{"id":4320,"date":"2023-07-12T01:20:23","date_gmt":"2023-07-12T01:20:23","guid":{"rendered":"https:\/\/statorials.org\/cn\/pandas-%e6%b2%a1%e6%9c%89%e6%8f%8f%e8%bf%b0%e7%a7%91%e5%ad%a6%e8%ae%b0%e6%95%b0%e6%b3%95\/"},"modified":"2023-07-12T01:20:23","modified_gmt":"2023-07-12T01:20:23","slug":"pandas-%e6%b2%a1%e6%9c%89%e6%8f%8f%e8%bf%b0%e7%a7%91%e5%ad%a6%e8%ae%b0%e6%95%b0%e6%b3%95","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/pandas-%e6%b2%a1%e6%9c%89%e6%8f%8f%e8%bf%b0%e7%a7%91%e5%ad%a6%e8%ae%b0%e6%95%b0%e6%b3%95\/","title":{"rendered":"Pandas\uff1a\u5982\u4f55\u4f7f\u7528describe()\u5e76\u5220\u9664\u79d1\u5b66\u8bb0\u6570\u6cd5"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u60a8\u53ef\u4ee5\u4f7f\u7528<strong>describe()<\/strong>\u51fd\u6570\u4e3apandas DataFrame\u4e2d\u7684\u53d8\u91cf\u751f\u6210<a href=\"https:\/\/statorials.org\/cn\/\u63cf\u8ff0\u6027\u63a8\u8bba\u7edf\u8ba1\/\" target=\"_blank\" rel=\"noopener\">\u63cf\u8ff0\u6027\u7edf\u8ba1\u6570\u636e<\/a>\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8981\u4ece<strong>describe()<\/strong>\u51fd\u6570\u7684\u8f93\u51fa\u4e2d\u5220\u9664\u79d1\u5b66\u8bb0\u6570\u6cd5\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u65b9\u6cd5\uff1a<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u65b9\u6cd5 1\uff1a\u5728\u5bf9\u5217\u4f7f\u7528describe() \u65f6\u5220\u9664\u79d1\u5b66\u8bb0\u6570\u6cd5<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong>df[' <span style=\"color: #ff0000;\">my_column<\/span> ']. <span style=\"color: #3366ff;\">describe<\/span> (). <span style=\"color: #3366ff;\">apply<\/span> ( <span style=\"color: #008000;\">lambda<\/span> x: <span style=\"color: #008000;\">format<\/span> (x, ' <span style=\"color: #ff0000;\">f<\/span> '))<\/strong><\/span><\/pre>\n<p><span style=\"color: #000000;\"><strong>\u65b9\u6cd5 2\uff1a\u5728\u5bf9\u591a\u5217\u4f7f\u7528describe() \u65f6\u5220\u9664\u79d1\u5b66\u8bb0\u6570\u6cd5<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong>df. <span style=\"color: #3366ff;\">describe<\/span> (). <span style=\"color: #3366ff;\">apply<\/span> ( <span style=\"color: #008000;\">lambda<\/span> x: <span style=\"color: #3366ff;\">x.apply<\/span> (' <span style=\"color: #ff0000;\">{0:.5f}<\/span> '. <span style=\"color: #3366ff;\">format<\/span> ))\n<\/strong><\/span><\/pre>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5b9e\u8df5\u4e2d\u4f7f\u7528\u4ee5\u4e0b pandas DataFrame \u7684\u6bcf\u79cd\u65b9\u6cd5\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#createDataFrame<\/span>\ndf = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">store<\/span> ': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],\n                   ' <span style=\"color: #ff0000;\">sales<\/span> ': [8450550, 406530, 53000, 6000, 2000, 4000, 5400, 6500],\n                   ' <span style=\"color: #ff0000;\">returns<\/span> ':[2212200, 145200, 300, 2500, 700, 600, 800, 1200]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n  store sales returns\n0 A 8450550 2212200\n1 A 406530 145200\n2 A 53000 300\n3 A 6000 2500\n4 B 2000 700\n5 B 4000 600\n6 B 5400 800\n7 B 6500 1200<\/strong><\/span><\/pre>\n<h2><span style=\"color: #000000;\"><strong>\u793a\u4f8b 