{"id":4229,"date":"2023-07-12T16:14:30","date_gmt":"2023-07-12T16:14:30","guid":{"rendered":"https:\/\/statorials.org\/uk\/%d0%bd%d0%b0%d0%b7%d0%b2%d0%b0-%d1%81%d1%82%d0%be%d0%b2%d0%bf%d1%86%d1%8f-panda-drop-%d0%bc%d1%96%d1%81%d1%82%d0%b8%d1%82%d1%8c\/"},"modified":"2023-07-12T16:14:30","modified_gmt":"2023-07-12T16:14:30","slug":"%d0%bd%d0%b0%d0%b7%d0%b2%d0%b0-%d1%81%d1%82%d0%be%d0%b2%d0%bf%d1%86%d1%8f-panda-drop-%d0%bc%d1%96%d1%81%d1%82%d0%b8%d1%82%d1%8c","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/%d0%bd%d0%b0%d0%b7%d0%b2%d0%b0-%d1%81%d1%82%d0%be%d0%b2%d0%bf%d1%86%d1%8f-panda-drop-%d0%bc%d1%96%d1%81%d1%82%d0%b8%d1%82%d1%8c\/","title":{"rendered":"Pandas: \u0432\u0438\u0434\u0430\u043b\u0438\u0442\u0438 \u0441\u0442\u043e\u0432\u043f\u0446\u0456, \u044f\u043a\u0449\u043e \u0456\u043c\u2019\u044f \u043c\u0456\u0441\u0442\u0438\u0442\u044c \u043f\u0435\u0432\u043d\u0438\u0439 \u0440\u044f\u0434\u043e\u043a"},"content":{"rendered":"

<\/p>\n

\n

\u0412\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0442\u0430\u043a\u0456 \u043c\u0435\u0442\u043e\u0434\u0438, \u0449\u043e\u0431 \u0432\u0438\u0434\u0430\u043b\u0438\u0442\u0438 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u0437 pandas DataFrame, \u0456\u043c\u2019\u044f \u044f\u043a\u043e\u0433\u043e \u043c\u0456\u0441\u0442\u0438\u0442\u044c \u043f\u0435\u0432\u043d\u0456 \u0440\u044f\u0434\u043a\u0438:<\/span><\/p>\n

\u0421\u043f\u043e\u0441\u0456\u0431 1: \u0432\u0438\u0434\u0430\u043b\u0456\u0442\u044c \u0441\u0442\u043e\u0432\u043f\u0446\u0456, \u044f\u043a\u0449\u043e \u0456\u043c\u2019\u044f \u043c\u0456\u0441\u0442\u0438\u0442\u044c \u043f\u0435\u0432\u043d\u0438\u0439 \u0440\u044f\u0434\u043e\u043a<\/strong><\/span><\/p>\n

 df. drop<\/span> (list(df. filter<\/span> (regex=' this_string<\/span> ')), axis= 1<\/span> , inplace= True<\/span> )\n<\/strong><\/pre>\n
 \u0421\u043f\u043e\u0441\u0456\u0431 2: \u0432\u0438\u0434\u0430\u043b\u0456\u0442\u044c \u0441\u0442\u043e\u0432\u043f\u0446\u0456, \u044f\u043a\u0449\u043e \u043d\u0430\u0437\u0432\u0430 \u043c\u0456\u0441\u0442\u0438\u0442\u044c \u043e\u0434\u0438\u043d \u0456\u0437 \u043a\u0456\u043b\u044c\u043a\u043e\u0445 \u043f\u0435\u0432\u043d\u0438\u0445 \u0440\u044f\u0434\u043a\u0456\u0432<\/strong><\/span><\/p>\n
 df. drop<\/span> (list(df. filter<\/span> (regex=' string1|string2|string3<\/span> ')), axis= 1<\/span> , inplace= True<\/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 \u043a\u043e\u0436\u0435\u043d \u043c\u0435\u0442\u043e\u0434 \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u0446\u0456 \u0437 \u0442\u0430\u043a\u0438\u043c\u0438 pandas DataFrame:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' team_name<\/span> ': ['A', 'B', 'C', 'D', 'E', 'F'],\n                   ' team_location<\/span> ': ['AU', 'AU', 'EU', 'EU', 'AU', 'EU'],\n                   ' player_name<\/span> ': ['Andy', 'Bob', 'Chad', 'Dan', 'Ed', 'Fran'],\n                   ' points<\/span> ': [22, 29, 35, 30, 18, 12]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n  team_name team_location player_name points\n0 A AU Andy 22\n1 B AU Bob 29\n2 C EU Chad 35\n3 D EU Dan 30\n4 E TO Ed 18\n5 F EU Fran 12\n<\/strong><\/pre>\n
