{"id":4160,"date":"2023-07-13T04:19:22","date_gmt":"2023-07-13T04:19:22","guid":{"rendered":"https:\/\/statorials.org\/uk\/pandas-%d0%bf%d0%b5%d1%80%d0%b5%d1%82%d0%b2%d0%be%d1%80%d1%8e%d1%94-%d1%80%d1%8f%d0%b4%d0%be%d0%ba-%d0%bd%d0%b0-%d0%b4%d0%b0%d1%82%d1%83-%d0%b8-%d1%87%d0%b0%d1%81\/"},"modified":"2023-07-13T04:19:22","modified_gmt":"2023-07-13T04:19:22","slug":"pandas-%d0%bf%d0%b5%d1%80%d0%b5%d1%82%d0%b2%d0%be%d1%80%d1%8e%d1%94-%d1%80%d1%8f%d0%b4%d0%be%d0%ba-%d0%bd%d0%b0-%d0%b4%d0%b0%d1%82%d1%83-%d0%b8-%d1%87%d0%b0%d1%81","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/pandas-%d0%bf%d0%b5%d1%80%d0%b5%d1%82%d0%b2%d0%be%d1%80%d1%8e%d1%94-%d1%80%d1%8f%d0%b4%d0%be%d0%ba-%d0%bd%d0%b0-%d0%b4%d0%b0%d1%82%d1%83-%d0%b8-%d1%87%d0%b0%d1%81\/","title":{"rendered":"\u042f\u043a \u043f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0440\u044f\u0434\u043e\u043a \u043d\u0430 datetime \u0443 pandas"},"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\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0440\u044f\u0434\u043a\u043e\u0432\u0438\u0439 \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c \u0443 \u0444\u043e\u0440\u043c\u0430\u0442 \u0434\u0430\u0442\u0438 \u0439 \u0447\u0430\u0441\u0443 \u0432 pandas DataFrame:<\/span><\/p>\n

\u0421\u043f\u043e\u0441\u0456\u0431 1. \u041f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0456\u0442\u044c \u0440\u044f\u0434\u043a\u043e\u0432\u0438\u0439 \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c \u043d\u0430 \u0434\u0430\u0442\u0443 \u0439 \u0447\u0430\u0441<\/strong><\/span><\/p>\n

 df[' col1<\/span> '] = pd. to_datetime<\/span> (df[' col1<\/span> '])\n<\/strong><\/pre>\n
 \u0421\u043f\u043e\u0441\u0456\u0431 2. \u041f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0435\u043d\u043d\u044f \u043a\u0456\u043b\u044c\u043a\u043e\u0445 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0456\u0437 \u0440\u044f\u0434\u043a\u0430 \u043d\u0430 \u0434\u0430\u0442\u0443 \u0439 \u0447\u0430\u0441<\/strong><\/span><\/p>\n
 df[[' col1<\/span> ', ' col2<\/span> ']] = df[[' col1<\/span> ', ' col2<\/span> ']]. apply<\/span> (pd. to_datetime<\/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 \u0456\u0437 \u0446\u0438\u0445 \u043c\u0435\u0442\u043e\u0434\u0456\u0432 \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\ndf = pd. DataFrame<\/span> ({' task<\/span> ': ['A', 'B', 'C', 'D'],\n                   ' due_date<\/span> ': ['4-15-2022', '5-19-2022', '6-14-2022', '10-24-2022'],\n                   ' comp_date<\/span> ': ['4-14-2022', '5-23-2022', '6-24-2022', '10-7-2022']})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n  task due_date comp_date\n0 A 2022-04-15 2022-04-14\n1 B 2022-05-19 2022-05-23\n2 C 2022-06-14 2022-06-24\n3 D 2022-10-24 2022-10-07\n\n#view data type of each column<\/span>\nprint<\/span> ( df.dtypes<\/span> )\n\ntask object\ndue_date object\ncomp_date object\ndtype:object\n<\/span><\/span><\/strong><\/pre>\n
 \u041c\u0438 \u0431\u0430\u0447\u0438\u043c\u043e, \u0449\u043e \u043a\u043e\u0436\u0435\u043d \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c \u0443 DataFrame \u043d\u0430\u0440\u0430\u0437\u0456 \u043c\u0430\u0454 \u043e\u0431\u2019\u0454\u043a\u0442\u043d\u0438\u0439<\/strong> \u0442\u0438\u043f \u0434\u0430\u043d\u0438\u0445, \u0442\u043e\u0431\u0442\u043e \u0440\u044f\u0434\u043e\u043a.<\/span><\/p>\n
 \u041f\u0440\u0438\u043a\u043b\u0430\u0434 1: \u043f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0435\u043d\u043d\u044f \u0441\u0442\u043e\u0432\u043f\u0446\u044f String \u043d\u0430 Datetime<\/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 \u043f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c due_date<\/strong> \u0456\u0437 \u0440\u044f\u0434\u043a\u0430 \u043d\u0430 datetime:<\/span><\/span><\/p>\n
 #convert due_date column to datetime\n<\/span>df[' due_date<\/span> '] = pd. to_datetime<\/span> (df[' due_date<\/span> '])\n\n#view updated DataFrame\n<\/span>print<\/span> (df)\n\n  task due_date comp_date\n0 A 2022-04-15 4-14-2022\n1 B 2022-05-19 5-23-2022\n2 C 2022-06-14 6-24-2022\n3 D 2022-10-24 10-7-2022\n\n#view data type of each column\n<\/span>print<\/span> ( df.dtypes<\/span> )\n\ntask object\ndue_date datetime64[ns]\ncomp_date object\ndtype:object<\/span><\/span><\/strong><\/pre>\n
