{"id":4164,"date":"2023-07-13T04:19:22","date_gmt":"2023-07-13T04:19:22","guid":{"rendered":"https:\/\/statorials.org\/cn\/pandas-%e5%b0%86%e5%ad%97%e7%ac%a6%e4%b8%b2%e8%bd%ac%e6%8d%a2%e4%b8%ba%e6%97%a5%e6%9c%9f%e6%97%b6%e9%97%b4\/"},"modified":"2023-07-13T04:19:22","modified_gmt":"2023-07-13T04:19:22","slug":"pandas-%e5%b0%86%e5%ad%97%e7%ac%a6%e4%b8%b2%e8%bd%ac%e6%8d%a2%e4%b8%ba%e6%97%a5%e6%9c%9f%e6%97%b6%e9%97%b4","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/pandas-%e5%b0%86%e5%ad%97%e7%ac%a6%e4%b8%b2%e8%bd%ac%e6%8d%a2%e4%b8%ba%e6%97%a5%e6%9c%9f%e6%97%b6%e9%97%b4\/","title":{"rendered":"\u5982\u4f55\u5728 pandas \u4e2d\u5c06\u5b57\u7b26\u4e32\u8f6c\u6362\u4e3a\u65e5\u671f\u65f6\u95f4"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u65b9\u6cd5\u5c06 pandas DataFrame \u4e2d\u7684\u5b57\u7b26\u4e32\u5217\u8f6c\u6362\u4e3a\u65e5\u671f\u65f6\u95f4\u683c\u5f0f\uff1a<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u65b9\u6cd5 1\uff1a\u5c06\u5b57\u7b26\u4e32\u5217\u8f6c\u6362\u4e3a\u65e5\u671f\u65f6\u95f4<\/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>\u65b9\u6cd5 2\uff1a\u5c06\u591a\u5217\u4ece\u5b57\u7b26\u4e32\u8f6c\u6362\u4e3a\u65e5\u671f\u65f6\u95f4<\/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;\">\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5b9e\u8df5\u4e2d\u901a\u8fc7\u4ee5\u4e0b pandas DataFrame \u4f7f\u7528\u8fd9\u4e9b\u65b9\u6cd5\uff1a<\/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;\">\u6211\u4eec\u53ef\u4ee5\u770b\u5230DataFrame\u4e2d\u7684\u6bcf\u4e00\u5217\u5f53\u524d\u90fd\u6709\u4e00\u4e2a<strong>\u5bf9\u8c61<\/strong>\u6570\u636e\u7c7b\u578b\uff0c\u5373\u5b57\u7b26\u4e32\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u793a\u4f8b 1\uff1a\u5c06\u5b57\u7b26\u4e32\u5217\u8f6c\u6362\u4e3a\u65e5\u671f\u65f6\u95f4<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u5c06<strong>due_date<\/strong>\u5217\u4ece\u5b57\u7b26\u4e32\u8f6c\u6362\u4e3a\u65e5\u671f\u65f6\u95f4\uff1a<\/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;\">\u6211\u4eec\u53ef\u4ee5\u770b\u5230<strong>due_date<\/strong>\u5217\u5df2\u8f6c\u6362\u4e3a\u65e5\u671f\u65f6\u95f4\uff0c\u800c\u6240\u6709\u5176\u4ed6\u5217\u4fdd\u6301\u4e0d\u53d8\u3002<\/span><\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u793a\u4f8b 2\uff1a\u5c06\u591a\u5217\u4ece\u5b57\u7b26\u4e32\u8f6c\u6362\u4e3a\u65e5\u671f\u65f6\u95f4<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u5c06<strong>due_date<\/strong>\u548c<strong>comp_date<\/strong>\u5217\u4ece\u5b57\u7b26\u4e32\u8f6c\u6362\u4e3a\u65e5\u671f\u65f6\u95f4\uff1a<\/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;\">\u6211\u4eec\u53ef\u4ee5\u770b\u5230<strong>due_date<\/strong>\u548c<strong>comp_date<\/strong>\u5217\u90fd\u5df2\u4ece\u5b57\u7b26\u4e32\u8f6c\u6362\u4e3a\u65e5\u671f\u65f6\u95f4\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u6ce8\u610f<\/strong>\uff1a\u60a8\u53ef\u4ee5<a href=\"https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.to_datetime.html\" target=\"_blank\" rel=\"noopener\">\u5728\u6b64\u5904<\/a>\u627e\u5230 pandas <strong>to_datetime()<\/strong>\u51fd\u6570\u7684\u5b8c\u6574\u6587\u6863\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u5728 pandas \u4e2d\u6267\u884c\u5176\u4ed6\u5e38\u89c1\u64cd\u4f5c\uff1a<\/span><\/p>\n<p><a href=\"https:\/\/statorials.org\/cn\/\u718a\u732b\u65e5\u671f\u8303\u56f4\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 Pandas \u4e2d\u521b\u5efa\u65e5\u671f\u8303\u56f4<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u718a\u732b\u65f6\u95f4\u6233\u65e5\u671f\u65f6\u95f4\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 Pandas \u4e2d\u5c06\u65f6\u95f4\u6233\u8f6c\u6362\u4e3a\u65e5\u671f\/\u65f6\u95f4<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u5927\u718a\u732b\u65e5\u671f\u5dee\u5f02\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u8ba1\u7b97pandas\u4e2d\u4e24\u4e2a\u65e5\u671f\u4e4b\u95f4\u7684\u5dee\u5f02<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u65b9\u6cd5\u5c06 pandas DataFrame \u4e2d\u7684\u5b57\u7b26\u4e32\u5217\u8f6c\u6362\u4e3a\u65e5\u671f\u65f6\u95f4\u683c\u5f0f\uff1a \u65b9\u6cd5 1\uff1a\u5c06\u5b57\u7b26\u4e32\u5217 [&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-4164","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u5982\u4f55\u5728 Pandas \u4e2d\u5c06\u5b57\u7b26\u4e32\u8f6c\u6362\u4e3a\u65e5\u671f\u65f6\u95f4 - Statorials<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u4ecb\u7ecd\u5982\u4f55\u5c06 pandas DataFrame \u4e2d\u7684\u4e00\u5217\u6216\u591a\u5217\u4ece\u5b57\u7b26\u4e32\u8f6c\u6362\u4e3a\u65e5\u671f\u65f6\u95f4\uff0c\u5305\u62ec\u793a\u4f8b\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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