u0443\u0432\u0430\u043d\u043d\u044f\u043c. \u041d\u0430\u043f\u0440\u0438\u043a\u043b\u0430\u0434, \u044f\u043a\u0449\u043e \u043c\u0438 \u0437\u043d\u0430\u0439\u0434\u0435\u043c\u043e \u0441\u0435\u0440\u0435\u0434\u043d\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0441\u0442\u043e\u0432\u043f\u0446\u044f \u00ab\u043f\u0456\u0434\u0431\u0438\u0440\u0430\u043d\u043d\u044f\u00bb, \u043f\u0435\u0440\u0448\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u00abNaN\u00bb \u0431\u0443\u0434\u0435 \u043f\u0440\u043e\u0441\u0442\u043e \u0432\u0438\u043a\u043b\u044e\u0447\u0435\u043d\u043e \u0437 \u0440\u043e\u0437\u0440\u0430\u0445\u0443\u043d\u043a\u0443:<\/span><\/p>\n
 df['rebounds']. mean<\/span> ()\n\n8.0\n<\/strong><\/pre>\n
 \u042f\u043a\u0449\u043e \u0432\u0438 \u0441\u043f\u0440\u043e\u0431\u0443\u0454\u0442\u0435 \u0437\u043d\u0430\u0439\u0442\u0438 \u0441\u0435\u0440\u0435\u0434\u043d\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0441\u0442\u043e\u0432\u043f\u0446\u044f, \u044f\u043a\u0438\u0439 \u043d\u0435 \u0454 \u0447\u0438\u0441\u043b\u043e\u0432\u0438\u043c, \u0432\u0438 \u043e\u0442\u0440\u0438\u043c\u0430\u0454\u0442\u0435 \u043f\u043e\u0432\u0456\u0434\u043e\u043c\u043b\u0435\u043d\u043d\u044f \u043f\u0440\u043e \u043f\u043e\u043c\u0438\u043b\u043a\u0443:<\/span><\/p>\n
 df['player'].<\/span> mean<\/span> ()<\/span>\n<\/span>\nTypeError: Could not convert ABCDEFGHIJ to numeric\n<\/strong><\/pre>\n
 \u041f\u0440\u0438\u043a\u043b\u0430\u0434 2: \u0417\u043d\u0430\u0439\u0434\u0456\u0442\u044c \u0441\u0435\u0440\u0435\u0434\u043d\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u043a\u0456\u043b\u044c\u043a\u043e\u0445 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432<\/strong><\/span><\/h3>\n
 \u041c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0437\u043d\u0430\u0439\u0442\u0438 \u0441\u0435\u0440\u0435\u0434\u043d\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u043a\u0456\u043b\u044c\u043a\u043e\u0445 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e \u0442\u0430\u043a\u043e\u0433\u043e \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441\u0443:<\/span><\/p>\n
 #find mean of points and rebounds columns<\/span>\ndf[['rebounds', 'points']]. mean<\/span> ()\n\nrebounds 8.0\npoints 18.2\ndtype:float64\n<\/strong><\/pre>\n
 \u041f\u0440\u0438\u043a\u043b\u0430\u0434 3: \u0417\u043d\u0430\u0439\u0434\u0456\u0442\u044c \u0441\u0435\u0440\u0435\u0434\u043d\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0432\u0441\u0456\u0445 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432<\/strong><\/span><\/h3>\n
 \u041c\u0438 \u0442\u0430\u043a\u043e\u0436 \u043c\u043e\u0436\u0435\u043c\u043e \u0437\u043d\u0430\u0439\u0442\u0438 \u0441\u0435\u0440\u0435\u0434\u043d\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0432\u0441\u0456\u0445 \u0447\u0438\u0441\u043b\u043e\u0432\u0438\u0445 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e \u0442\u0430\u043a\u043e\u0433\u043e \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441\u0443:<\/span><\/p>\n
 #find mean of all numeric columns in DataFrame<\/span>\ndf. mean<\/span> ()\n\npoints 18.2\nassists 6.8\nrebounds 8.0\ndtype:float64\n<\/strong><\/pre>\n
 \u0417\u0430\u0443\u0432\u0430\u0436\u0442\u0435, \u0449\u043e \u0444\u0443\u043d\u043a\u0446\u0456\u044f Mean() \u043f\u0440\u043e\u0441\u0442\u043e \u0456\u0433\u043d\u043e\u0440\u0443\u0432\u0430\u0442\u0438\u043c\u0435 \u0441\u0442\u043e\u0432\u043f\u0446\u0456, \u044f\u043a\u0456 \u043d\u0435 \u0454 \u0447\u0438\u0441\u043b\u043e\u0432\u0438\u043c\u0438.<\/span><\/p>\n
 \u0414\u043e\u0434\u0430\u0442\u043a\u043e\u0432\u0456 \u0440\u0435\u0441\u0443\u0440\u0441\u0438<\/strong><\/span><\/h3>\n
 \u042f\u043a \u0440\u043e\u0437\u0440\u0430\u0445\u0443\u0432\u0430\u0442\u0438 \u043c\u0435\u0434\u0456\u0430\u043d\u0443 \u0432 \u043f\u0430\u043d\u0434\u0430\u0445<\/a>