 \u041c\u0438 \u0431\u0430\u0447\u0438\u043c\u043e, \u0449\u043e \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c due_date<\/strong> \u0431\u0443\u043b\u043e \u043f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0435\u043d\u043e \u043d\u0430 datetime, \u0430 \u0432\u0441\u0456 \u0456\u043d\u0448\u0456 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u0437\u0430\u043b\u0438\u0448\u0438\u043b\u0438\u0441\u044f \u0431\u0435\u0437 \u0437\u043c\u0456\u043d.<\/span><\/span><\/p>\n
 \u041f\u0440\u0438\u043a\u043b\u0430\u0434 2: \u041f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0435\u043d\u043d\u044f \u043a\u0456\u043b\u044c\u043a\u043e\u0445 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0456\u0437 \u0440\u044f\u0434\u043a\u0430 \u043d\u0430 \u0434\u0430\u0442\u0443 \u0439 \u0447\u0430\u0441<\/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 \u043f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 due_date<\/strong> \u0456 comp_date<\/strong> \u0456\u0437 \u0440\u044f\u0434\u043a\u0430 \u043d\u0430 datetime:<\/span><\/p>\n
 #convert due_date and comp_date columns to datetime\n<\/span>df[[' due_date<\/span> ', ' comp_date<\/span> ']] = df[[' due_date<\/span> ', ' comp_date<\/span> ']]. apply<\/span> (pd. to_datetime<\/span> )\n\n#view updated DataFrame\n<\/span>print<\/span> (df)\n\n  task due_date comp_date\n0 A 2022-04-15 2022-04-14\n1 B 2022-05-19 2022-05-23\n2 C 2022-06-14 2022-06-24\n3 D 2022-10-24 2022-10-07\n\n#view data type of each column\n<\/span>print<\/span> ( df.dtypes<\/span> )\n\ntask object\ndue_date datetime64[ns]\ncomp_date datetime64[ns]\ndtype:object\n<\/span><\/span><\/strong><\/pre>\n
 \u041c\u0438 \u0431\u0430\u0447\u0438\u043c\u043e, \u0449\u043e \u043e\u0431\u0438\u0434\u0432\u0430 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 due_date<\/strong> \u0456 comp_date<\/strong> \u0431\u0443\u043b\u0438 \u043f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0435\u043d\u0456 \u0437 \u0440\u044f\u0434\u043a\u0430 \u043d\u0430 datetime.<\/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 to_datetime()<\/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 \u043e\u043f\u0435\u0440\u0430\u0446\u0456\u0457 \u0432 pandas:<\/span><\/p>\n
 \u042f\u043a \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d \u0434\u0430\u0442 \u0443 Pandas<\/a>
 \u042f\u043a \u043f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u043f\u043e\u0437\u043d\u0430\u0447\u043a\u0443 \u0447\u0430\u0441\u0443 \u0432 \u0434\u0430\u0442\u0443\/\u0447\u0430\u0441 \u0443 Pandas<\/a>
 \u042f\u043a \u0440\u043e\u0437\u0440\u0430\u0445\u0443\u0432\u0430\u0442\u0438 \u0440\u0456\u0437\u043d\u0438\u0446\u044e \u043c\u0456\u0436 \u0434\u0432\u043e\u043c\u0430 \u0434\u0430\u0442\u0430\u043c\u0438 \u0432 \u043f\u0430\u043d\u0434\u0430\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 \u0442\u0430\u043a\u0456 \u043c\u0435\u0442\u043e\u0434\u0438, \u0449\u043e\u0431 \u043f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0440\u044f\u0434\u043a\u043e\u0432\u0438\u0439 \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c \u0443 \u0444\u043e\u0440\u043c\u0430\u0442 \u0434\u0430\u0442\u0438 \u0439 \u0447\u0430\u0441\u0443 \u0432 pandas DataFrame: \u0421\u043f\u043e\u0441\u0456\u0431 1. \u041f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0456\u0442\u044c \u0440\u044f\u0434\u043a\u043e\u0432\u0438\u0439 \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c \u043d\u0430 \u0434\u0430\u0442\u0443 \u0439 \u0447\u0430\u0441 df[‘ col1 ‘] = pd. to_datetime (df[‘ col1 ‘]) \u0421\u043f\u043e\u0441\u0456\u0431 2. \u041f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0435\u043d\u043d\u044f \u043a\u0456\u043b\u044c\u043a\u043e\u0445 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0456\u0437 \u0440\u044f\u0434\u043a\u0430 \u043d\u0430 \u0434\u0430\u0442\u0443 \u0439 \u0447\u0430\u0441 df[[‘ col1 ‘, ‘ col2 ‘]] = df[[‘ col1 […]<\/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 \u043f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0440\u044f\u0434\u043e\u043a \u043d\u0430 \u0434\u0430\u0442\u0443 \u0456 \u0447\u0430\u0441 \u0443 Pandas - 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\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u043e\u0434\u0438\u043d \u0430\u0431\u043e \u043a\u0456\u043b\u044c\u043a\u0430 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 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