{"id":3406,"date":"2023-07-17T15:39:31","date_gmt":"2023-07-17T15:39:31","guid":{"rendered":"https:\/\/statorials.org\/uk\/%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8-%d1%81%d1%82%d1%80%d0%b8%d0%b1%d0%b0%d1%8e%d1%82%d1%8c-%d1%80%d1%8f%d0%b4%d0%b0%d0%bc%d0%b8\/"},"modified":"2023-07-17T15:39:31","modified_gmt":"2023-07-17T15:39:31","slug":"%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8-%d1%81%d1%82%d1%80%d0%b8%d0%b1%d0%b0%d1%8e%d1%82%d1%8c-%d1%80%d1%8f%d0%b4%d0%b0%d0%bc%d0%b8","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8-%d1%81%d1%82%d1%80%d0%b8%d0%b1%d0%b0%d1%8e%d1%82%d1%8c-%d1%80%d1%8f%d0%b4%d0%b0%d0%bc%d0%b8\/","title":{"rendered":"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"},"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 \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 \u0443 pandas DataFrame:<\/span><\/p>\n

\u0421\u043f\u043e\u0441\u0456\u0431 1: \u043f\u0440\u043e\u043f\u0443\u0441\u043a \u043f\u0435\u0432\u043d\u043e\u0433\u043e \u0440\u044f\u0434\u043a\u0430<\/strong><\/span><\/p>\n

 #import DataFrame and skip 2nd row\n<\/span>df = pd. read_csv<\/span> ('my_data.csv', skiprows=[ 2<\/span> ])\n<\/strong><\/pre>\n
 \u0421\u043f\u043e\u0441\u0456\u0431 2: \u0406\u0433\u043d\u043e\u0440\u0443\u0432\u0430\u043d\u043d\u044f \u043a\u0456\u043b\u044c\u043a\u043e\u0445 \u043f\u0435\u0432\u043d\u0438\u0445 \u0440\u044f\u0434\u043a\u0456\u0432<\/strong><\/span><\/p>\n
 #import DataFrame and skip 2nd and 4th row\n<\/span>df = pd. read_csv<\/span> (' my_data.csv', skiprows=[2,4<\/span> ]<\/span> )<\/strong><\/pre>\n
 \u0421\u043f\u043e\u0441\u0456\u0431 3: \u0456\u0433\u043d\u043e\u0440\u0443\u0432\u0430\u0442\u0438 \u043f\u0435\u0440\u0448\u0456 N \u0440\u044f\u0434\u043a\u0456\u0432<\/strong><\/span><\/p>\n
 #import DataFrame and skip first 2 rows\n<\/span>df = pd. read_csv<\/span> ('my_data.csv', skiprows= 2<\/span> )<\/strong><\/pre>\n
 \u0423 \u043d\u0430\u0432\u0435\u0434\u0435\u043d\u0438\u0445 \u043d\u0438\u0436\u0447\u0435 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0430\u0445 \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 \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 \u0444\u0430\u0439\u043b\u043e\u043c CSV \u043f\u0456\u0434 \u043d\u0430\u0437\u0432\u043e\u044e basketball_data.csv<\/strong> :<\/span> <\/p>\n
\"\"<\/p>\n
 \u041f\u0440\u0438\u043a\u043b\u0430\u0434 1: \u0406\u0433\u043d\u043e\u0440\u0443\u0432\u0430\u043d\u043d\u044f \u043f\u0435\u0432\u043d\u043e\u0433\u043e \u0440\u044f\u0434\u043a\u0430<\/strong><\/span><\/h2>\n
 \u041c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0442\u0430\u043a\u0438\u0439 \u043a\u043e\u0434, \u0449\u043e\u0431 \u0456\u043c\u043f\u043e\u0440\u0442\u0443\u0432\u0430\u0442\u0438 \u0444\u0430\u0439\u043b CSV \u0456 \u0456\u0433\u043d\u043e\u0440\u0443\u0432\u0430\u0442\u0438 \u0434\u0440\u0443\u0433\u0438\u0439 \u0440\u044f\u0434\u043e\u043a:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#import DataFrame and skip 2nd row\n<\/span>df = pd. read_csv<\/span> ('basketball_data.csv', skiprows=[ 2<\/span> ])\n\n#view DataFrame\n<\/span>df\n\n        team points rebounds\n0 to 22 10\n1 C 29 6\n2 D 30 2\n<\/b><\/pre>\n
