{"id":4294,"date":"2023-07-12T05:01:09","date_gmt":"2023-07-12T05:01:09","guid":{"rendered":"https:\/\/statorials.org\/uk\/%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8-%d1%83-%d1%84%d0%be%d1%80%d0%bc%d0%b0%d1%82%d1%96-%d0%b4%d0%b0%d1%82%d0%b8-%d0%b8-%d1%87%d0%b0%d1%81%d1%83\/"},"modified":"2023-07-12T05:01:09","modified_gmt":"2023-07-12T05:01:09","slug":"%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8-%d1%83-%d1%84%d0%be%d1%80%d0%bc%d0%b0%d1%82%d1%96-%d0%b4%d0%b0%d1%82%d0%b8-%d0%b8-%d1%87%d0%b0%d1%81%d1%83","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8-%d1%83-%d1%84%d0%be%d1%80%d0%bc%d0%b0%d1%82%d1%96-%d0%b4%d0%b0%d1%82%d0%b8-%d0%b8-%d1%87%d0%b0%d1%81%d1%83\/","title":{"rendered":"\u042f\u043a \u0432\u043a\u0430\u0437\u0430\u0442\u0438 \u0444\u043e\u0440\u043c\u0430\u0442 \u0443 pandas.to_datetime"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u0412\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 <strong>pandas.to_datetime()<\/strong> , \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 \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c datetime \u0443 pandas DataFrame.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u041f\u0456\u0434 \u0447\u0430\u0441 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u043d\u044f \u0446\u0456\u0454\u0457 \u0444\u0443\u043d\u043a\u0446\u0456\u0457 \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 <strong>\u0444\u043e\u0440\u043c\u0430\u0442\u0443<\/strong> , \u0449\u043e\u0431 \u0443\u043a\u0430\u0437\u0430\u0442\u0438 \u0444\u043e\u0440\u043c\u0430\u0442 \u0432\u0430\u0448\u043e\u0457 \u0434\u0430\u0442\u0438, \u0449\u043e\u0431 \u0443\u043d\u0438\u043a\u043d\u0443\u0442\u0438 \u043f\u043e\u043c\u0438\u043b\u043e\u043a \u043f\u0456\u0434 \u0447\u0430\u0441 \u0457\u0457 \u043f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0435\u043d\u043d\u044f \u0437 \u0440\u044f\u0434\u043a\u0430 \u043d\u0430 \u0434\u0430\u0442\u0443 \u0439 \u0447\u0430\u0441.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0426\u044f \u0444\u0443\u043d\u043a\u0446\u0456\u044f \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0454 \u0442\u0430\u043a\u0438\u0439 \u0431\u0430\u0437\u043e\u0432\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df[' <span style=\"color: #ff0000;\">datetime<\/span> '] = pd. <span style=\"color: #3366ff;\">to_datetime<\/span> (df[' <span style=\"color: #ff0000;\">my_date_column<\/span> '], format=' <span style=\"color: #ff0000;\">%m%d%Y %H:%M:%S<\/span> '])\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">\u041e\u0441\u044c \u043d\u0430\u0439\u043f\u043e\u0448\u0438\u0440\u0435\u043d\u0456\u0448\u0456 \u0434\u0438\u0440\u0435\u043a\u0442\u0438\u0432\u0438, \u044f\u043a\u0456 \u0432\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u043d\u0430\u0434\u0430\u0442\u0438 \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442\u0443 <strong>format<\/strong> :<\/span><\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>%m<\/strong> : \u043c\u0456\u0441\u044f\u0446\u044c \u044f\u043a \u0447\u0438\u0441\u043b\u043e \u0437 \u043d\u0443\u043b\u0435\u043c (01, 02, \u2026 12)<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>%d<\/strong> : \u0414\u0435\u043d\u044c \u043c\u0456\u0441\u044f\u0446\u044f \u044f\u043a \u0447\u0438\u0441\u043b\u043e \u0437 \u043d\u0443\u043b\u0435\u043c (01, 02, \u2026 31)<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>%y<\/strong> : \u0440\u0456\u043a \u0456\u0437 \u0447\u0438\u0441\u043b\u043e\u043c \u0441\u0442\u043e\u043b\u0456\u0442\u0442\u044f (2020, 2021, 2022 \u0442\u043e\u0449\u043e)<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>%H<\/strong> : \u0433\u043e\u0434\u0438\u043d\u0430 (24-\u0433\u043e\u0434\u0438\u043d\u043d\u0438\u0439 \u0444\u043e\u0440\u043c\u0430\u0442) \u044f\u043a \u0447\u0438\u0441\u043b\u043e, \u0434\u043e\u043f\u043e\u0432\u043d\u0435\u043d\u0435 \u043d\u0443\u043b\u044f\u043c\u0438 (00, 01, \u2026 23)<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>%I<\/strong> : \u0427\u0430\u0441 (12-\u0433\u043e\u0434\u0438\u043d\u043d\u0438\u0439 \u0433\u043e\u0434\u0438\u043d\u043d\u0438\u043a) \u044f\u043a \u0447\u0438\u0441\u043b\u043e, \u0434\u043e\u043f\u043e\u0432\u043d\u0435\u043d\u0435 \u043d\u0443\u043b\u044f\u043c\u0438 (01, 02, \u2026 12)<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>%p<\/strong> : \u0440\u0430\u043d\u043e\u043a \u0430\u0431\u043e \u043e\u0431\u0456\u0434<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>%M<\/strong> : \u0425\u0432\u0438\u043b\u0438\u043d\u0430 \u0443 \u0432\u0438\u0433\u043b\u044f\u0434\u0456 \u0447\u0438\u0441\u043b\u0430 \u0437 \u043d\u0443\u043b\u044f\u043c\u0438 (00, 01, \u2026 59)<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>%S<\/strong> : \u0441\u0435\u043a\u0443\u043d\u0434\u0430 \u0443 \u0432\u0438\u0433\u043b\u044f\u0434\u0456 \u0447\u0438\u0441\u043b\u0430 \u0437 \u043d\u0443\u043b\u044f\u043c\u0438 (00, 01, \u2026 59)<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u0429\u043e\u0431 \u043e\u0442\u0440\u0438\u043c\u0430\u0442\u0438 \u043f\u043e\u0432\u043d\u0438\u0439 \u0441\u043f\u0438\u0441\u043e\u043a \u0456\u043d\u0441\u0442\u0440\u0443\u043a\u0446\u0456\u0439, \u043f\u0435\u0440\u0435\u0433\u043b\u044f\u043d\u044c\u0442\u0435 <a href=\"https:\/\/docs.python.org\/3\/library\/datetime.html#strftime-and-strptime-behavior\" target=\"_blank\" rel=\"noopener\">\u0446\u044e \u0441\u0442\u043e\u0440\u0456\u043d\u043a\u0443<\/a> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">\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 \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442 <strong>format<\/strong> \u0443 \u0444\u0443\u043d\u043a\u0446\u0456\u0457 <strong>to_datetime()<\/strong> \u0443 \u0440\u0456\u0437\u043d\u0438\u0445 \u0441\u0446\u0435\u043d\u0430\u0440\u0456\u044f\u0445.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>\u041f\u0440\u0438\u043a\u043b\u0430\u0434: \u0443\u043a\u0430\u0436\u0456\u0442\u044c \u0444\u043e\u0440\u043c\u0430\u0442 \u0443 pandas.to_datetime<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\u041f\u0440\u0438\u043f\u0443\u0441\u0442\u0456\u043c\u043e, \u0449\u043e \u043c\u0438 \u043c\u0430\u0454\u043c\u043e \u0442\u0430\u043a\u0438\u0439 \u0444\u0440\u0435\u0439\u043c \u0434\u0430\u043d\u0438\u0445 pandas, \u044f\u043a\u0438\u0439 \u043c\u0456\u0441\u0442\u0438\u0442\u044c \u0456\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0456\u044e \u043f\u0440\u043e \u0437\u0430\u0433\u0430\u043b\u044c\u043d\u0456 \u043f\u0440\u043e\u0434\u0430\u0436\u0456, \u0437\u0434\u0456\u0439\u0441\u043d\u0435\u043d\u0456 \u0432 \u0440\u0456\u0437\u043d\u0456 \u0434\u0430\u0442\u0438 \u0432 \u0440\u043e\u0437\u0434\u0440\u0456\u0431\u043d\u043e\u043c\u0443 \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0456:<\/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<span style=\"color: #000000;\">\ndf = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">date<\/span> ': ['10012023 4:15:30', '10042023 7:16:04', '10062023 9:25:00',\n                            '10142023 15:30:50', '10152023 18:15:00'],\n                   ' <span style=\"color: #ff0000;\">sales<\/span> ': [100, 140, 235, 120, 250]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n                dirty dates\n0 10012023 4:15:30 100\n1 10042023 7:16:04 140\n2 10062023 9:25:00 235\n3 10142023 15:30:50 120\n4 10152023 18:15:00 250\n\n<span style=\"color: #008080;\">#view data type of each column in DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">df.dtypes<\/span> )\n\ndate object\ndirty int64\ndtype:object\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">\u041c\u0438 \u0431\u0430\u0447\u0438\u043c\u043e, \u0449\u043e \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c <strong>\u0434\u0430\u0442\u0438<\/strong> \u043d\u0430\u0440\u0430\u0437\u0456 \u0454 \u0441\u0442\u043e\u0432\u043f\u0446\u0435\u043c \u0440\u044f\u0434\u043a\u0430 (\u0442\u043e\u0431\u0442\u043e \u043e\u0431\u2019\u0454\u043a\u0442\u0430).