{"id":4081,"date":"2023-07-13T17:18:47","date_gmt":"2023-07-13T17:18:47","guid":{"rendered":"https:\/\/statorials.org\/uk\/pandas-%d1%87%d0%b8%d1%82%d0%b0%d1%94-csv-%d0%b7-%d1%80%d1%96%d0%b7%d0%bd%d0%be%d1%8e-%d0%ba%d1%96%d0%bb%d1%8c%d0%ba%d1%96%d1%81%d1%82%d1%8e-%d1%81%d1%82%d0%be%d0%b2%d0%bf%d1%86%d1%96%d0%b2\/"},"modified":"2023-07-13T17:18:47","modified_gmt":"2023-07-13T17:18:47","slug":"pandas-%d1%87%d0%b8%d1%82%d0%b0%d1%94-csv-%d0%b7-%d1%80%d1%96%d0%b7%d0%bd%d0%be%d1%8e-%d0%ba%d1%96%d0%bb%d1%8c%d0%ba%d1%96%d1%81%d1%82%d1%8e-%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\/pandas-%d1%87%d0%b8%d1%82%d0%b0%d1%94-csv-%d0%b7-%d1%80%d1%96%d0%b7%d0%bd%d0%be%d1%8e-%d0%ba%d1%96%d0%bb%d1%8c%d0%ba%d1%96%d1%81%d1%82%d1%8e-%d1%81%d1%82%d0%be%d0%b2%d0%bf%d1%86%d1%96%d0%b2\/","title":{"rendered":"Pandas: \u0456\u043c\u043f\u043e\u0440\u0442 csv \u0437 \u0440\u0456\u0437\u043d\u043e\u044e \u043a\u0456\u043b\u044c\u043a\u0456\u0441\u0442\u044e \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 \u0440\u044f\u0434\u043a\u0443"},"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\u0438\u0439 \u0431\u0430\u0437\u043e\u0432\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441, \u0449\u043e\u0431 \u0456\u043c\u043f\u043e\u0440\u0442\u0443\u0432\u0430\u0442\u0438 \u0444\u0430\u0439\u043b CSV \u0443 pandas, \u044f\u043a\u0449\u043e \u043a\u0456\u043b\u044c\u043a\u0456\u0441\u0442\u044c \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 \u0440\u044f\u0434\u043a\u0443 \u0440\u0456\u0437\u043d\u0430:<\/span><\/p>\n

 df = pd. read_csv<\/span> (' uneven_data.csv<\/span> ', header= None<\/span> , names=range( 4<\/span> ))<\/span>\n<\/span><\/strong><\/pre>\n
 \u0417\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0432\u0441\u0435\u0440\u0435\u0434\u0438\u043d\u0456 \u0444\u0443\u043d\u043a\u0446\u0456\u0457 range()<\/strong> \u043c\u0430\u0454 \u0431\u0443\u0442\u0438 \u043a\u0456\u043b\u044c\u043a\u0456\u0441\u0442\u044e \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 \u0440\u044f\u0434\u043a\u0443 \u0437 \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u043e\u044e \u043a\u0456\u043b\u044c\u043a\u0456\u0441\u0442\u044e \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432.<\/span><\/p>\n
 \u0423 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u043e\u043c\u0443 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0456 \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u043e, \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.<\/span><\/p>\n
 \u041f\u0440\u0438\u043a\u043b\u0430\u0434: \u0456\u043c\u043f\u043e\u0440\u0442 CSV \u0443 Pandas \u0437 \u0440\u0456\u0437\u043d\u043e\u044e \u043a\u0456\u043b\u044c\u043a\u0456\u0441\u0442\u044e \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 \u0440\u044f\u0434\u043a\u0443<\/strong><\/span><\/h2>\n
 \u0421\u043a\u0430\u0436\u0456\u043c\u043e, \u0443 \u043d\u0430\u0441 \u0454 \u0442\u0430\u043a\u0438\u0439 \u0444\u0430\u0439\u043b CSV \u043f\u0456\u0434 \u043d\u0430\u0437\u0432\u043e\u044e uneven_data.csv<\/strong> :<\/span> <\/p>\n
\"\"<\/p>\n
 \u0417\u0430\u0443\u0432\u0430\u0436\u0442\u0435, \u0449\u043e \u043a\u043e\u0436\u0435\u043d \u0440\u044f\u0434\u043e\u043a \u043d\u0435 \u043c\u0430\u0454 \u043e\u0434\u043d\u0430\u043a\u043e\u0432\u043e\u0457 \u043a\u0456\u043b\u044c\u043a\u043e\u0441\u0442\u0456 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432.<\/span><\/p>\n
 \u042f\u043a\u0449\u043e \u043c\u0438 \u0441\u043f\u0440\u043e\u0431\u0443\u0454\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u044e read_csv()<\/strong> , \u0449\u043e\u0431 \u0456\u043c\u043f\u043e\u0440\u0442\u0443\u0432\u0430\u0442\u0438 \u0446\u0435\u0439 \u0444\u0430\u0439\u043b CSV \u0443 pandas DataFrame, \u043c\u0438 \u043e\u0442\u0440\u0438\u043c\u0430\u0454\u043c\u043e \u043f\u043e\u043c\u0438\u043b\u043a\u0443:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#attempt to import CSV file with differing number of columns per row\ndf = pd. read_csv<\/span> (' uneven_data.csv<\/span> ', header= None<\/span> )\n\nParserError: Error tokenizing data. C error: Expected 2 fields in line 2, saw 4\n<\/span><\/span><\/strong><\/span><\/b><\/pre>\n