1\uff1a\u5728\u5bf9\u5217\u4f7f\u7528describe() \u65f6\u5220\u9664\u79d1\u5b66\u8bb0\u6570\u6cd5<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u5982\u679c\u6211\u4eec\u4f7f\u7528<strong>describe()<\/strong>\u51fd\u6570\u8ba1\u7b97<strong>sales<\/strong>\u5217\u7684\u63cf\u8ff0\u6027\u7edf\u8ba1\uff0c\u8f93\u51fa\u503c\u5c06\u4ee5\u79d1\u5b66\u8ba1\u6570\u6cd5\u663e\u793a\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate descriptive statistics for sales column\n<span style=\"color: #000000;\">df[' <span style=\"color: #ff0000;\">sales<\/span> ']. <span style=\"color: #3366ff;\">describe<\/span> ()\n<\/span>\n<\/span>count 8.000000e+00\nmean 1.116748e+06\nstd 2.966552e+06\nmin 2.000000e+03\n25% 5.050000e+03\n50% 6.250000e+03\n75% 1.413825e+05\nmax 8.450550e+06\nName: sales, dtype: float64<\/strong><\/pre>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\">\u8bf7\u6ce8\u610f\uff0c\u6bcf\u4e2a\u8f93\u51fa\u503c\u5747\u4f7f\u7528\u79d1\u5b66\u8ba1\u6570\u6cd5\u663e\u793a\u3002<\/span><\/span><\/p>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u4ece\u8f93\u51fa\u4e2d\u5220\u9664\u79d1\u5b66\u8bb0\u6570\u6cd5\uff1a<\/span><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#calculate descriptive statistics for sales column and suppress scientific notation<\/span>\ndf[' <span style=\"color: #ff0000;\">sales<\/span> ']. <span style=\"color: #3366ff;\">describe<\/span> (). <span style=\"color: #3366ff;\">apply<\/span> ( <span style=\"color: #008000;\">lambda<\/span> x: <span style=\"color: #008000;\">format<\/span> (x, ' <span style=\"color: #ff0000;\">f<\/span> '))\n\ncount 8.000000\nmean 1116747.500000\nstd 2966551.594104\nmin 2000.000000\n25% 5050.000000\n50% 6250.000000\n75% 141382.500000\nmax 8450550.000000\nName: sales, dtype: object\n<\/strong><\/span><\/pre>\n<p><span style=\"color: #000000;\">\u8bf7\u6ce8\u610f\uff0c\u8f93\u51fa\u4e2d\u7684\u503c\u73b0\u5728\u663e\u793a\u65f6\u4e0d\u5e26\u79d1\u5b66\u8ba1\u6570\u6cd5\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u793a\u4f8b 2\uff1a\u5728\u5bf9\u591a\u5217\u4f7f\u7528describe() \u65f6\u5220\u9664\u79d1\u5b66\u8bb0\u6570\u6cd5<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u5982\u679c\u6211\u4eec\u4f7f\u7528<strong>describe()<\/strong>\u51fd\u6570\u8ba1\u7b97\u6bcf\u4e2a\u6570\u503c\u5217\u7684\u63cf\u8ff0\u6027\u7edf\u8ba1\u91cf\uff0c\u8f93\u51fa\u503c\u5c06\u4ee5\u79d1\u5b66\u8ba1\u6570\u6cd5\u663e\u793a\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate descriptive statistics for each numeric column\n<span style=\"color: #000000;\">df. <span style=\"color: #3366ff;\">describe<\/span> ()\n<\/span><\/span>\n               sales returns\ncount 8.000000e+00 8.000000e+00\nmean 1.116748e+06 2.954375e+05\nstd 2.966552e+06 7.761309e+05\nmin 2.000000e+03 3.000000e+02\n25% 5.050000e+03 6.750000e+02\n50% 6.250000e+03 1.000000e+03\n75% 1.413825e+05 3.817500e+04\nmax 8.450550e+06 2.212200e+06<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u8bf7\u6ce8\u610f\uff0c\u6bcf\u4e2a\u8f93\u51fa\u503c\u5747\u4f7f\u7528\u79d1\u5b66\u8ba1\u6570\u6cd5\u663e\u793a\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u4ece\u8f93\u51fa\u4e2d\u5220\u9664\u79d1\u5b66\u8bb0\u6570\u6cd5\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#calculate