{"id":4161,"date":"2023-07-13T04:16:13","date_gmt":"2023-07-13T04:16:13","guid":{"rendered":"https:\/\/statorials.org\/uk\/%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8-%d1%81%d0%ba%d0%b8%d0%b4%d0%b0%d1%8e%d1%82%d1%8c-%d0%ba%d1%96%d0%bb%d1%8c%d0%ba%d0%b0-%d1%81%d1%82%d0%be%d0%b2%d0%bf%d1%86%d1%96%d0%b2\/"},"modified":"2023-07-13T04:16:13","modified_gmt":"2023-07-13T04:16:13","slug":"%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8-%d1%81%d0%ba%d0%b8%d0%b4%d0%b0%d1%8e%d1%82%d1%8c-%d0%ba%d1%96%d0%bb%d1%8c%d0%ba%d0%b0-%d1%81%d1%82%d0%be%d0%b2%d0%bf%d1%86%d1%96%d0%b2","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8-%d1%81%d0%ba%d0%b8%d0%b4%d0%b0%d1%8e%d1%82%d1%8c-%d0%ba%d1%96%d0%bb%d1%8c%d0%ba%d0%b0-%d1%81%d1%82%d0%be%d0%b2%d0%bf%d1%86%d1%96%d0%b2\/","title":{"rendered":"\u042f\u043a \u0432\u0438\u0434\u0430\u043b\u0438\u0442\u0438 \u043a\u0456\u043b\u044c\u043a\u0430 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 pandas (4 \u043c\u0435\u0442\u043e\u0434\u0438)"},"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 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0456 \u043c\u0435\u0442\u043e\u0434\u0438, \u0449\u043e\u0431 \u0432\u0438\u0434\u0430\u043b\u0438\u0442\u0438 \u043a\u0456\u043b\u044c\u043a\u0430 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0456\u0437 pandas DataFrame:<\/span><\/span><\/p>\n

\u0421\u043f\u043e\u0441\u0456\u0431 1. \u0412\u0438\u0434\u0430\u043b\u0435\u043d\u043d\u044f \u043a\u0456\u043b\u044c\u043a\u043e\u0445 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0437\u0430 \u043d\u0430\u0437\u0432\u043e\u044e<\/strong><\/span><\/p>\n

 df. drop<\/span> (columns=[' col1<\/span> ', ' col2<\/span> ', ' col4<\/span> '], inplace= True<\/span> )\n<\/strong><\/pre>\n
 \u0421\u043f\u043e\u0441\u0456\u0431 2. \u0412\u0438\u0434\u0430\u043b\u0435\u043d\u043d\u044f \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 \u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d\u0456 \u0437\u0430 \u043d\u0430\u0437\u0432\u043e\u044e<\/strong><\/span><\/p>\n
 df. drop<\/span> (columns= df.loc<\/span> [:, ' col1<\/span> ':' col4<\/span> '], inplace= True<\/span> )<\/strong><\/pre>\n
 \u0421\u043f\u043e\u0441\u0456\u0431 3: \u0412\u0438\u0434\u0430\u043b\u0435\u043d\u043d\u044f \u043a\u0456\u043b\u044c\u043a\u043e\u0445 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0437\u0430 \u0456\u043d\u0434\u0435\u043a\u0441\u043e\u043c<\/strong><\/span><\/p>\n
 df. drop<\/span> (columns=df. columns<\/span> [[0, 3, 4]], inplace= True<\/span> )\n<\/strong><\/pre>\n
 \u0421\u043f\u043e\u0441\u0456\u0431 4. \u0412\u0438\u0434\u0430\u043b\u0435\u043d\u043d\u044f \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 \u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d\u0456 \u0437\u0430 \u0456\u043d\u0434\u0435\u043a\u0441\u043e\u043c<\/strong><\/span><\/p>\n
 df. drop<\/span> (columns= df.columns<\/span> [1:4], inplace= True<\/span> )<\/strong><\/pre>\n
 \u041f\u0440\u0438\u043c\u0456\u0442\u043a\u0430<\/strong> . \u0410\u0440\u0433\u0443\u043c\u0435\u043d\u0442 inplace=True<\/strong> \u043f\u043e\u0432\u0456\u0434\u043e\u043c\u043b\u044f\u0454 pandas \u0432\u0438\u0434\u0430\u043b\u0438\u0442\u0438 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 inplace \u0431\u0435\u0437 \u043f\u043e\u0432\u0442\u043e\u0440\u043d\u043e\u0433\u043e \u043f\u0440\u0438\u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f DataFrame.<\/span><\/p>\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<\/span>\n\n#createDataFrame<\/span>\ndf = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'],\n                   ' points<\/span> ': [18, 22, 19, 14, 14, 11, 20, 28],\n                   ' assists<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12],\n                   ' steals<\/span> ': [4, 5, 10, 12, 4, 8, 7, 2]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n  team points assists rebounds steals\n0 A 18 5 11 4\n1 B 22 7 8 5\n2 C 19 7 10 10\n3 D 14 9 6 12\n4 E 14 12 6 4\n5 F 11 9 5 8\n6 G 20 9 9 7\n7:28 4 12 2<\/strong><\/pre>\n