 \u041c\u0438 \u043e\u0442\u0440\u0438\u043c\u0443\u0454\u043c\u043e \u043f\u043e\u043c\u0438\u043b\u043a\u0443 ParserError<\/strong> , \u044f\u043a\u0430 \u043f\u043e\u0432\u0456\u0434\u043e\u043c\u043b\u044f\u0454 \u043d\u0430\u043c, \u0449\u043e pandas \u043e\u0447\u0456\u043a\u0443\u0432\u0430\u043b\u0430 2<\/strong> \u043f\u043e\u043b\u044f (\u043e\u0441\u043a\u0456\u043b\u044c\u043a\u0438 \u0446\u0435 \u0431\u0443\u043b\u0430 \u043a\u0456\u043b\u044c\u043a\u0456\u0441\u0442\u044c \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 \u043f\u0435\u0440\u0448\u043e\u043c\u0443 \u0440\u044f\u0434\u043a\u0443), \u0430\u043b\u0435 \u043f\u043e\u0431\u0430\u0447\u0438\u043b\u0430 4<\/strong> .<\/span><\/span><\/p>\n
 \u0426\u044f \u043f\u043e\u043c\u0438\u043b\u043a\u0430 \u043f\u043e\u0432\u0456\u0434\u043e\u043c\u043b\u044f\u0454 \u043d\u0430\u043c, \u0449\u043e \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u0430 \u043a\u0456\u043b\u044c\u043a\u0456\u0441\u0442\u044c \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 \u0434\u0430\u043d\u043e\u043c\u0443 \u0440\u044f\u0434\u043a\u0443 \u0441\u0442\u0430\u043d\u043e\u0432\u0438\u0442\u044c 4<\/strong> .<\/span><\/p>\n
 \u041e\u0442\u0436\u0435, \u043c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0456\u043c\u043f\u043e\u0440\u0442\u0443\u0432\u0430\u0442\u0438 \u0444\u0430\u0439\u043b CSV \u0456 \u043d\u0430\u0434\u0430\u0442\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d\u0443 (4)<\/strong> \u0434\u043b\u044f \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442\u0443 \u0456\u043c\u0435\u043d<\/strong> :<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#import CSV file with differing number of columns per row\ndf = pd. read_csv<\/span> (' uneven_data.csv<\/span> ', header= None<\/span> , names=range( 4<\/span> )))\n\n#view DataFrame<\/span>\nprint<\/span> (df)\n\n   0 1 2 3\n0 to 22 NaN NaN\n1 B 16 10.0 12.0\n2 C 25 10.0 NaN\n3 D 14 2.0 7.0\n4 E 20 4.0 NaN\n<\/span><\/span><\/strong><\/span><\/b><\/pre>\n
 \u0417\u0432\u0435\u0440\u043d\u0456\u0442\u044c \u0443\u0432\u0430\u0433\u0443, \u0449\u043e \u043c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0443\u0441\u043f\u0456\u0448\u043d\u043e \u0456\u043c\u043f\u043e\u0440\u0442\u0443\u0432\u0430\u0442\u0438 \u0444\u0430\u0439\u043b CSV \u0443 pandas DataFrame \u0431\u0435\u0437 \u0431\u0443\u0434\u044c-\u044f\u043a\u0438\u0445 \u043f\u043e\u043c\u0438\u043b\u043e\u043a, \u043e\u0441\u043a\u0456\u043b\u044c\u043a\u0438 \u043c\u0438 \u044f\u0432\u043d\u043e \u0441\u043a\u0430\u0437\u0430\u043b\u0438 pandas \u043e\u0447\u0456\u043a\u0443\u0432\u0430\u0442\u0438 4 \u0441\u0442\u043e\u0432\u043f\u0446\u0456.<\/span><\/p>\n
 \u0417\u0430 \u0437\u0430\u043c\u043e\u0432\u0447\u0443\u0432\u0430\u043d\u043d\u044f\u043c pandas \u0437\u0430\u043f\u043e\u0432\u043d\u044e\u0454 \u0432\u0441\u0456 \u0432\u0456\u0434\u0441\u0443\u0442\u043d\u0456 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0432 \u043a\u043e\u0436\u043d\u043e\u043c\u0443 \u0440\u044f\u0434\u043a\u0443 NaN.<\/span><\/p>\n
 \u042f\u043a\u0449\u043e \u0432\u0438 \u0445\u043e\u0447\u0435\u0442\u0435, \u0449\u043e\u0431 \u043f\u0440\u043e\u043f\u0443\u0449\u0435\u043d\u0456 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0436\u0430\u043b\u0438\u0441\u044f \u044f\u043a \u043d\u0443\u043b\u044c, \u0432\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u044e fillna()<\/strong> \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u043c \u0447\u0438\u043d\u043e\u043c:<\/span><\/p>\n
