{"id":4161,"date":"2023-07-13T04:19:22","date_gmt":"2023-07-13T04:19:22","guid":{"rendered":"https:\/\/statorials.org\/ru\/pandas-%d0%ba%d0%be%d0%bd%d0%b2%d0%b5%d1%80%d1%82%d0%b8%d1%80%d1%83%d0%b5%d1%82-%d1%81%d1%82%d1%80%d0%be%d0%ba%d1%83-%d0%b2-%d0%b4%d0%b0%d1%82%d1%83-%d0%b8-%d0%b2%d1%80%d0%b5%d0%bc%d1%8f\/"},"modified":"2023-07-13T04:19:22","modified_gmt":"2023-07-13T04:19:22","slug":"pandas-%d0%ba%d0%be%d0%bd%d0%b2%d0%b5%d1%80%d1%82%d0%b8%d1%80%d1%83%d0%b5%d1%82-%d1%81%d1%82%d1%80%d0%be%d0%ba%d1%83-%d0%b2-%d0%b4%d0%b0%d1%82%d1%83-%d0%b8-%d0%b2%d1%80%d0%b5%d0%bc%d1%8f","status":"publish","type":"post","link":"https:\/\/statorials.org\/ru\/pandas-%d0%ba%d0%be%d0%bd%d0%b2%d0%b5%d1%80%d1%82%d0%b8%d1%80%d1%83%d0%b5%d1%82-%d1%81%d1%82%d1%80%d0%be%d0%ba%d1%83-%d0%b2-%d0%b4%d0%b0%d1%82%d1%83-%d0%b8-%d0%b2%d1%80%d0%b5%d0%bc%d1%8f\/","title":{"rendered":"\u041a\u0430\u043a \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u0442\u044c \u0441\u0442\u0440\u043e\u043a\u0443 \u0432 datetime \u0432 pandas"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u0412\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0435 \u043c\u0435\u0442\u043e\u0434\u044b \u0434\u043b\u044f \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u044f \u0441\u0442\u0440\u043e\u043a\u043e\u0432\u043e\u0433\u043e \u0441\u0442\u043e\u043b\u0431\u0446\u0430 \u0432 \u0444\u043e\u0440\u043c\u0430\u0442 \u0434\u0430\u0442\u044b \u0438 \u0432\u0440\u0435\u043c\u0435\u043d\u0438 \u0432 DataFrame pandas:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\u0421\u043f\u043e\u0441\u043e\u0431 1: \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u0442\u044c \u0441\u0442\u0440\u043e\u043a\u043e\u0432\u044b\u0439 \u0441\u0442\u043e\u043b\u0431\u0435\u0446 \u0432 Datetime<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df[' <span style=\"color: #ff0000;\">col1<\/span> '] = pd. <span style=\"color: #3366ff;\">to_datetime<\/span> (df[' <span style=\"color: #ff0000;\">col1<\/span> '])\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><strong>\u0421\u043f\u043e\u0441\u043e\u0431 2. \u041f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u0445 \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 \u0438\u0437 \u0441\u0442\u0440\u043e\u043a\u0438 \u0432 \u0434\u0430\u0442\u0443 \u0438 \u0432\u0440\u0435\u043c\u044f<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df[[' <span style=\"color: #ff0000;\">col1<\/span> ', ' <span style=\"color: #ff0000;\">col2<\/span> ']] = df[[' <span style=\"color: #ff0000;\">col1<\/span> ', ' <span style=\"color: #ff0000;\">col2<\/span> ']]. <span style=\"color: #3366ff;\">apply<\/span> (pd. <span style=\"color: #3366ff;\">to_datetime<\/span> )<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0412 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0445 \u043f\u0440\u0438\u043c\u0435\u0440\u0430\u0445 \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u043e, \u043a\u0430\u043a \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u043a\u0430\u0436\u0434\u044b\u0439 \u0438\u0437 \u044d\u0442\u0438\u0445 \u043c\u0435\u0442\u043e\u0434\u043e\u0432 \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u043a\u0435 \u0441\u043e \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u043c DataFrame pandas:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#createDataFrame\n<span style=\"color: #000000;\">df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">task<\/span> ': ['A', 'B', 'C', 'D'],\n                   ' <span style=\"color: #ff0000;\">due_date<\/span> ': ['4-15-2022', '5-19-2022', '6-14-2022', '10-24-2022'],\n                   ' <span style=\"color: #ff0000;\">comp_date<\/span> ': ['4-14-2022', '5-23-2022', '6-24-2022', '10-7-2022']})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">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<span style=\"color: #008080;\">#view data type of each column<\/span>\n<span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">df.dtypes<\/span> )\n\ntask object\ndue_date object\ncomp_date object\ndtype:object\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u041c\u044b \u0432\u0438\u0434\u0438\u043c, \u0447\u0442\u043e \u043a\u0430\u0436\u0434\u044b\u0439 \u0441\u0442\u043e\u043b\u0431\u0435\u0446 \u0432 DataFrame \u0432 \u043d\u0430\u0441\u0442\u043e\u044f\u0449\u0435\u0435 \u0432\u0440\u0435\u043c\u044f \u0438\u043c\u0435\u0435\u0442 \u0442\u0438\u043f \u0434\u0430\u043d\u043d\u044b\u0445 <strong>\u043e\u0431\u044a\u0435\u043a\u0442\u0430<\/strong> , \u0442\u043e \u0435\u0441\u0442\u044c \u0441\u0442\u0440\u043e\u043a\u0443.