 \u041f\u0440\u0438\u043a\u043b\u0430\u0434 1: \u0432\u0438\u0434\u0430\u043b\u0435\u043d\u043d\u044f \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432, \u044f\u043a\u0449\u043e \u0456\u043c\u2019\u044f \u043c\u0456\u0441\u0442\u0438\u0442\u044c \u043f\u0435\u0432\u043d\u0438\u0439 \u0440\u044f\u0434\u043e\u043a<\/strong><\/span><\/h2>\n
 \u041c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u0442\u0438 \u0442\u0430\u043a\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441, \u0449\u043e\u0431 \u0432\u0438\u0434\u0430\u043b\u0438\u0442\u0438 \u0431\u0443\u0434\u044c-\u044f\u043a\u0456 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u0437 DataFrame, \u044f\u043a\u0456 \u043c\u0456\u0441\u0442\u044f\u0442\u044c \u00ab\u043a\u043e\u043c\u0430\u043d\u0434\u0430\u00bb \u0431\u0443\u0434\u044c-\u0434\u0435 \u0432 \u043d\u0430\u0437\u0432\u0456 \u0441\u0442\u043e\u0432\u043f\u0446\u044f:<\/span><\/p>\n
 #drop columns whose name contains 'team'\n<\/span>df. drop<\/span> (list(df. filter<\/span> (regex=' team<\/span> ')), axis= 1<\/span> , inplace= True<\/span> )\n\n#view updated DataFrame\n<\/span>print<\/span> (df)\n\n  player_name points\n0 Andy 22\n1 Bob 29\n2 Chad 35\n3 Dan 30\n4 Ed 18\n5 Fran 12\n<\/strong><\/pre>\n
 \u0417\u0430\u0443\u0432\u0430\u0436\u0442\u0435, \u0449\u043e \u0434\u0432\u0430 \u0441\u0442\u043e\u0432\u043f\u0446\u0456, \u044f\u043a\u0456 \u043c\u0456\u0441\u0442\u044f\u0442\u044c \u0441\u043b\u043e\u0432\u043e \u00ab\u043a\u043e\u043c\u0430\u043d\u0434\u0430\u00bb \u0432 \u043d\u0430\u0437\u0432\u0456, \u0432\u0438\u0434\u0430\u043b\u0435\u043d\u043e \u0437 DataFrame.<\/span><\/span><\/p>\n
 \u041f\u0440\u0438\u043a\u043b\u0430\u0434 2: \u0432\u0438\u0434\u0430\u043b\u0435\u043d\u043d\u044f \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432, \u044f\u043a\u0449\u043e \u0456\u043c\u2019\u044f \u043c\u0456\u0441\u0442\u0438\u0442\u044c \u043e\u0434\u0438\u043d \u0456\u0437 \u043a\u0456\u043b\u044c\u043a\u043e\u0445 \u043a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u0438\u0445 \u0440\u044f\u0434\u043a\u0456\u0432<\/strong><\/span><\/h2>\n
 \u041c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u0442\u0438 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441, \u0449\u043e\u0431 \u0432\u0438\u0434\u0430\u043b\u0438\u0442\u0438 \u0431\u0443\u0434\u044c-\u044f\u043a\u0456 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u0437 DataFrame, \u044f\u043a\u0456 \u043c\u0456\u0441\u0442\u044f\u0442\u044c \u00abplayer\u00bb \u0430\u0431\u043e \u00abpoints\u00bb \u0431\u0443\u0434\u044c-\u0434\u0435 \u0432 \u043d\u0430\u0437\u0432\u0456 \u0441\u0442\u043e\u0432\u043f\u0446\u044f:<\/span><\/p>\n