 \u0417\u0430\u0443\u0432\u0430\u0436\u0442\u0435, \u0449\u043e \u0434\u0440\u0443\u0433\u0438\u0439 \u0440\u044f\u0434\u043e\u043a (\u0437 \u043a\u043e\u043c\u0430\u043d\u0434\u043e\u044e \u00abB\u00bb) \u0431\u0443\u043b\u043e \u043f\u0440\u043e\u0456\u0433\u043d\u043e\u0440\u043e\u0432\u0430\u043d\u043e \u043f\u0456\u0434 \u0447\u0430\u0441 \u0456\u043c\u043f\u043e\u0440\u0442\u0443\u0432\u0430\u043d\u043d\u044f \u0444\u0430\u0439\u043b\u0443 CSV \u0443 pandas DataFrame.<\/span><\/p>\n
 \u041f\u0440\u0438\u043c\u0456\u0442\u043a\u0430<\/strong> : \u043f\u0435\u0440\u0448\u0438\u0439 \u0440\u044f\u0434\u043e\u043a \u0444\u0430\u0439\u043b\u0443 CSV \u0432\u0432\u0430\u0436\u0430\u0454\u0442\u044c\u0441\u044f \u0440\u044f\u0434\u043a\u043e\u043c 0.<\/span><\/p>\n
 \u041f\u0440\u0438\u043a\u043b\u0430\u0434 2: \u0456\u0433\u043d\u043e\u0440\u0443\u0432\u0430\u043d\u043d\u044f \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 \u0442\u0430\u043a\u0438\u0439 \u043a\u043e\u0434, \u0449\u043e\u0431 \u0456\u043c\u043f\u043e\u0440\u0442\u0443\u0432\u0430\u0442\u0438 \u0444\u0430\u0439\u043b CSV \u0456 \u043f\u0440\u043e\u043f\u0443\u0441\u0442\u0438\u0442\u0438 \u0434\u0440\u0443\u0433\u0438\u0439 \u0456 \u0447\u0435\u0442\u0432\u0435\u0440\u0442\u0438\u0439 \u0440\u044f\u0434\u043a\u0438:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#import DataFrame and skip 2nd and 4th rows\n<\/span>df = pd. read_csv<\/span> ('basketball_data.csv', skiprows=[ 2<\/span> , 4<\/span> ])\n\n#view DataFrame\n<\/span>df\n\n        team points rebounds\n0 to 22 10\n1 C 29 6\n<\/b><\/pre>\n
 \u0417\u0432\u0435\u0440\u043d\u0456\u0442\u044c \u0443\u0432\u0430\u0433\u0443, \u0449\u043e \u0434\u0440\u0443\u0433\u0438\u0439 \u0456 \u0447\u0435\u0442\u0432\u0435\u0440\u0442\u0438\u0439 \u0440\u044f\u0434\u043a\u0438 (\u0437 \u043a\u043e\u043c\u0430\u043d\u0434\u0430\u043c\u0438 \u00abB\u00bb \u0456 \u00abD\u00bb) \u0431\u0443\u043b\u0438 \u043f\u0440\u043e\u0456\u0433\u043d\u043e\u0440\u043e\u0432\u0430\u043d\u0456 \u043f\u0456\u0434 \u0447\u0430\u0441 \u0456\u043c\u043f\u043e\u0440\u0442\u0443\u0432\u0430\u043d\u043d\u044f \u0444\u0430\u0439\u043b\u0443 CSV \u0443 pandas DataFrame.<\/span><\/p>\n
 \u041f\u0440\u0438\u043a\u043b\u0430\u0434 3: \u0456\u0433\u043d\u043e\u0440\u0443\u0432\u0430\u0442\u0438 \u043f\u0435\u0440\u0448\u0456 N \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 \u0442\u0430\u043a\u0438\u0439 \u043a\u043e\u0434, \u0449\u043e\u0431 \u0456\u043c\u043f\u043e\u0440\u0442\u0443\u0432\u0430\u0442\u0438 \u0444\u0430\u0439\u043b CSV \u0456 \u043f\u0440\u043e\u043f\u0443\u0441\u0442\u0438\u0442\u0438 \u043f\u0435\u0440\u0448\u0456 \u0434\u0432\u0430 \u0440\u044f\u0434\u043a\u0438:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#import DataFrame and skip first 2 rows\n<\/span>df = pd. read_csv<\/span> ('basketball_data.csv', skiprows= 2<\/span> )\n\n#view DataFrame\n<\/span>df\n\n        B 14 9\n0 C 29 6\n1 D 30 2\n<\/b><\/pre>\n
