{"id":4077,"date":"2023-07-13T18:02:21","date_gmt":"2023-07-13T18:02:21","guid":{"rendered":"https:\/\/statorials.org\/uk\/pandasa-%d1%87%d0%b8%d1%82%d0%b0%d1%94-csv-%d0%b1%d0%b5%d0%b7-%d0%b7%d0%b0%d0%b3%d0%be%d0%bb%d0%be%d0%b2%d0%ba%d0%b0\/"},"modified":"2023-07-13T18:02:21","modified_gmt":"2023-07-13T18:02:21","slug":"pandasa-%d1%87%d0%b8%d1%82%d0%b0%d1%94-csv-%d0%b1%d0%b5%d0%b7-%d0%b7%d0%b0%d0%b3%d0%be%d0%bb%d0%be%d0%b2%d0%ba%d0%b0","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/pandasa-%d1%87%d0%b8%d1%82%d0%b0%d1%94-csv-%d0%b1%d0%b5%d0%b7-%d0%b7%d0%b0%d0%b3%d0%be%d0%bb%d0%be%d0%b2%d0%ba%d0%b0\/","title":{"rendered":"\u042f\u043a \u0447\u0438\u0442\u0430\u0442\u0438 csv \u0431\u0435\u0437 \u0437\u0430\u0433\u043e\u043b\u043e\u0432\u043a\u0456\u0432 \u0443 pandas (\u0437 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u043e\u043c)"},"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 \u043f\u0440\u043e\u0447\u0438\u0442\u0430\u0442\u0438 \u0444\u0430\u0439\u043b CSV \u0431\u0435\u0437 \u0437\u0430\u0433\u043e\u043b\u043e\u0432\u043a\u0456\u0432 \u0443 pandas DataFrame:<\/span><\/p>\n

 df = pd. read_csv<\/span> (' my_data.csv<\/span> ', header= None<\/span> )\n<\/strong><\/pre>\n
 \u0410\u0440\u0433\u0443\u043c\u0435\u043d\u0442 header=None<\/strong> \u043f\u043e\u0432\u0456\u0434\u043e\u043c\u043b\u044f\u0454 pandas, \u0449\u043e \u043f\u0435\u0440\u0448\u0438\u0439 \u0440\u044f\u0434\u043e\u043a \u043d\u0435 \u0441\u043b\u0456\u0434 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u044f\u043a \u0440\u044f\u0434\u043e\u043a \u0437\u0430\u0433\u043e\u043b\u043e\u0432\u043a\u0430.<\/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: \u0447\u0438\u0442\u0430\u0439\u0442\u0435 \u0444\u0430\u0439\u043b CSV \u0431\u0435\u0437 \u0437\u0430\u0433\u043e\u043b\u043e\u0432\u043a\u0456\u0432 \u0443 Pandas<\/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 Players_data.csv<\/strong> :<\/span> <\/p>\n
\"pandas<\/p>\n
 \u0417 \u0444\u0430\u0439\u043b\u0443 \u043c\u0438 \u0431\u0430\u0447\u0438\u043c\u043e, \u0449\u043e \u043f\u0435\u0440\u0448\u0438\u0439 \u0440\u044f\u0434\u043e\u043a \u043d\u0435 \u043c\u0456\u0441\u0442\u0438\u0442\u044c \u0456\u043c\u0435\u043d \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432.<\/span><\/p>\n
 \u042f\u043a\u0449\u043e \u043c\u0438 \u0456\u043c\u043f\u043e\u0440\u0442\u0443\u0454\u043c\u043e \u0444\u0430\u0439\u043b CSV \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e \u0444\u0443\u043d\u043a\u0446\u0456\u0457 read_csv()<\/strong> , pandas \u0441\u043f\u0440\u043e\u0431\u0443\u0454 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u0442\u0438 \u043f\u0435\u0440\u0448\u0438\u0439 \u0440\u044f\u0434\u043e\u043a \u044f\u043a \u0440\u044f\u0434\u043e\u043a \u0437\u0430\u0433\u043e\u043b\u043e\u0432\u043a\u0430:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#import CSV file\n<\/span>df = pd. read_csv<\/span> (' players_data.csv<\/span> ')\n\n#view resulting DataFrame\n<\/span>print<\/span> (df)\n\n   At 22 10\n0 B 14 9\n1 C 29 6\n2 D 30 2\n3 E 22 9\n4 F 31 10\n<\/strong><\/span><\/b><\/pre>\n
 \u041e\u0434\u043d\u0430\u043a \u043c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0432\u043a\u0430\u0437\u0430\u0442\u0438 header=None<\/strong> , \u0449\u043e\u0431 pandas \u0437\u043d\u0430\u043b\u0430, \u0449\u043e \u043d\u0435 \u043f\u043e\u0442\u0440\u0456\u0431\u043d\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u043f\u0435\u0440\u0448\u0438\u0439 \u0440\u044f\u0434\u043e\u043a \u044f\u043a \u0440\u044f\u0434\u043e\u043a \u0437\u0430\u0433\u043e\u043b\u043e\u0432\u043a\u0430:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#import CSV file without header\n<\/span>df = pd. read_csv<\/span> (' players_data.csv<\/span> ', header =<\/span> None<\/span> )\n\n#view resulting DataFrame\n<\/span>print<\/span> (df)\n\n   0 1 2\n0 to 22 10\n1 B 14 9\n2 C 29 6\n3 D 30 2\n4 E 22 9\n5 F 31 10\n<\/strong><\/span><\/b><\/pre>\n