 #fill NaN values with zeros\n<\/span>df_new = df. fillna<\/span> ( 0<\/span> )\n\n#view new DataFrame\n<\/span>print<\/span> (df_new)\n\n   0 1 2 3\n0 to 22 0.0 0.0\n1 B 16 10.0 12.0\n2 C 25 10.0 0.0\n3 D 14 2.0 7.0\n4 E 20 4.0 0.0<\/b><\/pre>\n
 \u041a\u043e\u0436\u043d\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f NaN \u0443 DataFrame \u0442\u0435\u043f\u0435\u0440 \u0437\u0430\u043c\u0456\u043d\u0435\u043d\u043e \u043d\u0443\u043b\u0435\u043c.<\/span><\/p>\n
 \u041f\u0440\u0438\u043c\u0456\u0442\u043a\u0430<\/strong> : \u0412\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u0437\u043d\u0430\u0439\u0442\u0438 \u043f\u043e\u0432\u043d\u0443 \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u0430\u0446\u0456\u044e \u0444\u0443\u043d\u043a\u0446\u0456\u0457 pandas read_csv()<\/strong> \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 Python:<\/span><\/p>\n
 Pandas: \u044f\u043a \u043f\u0440\u043e\u043f\u0443\u0441\u043a\u0430\u0442\u0438 \u0440\u044f\u0434\u043a\u0438 \u043f\u0456\u0434 \u0447\u0430\u0441 \u0447\u0438\u0442\u0430\u043d\u043d\u044f \u0444\u0430\u0439\u043b\u0443 CSV<\/a>
 Pandas: \u042f\u043a \u0434\u043e\u0434\u0430\u0442\u0438 \u0434\u0430\u043d\u0456 \u0434\u043e \u043d\u0430\u044f\u0432\u043d\u043e\u0433\u043e \u0444\u0430\u0439\u043b\u0443 CSV<\/a>
 Pandas: \u044f\u043a \u0443\u043a\u0430\u0437\u0430\u0442\u0438 \u0442\u0438\u043f\u0438 \u043f\u0456\u0434 \u0447\u0430\u0441 \u0456\u043c\u043f\u043e\u0440\u0442\u0443\u0432\u0430\u043d\u043d\u044f \u0444\u0430\u0439\u043b\u0443 CSV<\/a>
 Pandas: \u0443\u0441\u0442\u0430\u043d\u043e\u0432\u0456\u0442\u044c \u043d\u0430\u0437\u0432\u0438 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u043f\u0456\u0434 \u0447\u0430\u0441 \u0456\u043c\u043f\u043e\u0440\u0442\u0443 \u0444\u0430\u0439\u043b\u0443 CSV<\/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\u0438\u0439 \u0431\u0430\u0437\u043e\u0432\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441, \u0449\u043e\u0431 \u0456\u043c\u043f\u043e\u0440\u0442\u0443\u0432\u0430\u0442\u0438 \u0444\u0430\u0439\u043b CSV \u0443 pandas, \u044f\u043a\u0449\u043e \u043a\u0456\u043b\u044c\u043a\u0456\u0441\u0442\u044c \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 \u0440\u044f\u0434\u043a\u0443 \u0440\u0456\u0437\u043d\u0430: df = pd. read_csv (‘ uneven_data.csv ‘, header= None , names=range( 4 )) \u0417\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0432\u0441\u0435\u0440\u0435\u0434\u0438\u043d\u0456 \u0444\u0443\u043d\u043a\u0446\u0456\u0457 range() \u043c\u0430\u0454 \u0431\u0443\u0442\u0438 \u043a\u0456\u043b\u044c\u043a\u0456\u0441\u0442\u044e \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 \u0440\u044f\u0434\u043a\u0443 \u0437 \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u043e\u044e \u043a\u0456\u043b\u044c\u043a\u0456\u0441\u0442\u044e \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432. \u0423 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u043e\u043c\u0443 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0456 \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u043e, \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. […]<\/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: \u0456\u043c\u043f\u043e\u0440\u0442\u0443\u0439\u0442\u0435 \u0444\u0430\u0439\u043b CSV \u0437 \u0440\u0456\u0437\u043d\u043e\u044e \u043a\u0456\u043b\u044c\u043a\u0456\u0441\u0442\u044e \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 \u0440\u044f\u0434\u043a\u0443 \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"\u0423 \u0446\u044c\u043e\u043c\u0443 \u043f\u0456\u0434\u0440\u0443\u0447\u043d\u0438\u043a\u0443 \u043f\u043e\u044f\u0441\u043d\u044e\u0454\u0442\u044c\u0441\u044f, \u044f\u043a \u0456\u043c\u043f\u043e\u0440\u0442\u0443\u0432\u0430\u0442\u0438 CSV \u0443 pandas, \u044f\u043a\u0449\u043e \u043a\u0456\u043b\u044c\u043a\u0456\u0441\u0442\u044c \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 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