{"id":2091,"date":"2023-07-23T17:39:42","date_gmt":"2023-07-23T17:39:42","guid":{"rendered":"https:\/\/statorials.org\/uk\/%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8-%d0%bf%d0%be%d0%b4%d0%b0%d1%82%d0%b8-%d0%b7%d0%b0%d1%8f%d0%b2%d0%ba%d1%83-%d0%bd%d0%b0-%d1%81%d0%b0%d0%b8%d1%82%d1%96\/"},"modified":"2023-07-23T17:39:42","modified_gmt":"2023-07-23T17:39:42","slug":"%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8-%d0%bf%d0%be%d0%b4%d0%b0%d1%82%d0%b8-%d0%b7%d0%b0%d1%8f%d0%b2%d0%ba%d1%83-%d0%bd%d0%b0-%d1%81%d0%b0%d0%b8%d1%82%d1%96","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8-%d0%bf%d0%be%d0%b4%d0%b0%d1%82%d0%b8-%d0%b7%d0%b0%d1%8f%d0%b2%d0%ba%d1%83-%d0%bd%d0%b0-%d1%81%d0%b0%d0%b8%d1%82%d1%96\/","title":{"rendered":"\u042f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 panda apply() \u043d\u0430 \u043c\u0456\u0441\u0446\u0456"},"content":{"rendered":"

<\/p>\n

\n

\u0424\u0443\u043d\u043a\u0446\u0456\u044e pandas apply()<\/a> \u043c\u043e\u0436\u043d\u0430 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0434\u043b\u044f \u0437\u0430\u0441\u0442\u043e\u0441\u0443\u0432\u0430\u043d\u043d\u044f \u0444\u0443\u043d\u043a\u0446\u0456\u0457 \u0434\u043e \u0440\u044f\u0434\u043a\u0456\u0432 \u0430\u0431\u043e \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 pandas DataFrame.<\/span><\/p>\n

\u0426\u044f \u0444\u0443\u043d\u043a\u0446\u0456\u044f \u0432\u0456\u0434\u0440\u0456\u0437\u043d\u044f\u0454\u0442\u044c\u0441\u044f \u0432\u0456\u0434 \u0456\u043d\u0448\u0438\u0445 \u0444\u0443\u043d\u043a\u0446\u0456\u0439, \u0442\u0430\u043a\u0438\u0445 \u044f\u043a drop()<\/strong> \u0456 replace()<\/strong> , \u044f\u043a\u0456 \u043d\u0430\u0434\u0430\u044e\u0442\u044c \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442 \u043d\u0430 \u043c\u0456\u0441\u0446\u0456:<\/span><\/p>\n