 \u042f\u043a \u043e\u0431\u0447\u0438\u0441\u043b\u0438\u0442\u0438 \u0441\u0443\u043c\u0443 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 Pandas<\/a>
 \u042f\u043a \u0437\u043d\u0430\u0439\u0442\u0438 \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 Pandas<\/p>\n","protected":false},"excerpt":{"rendered":"
\u0427\u0430\u0441\u0442\u043e \u0432\u0430\u043c \u043c\u043e\u0436\u0435 \u0431\u0443\u0442\u0438 \u0446\u0456\u043a\u0430\u0432\u043e \u043e\u0431\u0447\u0438\u0441\u043b\u0438\u0442\u0438 \u0441\u0435\u0440\u0435\u0434\u043d\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u043e\u0434\u043d\u043e\u0433\u043e \u0430\u0431\u043e \u043a\u0456\u043b\u044c\u043a\u043e\u0445 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 pandas DataFrame. \u041d\u0430 \u0449\u0430\u0441\u0442\u044f, \u0432\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u043b\u0435\u0433\u043a\u043e \u0437\u0440\u043e\u0431\u0438\u0442\u0438 \u0446\u0435 \u0432 pandas \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e \u0444\u0443\u043d\u043a\u0446\u0456\u0457 Mean() . \u0426\u0435\u0439 \u043f\u0456\u0434\u0440\u0443\u0447\u043d\u0438\u043a \u043f\u043e\u043a\u0430\u0437\u0443\u0454 \u043a\u0456\u043b\u044c\u043a\u0430 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0456\u0432 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u043d\u044f \u0446\u0456\u0454\u0457 \u0444\u0443\u043d\u043a\u0446\u0456\u0457. \u041f\u0440\u0438\u043a\u043b\u0430\u0434 1: \u0417\u043d\u0430\u0439\u0434\u0456\u0442\u044c \u0441\u0435\u0440\u0435\u0434\u043d\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u043e\u0434\u043d\u043e\u0433\u043e \u0441\u0442\u043e\u0432\u043f\u0446\u044f \u041f\u0440\u0438\u043f\u0443\u0441\u0442\u0456\u043c\u043e, \u0449\u043e \u0443 \u043d\u0430\u0441 \u0454 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0456 pandas DataFrame: import pandas as […]<\/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 \u0440\u043e\u0437\u0440\u0430\u0445\u0443\u0432\u0430\u0442\u0438 \u0441\u0435\u0440\u0435\u0434\u043d\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0441\u0442\u043e\u0432\u043f\u0446\u044f \u0432 \u043f\u0430\u043d\u0434\u0430\u0445 \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"\u041f\u0440\u043e\u0441\u0442\u0435 \u043f\u043e\u044f\u0441\u043d\u0435\u043d\u043d\u044f \u0442\u043e\u0433\u043e, \u044f\u043a \u043e\u0431\u0447\u0438\u0441\u043b\u0438\u0442\u0438 \u0441\u0435\u0440\u0435\u0434\u043d\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u043e\u0434\u043d\u043e\u0433\u043e \u0430\u0431\u043e \u043a\u0456\u043b\u044c\u043a\u043e\u0445 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 pandas DataFrame.\" \/>\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\/\u0441\u0435\u0440\u0435\u0434\u043d\u0456\u0438-\u0441\u0442\u043e\u0432\u043f\u0447\u0438\u043a-\u043f\u0430\u043d\u0434\/\" \/>\n<meta property=\"og:locale\" content=\"uk_UA\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u042f\u043a \u0440\u043e\u0437\u0440\u0430\u0445\u0443\u0432\u0430\u0442\u0438 \u0441\u0435\u0440\u0435\u0434\u043d\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0441\u0442\u043e\u0432\u043f\u0446\u044f \u0432 \u043f\u0430\u043d\u0434\u0430\u0445 \u2013 Statorials\" \/>\n<meta property=\"og:description\" content=\"\u041f\u0440\u043e\u0441\u0442\u0435 \u043f\u043e\u044f\u0441\u043d\u0435\u043d\u043d\u044f \u0442\u043e\u0433\u043e, \u044f\u043a \u043e\u0431\u0447\u0438\u0441\u043b\u0438\u0442\u0438 \u0441\u0435\u0440\u0435\u0434\u043d\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u043e\u0434\u043d\u043e\u0433\u043e \u0430\u0431\u043e \u043a\u0456\u043b\u044c\u043a\u043e\u0445 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 pandas DataFrame.\" \/>\n<meta property=\"og:url\" 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