 \u041f\u0440\u0438\u043a\u043b\u0430\u0434 1. \u0412\u0438\u0434\u0430\u043b\u0435\u043d\u043d\u044f \u043a\u0456\u043b\u044c\u043a\u043e\u0445 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0437\u0430 \u043d\u0430\u0437\u0432\u043e\u044e<\/strong><\/span><\/h2>\n
 \u041d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u043a\u043e\u0434 \u043f\u043e\u043a\u0430\u0437\u0443\u0454, \u044f\u043a \u0432\u0438\u0434\u0430\u043b\u0438\u0442\u0438 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 Points<\/strong> , Rebounds<\/strong> \u0456 \u043f\u0435\u0440\u0435\u0445\u043e\u043f\u043b\u0435\u043d\u043d\u044f<\/strong> \u0437\u0430 \u043d\u0430\u0437\u0432\u043e\u044e:<\/span><\/p>\n
 #drop multiple columns by name\n<\/span>df. drop<\/span> (columns=[' points<\/span> ', ' rebounds<\/span> ', ' steals<\/span> '], inplace= True<\/span> )\n\n#view updated Dataframe\n<\/span>print<\/span> (df)\n\n  team assists\n0 to 5\n1 B 7\n2 C 7\n3 D 9\n4 E 12\n5 F 9\n6 G 9\n7:04 a.m.<\/strong>\n<\/pre>\n
 \u041f\u0440\u0438\u043a\u043b\u0430\u0434 2: \u0412\u0438\u0434\u0430\u043b\u0435\u043d\u043d\u044f \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 \u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d\u0456 \u0437\u0430 \u043d\u0430\u0437\u0432\u043e\u044e<\/strong><\/span><\/h2>\n
 \u041d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u043a\u043e\u0434 \u043f\u043e\u043a\u0430\u0437\u0443\u0454, \u044f\u043a \u043f\u0435\u0440\u0435\u043a\u0438\u043d\u0443\u0442\u0438 \u043a\u043e\u0436\u0435\u043d \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c \u043c\u0456\u0436 \u0441\u0442\u043e\u0432\u043f\u0446\u044f\u043c\u0438 \u0442\u043e\u0447\u043e\u043a<\/strong> \u0456 \u0432\u0456\u0434\u0441\u043a\u043e\u043a\u0456\u0432<\/strong> \u0437\u0430 \u043d\u0430\u0437\u0432\u043e\u044e:<\/span><\/p>\n
 #drop columns in range by name\n<\/span>df. drop<\/span> (columns= df.loc<\/span> [:, ' points<\/span> ':' rebounds<\/span> '], inplace= True<\/span> )\n\n#view updated Dataframe\n<\/span>print<\/span> (df)\n\n  team steals\n0 to 4\n1 B 5\n2 C 10\n3 D 12\n4 E 4\n5 F 8\n6 G 7\n7 A.M. 2<\/strong><\/pre>\n
 \u041f\u0440\u0438\u043a\u043b\u0430\u0434 3: \u0432\u0438\u0434\u0430\u043b\u0435\u043d\u043d\u044f \u043a\u0456\u043b\u044c\u043a\u043e\u0445 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0437\u0430 \u0456\u043d\u0434\u0435\u043a\u0441\u043e\u043c<\/strong><\/span><\/h2>\n
 \u041d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u043a\u043e\u0434 \u043f\u043e\u043a\u0430\u0437\u0443\u0454, \u044f\u043a \u0432\u0438\u0434\u0430\u043b\u0438\u0442\u0438 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u0432 \u043f\u043e\u0437\u0438\u0446\u0456\u044f\u0445 \u0456\u043d\u0434\u0435\u043a\u0441\u0443 0, 3 \u0456 4 \u0437 DataFrame:<\/span><\/p>\n
 #drop multiple columns by index\n<\/span>df. drop<\/span> (columns=df. columns<\/span> [[0, 3, 4]], inplace= True<\/span> )\n\n#view updated Dataframe\n<\/span>print<\/span> (df)\n\n   assist points\n0 18 5\n1 22 7\n2 19 7\n3 14 9\n4 14 12\n5 11 9\n6 20 9\n7 28 4<\/strong><\/pre>\n
 \u041f\u0440\u0438\u043a\u043b\u0430\u0434 4: \u0412\u0438\u0434\u0430\u043b\u0435\u043d\u043d\u044f \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 \u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d\u0456 \u0437\u0430 \u0456\u043d\u0434\u0435\u043a\u0441\u043e\u043c<\/strong><\/span><\/h2>\n
 \u041d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u043a\u043e\u0434 \u043f\u043e\u043a\u0430\u0437\u0443\u0454, \u044f\u043a \u0432\u0438\u0434\u0430\u043b\u0438\u0442\u0438 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u0432 \u043f\u043e\u0437\u0438\u0446\u0456\u044f\u0445 \u0456\u043d\u0434\u0435\u043a\u0441\u0443 0, 3 \u0456 4 \u0437 DataFrame:<\/span><\/p>\n
