{"id":4069,"date":"2025-10-17T17:27:56","date_gmt":"2025-10-17T09:27:56","guid":{"rendered":"http:\/\/viplao.com\/?p=4069"},"modified":"2025-10-18T11:29:32","modified_gmt":"2025-10-18T03:29:32","slug":"%e3%80%90%e8%bf%90%e8%90%a5%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90-%e5%9f%ba%e7%a1%80%e7%af%87%e3%80%91pandas%e6%95%b0%e6%8d%ae%e5%a4%84%e7%90%86%e6%a0%b8%e5%bf%83%e6%8a%80%e8%83%bd","status":"publish","type":"post","link":"http:\/\/viplao.com\/index.php\/2025\/10\/17\/%e3%80%90%e8%bf%90%e8%90%a5%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90-%e5%9f%ba%e7%a1%80%e7%af%87%e3%80%91pandas%e6%95%b0%e6%8d%ae%e5%a4%84%e7%90%86%e6%a0%b8%e5%bf%83%e6%8a%80%e8%83%bd\/","title":{"rendered":"\u3010\u8fd0\u8425\u6570\u636e\u5206\u6790-\u57fa\u7840\u7bc7\u3011Pandas\u6570\u636e\u5904\u7406\u6838\u5fc3\u6280\u80fd"},"content":{"rendered":"\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_71 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">\u6587\u7ae0\u76ee\u5f55<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 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href=\"http:\/\/viplao.com\/index.php\/2025\/10\/17\/%e3%80%90%e8%bf%90%e8%90%a5%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90-%e5%9f%ba%e7%a1%80%e7%af%87%e3%80%91pandas%e6%95%b0%e6%8d%ae%e5%a4%84%e7%90%86%e6%a0%b8%e5%bf%83%e6%8a%80%e8%83%bd\/#21_DataFrame%E5%9F%BA%E6%9C%AC%E6%93%8D%E4%BD%9C\" title=\"2.1 DataFrame\u57fa\u672c\u64cd\u4f5c\">2.1 DataFrame\u57fa\u672c\u64cd\u4f5c<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"http:\/\/viplao.com\/index.php\/2025\/10\/17\/%e3%80%90%e8%bf%90%e8%90%a5%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90-%e5%9f%ba%e7%a1%80%e7%af%87%e3%80%91pandas%e6%95%b0%e6%8d%ae%e5%a4%84%e7%90%86%e6%a0%b8%e5%bf%83%e6%8a%80%e8%83%bd\/#22_%E6%95%B0%E6%8D%AE%E6%B8%85%E6%B4%97%E4%B8%8E%E9%A2%84%E5%A4%84%E7%90%86\" title=\"2.2 \u6570\u636e\u6e05\u6d17\u4e0e\u9884\u5904\u7406\">2.2 \u6570\u636e\u6e05\u6d17\u4e0e\u9884\u5904\u7406<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"http:\/\/viplao.com\/index.php\/2025\/10\/17\/%e3%80%90%e8%bf%90%e8%90%a5%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90-%e5%9f%ba%e7%a1%80%e7%af%87%e3%80%91pandas%e6%95%b0%e6%8d%ae%e5%a4%84%e7%90%86%e6%a0%b8%e5%bf%83%e6%8a%80%e8%83%bd\/#23_%E6%95%B0%E6%8D%AE%E5%90%88%E5%B9%B6%E4%B8%8E%E8%BF%9E%E6%8E%A5\" title=\"2.3 \u6570\u636e\u5408\u5e76\u4e0e\u8fde\u63a5\">2.3 \u6570\u636e\u5408\u5e76\u4e0e\u8fde\u63a5<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"http:\/\/viplao.com\/index.php\/2025\/10\/17\/%e3%80%90%e8%bf%90%e8%90%a5%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90-%e5%9f%ba%e7%a1%80%e7%af%87%e3%80%91pandas%e6%95%b0%e6%8d%ae%e5%a4%84%e7%90%86%e6%a0%b8%e5%bf%83%e6%8a%80%e8%83%bd\/#24_%E6%95%B0%E6%8D%AE%E5%88%86%E7%BB%84%E4%B8%8E%E8%81%9A%E5%90%88\" title=\"2.4 \u6570\u636e\u5206\u7ec4\u4e0e\u805a\u5408\">2.4 \u6570\u636e\u5206\u7ec4\u4e0e\u805a\u5408<\/a><\/li><\/ul><\/nav><\/div>\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%E3%80%90%E7%90%86%E8%AE%BA%E8%AE%B2%E8%A7%A3%E3%80%91\"><\/span><strong>\u3010\u7406\u8bba\u8bb2\u89e3\u3011<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Pandas\u662f\u6570\u636e\u5206\u6790\u7684\u745e\u58eb\u519b\u5200\uff0c\u5b83\u7684\u5f3a\u5927\u4e4b\u5904\u5728\u4e8e\u80fd\u9ad8\u6548\u5730\u5904\u7406\u8868\u683c\u6570\u636e\u3002\u672c\u7ae0\u6211\u4eec\u5c06\u5b66\u4e60\u5982\u4f55\u5bf9DataFrame\u8fdb\u884c\u5404\u79cd\u64cd\u4f5c\uff0c\u5305\u62ec\u67e5\u770b\u3001\u9009\u62e9\u3001\u6e05\u6d17\u3001\u5408\u5e76\u3001\u5206\u7ec4\u548c\u805a\u5408\u3002\u8fd9\u4e9b\u6280\u80fd\u662f\u8fdb\u884c\u4efb\u4f55\u6570\u636e\u5206\u6790\u7684\u524d\u63d0\u3002<\/p>\n\n\n\n<p><strong>\u3010\u63d0\u4f9b\u6570\u636e\u96c6\u3011<\/strong><\/p>\n\n\n\n<p>\u6211\u4eec\u5c06\u4f7f\u7528\u4e00\u4e2a\u6a21\u62df\u7684\u7535\u5546\u8ba2\u5355\u6570\u636e\u96c6&nbsp;<code>ecommerce_orders.csv<\/code>\u3002\u8bf7\u5b66\u5458\u4e0b\u8f7d\u6216\u81ea\u884c\u521b\u5efa\u6b64\u6587\u4ef6\u3002<\/p>\n\n\n\n<p><strong><code>ecommerce_orders.csv<\/code>&nbsp;\u793a\u4f8b\u5185\u5bb9\uff1a<\/strong><\/p>\n\n\n\n<p>csv<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>order_id,user_id,product_id,product_name,category,price,quantity,order_date,payment_method,shipping_city\nORD001,U001,P101,T-shirt,Apparel,59.9,2,2023-01-01,Credit Card,Shanghai\nORD002,U002,P102,Jeans,Apparel,129.0,1,2023-01-01,Alipay,Beijing\nORD003,U001,P103,Sneakers,Footwear,399.0,1,2023-01-02,WeChat Pay,Shanghai\nORD004,U003,P101,T-shirt,Apparel,59.9,3,2023-01-02,Credit Card,Guangzhou\nORD005,U002,P104,Hat,Accessories,49.0,1,2023-01-03,Alipay,Beijing\nORD006,U004,P105,Backpack,Bags,250.0,1,2023-01-03,Credit Card,Shenzhen\nORD007,U001,P106,Watch,Accessories,800.0,1,2023-01-04,WeChat Pay,Shanghai\nORD008,U005,P101,T-shirt,Apparel,59.9,1,2023-01-04,Alipay,Chengdu\nORD009,U003,P107,Dress,Apparel,180.0,1,2023-01-05,Credit