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>\u041f\u0440\u0438\u043c\u0435\u0440 1. \u041f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u0441\u0442\u0440\u043e\u043a\u043e\u0432\u043e\u0433\u043e \u0441\u0442\u043e\u043b\u0431\u0446\u0430 \u0432 Datetime<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">\u041c\u044b \u043c\u043e\u0436\u0435\u043c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441 \u0434\u043b\u044f \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u044f \u0441\u0442\u043e\u043b\u0431\u0446\u0430 <strong>Due_date<\/strong> \u0438\u0437 \u0441\u0442\u0440\u043e\u043a\u0438 \u0432 \u0434\u0430\u0442\u0443 \u0438 \u0432\u0440\u0435\u043c\u044f:<\/span><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#convert due_date column to datetime\n<\/span>df[' <span style=\"color: #ff0000;\">due_date<\/span> '] = pd. <span style=\"color: #3366ff;\">to_datetime<\/span> (df[' <span style=\"color: #ff0000;\">due_date<\/span> '])\n\n<span style=\"color: #008080;\">#view updated DataFrame\n<\/span><span style=\"color: #008000;\">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<span style=\"color: #008080;\">#view data type of each column\n<\/span><span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">df.dtypes<\/span> )\n\ntask object\ndue_date datetime64[ns]\ncomp_date object\ndtype:object<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">\u041c\u044b \u0432\u0438\u0434\u0438\u043c, \u0447\u0442\u043e \u0441\u0442\u043e\u043b\u0431\u0435\u0446 <strong>Due_date<\/strong> \u0431\u044b\u043b \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d \u0432 DateTime, \u0432 \u0442\u043e \u0432\u0440\u0435\u043c\u044f \u043a\u0430\u043a \u0432\u0441\u0435 \u043e\u0441\u0442\u0430\u043b\u044c\u043d\u044b\u0435 \u0441\u0442\u043e\u043b\u0431\u0446\u044b \u043e\u0441\u0442\u0430\u043b\u0438\u0441\u044c \u043d\u0435\u0438\u0437\u043c\u0435\u043d\u043d\u044b\u043c\u0438.<\/span><\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>\u041f\u0440\u0438\u043c\u0435\u0440 2. \u041f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u0445 \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 \u0438\u0437 \u0441\u0442\u0440\u043e\u043a\u0438 \u0432 \u0434\u0430\u0442\u0443 \u0438 \u0432\u0440\u0435\u043c\u044f<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\u041c\u044b \u043c\u043e\u0436\u0435\u043c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441 \u0434\u043b\u044f \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u044f \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 <strong>Due_date<\/strong> \u0438 <strong>Comp_date<\/strong> \u0438\u0437 \u0441\u0442\u0440\u043e\u043a\u0438 \u0432 DateTime:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#convert due_date and comp_date columns to datetime\n<\/span>df[[' <span style=\"color: #ff0000;\">due_date<\/span> ', ' <span style=\"color: #ff0000;\">comp_date<\/span> ']] = df[[' <span style=\"color: #ff0000;\">due_date<\/span> ', ' <span style=\"color: #ff0000;\">comp_date<\/span> ']]. <span style=\"color: #3366ff;\">apply<\/span> (pd. <span style=\"color: #3366ff;\">to_datetime<\/span> )\n\n<span style=\"color: #008080;\">#view updated DataFrame\n<\/span><span style=\"color: #008000;\">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<span style=\"color: #008080;\">#view data type of each column\n<\/span><span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">df.dtypes<\/span> )\n\ntask object\ndue_date datetime64[ns]\ncomp_date datetime64[ns]\ndtype:object\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u041c\u044b \u0432\u0438\u0434\u0438\u043c, \u0447\u0442\u043e \u0441\u0442\u043e\u043b\u0431\u0446\u044b <strong>Due_date<\/strong> \u0438 <strong>Comp_date<\/strong> \u0431\u044b\u043b\u0438 \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u044b \u0438\u0437 \u0441\u0442\u0440\u043e\u043a\u0438 \u0432 \u0434\u0430\u0442\u0443 \u0438 \u0432\u0440\u0435\u043c\u044f.