{"id":2572,"date":"2023-07-21T16:37:26","date_gmt":"2023-07-21T16:37:26","guid":{"rendered":"https:\/\/statorials.org\/cn\/pandas-%e7%b4%af%e8%ae%a1%e7%99%be%e5%88%86%e6%af%94\/"},"modified":"2023-07-21T16:37:26","modified_gmt":"2023-07-21T16:37:26","slug":"pandas-%e7%b4%af%e8%ae%a1%e7%99%be%e5%88%86%e6%af%94","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/pandas-%e7%b4%af%e8%ae%a1%e7%99%be%e5%88%86%e6%af%94\/","title":{"rendered":"\u5982\u4f55\u8ba1\u7b97 pandas \u7684\u7d2f\u79ef\u767e\u5206\u6bd4"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u57fa\u672c\u8bed\u6cd5\u6765\u8ba1\u7b97 pandas DataFrame \u7684\u5217\u4e2d\u503c\u7684\u7d2f\u79ef\u767e\u5206\u6bd4\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate cumulative sum of column\n<\/span>df[' <span style=\"color: #ff0000;\">cum_sum<\/span> '] = df[' <span style=\"color: #ff0000;\">col1<\/span> ']. <span style=\"color: #3366ff;\">cumsum<\/span> ()\n\n<span style=\"color: #008080;\">#calculate cumulative percentage of column (rounded to 2 decimal places)\n<\/span>df[' <span style=\"color: #ff0000;\">cum_percent<\/span> '] = round( <span style=\"color: #008000;\">100<\/span> *df. <span style=\"color: #3366ff;\">cum_sum<\/span> \/df[' <span style=\"color: #ff0000;\">col1<\/span> ']. <span style=\"color: #3366ff;\">sum<\/span> (), <span style=\"color: #008000;\">2<\/span> )\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5b9e\u8df5\u4e2d\u4f7f\u7528\u6b64\u8bed\u6cd5\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u793a\u4f8b\uff1a\u8ba1\u7b97 pandas \u4e4b\u95f4\u7684\u7d2f\u8ba1\u767e\u5206\u6bd4<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u5047\u8bbe\u6211\u4eec\u6709\u4ee5\u4e0b pandas DataFrame\uff0c\u663e\u793a\u516c\u53f8\u8fde\u7eed\u51e0\u5e74\u9500\u552e\u7684\u5355\u4f4d\u6570\u91cf\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;\">year<\/span> ': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                   ' <span style=\"color: #ff0000;\">units_sold<\/span> ': [60, 75, 77, 87, 104, 134, 120, 125, 140, 150]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n   year units_sold\n0 1 60\n1 2 75\n2 3 77\n3 4 87\n4 5 104\n5 6 134\n6 7 120\n7 8 125\n8 9 140\n9 10 150\n<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\">\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u6dfb\u52a0\u4e00\u5217\uff0c\u663e\u793a\u7d2f\u8ba1\u9500\u552e\u5355\u4f4d\u6570\u548c\u7d2f\u8ba1\u9500\u552e\u5355\u4f4d\u767e\u5206\u6bd4\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;\">#calculate cumulative sum of units sold\n<\/span>df[' <span style=\"color: #ff0000;\">cum_sum<\/span> '] = df[' <span style=\"color: #ff0000;\">units_sold<\/span> ']. <span style=\"color: #3366ff;\">cumsum<\/span> ()\n\n<span style=\"color: #008080;\">#calculate cumulative percentage of units sold\n<\/span>df[' <span style=\"color: #ff0000;\">cum_percent<\/span> '] = round( <span style=\"color: #008000;\">100<\/span> *df. <span style=\"color: #3366ff;\">cum_sum<\/span> \/df[' <span style=\"color: #ff0000;\">units_sold<\/span> ']. <span style=\"color: #3366ff;\">sum<\/span> (), <span style=\"color: #008000;\">2<\/span> )\n\n<span style=\"color: #008080;\">#view updated DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n   year units_sold cum_sum cum_percent\n0 1 60 60 5.60\n1 2 75 135 12.59\n2 3 77 212 19.78\n3 4 87 299 27.89\n4 5 104 403 37.59\n5 6 134 537 50.09\n6 7 120 657 61.29\n7 8 125 782 72.95\n8 9 140 922 86.01\n9 