Card,Guangzhou\nORD010,U006,P108,Headphones,Electronics,350.0,1,2023-01-05,WeChat Pay,Hangzhou\nORD011,U001,P102,Jeans,Apparel,129.0,1,2023-01-06,Credit Card,Shanghai\nORD012,U007,P109,Keyboard,Electronics,450.0,1,2023-01-06,Alipay,Nanjing\nORD013,U008,P101,T-shirt,Apparel,59.9,2,2023-01-07,WeChat Pay,Wuhan\nORD014,U009,P110,Mouse,Electronics,80.0,1,2023-01-07,Credit Card,Suzhou\nORD015,U001,P104,Hat,Accessories,49.0,1,2023-01-08,Alipay,Shanghai\nORD016,U002,P111,Socks,Apparel,20.0,3,2023-01-08,WeChat Pay,Beijing\nORD017,U010,P101,T-shirt,Apparel,59.9,1,2023-01-09,Credit Card,Tianjin\nORD018,U003,P103,Sneakers,Footwear,399.0,1,2023-01-09,Alipay,Guangzhou\nORD019,U001,P101,T-shirt,Apparel,59.9,2,2023-01-10,Credit Card,Shanghai\nORD020,U002,P102,Jeans,Apparel,129.0,1,2023-01-10,Alipay,Beijing<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"21_DataFrame%E5%9F%BA%E6%9C%AC%E6%93%8D%E4%BD%9C\"><\/span><strong>2.1 DataFrame\u57fa\u672c\u64cd\u4f5c<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p><strong>\u3010\u4ee3\u7801\u5b9e\u4f8b\u4e0e\u7535\u5546\u573a\u666f\u6848\u4f8b\u3011<\/strong><\/p>\n\n\n\n<p>python<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>import pandas as pd\n\n# \u52a0\u8f7d\u7535\u5546\u8ba2\u5355\u6570\u636e\ndf = pd.read_csv('ecommerce_orders.csv')\n\nprint(\"\u539f\u59cb\u6570\u636e\u9884\u89c8 (\u524d5\u884c):\")\nprint(df.head())\n\nprint(\"\\n\u6570\u636e\u57fa\u672c\u4fe1\u606f (\u5305\u542b\u5217\u540d\u3001\u975e\u7a7a\u503c\u6570\u91cf\u3001\u6570\u636e\u7c7b\u578b):\")\ndf.info()\n\nprint(\"\\n\u6570\u636e\u7edf\u8ba1\u6458\u8981 (\u6570\u503c\u5217\u7684\u5747\u503c\u3001\u6807\u51c6\u5dee\u3001\u6700\u5927\u503c\u3001\u6700\u5c0f\u503c\u7b49):\")\nprint(df.describe())\n\n# \u7535\u5546\u573a\u666f\uff1a\u67e5\u770b\u6700\u8fd1\u7684\u8ba2\u5355\nprint(\"\\n\u67e5\u770b\u6700\u8fd1\u76845\u6761\u8ba2\u5355 (\u5c3e\u90e8\u6570\u636e):\")\nprint(df.tail())\n\n# \u9009\u62e9\u6570\u636e\n# \u7535\u5546\u573a\u666f\uff1a\u53ea\u67e5\u770b\u8ba2\u5355ID\u548c\u5546\u54c1\u540d\u79f0\nprint(\"\\n\u9009\u62e9\u7279\u5b9a\u5217 (\u8ba2\u5355ID\u548c\u5546\u54c1\u540d\u79f0):\")\nprint(df&#91;&#91;'order_id', 'product_name']].head())\n\n# \u7535\u5546\u573a\u666f\uff1a\u9009\u62e9\u7279\u5b9a\u884c\uff08\u4f8b\u5982\uff0c\u524d3\u6761\u8ba2\u5355\uff09\nprint(\"\\n\u9009\u62e9\u7279\u5b9a\u884c (\u524d3\u6761\u8ba2\u5355):\")\nprint(df.iloc&#91;0:3]) # \u4f7f\u7528\u6574\u6570\u4f4d\u7f6e\u7d22\u5f15\n\n# \u7535\u5546\u573a\u666f\uff1a\u9009\u62e9\u67d0\u4e2a\u7528\u6237\uff08U001\uff09\u7684\u6240\u6709\u8ba2\u5355\nprint(\"\\n\u9009\u62e9\u7528\u6237U001\u7684\u6240\u6709\u8ba2\u5355:\")\nprint(df&#91;df&#91;'user_id'] == 'U001'])\n\n# \u7535\u5546\u573a\u666f\uff1a\u9009\u62e9\u4ef7\u683c\u9ad8\u4e8e100\u5143\u7684\u8ba2\u5355\nprint(\"\\n\u9009\u62e9\u4ef7\u683c\u9ad8\u4e8e100\u5143\u7684\u8ba2\u5355:\")\nprint(df&#91;df&#91;'price'] &gt; 100])\n\n# \u7ec4\u5408\u6761\u4ef6\u9009\u62e9\n# \u7535\u5546\u573a\u666f\uff1a\u9009\u62e9\u7528\u6237U001\u4e14\u4ef7\u683c\u9ad8\u4e8e100\u5143\u7684\u8ba2\u5355\nprint(\"\\n\u9009\u62e9\u7528\u6237U001\u4e14\u4ef7\u683c\u9ad8\u4e8e100\u5143\u7684\u8ba2\u5355:\")\nprint(df&#91;(df&#91;'user_id'] == 'U001') &amp; (df&#91;'price'] &gt; 100)])\n\n# \u6570\u636e\u6392\u5e8f\n# \u7535\u5546\u573a\u666f\uff1a\u6309\u8ba2\u5355\u65e5\u671f\u964d\u5e8f\u6392\u5217\uff0c\u67e5\u770b\u6700\u65b0\u8ba2\u5355\nprint(\"\\n\u6309\u8ba2\u5355\u65e5\u671f\u964d\u5e8f\u6392\u5217\u7684\u8ba2\u5355 (\u6700\u65b0\u8ba2\u5355\u5728\u524d):\")\nprint(df.sort_values(by='order_date', ascending=False).head())\n\n# \u7535\u5546\u573a\u666f\uff1a\u6309\u5546\u54c1\u7c7b\u522b\u548c\u4ef7\u683c\u5347\u5e8f\u6392\u5217\nprint(\"\\n\u6309\u5546\u54c1\u7c7b\u522b\u548c\u4ef7\u683c\u5347\u5e8f\u6392\u5217\u7684\u8ba2\u5355:\")\nprint(df.sort_values(by=&#91;'category', 'price']).head())\n\n# \u6570\u636e\u6392\u540d (Rank)\n# \u7535\u5546\u573a\u666f\uff1a\u7ed9\u5546\u54c1\u4ef7\u683c\u8fdb\u884c\u6392\u540d\ndf&#91;'price_rank'] = df&#91;'price'].rank(ascending=False) # \u4ef7\u683c\u8d8a\u9ad8\uff0c\u6392\u540d\u8d8a\u9760\u524d\nprint(\"\\n\u6dfb\u52a0\u4ef7\u683c\u6392\u540d\u5217:\\n\", df.sort_values(by='price_rank').head())<\/code><\/pre>\n\n\n\n<p><strong>\u3010\u4e92\u52a8\u95ee\u7b54\u3011<\/strong><\/p>\n\n\n\n<ul>\n<li><code>df.head()<\/code>&nbsp;\u548c&nbsp;<code>df.info()<\/code>&nbsp;\u5728\u6570\u636e\u63a2\u7d22\u521d\u671f\u5404\u6709\u4ec0\u4e48\u7528\u9014\uff1f<\/li>\n\n\n\n<li>\u5982\u4f55\u9009\u62e9DataFrame\u4e2d\u7684\u5355\u5217\uff1f\u5982\u4f55\u9009\u62e9\u591a\u5217\uff1f<\/li>\n\n\n\n<li><code>iloc<\/code>&nbsp;\u548c&nbsp;<code>loc<\/code>&nbsp;\u5728\u9009\u62e9\u884c\u548c\u5217\u65f6\u6709\u4ec0\u4e48\u533a\u522b\uff1f<\/li>\n\n\n\n