 \u0417\u0430\u0443\u0432\u0430\u0436\u0442\u0435, \u0449\u043e \u043f\u0435\u0440\u0448\u0456 \u0434\u0432\u0430 \u0440\u044f\u0434\u043a\u0438 \u0444\u0430\u0439\u043b\u0443 CSV \u0431\u0443\u043b\u043e \u043f\u0440\u043e\u043f\u0443\u0449\u0435\u043d\u043e, \u0430 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u0434\u043e\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u0440\u044f\u0434\u043e\u043a (\u0437 \u043a\u043e\u043c\u0430\u043d\u0434\u043e\u044e \u00abB\u00bb) \u0441\u0442\u0430\u0432 \u0440\u044f\u0434\u043a\u043e\u043c \u0437\u0430\u0433\u043e\u043b\u043e\u0432\u043a\u0430 DataFrame.<\/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
\u042f\u043a \u0447\u0438\u0442\u0430\u0442\u0438 \u0444\u0430\u0439\u043b\u0438 Excel \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e Pandas<\/a>
 \u042f\u043a \u0435\u043a\u0441\u043f\u043e\u0440\u0442\u0443\u0432\u0430\u0442\u0438 Pandas DataFrame \u0432 Excel<\/a>
 \u042f\u043a \u0435\u043a\u0441\u043f\u043e\u0440\u0442\u0443\u0432\u0430\u0442\u0438 \u043c\u0430\u0441\u0438\u0432 NumPy \u0443 \u0444\u0430\u0439\u043b 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 \u0442\u0430\u043a\u0456 \u043c\u0435\u0442\u043e\u0434\u0438, \u0449\u043e\u0431 \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 \u0443 pandas DataFrame: \u0421\u043f\u043e\u0441\u0456\u0431 1: \u043f\u0440\u043e\u043f\u0443\u0441\u043a \u043f\u0435\u0432\u043d\u043e\u0433\u043e \u0440\u044f\u0434\u043a\u0430 #import DataFrame and skip 2nd row df = pd. read_csv (‘my_data.csv’, skiprows=[ 2 ]) \u0421\u043f\u043e\u0441\u0456\u0431 2: \u0406\u0433\u043d\u043e\u0440\u0443\u0432\u0430\u043d\u043d\u044f \u043a\u0456\u043b\u044c\u043a\u043e\u0445 \u043f\u0435\u0432\u043d\u0438\u0445 \u0440\u044f\u0434\u043a\u0456\u0432 #import DataFrame and skip 2nd and 4th row df = pd. read_csv (‘ my_data.csv’, […]<\/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: \u042f\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 - 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 \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 \u0443 pandas \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\/\u043f\u0430\u043d\u0434\u0438-\u0441\u0442\u0440\u0438\u0431\u0430\u044e\u0442\u044c-\u0440\u044f\u0434\u0430\u043c\u0438\/\" \/>\n<meta property=\"og:locale\" content=\"uk_UA\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandas: \u042f\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 - 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 \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 \u0443 pandas \u0456\u0437 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u043e\u043c.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/uk\/\u043f\u0430\u043d\u0434\u0438-\u0441\u0442\u0440\u0438\u0431\u0430\u044e\u0442\u044c-\u0440\u044f\u0434\u0430\u043c\u0438\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-17T15:39:31+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/sauter1-1.jpg\" \/>\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\" 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