<\/span><\/span><\/p>\n<p> <span style=\"color: #000000;\">\u041f\u0440\u0438\u043f\u0443\u0441\u0442\u0456\u043c\u043e, \u043c\u0438 \u0441\u043f\u0440\u043e\u0431\u0443\u0454\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u0442\u0438 <strong>pandas.to_datetime()<\/strong> , \u0449\u043e\u0431 \u043f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0446\u0435\u0439 \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c \u043d\u0430 datetime:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#attempt to convert date column to datetime format\n<span style=\"color: #000000;\">df[' <span style=\"color: #ff0000;\">date<\/span> '] = pd. <span style=\"color: #3366ff;\">to_datetime<\/span> (df[' <span style=\"color: #ff0000;\">date<\/span> '])\n\nParserError: month must be in 1..12: 10012023 4:15:30 present at position 0<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">\u041c\u0438 \u043e\u0442\u0440\u0438\u043c\u0443\u0454\u043c\u043e \u043f\u043e\u0432\u0456\u0434\u043e\u043c\u043b\u0435\u043d\u043d\u044f \u043f\u0440\u043e \u043f\u043e\u043c\u0438\u043b\u043a\u0443, \u043e\u0441\u043a\u0456\u043b\u044c\u043a\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u044f <strong>pandas.to_datetime()<\/strong> \u043d\u0435 \u0440\u043e\u0437\u043f\u0456\u0437\u043d\u0430\u0454 \u0444\u043e\u0440\u043c\u0430\u0442 \u0434\u0430\u0442\u0438 \u0439 \u0447\u0430\u0441\u0443, \u0443 \u044f\u043a\u043e\u043c\u0443 \u043d\u0430\u0440\u0430\u0437\u0456 \u043c\u0456\u0441\u0442\u0438\u0442\u044c\u0441\u044f \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c <strong>\u0434\u0430\u0442\u0438<\/strong> .<\/span><\/span><\/p>\n<p> <span style=\"color: #000000;\">\u041c\u0438 \u0442\u0430\u043a\u043e\u0436 \u043c\u043e\u0436\u0435\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442 <b>format<\/b> , \u0449\u043e\u0431 \u0432\u043a\u0430\u0437\u0430\u0442\u0438 \u0444\u043e\u0440\u043c\u0430\u0442 \u0441\u0442\u043e\u0432\u043f\u0446\u044f:<\/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 date column to datetime format\n<\/span>df[' <span style=\"color: #ff0000;\">date<\/span> '] = pd. <span style=\"color: #3366ff;\">to_datetime<\/span> (df[' <span style=\"color: #ff0000;\">date<\/span> '], format=' <span style=\"color: #ff0000;\">%m%d%Y %H:%M:%S<\/span> ')\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n                 dirty dates\n0 2023-10-01 04:15:30 100\n1 2023-10-04 07:16:04 140\n2 2023-10-06 09:25:00 235\n3 2023-10-14 15:30:50 120\n4 2023-10-15 18:15:00 250\n\n<span style=\"color: #008080;\">#view updated type of each column\n<\/span><span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">df.dtypes<\/span> )\n\ndate datetime64[ns]\ndirty int64\ndtype:object<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u041c\u0438 \u0431\u0430\u0447\u0438\u043c\u043e, \u0449\u043e \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c <strong>\u0434\u0430\u0442\u0438<\/strong> \u0431\u0443\u043b\u043e \u0443\u0441\u043f\u0456\u0448\u043d\u043e \u043f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0435\u043d\u043e \u043d\u0430 \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c \u0434\u0430\u0442\u0438 \u0442\u0430 \u0447\u0430\u0441\u0443, \u0456 \u043c\u0438 \u043d\u0435 \u043e\u0442\u0440\u0438\u043c\u0430\u043b\u0438 \u0436\u043e\u0434\u043d\u0438\u0445 \u043f\u043e\u043c\u0438\u043b\u043e\u043a, \u043e\u0441\u043a\u0456\u043b\u044c\u043a\u0438 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u043b\u0438 \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442 <strong>\u0444\u043e\u0440\u043c\u0430\u0442\u0443<\/strong> , \u0449\u043e\u0431 \u0443\u043a\u0430\u0437\u0430\u0442\u0438 \u0442\u043e\u0447\u043d\u0438\u0439 \u0444\u043e\u0440\u043c\u0430\u0442, \u0443 \u044f\u043a\u043e\u043c\u0443 \u0431\u0443\u0432 \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c <strong>\u0434\u0430\u0442\u0438<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\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 <strong>to_datetime()<\/strong> <a href=\"https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.to_datetime.html\" target=\"_blank\" rel=\"noopener\">\u0442\u0443\u0442<\/a> .<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>\u0414\u043e\u0434\u0430\u0442\u043a\u043e\u0432\u0456 \u0440\u0435\u0441\u0443\u0440\u0441\u0438<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\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<p> <a href=\"https:\/\/statorials.org\/uk\/\u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d-\u0434\u0430\u0442-panda\/\" target=\"_blank\" rel=\"noopener\">\u042f\u043a \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0434\u0456\u0430\u043f\u0430\u0437\u043e\u043d \u0434\u0430\u0442 \u0443 Pandas<\/a><br \/> <a href=\"https:\/\/statorials.org\/uk\/pandas-timestamp-\u0434\u0430\u0442\u0430-\u0447\u0430\u0441\/\" target=\"_blank\" rel=\"noopener\">\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><br \/> <a href=\"https:\/\/statorials.org\/uk\/\u0440\u0456\u0437\u043d\u0438\u0446\u044f-\u0432-\u0434\u0430\u0442\u0456-\u043f\u0430\u043d\u0434\/\" target=\"_blank\" rel=\"noopener\">\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":"<p>\u0412\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 pandas.to_datetime() , \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 \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c datetime \u0443 pandas DataFrame. \u041f\u0456\u0434 \u0447\u0430\u0441 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u043d\u044f \u0446\u0456\u0454\u0457 \u0444\u0443\u043d\u043a\u0446\u0456\u0457 \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 \u0444\u043e\u0440\u043c\u0430\u0442\u0443 , \u0449\u043e\u0431 \u0443\u043a\u0430\u0437\u0430\u0442\u0438 \u0444\u043e\u0440\u043c\u0430\u0442 \u0432\u0430\u0448\u043e\u0457 \u0434\u0430\u0442\u0438, \u0449\u043e\u0431 \u0443\u043d\u0438\u043a\u043d\u0443\u0442\u0438 \u043f\u043e\u043c\u0438\u043b\u043e\u043a \u043f\u0456\u0434 \u0447\u0430\u0441 \u0457\u0457 \u043f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0435\u043d\u043d\u044f \u0437 \u0440\u044f\u0434\u043a\u0430 \u043d\u0430 \u0434\u0430\u0442\u0443 \u0439 \u0447\u0430\u0441. \u0426\u044f \u0444\u0443\u043d\u043a\u0446\u0456\u044f \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0454 \u0442\u0430\u043a\u0438\u0439 \u0431\u0430\u0437\u043e\u0432\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441: df[&#8216; datetime &#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":[3],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u042f\u043a \u0432\u043a\u0430\u0437\u0430\u0442\u0438 \u0444\u043e\u0440\u043c\u0430\u0442 \u0443 pandas.to_datetime \u2013 Statorials<\/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 \u0432\u043a\u0430\u0437\u0430\u0442\u0438 \u0444\u043e\u0440\u043c\u0430\u0442 \u0434\u0430\u0442\u0438 \u043f\u0456\u0434 \u0447\u0430\u0441 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u043d\u044f \u0444\u0443\u043d\u043a\u0446\u0456\u0457 pandas.to_datetime, \u0437 \u043a\u0456\u043b\u044c\u043a\u043e\u043c\u0430 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