<li>\u5982\u4f55\u5b9e\u73b0\u590d\u6742\u7684\u6570\u636e\u7b5b\u9009\uff0c\u4f8b\u5982\u7b5b\u9009\u51fa\u67d0\u4e2a\u57ce\u5e02\u3001\u67d0\u4e2a\u54c1\u7c7b\u7684\u5546\u54c1\uff1f<\/li>\n\n\n\n<li><code>sort_values()<\/code>&nbsp;\u51fd\u6570\u7684&nbsp;<code>by<\/code>&nbsp;\u548c&nbsp;<code>ascending<\/code>&nbsp;\u53c2\u6570\u6709\u4ec0\u4e48\u4f5c\u7528\uff1f<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"22_%E6%95%B0%E6%8D%AE%E6%B8%85%E6%B4%97%E4%B8%8E%E9%A2%84%E5%A4%84%E7%90%86\"><\/span><strong>2.2 \u6570\u636e\u6e05\u6d17\u4e0e\u9884\u5904\u7406<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p><strong>\u3010\u7406\u8bba\u8bb2\u89e3\u3011<\/strong><\/p>\n\n\n\n<p>\u6570\u636e\u6e05\u6d17\u662f\u6570\u636e\u5206\u6790\u4e2d\u6700\u8017\u65f6\u4f46\u4e5f\u6700\u91cd\u8981\u7684\u4e00\u6b65\u3002\u810f\u4e71\u7684\u6570\u636e\u4f1a\u5bfc\u81f4\u9519\u8bef\u7684\u5206\u6790\u7ed3\u679c\u3002\u6211\u4eec\u5c06\u91cd\u70b9\u5b66\u4e60\u5982\u4f55\u5904\u7406\u7f3a\u5931\u503c\u3001\u5f02\u5e38\u503c\u548c\u91cd\u590d\u503c\u3002<\/p>\n\n\n\n<ul>\n<li><strong>\u7f3a\u5931\u503c\uff1a<\/strong>&nbsp;\u6570\u636e\u7f3a\u5931\u4e86\uff0c\u5c31\u50cf\u62fc\u56fe\u5c11\u4e86\u4e00\u5757\u3002\u6211\u4eec\u9700\u8981\u51b3\u5b9a\u662f\u6254\u6389\u8fd9\u5757\u62fc\u56fe\uff0c\u8fd8\u662f\u7528\u5176\u4ed6\u65b9\u6cd5\u8865\u4e0a\u3002<\/li>\n\n\n\n<li><strong>\u5f02\u5e38\u503c\uff1a<\/strong>&nbsp;\u6570\u636e\u4e2d\u201c\u6363\u86cb\u9b3c\u201d\uff0c\u4e0e\u5176\u4ed6\u6570\u636e\u683c\u683c\u4e0d\u5165\u3002\u5b83\u4eec\u53ef\u80fd\u662f\u5f55\u5165\u9519\u8bef\uff0c\u4e5f\u53ef\u80fd\u662f\u7279\u6b8a\u60c5\u51b5\uff0c\u9700\u8981\u8c28\u614e\u5904\u7406\u3002<\/li>\n\n\n\n<li><strong>\u91cd\u590d\u503c\uff1a<\/strong>&nbsp;\u201c\u53cc\u80de\u80ce\u201d\u6570\u636e\uff0c\u5360\u7528\u7a7a\u95f4\u5e76\u53ef\u80fd\u8bef\u5bfc\u5206\u6790\u3002<\/li>\n<\/ul>\n\n\n\n<p><strong>\u3010\u63d0\u4f9b\u6570\u636e\u96c6\u3011<\/strong><\/p>\n\n\n\n<p>\u4e3a\u4e86\u6f14\u793a\u6570\u636e\u6e05\u6d17\uff0c\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u5305\u542b\u7f3a\u5931\u503c\u3001\u5f02\u5e38\u503c\u548c\u91cd\u590d\u503c\u7684\u6a21\u62df\u6570\u636e\u96c6&nbsp;<code>dirty_ecommerce_data.csv<\/code>\u3002<\/p>\n\n\n\n<p><strong><code>dirty_ecommerce_data.csv<\/code>&nbsp;\u793a\u4f8b\u5185\u5bb9\uff1a<\/strong><\/p>\n\n\n\n<p>csv<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