 df. drop<\/span> ([' column1<\/span> '], inplace= True<\/span> )\n\ndf. rename<\/span> ({' old_column<\/span> ': ' new_column<\/span> '}, inplace= True<\/span> )\n<\/strong><\/pre>\n
 \u0424\u0443\u043d\u043a\u0446\u0456\u044f apply()<\/strong> \u043d\u0435 \u043c\u0430\u0454 \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442\u0443 inplace, \u0442\u043e\u043c\u0443 \u043c\u0438 \u043f\u043e\u0432\u0438\u043d\u043d\u0456 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441 \u0434\u043b\u044f \u043f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0435\u043d\u043d\u044f inplace DataFrame:<\/span><\/p>\n
 df = df. apply<\/span> ( lambda<\/span> x: x* 2<\/span> )<\/strong><\/pre>\n
 \u041d\u0430\u0441\u0442\u0443\u043f\u043d\u0456 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0438 \u043f\u043e\u043a\u0430\u0437\u0443\u044e\u0442\u044c, \u044f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0446\u0435\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441 \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u0446\u0456 \u0437 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u043c\u0438 pandas DataFrame:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' points<\/span> ': [25, 12, 15, 14, 19, 23, 25, 29],\n                   ' assists<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12]})\n\n#view DataFrame\n<\/span>df\n\n        points assists rebounds\n0 25 5 11\n1 12 7 8\n2 15 7 10\n3 14 9 6\n4 19 12 6\n5 23 9 5\n6 25 9 9\n7 29 4 12\n<\/strong><\/pre>\n
 \u041f\u0440\u0438\u043a\u043b\u0430\u0434 1: \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0439\u0442\u0435 apply() \u0437\u0430\u043c\u0456\u0441\u0442\u044c \u0441\u0442\u043e\u0432\u043f\u0446\u044f<\/strong><\/span><\/h3>\n
 \u041d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u043a\u043e\u0434 \u0434\u0435\u043c\u043e\u043d\u0441\u0442\u0440\u0443\u0454, \u044f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 apply()<\/strong> \u0434\u043b\u044f \u043f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0435\u043d\u043d\u044f \u0441\u0442\u043e\u0432\u043f\u0446\u044f \u043a\u0430\u0434\u0440\u0443 \u0434\u0430\u043d\u0438\u0445 \u043d\u0430 \u043c\u0456\u0441\u0446\u0456:<\/span><\/p>\n
 #multiply all values in 'points' column by 2 in place\n<\/span>df. loc<\/span> [:, ' points<\/span> '] = df. points<\/span> . apply<\/span> ( lambda<\/span> x: x* 2<\/span> )\n\n#view updated DataFrame<\/span>\ndf\n\npoints assists rebounds\n0 50 5 11\n1 24 7 8\n2 30 7 10\n3 28 9 6\n4 38 12 6\n5 46 9 5\n6 50 9 9\n7 58 4 12\n<\/strong><\/pre>\n
 \u041f\u0440\u0438\u043a\u043b\u0430\u0434 2: \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0439\u0442\u0435 apply() \u0437\u0430\u043c\u0456\u0441\u0442\u044c \u043a\u0456\u043b\u044c\u043a\u043e\u0445 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432<\/strong><\/span><\/h3>\n
 \u041d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u043a\u043e\u0434 \u0434\u0435\u043c\u043e\u043d\u0441\u0442\u0440\u0443\u0454, \u044f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 apply()<\/strong> \u0434\u043b\u044f \u043f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0435\u043d\u043d\u044f \u043a\u0456\u043b\u044c\u043a\u043e\u0445 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0444\u0440\u0435\u0439\u043c\u0443 \u0434\u0430\u043d\u0438\u0445 \u043d\u0430 \u043c\u0456\u0441\u0446\u0456:<\/span><\/p>\n
 multiply all values in 'points' and 'rebounds' column by 2 in place<\/span>\ndf[[' points<\/span> ', ' rebounds<\/span> ']] = df[[' points<\/span> ', ' rebounds<\/span> ']]. apply<\/span> ( lambda<\/span> x: x* 2<\/span> )\n\n#view updated DataFrame\n<\/span>df\n\n\tpoints assists rebounds\n0 50 5 22\n1 24 7 16\n2 30 7 20\n3 28 9 12\n4 38 12 12\n5 46 9 10\n6 50 9 18\n7 58 4 24\n<\/strong><\/pre>\n
 \u041f\u0440\u0438\u043a\u043b\u0430\u0434 3: \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0439\u0442\u0435 apply() \u0437\u0430\u043c\u0456\u0441\u0442\u044c \u0443\u0441\u0456\u0445 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432<\/strong><\/span><\/h3>\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\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 apply()<\/strong> \u0434\u043b\u044f \u043f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0435\u043d\u043d\u044f \u0432\u0441\u0456\u0445 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 \u0444\u0440\u0435\u0439\u043c\u0456 \u0434\u0430\u043d\u0438\u0445 \u043d\u0430 \u043c\u0456\u0441\u0446\u0456:<\/span><\/p>\n