10 150 1072 100.00\n<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u5bf9\u7d2f\u8ba1\u767e\u5206\u6bd4\u7684\u89e3\u91ca\u5982\u4e0b\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u7b2c\u4e00\u5e74\u9500\u552e\u989d\u5360\u603b\u9500\u552e\u989d\u7684<strong>5.60%<\/strong> \u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u7b2c 1 \u5e74\u548c\u7b2c 2 \u5e74\u5408\u8ba1\u5360\u6240\u6709\u9500\u552e\u989d\u7684<strong>12.59%<\/strong> \u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u7b2c 1 \u5e74\u3001\u7b2c 2 \u5e74\u548c\u7b2c 3 \u5e74\u5408\u8ba1\u5360\u603b\u9500\u552e\u989d\u7684<strong>19.78%<\/strong> \u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u7b49\u7b49\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8bf7\u6ce8\u610f\uff0c\u60a8\u53ea\u9700\u66f4\u6539<strong>round()<\/strong>\u51fd\u6570\u4e2d\u7684\u503c\u5373\u53ef\u66f4\u6539\u663e\u793a\u7684\u5c0f\u6570\u4f4d\u6570\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4f8b\u5982\uff0c\u6211\u4eec\u53ef\u4ee5\u5c06\u7d2f\u79ef\u767e\u5206\u6bd4\u56db\u820d\u4e94\u5165\u5230\u5c0f\u6570\u70b9\u540e\u96f6\u4f4d\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;\">#calculate cumulative sum of units sold\n<\/span>df[' <span style=\"color: #ff0000;\">cum_sum<\/span> '] = df[' <span style=\"color: #ff0000;\">units_sold<\/span> ']. <span style=\"color: #3366ff;\">cumsum<\/span> ()\n\n<span style=\"color: #008080;\">#calculate cumulative percentage of units sold\n<\/span>df[' <span style=\"color: #ff0000;\">cum_percent<\/span> '] = round( <span style=\"color: #008000;\">100<\/span> *df. <span style=\"color: #3366ff;\">cum_sum<\/span> \/df[' <span style=\"color: #ff0000;\">units_sold<\/span> ']. <span style=\"color: #3366ff;\">sum<\/span> (), <span style=\"color: #008000;\">0<\/span> )\n\n<span style=\"color: #008080;\">#view updated DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n   year units_sold cum_sum cum_percent\n0 1 60 60 6.0\n1 2 75 135 13.0\n2 3 77 212 20.0\n3 4 87 299 28.0\n4 5 104 403 38.0\n5 6 134 537 50.0\n6 7 120 657 61.0\n7 8 125 782 73.0\n8 9 140 922 86.0\n9 10 150 1072 100.0<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u7d2f\u79ef\u767e\u5206\u6bd4\u73b0\u5728\u56db\u820d\u4e94\u5165\u5230\u5c0f\u6570\u70b9\u540e\u96f6\u4f4d\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u5176\u4ed6\u5e38\u89c1\u64cd\u4f5c\uff1a<\/span><\/p>\n<p><a href=\"https:\/\/statorials.org\/cn\/python-\u9891\u7387\u8868\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 Python \u4e2d\u521b\u5efa\u9891\u7387\u8868<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u76f8\u5bf9\u9891\u7387-python\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 Python \u4e2d\u8ba1\u7b97\u76f8\u5bf9\u9891\u7387<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u57fa\u672c\u8bed\u6cd5\u6765\u8ba1\u7b97 pandas DataFrame \u7684\u5217\u4e2d\u503c\u7684\u7d2f\u79ef\u767e\u5206\u6bd4\uff1a #calculate  [&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-2572","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\u8ba1\u7b97 pandas \u7684\u7d2f\u79ef\u767e\u5206\u6bd4 \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u901a\u8fc7\u793a\u4f8b\u89e3\u91ca\u4e86\u5982\u4f55\u8ba1\u7b97 pandas \u4e2d\u5217\u7684\u7d2f\u79ef\u767e\u5206\u6bd4\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-\u7d2f\u8ba1\u767e\u5206\u6bd4\/\" \/>\n<meta property=\"og:locale\" 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