 \u0417\u0430\u0443\u0432\u0430\u0436\u0442\u0435, \u0449\u043e \u043f\u0435\u0440\u0448\u0438\u0439 \u0440\u044f\u0434\u043e\u043a \u0444\u0430\u0439\u043b\u0443 CSV \u0431\u0456\u043b\u044c\u0448\u0435 \u043d\u0435 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0454\u0442\u044c\u0441\u044f \u044f\u043a \u0440\u044f\u0434\u043e\u043a \u0437\u0430\u0433\u043e\u043b\u043e\u0432\u043a\u0430.<\/span><\/span><\/p>\n
 \u0422\u0430\u043a\u043e\u0436 \u0437\u0430\u0443\u0432\u0430\u0436\u0442\u0435, \u0449\u043e pandas \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0454 \u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d \u0447\u0438\u0441\u043b\u043e\u0432\u0438\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u044c (0, 1, 2) \u044f\u043a \u0456\u043c\u0435\u043d\u0430 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0437\u0430 \u0437\u0430\u043c\u043e\u0432\u0447\u0443\u0432\u0430\u043d\u043d\u044f\u043c.<\/span><\/p>\n
 \u0429\u043e\u0431 \u0443\u043a\u0430\u0437\u0430\u0442\u0438 \u0432\u043b\u0430\u0441\u043d\u0456 \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, \u0432\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442 \u0456\u043c\u0435\u043d<\/strong> \u0442\u0430\u043a\u0438\u043c \u0447\u0438\u043d\u043e\u043c:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#specify column names<\/span>\ncols = [' team<\/span> ', ' points<\/span> ', ' rebounds<\/span> ']\n\n#import CSV file without header and specify column names\n<\/span>df = pd. read_csv<\/span> (' players_data.csv<\/span> ', header =<\/span> None<\/span> , names=cols)\n\n#view resulting DataFrame\n<\/span>print<\/span> (df)\n\n  team points rebounds\n0 to 22 10\n1 B 14 9\n2 C 29 6\n3 D 30 2\n4 E 22 9\n5 F 31 10\n<\/strong><\/span><\/b><\/pre>\n
 DataFrame \u0442\u0435\u043f\u0435\u0440 \u043c\u0430\u0454 \u043d\u0430\u0437\u0432\u0438 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432, \u044f\u043a\u0456 \u043c\u0438 \u0432\u043a\u0430\u0437\u0430\u043b\u0438 \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442\u0443 names<\/strong> .<\/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 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 read_csv \u0437 \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442\u043e\u043c usecols<\/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 \u043f\u0440\u043e\u0447\u0438\u0442\u0430\u0442\u0438 \u0444\u0430\u0439\u043b CSV \u0431\u0435\u0437 \u0437\u0430\u0433\u043e\u043b\u043e\u0432\u043a\u0456\u0432 \u0443 pandas DataFrame: df = pd. read_csv (‘ my_data.csv ‘, header= None ) \u0410\u0440\u0433\u0443\u043c\u0435\u043d\u0442 header=None \u043f\u043e\u0432\u0456\u0434\u043e\u043c\u043b\u044f\u0454 pandas, \u0449\u043e \u043f\u0435\u0440\u0448\u0438\u0439 \u0440\u044f\u0434\u043e\u043a \u043d\u0435 \u0441\u043b\u0456\u0434 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u044f\u043a \u0440\u044f\u0434\u043e\u043a \u0437\u0430\u0433\u043e\u043b\u043e\u0432\u043a\u0430. \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. \u041f\u0440\u0438\u043a\u043b\u0430\u0434: \u0447\u0438\u0442\u0430\u0439\u0442\u0435 \u0444\u0430\u0439\u043b CSV \u0431\u0435\u0437 \u0437\u0430\u0433\u043e\u043b\u043e\u0432\u043a\u0456\u0432 \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 \u0447\u0438\u0442\u0430\u0442\u0438 CSV \u0431\u0435\u0437 \u0437\u0430\u0433\u043e\u043b\u043e\u0432\u043a\u0456\u0432 \u0443 Pandas (\u0437 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u043e\u043c) - \u0421\u0442\u0430\u0442\u043e\u043b\u043e\u0433\u0456\u044f<\/title>\n<meta name=\"description\" content=\"\u0426\u0435\u0439 \u043f\u043e\u0441\u0456\u0431\u043d\u0438\u043a \u043f\u043e\u044f\u0441\u043d\u044e\u0454, \u044f\u043a \u0447\u0438\u0442\u0430\u0442\u0438 \u0444\u0430\u0439\u043b CSV \u0443 pandas \u0431\u0435\u0437 \u0437\u0430\u0433\u043e\u043b\u043e\u0432\u043a\u0456\u0432 \u0456\u0437 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u043e\u043c.\" \/>\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\/pandasa-\u0447\u0438\u0442\u0430\u0454-csv-\u0431\u0435\u0437-\u0437\u0430\u0433\u043e\u043b\u043e\u0432\u043a\u0430\/\" \/>\n<meta property=\"og:locale\" content=\"uk_UA\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u042f\u043a 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