 #multiply values in all columns by 2\n<\/span>df = df. apply<\/span> ( lambda<\/span> x: x* 2<\/span> )\n\n#view updated DataFrame<\/span>\ndf\n\n\tpoints assists rebounds\n0 50 10 22\n1 24 14 16\n2 30 14 20\n3 28 18 12\n4 38 24 12\n5 46 18 10\n6 50 18 18\n7 58 8 24\n<\/strong><\/pre>\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\u043a\u043e\u043d\u0443\u0432\u0430\u0442\u0438 \u0456\u043d\u0448\u0456 \u0442\u0438\u043f\u043e\u0432\u0456 \u0444\u0443\u043d\u043a\u0446\u0456\u0457 \u0432 pandas:<\/span><\/p>\n
 \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 \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<\/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":"
\u0424\u0443\u043d\u043a\u0446\u0456\u044e pandas apply() \u043c\u043e\u0436\u043d\u0430 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0434\u043b\u044f \u0437\u0430\u0441\u0442\u043e\u0441\u0443\u0432\u0430\u043d\u043d\u044f \u0444\u0443\u043d\u043a\u0446\u0456\u0457 \u0434\u043e \u0440\u044f\u0434\u043a\u0456\u0432 \u0430\u0431\u043e \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 pandas DataFrame. \u0426\u044f \u0444\u0443\u043d\u043a\u0446\u0456\u044f \u0432\u0456\u0434\u0440\u0456\u0437\u043d\u044f\u0454\u0442\u044c\u0441\u044f \u0432\u0456\u0434 \u0456\u043d\u0448\u0438\u0445 \u0444\u0443\u043d\u043a\u0446\u0456\u0439, \u0442\u0430\u043a\u0438\u0445 \u044f\u043a drop() \u0456 replace() , \u044f\u043a\u0456 \u043d\u0430\u0434\u0430\u044e\u0442\u044c \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442 \u043d\u0430 \u043c\u0456\u0441\u0446\u0456: df. drop ([‘ column1 ‘], inplace= True ) df. rename ({‘ old_column ‘: ‘ new_column ‘}, inplace= True ) \u0424\u0443\u043d\u043a\u0446\u0456\u044f apply() \u043d\u0435 \u043c\u0430\u0454 \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442\u0443 […]<\/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\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 Pandas apply() \u043d\u0430 \u043c\u0456\u0441\u0446\u0456 \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"\u0426\u0435\u0439 \u043f\u0456\u0434\u0440\u0443\u0447\u043d\u0438\u043a \u043f\u043e\u044f\u0441\u043d\u044e\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 pandas apply() \u043d\u0430 \u043c\u0456\u0441\u0446\u0456, \u0432\u043a\u043b\u044e\u0447\u0430\u044e\u0447\u0438 \u043a\u0456\u043b\u044c\u043a\u0430 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0456\u0432.\" \/>\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\/\u043f\u0430\u043d\u0434\u0438-\u043f\u043e\u0434\u0430\u0442\u0438-\u0437\u0430\u044f\u0432\u043a\u0443-\u043d\u0430-\u0441\u0430\u0438\u0442\u0456\/\" \/>\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\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 Pandas apply() \u043d\u0430 \u043c\u0456\u0441\u0446\u0456 \u2013 Statorials\" \/>\n<meta property=\"og:description\" content=\"\u0426\u0435\u0439 \u043f\u0456\u0434\u0440\u0443\u0447\u043d\u0438\u043a \u043f\u043e\u044f\u0441\u043d\u044e\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 pandas apply() \u043d\u0430 \u043c\u0456\u0441\u0446\u0456, \u0432\u043a\u043b\u044e\u0447\u0430\u044e\u0447\u0438 \u043a\u0456\u043b\u044c\u043a\u0430 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0456\u0432.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/uk\/\u043f\u0430\u043d\u0434\u0438-\u043f\u043e\u0434\u0430\u0442\u0438-\u0437\u0430\u044f\u0432\u043a\u0443-\u043d\u0430-\u0441\u0430\u0438\u0442\u0456\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-23T17:39:42+00:00\" \/>\n<meta name=\"author\" content=\"\u0420\u0435\u0434\u0430\u043a\u0446\u0456\u044f\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u041d\u0430\u043f\u0438\u0441\u0430\u043d\u043e\" \/>\n\t<meta name=\"twitter:data1\" 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