{"id":4157,"date":"2023-07-13T05:34:42","date_gmt":"2023-07-13T05:34:42","guid":{"rendered":"https:\/\/statorials.org\/cn\/pandas-%e6%af%94%e8%be%83%e6%97%a5%e6%9c%9f\/"},"modified":"2023-07-13T05:34:42","modified_gmt":"2023-07-13T05:34:42","slug":"pandas-%e6%af%94%e8%be%83%e6%97%a5%e6%9c%9f","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/pandas-%e6%af%94%e8%be%83%e6%97%a5%e6%9c%9f\/","title":{"rendered":"\u5982\u4f55\u6bd4\u8f83 pandas \u4e2d\u7684\u65e5\u671f\uff1a\u793a\u4f8b"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u65b9\u6cd5\u6765\u6bd4\u8f83 pandas DataFrame \u4e2d\u4e24\u5217\u4e4b\u95f4\u7684\u65e5\u671f\uff1a<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u65b9\u6cd5 1\uff1a\u5411 DataFrame \u6dfb\u52a0\u65b0\u5217\u4ee5\u663e\u793a\u65e5\u671f\u6bd4\u8f83<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df[' <span style=\"color: #ff0000;\">met_due_date<\/span> '] = df[' <span style=\"color: #ff0000;\">comp_date<\/span> '] <span style=\"color: #800080;\">&lt;<\/span> df[' <span style=\"color: #ff0000;\">due_date<\/span> ']\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6b64\u7279\u5b9a\u793a\u4f8b\u6dfb\u52a0\u4e86\u4e00\u4e2a\u540d\u4e3a<strong>met_due_date<\/strong>\u7684\u65b0\u5217\uff0c\u8be5\u5217\u6839\u636e<strong>comp_date<\/strong>\u5217\u4e2d\u7684\u65e5\u671f\u662f\u5426\u65e9\u4e8e<strong>due_date<\/strong>\u5217\u4e2d\u7684\u65e5\u671f\u8fd4\u56de True \u6216 False\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u65b9\u6cd52\uff1a\u6839\u636e\u65e5\u671f\u6bd4\u8f83\u8fc7\u6ee4DataFrame<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df_met_due_date = df[df[' <span style=\"color: #ff0000;\">comp_date<\/span> '] <span style=\"color: #800080;\">&lt;<\/span> df[' <span style=\"color: #ff0000;\">due_date<\/span> ']]<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6b64\u7279\u5b9a\u793a\u4f8b\u8fc7\u6ee4 DataFrame \u4ee5\u4ec5\u4fdd\u7559<strong>comp_date<\/strong>\u5217\u4e2d\u7684\u65e5\u671f\u65e9\u4e8e<strong>due_date<\/strong>\u5217\u4e2d\u7684\u65e5\u671f\u7684\u884c\u3002<\/span><\/p>\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;\">#convert due_date and comp_date columns to datetime format\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 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<\/span><\/span><\/strong><\/pre>\n<h2><span style=\"color: #000000;\"><strong>\u793a\u4f8b 1\uff1a\u5411 DataFrame \u6dfb\u52a0\u4e00\u4e2a\u65b0\u5217\u4ee5\u663e\u793a\u65e5\u671f\u6bd4\u8f83<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u6f14\u793a\u5982\u4f55\u6dfb\u52a0\u540d\u4e3a<strong>met_due_date<\/strong>\u7684\u65b0\u5217\uff0c\u8be5\u5217\u6839\u636e<strong>comp_date<\/strong>\u5217\u4e2d\u7684\u65e5\u671f\u662f\u5426\u65e9\u4e8e<strong>due_date<\/strong>\u5217\u4e2d\u7684\u65e5\u671f\u8fd4\u56de True \u6216 False\u3002<\/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;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#create new column that shows if completion date is before due date\n<\/span>df[' <span style=\"color: #ff0000;\">met_due_date<\/span> '] = df[' <span style=\"color: #ff0000;\">comp_date<\/span> '] <span style=\"color: #800080;\">&lt;<\/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 met_due_date\n0 A 2022-04-15 2022-04-14 True\n1 B 2022-05-19 2022-05-23 False\n2 C 2022-06-14 2022-06-24 False\n3 D 2022-10-24 2022-10-07 True\n<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u5bf9\u4e8e DataFrame \u4e2d\u7684\u6bcf\u4e00\u884c\uff0c\u65b0\u7684<strong>met_due_date<\/strong>\u5217\u6307\u793a<strong>comp_date<\/strong>\u5217\u4e2d\u7684\u65e5\u671f\u662f\u5426\u65e9\u4e8e<strong>due_date<\/strong>\u5217\u4e2d\u7684\u65e5\u671f\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4f8b\u5982\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u4efb\u52a1 A \u7684\u622a\u6b62\u65e5\u671f\u4e3a 04\/15\/2022\uff0c\u5b8c\u6210\u65e5\u671f\u4e3a 04\/14\/2022\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u7531\u4e8e\u5b8c\u6210\u65e5\u671f\u65e9\u4e8e\u622a\u6b62\u65e5\u671f\uff0c\u56e0\u6b64<strong>met_due_date<\/strong>\u5217\u7684\u503c\u4e3a True\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u793a\u4f8b 2\uff1a\u6839\u636e\u65e5\u671f\u6bd4\u8f83\u8fc7\u6ee4 DataFrame<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u5982\u4f55\u8fc7\u6ee4 DataFrame \u4ee5\u4ec5\u5305\u542b<strong>comp_date<\/strong>\u5217\u4e2d\u7684\u65e5\u671f\u65e9\u4e8e<strong>due_date<\/strong>\u5217\u4e2d\u7684\u65e5\u671f\u7684\u884c\u3002<\/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<span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">\n#filter for rows where completion date is before due date\n<\/span>df_met_due_date = df[df[' <span style=\"color: #ff0000;\">comp_date<\/span> '] <span style=\"color: #800080;\">&lt;<\/span> df[' <span style=\"color: #ff0000;\">due_date<\/span> ']]\n\n<span style=\"color: #008080;\">#view results\n<\/span><span style=\"color: #008000;\">print<\/span> (df_met_due_date)\n\n  task due_date comp_date\n0 A 2022-04-15 2022-04-14\n3 D 2022-10-24 2022-10-07<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u65b0\u7684 DataFrame \u5df2\u88ab\u8fc7\u6ee4\u4e3a\u4ec5\u5305\u542b<strong>comp_date<\/strong>\u5217\u4e2d\u7684\u65e5\u671f\u65e9\u4e8e<strong>due_date<\/strong>\u5217\u4e2d\u7684\u65e5\u671f\u7684\u884c\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\u6765\u6bd4\u8f83 pandas DataFrame \u4e2d\u4e24\u5217\u4e4b\u95f4\u7684\u65e5\u671f\uff1a \u65b9\u6cd5 1\uff1a\u5411 DataFram [&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-4157","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\u6bd4\u8f83 Pandas \u4e2d\u7684\u65e5\u671f\uff08\u5e26\u6709\u793a\u4f8b\uff09 - Statorials<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u901a\u8fc7\u51e0\u4e2a\u793a\u4f8b\u89e3\u91ca\u4e86\u5982\u4f55\u6bd4\u8f83 pandas DataFrame \u4e2d\u7684\u65e5\u671f\u3002\" \/>\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\/cn\/pandas-\u6bd4\u8f83\u65e5\u671f\/\" \/>\n<meta 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