descriptive statistics for numerical columns and suppress scientific notation<\/span>\ndf. <span style=\"color: #3366ff;\">describe<\/span> (). <span style=\"color: #3366ff;\">apply<\/span> ( <span style=\"color: #008000;\">lambda<\/span> x: <span style=\"color: #3366ff;\">x.apply<\/span> (' <span style=\"color: #ff0000;\">{0:.5f}<\/span> '. <span style=\"color: #3366ff;\">format<\/span> ))\n\n                sales returns\ncount 8.00000 8.00000\nmean 1116747.50000 295437.50000\nstd 2966551.59410 776130.93692\nmin 2000.00000 300.00000\n25% 5050.00000 675.00000\n50% 6250.00000 1000.00000\n75% 141382.50000 38175.00000\nmax 8450550.00000 2212200.00000\n<\/strong><\/span><\/pre>\n<p><span style=\"color: #000000;\">\u8bf7\u6ce8\u610f\uff0c\u8f93\u51fa\u4e2d\u7684\u503c\u73b0\u5728\u663e\u793a\u65f6\u4e0d\u5e26\u79d1\u5b66\u8ba1\u6570\u6cd5\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8bf7\u6ce8\u610f\uff0c\u5728\u6b64\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528<strong>0:.5f<\/strong>\u5728\u8f93\u51fa\u4e2d\u663e\u793a<strong>5<\/strong>\u4f4d\u5c0f\u6570\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8bf7\u968f\u610f\u5c06<strong>5<\/strong>\u66ff\u6362\u4e3a\u4e0d\u540c\u7684\u6570\u5b57\u4ee5\u663e\u793a\u4e0d\u540c\u7684\u5c0f\u6570\u4f4d\u6570\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\/pandas-\u6bcf\u7ec4\u7d2f\u8ba1\u603b\u548c\/\" target=\"_blank\" rel=\"noopener\">Pandas\uff1a\u5982\u4f55\u8ba1\u7b97\u6bcf\u7ec4\u7684\u7d2f\u8ba1\u548c<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/pandas-groupby-\u8ba1\u6570\u552f\u4e00\/\" target=\"_blank\" rel=\"noopener\">Pandas\uff1a\u5982\u4f55\u6309\u7ec4\u8ba1\u7b97\u552f\u4e00\u503c<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/pandas-\u5206\u7ec4\u76f8\u5173\u6027\/\" target=\"_blank\" rel=\"noopener\">Pandas\uff1a\u5982\u4f55\u6309\u7ec4\u8ba1\u7b97\u76f8\u5173\u6027<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u60a8\u53ef\u4ee5\u4f7f\u7528describe()\u51fd\u6570\u4e3apandas DataFrame\u4e2d\u7684\u53d8\u91cf\u751f\u6210\u63cf\u8ff0\u6027\u7edf\u8ba1\u6570\u636e\u3002 \u8981\u4ecedesc [&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-4320","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>Pandas\uff1a\u5982\u4f55\u4f7f\u7528describe()\u5e76\u5220\u9664\u79d1\u5b66\u8bb0\u6570\u6cd5 - Statorials<\/title>\n<meta name=\"description\" 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\u51fd\u6570\u5e76\u5220\u9664\u8f93\u51fa\u4e2d\u7684\u79d1\u5b66\u8bb0\u6570\u6cd5\u3002\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/cn\/pandas-\u6ca1\u6709\u63cf\u8ff0\u79d1\u5b66\u8bb0\u6570\u6cd5\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-12T01:20:23+00:00\" \/>\n<meta name=\"author\" content=\"\u672c\u6770\u660e\u00b7\u5b89\u5fb7\u68ee\u535a\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u4f5c\u8005\" \/>\n\t<meta name=\"twitter:data1\" content=\"\u672c\u6770\u660e\u00b7\u5b89\u5fb7\u68ee\u535a\" \/>\n\t<meta name=\"twitter:label2\" content=\"\u9884\u8ba1\u9605\u8bfb\u65f6\u95f4\" \/>\n\t<meta name=\"twitter:data2\" content=\"1 \u5206\" \/>\n<script type=\"application\/ld+json\" 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