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\u041f\u0440\u0438\u043c\u0435\u0447\u0430\u043d\u0438\u0435<\/strong> . \u041f\u043e\u043b\u043d\u0443\u044e \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u0430\u0446\u0438\u044e \u043f\u043e \u0444\u0443\u043d\u043a\u0446\u0438\u0438 pandas <strong>to_datetime()<\/strong> \u043c\u043e\u0436\u043d\u043e \u043d\u0430\u0439\u0442\u0438 <a href=\"https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.to_datetime.html\" target=\"_blank\" rel=\"noopener\">\u0437\u0434\u0435\u0441\u044c<\/a> .<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>\u0414\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0435 \u0440\u0435\u0441\u0443\u0440\u0441\u044b<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\u0412 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0445 \u0440\u0443\u043a\u043e\u0432\u043e\u0434\u0441\u0442\u0432\u0430\u0445 \u043e\u0431\u044a\u044f\u0441\u043d\u044f\u0435\u0442\u0441\u044f, \u043a\u0430\u043a \u0432\u044b\u043f\u043e\u043b\u043d\u044f\u0442\u044c \u0434\u0440\u0443\u0433\u0438\u0435 \u0440\u0430\u0441\u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u0435\u043d\u043d\u044b\u0435 \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0438 \u0432 pandas:<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/ru\/\u0434\u0438\u0430\u043f\u0430\u0437\u043e\u043d-\u0434\u0430\u0442-\u043f\u0430\u043d\u0434\u044b\/\" target=\"_blank\" rel=\"noopener\">\u041a\u0430\u043a \u0441\u043e\u0437\u0434\u0430\u0442\u044c \u0434\u0438\u0430\u043f\u0430\u0437\u043e\u043d \u0434\u0430\u0442 \u0432 Pandas<\/a><br \/> <a href=\"https:\/\/statorials.org\/ru\/\u0434\u0430\u0442\u0430-\u0438-\u0432\u0440\u0435\u043c\u044f-\u043f\u0430\u043d\u0434\u044b\/\" target=\"_blank\" rel=\"noopener\">\u041a\u0430\u043a \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u0442\u044c \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u0443\u044e \u043c\u0435\u0442\u043a\u0443 \u0432 \u0434\u0430\u0442\u0443\/\u0432\u0440\u0435\u043c\u044f \u0432 Pandas<\/a><br \/> <a href=\"https:\/\/statorials.org\/ru\/\u0440\u0430\u0437\u043d\u0438\u0446\u0430-\u0432-\u0434\u0430\u0442\u0430\u0445-\u043f\u0430\u043d\u0434\/\" target=\"_blank\" rel=\"noopener\">\u041a\u0430\u043a \u0440\u0430\u0441\u0441\u0447\u0438\u0442\u0430\u0442\u044c \u0440\u0430\u0437\u043d\u0438\u0446\u0443 \u043c\u0435\u0436\u0434\u0443 \u0434\u0432\u0443\u043c\u044f \u0434\u0430\u0442\u0430\u043c\u0438 \u0432 \u043f\u0430\u043d\u0434\u0430\u0445<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u0412\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0435 \u043c\u0435\u0442\u043e\u0434\u044b \u0434\u043b\u044f \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u044f \u0441\u0442\u0440\u043e\u043a\u043e\u0432\u043e\u0433\u043e \u0441\u0442\u043e\u043b\u0431\u0446\u0430 \u0432 \u0444\u043e\u0440\u043c\u0430\u0442 \u0434\u0430\u0442\u044b \u0438 \u0432\u0440\u0435\u043c\u0435\u043d\u0438 \u0432 DataFrame pandas: \u0421\u043f\u043e\u0441\u043e\u0431 1: \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u0442\u044c \u0441\u0442\u0440\u043e\u043a\u043e\u0432\u044b\u0439 \u0441\u0442\u043e\u043b\u0431\u0435\u0446 \u0432 Datetime df[&#8216; col1 &#8216;] = pd. to_datetime (df[&#8216; col1 &#8216;]) \u0421\u043f\u043e\u0441\u043e\u0431 2. \u041f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u0445 \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 \u0438\u0437 \u0441\u0442\u0440\u043e\u043a\u0438 \u0432 \u0434\u0430\u0442\u0443 \u0438 \u0432\u0440\u0435\u043c\u044f df[[&#8216; col1 &#8216;, &#8216; col2 &#8216;]] = df[[&#8216; col1 &#8216;, &#8216; [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-4161","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- This site is optimized with the Yoast 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