 #drop columns by index range\n<\/span>df. drop<\/span> (columns= df.columns<\/span> [1:4], inplace= True<\/span> )\n\n#view updated Dataframe\n<\/span>print<\/span> (df)\n\n  team steals\n0 to 4\n1 B 5\n2 C 10\n3 D 12\n4 E 4\n5 F 8\n6 G 7\n7 A.M. 2<\/strong><\/pre>\n
 \u0417\u0430\u0443\u0432\u0430\u0436\u0442\u0435, \u0449\u043e \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441 df.columns[1:4]<\/strong> \u0432\u0438\u0437\u043d\u0430\u0447\u0430\u0454 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u0432 \u043f\u043e\u0437\u0438\u0446\u0456\u044f\u0445 \u0456\u043d\u0434\u0435\u043a\u0441\u0443 \u0437 1 \u043f\u043e<\/em> 4.<\/span><\/p>\n
 \u041e\u0442\u0436\u0435, \u0446\u0435\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441 \u0432\u0438\u0434\u0430\u043b\u044f\u0454 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u0432 \u043f\u043e\u0437\u0438\u0446\u0456\u044f\u0445 \u0456\u043d\u0434\u0435\u043a\u0441\u0443 1, 2 \u0456 3.<\/span><\/p>\n
 \u041f\u0440\u0438\u043c\u0456\u0442\u043a\u0430<\/strong> . \u041f\u043e\u0432\u043d\u0443 \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u0430\u0446\u0456\u044e \u0449\u043e\u0434\u043e \u0444\u0443\u043d\u043a\u0446\u0456\u0457 pandas drop()<\/strong> \u043c\u043e\u0436\u043d\u0430 \u0437\u043d\u0430\u0439\u0442\u0438 \u0442\u0443\u0442<\/a> .<\/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
 Pandas: \u044f\u043a \u0432\u0438\u0434\u0430\u043b\u0438\u0442\u0438 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u0437\u0456 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f\u043c\u0438 NaN<\/a>
 Pandas: \u044f\u043a \u0432\u0438\u0434\u0430\u043b\u0438\u0442\u0438 \u0441\u0442\u043e\u0432\u043f\u0446\u0456, \u044f\u043a\u0438\u0445 \u043d\u0435\u043c\u0430\u0454 \u0432 \u0441\u043f\u0438\u0441\u043a\u0443<\/a>
 Pandas: \u044f\u043a \u0432\u0438\u0434\u0430\u043b\u0438\u0442\u0438 \u0432\u0441\u0456 \u0441\u0442\u043e\u0432\u043f\u0446\u0456, \u043a\u0440\u0456\u043c \u043e\u043a\u0440\u0435\u043c\u0438\u0445<\/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 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0456 \u043c\u0435\u0442\u043e\u0434\u0438, \u0449\u043e\u0431 \u0432\u0438\u0434\u0430\u043b\u0438\u0442\u0438 \u043a\u0456\u043b\u044c\u043a\u0430 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0456\u0437 pandas DataFrame: \u0421\u043f\u043e\u0441\u0456\u0431 1. \u0412\u0438\u0434\u0430\u043b\u0435\u043d\u043d\u044f \u043a\u0456\u043b\u044c\u043a\u043e\u0445 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0437\u0430 \u043d\u0430\u0437\u0432\u043e\u044e df. drop (columns=[‘ col1 ‘, ‘ col2 ‘, ‘ col4 ‘], inplace= True ) \u0421\u043f\u043e\u0441\u0456\u0431 2. \u0412\u0438\u0434\u0430\u043b\u0435\u043d\u043d\u044f \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 \u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d\u0456 \u0437\u0430 \u043d\u0430\u0437\u0432\u043e\u044e df. drop (columns= df.loc [:, ‘ col1 ‘:’ col4 ‘], inplace= True ) \u0421\u043f\u043e\u0441\u0456\u0431 […]<\/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\u0434\u0430\u043b\u0438\u0442\u0438 \u043a\u0456\u043b\u044c\u043a\u0430 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 Pandas (4 \u043c\u0435\u0442\u043e\u0434\u0438) \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 \u043a\u0456\u043b\u044c\u043a\u0430 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0456\u0437 pandas DataFrame, \u0456\u0437 \u043a\u0456\u043b\u044c\u043a\u043e\u043c\u0430 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0430\u043c\u0438.\" \/>\n<meta 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