 #drop columns whose name contains 'player' or 'points'\n<\/span>df. drop<\/span> (list(df. filter<\/span> (regex=' player|points<\/span> ')), axis= 1<\/span> , inplace= True<\/span> )\n\n#view updated DataFrame\n<\/span>print<\/span> (df)\n\n  team_name team_location\n0 A AU\n1 B AU\n2 C EU\n3D EU\n4 E AU\n5 F EU\n<\/strong><\/pre>\n
 \u0417\u0430\u0443\u0432\u0430\u0436\u0442\u0435, \u0449\u043e \u0434\u0432\u0430 \u0441\u0442\u043e\u0432\u043f\u0446\u0456, \u044f\u043a\u0456 \u043c\u0456\u0441\u0442\u044f\u0442\u044c \u00ab\u0433\u0440\u0430\u0432\u0435\u0446\u044c\u00bb \u0430\u0431\u043e \u00ab\u043e\u0447\u043a\u0438\u00bb<\/span> \u0432 \u0456\u043c\u0435\u043d\u0456, \u0431\u0443\u043b\u0438 \u0432\u0438\u0434\u0430\u043b\u0435\u043d\u0456 \u0437 DataFrame.<\/span><\/p>\n
 \u041f\u0440\u0438\u043c\u0456\u0442\u043a\u0430<\/strong> : |<\/strong> \u0421\u0438\u043c\u0432\u043e\u043b \u0443 pandas \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0454\u0442\u044c\u0441\u044f \u044f\u043a \u043e\u043f\u0435\u0440\u0430\u0442\u043e\u0440 \u00ab\u0410\u0411\u041e\u00bb.<\/span><\/p>\n
 \u0414\u043e\u0434\u0430\u0442\u043a\u043e\u0432\u0456 \u0440\u0435\u0441\u0443\u0440\u0441\u0438<\/strong><\/span><\/h2>\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 \u0437\u0430\u0432\u0434\u0430\u043d\u043d\u044f \u0432 pandas:<\/span><\/p>\n
 \u042f\u043a \u0432\u0438\u0434\u0430\u043b\u0438\u0442\u0438 \u043f\u0435\u0440\u0448\u0438\u0439 \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c \u0443 Pandas<\/a>
 \u042f\u043a \u0432\u0438\u0434\u0430\u043b\u0438\u0442\u0438 \u0434\u0443\u0431\u043b\u0456\u043a\u0430\u0442\u0438 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 Pandas<\/a>
 \u042f\u043a \u0432\u0438\u0434\u0430\u043b\u0438\u0442\u0438 \u0432\u0441\u0456 \u0441\u0442\u043e\u0432\u043f\u0446\u0456, \u043a\u0440\u0456\u043c \u0434\u0435\u044f\u043a\u0438\u0445, \u0443 Pandas<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u0412\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0442\u0430\u043a\u0456 \u043c\u0435\u0442\u043e\u0434\u0438, \u0449\u043e\u0431 \u0432\u0438\u0434\u0430\u043b\u0438\u0442\u0438 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u0437 pandas DataFrame, \u0456\u043c\u2019\u044f \u044f\u043a\u043e\u0433\u043e \u043c\u0456\u0441\u0442\u0438\u0442\u044c \u043f\u0435\u0432\u043d\u0456 \u0440\u044f\u0434\u043a\u0438: \u0421\u043f\u043e\u0441\u0456\u0431 1: \u0432\u0438\u0434\u0430\u043b\u0456\u0442\u044c \u0441\u0442\u043e\u0432\u043f\u0446\u0456, \u044f\u043a\u0449\u043e \u0456\u043c\u2019\u044f \u043c\u0456\u0441\u0442\u0438\u0442\u044c \u043f\u0435\u0432\u043d\u0438\u0439 \u0440\u044f\u0434\u043e\u043a df. drop (list(df. filter (regex=’ this_string ‘)), axis= 1 , inplace= True ) \u0421\u043f\u043e\u0441\u0456\u0431 2: \u0432\u0438\u0434\u0430\u043b\u0456\u0442\u044c \u0441\u0442\u043e\u0432\u043f\u0446\u0456, \u044f\u043a\u0449\u043e \u043d\u0430\u0437\u0432\u0430 \u043c\u0456\u0441\u0442\u0438\u0442\u044c \u043e\u0434\u0438\u043d \u0456\u0437 \u043a\u0456\u043b\u044c\u043a\u043e\u0445 \u043f\u0435\u0432\u043d\u0438\u0445 \u0440\u044f\u0434\u043a\u0456\u0432 df. drop (list(df. filter (regex=’ […]<\/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":"\nPandas: \u0432\u0438\u0434\u0430\u043b\u0456\u0442\u044c \u0441\u0442\u043e\u0432\u043f\u0446\u0456, \u044f\u043a\u0449\u043e \u0456\u043c\u2019\u044f \u043c\u0456\u0441\u0442\u0438\u0442\u044c \u043f\u0435\u0432\u043d\u0438\u0439 \u0440\u044f\u0434\u043e\u043a \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\u0434\u0430\u043b\u0438\u0442\u0438 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u0443 pandas DataFrame, \u0456\u043c\u2019\u044f \u044f\u043a\u043e\u0433\u043e \u043c\u0456\u0441\u0442\u0438\u0442\u044c \u043f\u0435\u0432\u043d\u0438\u0439 \u0440\u044f\u0434\u043e\u043a, \u0456\u0437 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0430\u043c\u0438.\" \/>\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\/\u043d\u0430\u0437\u0432\u0430-\u0441\u0442\u043e\u0432\u043f\u0446\u044f-panda-drop-\u043c\u0456\u0441\u0442\u0438\u0442\u044c\/\" \/>\n<meta property=\"og:locale\" content=\"uk_UA\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandas: \u0432\u0438\u0434\u0430\u043b\u0456\u0442\u044c \u0441\u0442\u043e\u0432\u043f\u0446\u0456, \u044f\u043a\u0449\u043e \u0456\u043c\u2019\u044f \u043c\u0456\u0441\u0442\u0438\u0442\u044c \u043f\u0435\u0432\u043d\u0438\u0439 \u0440\u044f\u0434\u043e\u043a \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\u0434\u0430\u043b\u0438\u0442\u0438 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u0443 pandas DataFrame, \u0456\u043c\u2019\u044f \u044f\u043a\u043e\u0433\u043e \u043c\u0456\u0441\u0442\u0438\u0442\u044c \u043f\u0435\u0432\u043d\u0438\u0439 \u0440\u044f\u0434\u043e\u043a, \u0456\u0437 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0430\u043c\u0438.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/uk\/\u043d\u0430\u0437\u0432\u0430-\u0441\u0442\u043e\u0432\u043f\u0446\u044f-panda-drop-\u043c\u0456\u0441\u0442\u0438\u0442\u044c\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-12T16:14:30+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\" content=\"\u0420\u0435\u0434\u0430\u043a\u0446\u0456\u044f\" \/>\n\t<meta name=\"twitter:label2\" content=\"\u041f\u0440\u0438\u0431\u043b. \u0447\u0430\u0441 \u0447\u0438\u0442\u0430\u043d\u043d\u044f\" \/>\n\t<meta name=\"twitter:data2\" content=\"1 \u0445\u0432\u0438\u043b\u0438\u043d\u0430\" \/>\n<script type=\"application\/ld+json\" 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