>order_id,user_id,product_name,price,quantity,order_date,category,rating\nORD001,U001,T-shirt,59.9,2,2023-01-01,Apparel,4.5\nORD002,U002,Jeans,129.0,1,2023-01-01,Apparel,4.0\nORD003,U001,Sneakers,399.0,1,2023-01-02,Footwear,5.0\nORD004,U003,T-shirt,59.9,3,2023-01-02,Apparel,NaN\nORD005,U002,Hat,49.0,1,2023-01-03,Accessories,3.5\nORD006,U004,Backpack,250.0,1,2023-01-03,Bags,4.8\nORD007,U001,Watch,800.0,1,2023-01-04,Accessories,NaN\nORD008,U005,T-shirt,59.9,1,2023-01-04,Apparel,4.2\nORD009,U003,Dress,180.0,1,2023-01-05,Apparel,4.1\nORD010,U006,Headphones,3500.0,1,2023-01-05,Electronics,4.7  # \u5f02\u5e38\u503c\uff1a\u4ef7\u683c\u8fc7\u9ad8\nORD011,U001,Jeans,129.0,1,2023-01-06,Apparel,4.0\nORD012,U007,Keyboard,450.0,1,2023-01-06,Electronics,4.3\nORD013,U008,T-shirt,59.9,2,2023-01-07,Apparel,4.6\nORD014,U009,Mouse,80.0,1,2023-01-07,Electronics,4.0\nORD015,U001,Hat,49.0,1,2023-01-08,Accessories,3.5 # \u91cd\u590d\u503c\uff08\u4e0eORD005\u76f8\u4f3c\uff0c\u5047\u8bbe\u662f\u91cd\u590d\u8ba2\u5355\uff09\nORD016,U002,Socks,20.0,3,2023-01-08,Apparel,3.8\nORD017,U010,T-shirt,59.9,1,2023-01-09,Apparel,4.5\nORD018,U003,Sneakers,399.0,1,2023-01-09,Footwear,5.0\nORD019,U001,T-shirt,59.9,2,2023-01-10,Apparel,4.5\nORD020,U002,Jeans,129.0,1,2023-01-10,Apparel,4.0\nORD021,U001,T-shirt,59.9,2,2023-01-01,Apparel,4.5 # \u91cd\u590d\u503c\uff08\u4e0eORD001\u5b8c\u5168\u4e00\u81f4\uff09<\/code><\/pre>\n\n\n\n<p><strong>\u3010\u4ee3\u7801\u5b9e\u4f8b\u4e0e\u7535\u5546\u573a\u666f\u6848\u4f8b\u3011<\/strong><\/p>\n\n\n\n<p>python<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>import pandas as pd\nimport numpy as np\nfrom sklearn.impute import SimpleImputer # \u7528\u4e8e\u9ad8\u7ea7\u7f3a\u5931\u503c\u5904\u7406\n\n# \u52a0\u8f7d\u810f\u6570\u636e\ndf_dirty = pd.read_csv('dirty_ecommerce_data.csv')\nprint(\"\u539f\u59cb\u810f\u6570\u636e\u9884\u89c8:\\n\", df_dirty)\nprint(\"\\n\u6570\u636e\u4fe1\u606f:\\n\")\ndf_dirty.info()\n\n# --- \u7f3a\u5931\u503c\u5904\u7406 ---\nprint(\"\\n--- \u7f3a\u5931\u503c\u5904\u7406 ---\")\n\n# 1. \u68c0\u6d4b\u7f3a\u5931\u503c\nprint(\"\\n\u6bcf\u5217\u7684\u7f3a\u5931\u503c\u6570\u91cf:\\n\", df_dirty.isnull().sum())\nprint(\"\\n\u6574\u4e2aDataFrame\u662f\u5426\u6709\u7f3a\u5931\u503c:\", df_dirty.isnull().any().any()) # \u5feb\u901f\u5224\u65ad\u662f\u5426\u6709\u7f3a\u5931\n\n# 2. \u5220\u9664\u7f3a\u5931\u503c\n# \u7535\u5546\u573a\u666f\uff1a\u5982\u679c\u8ba2\u5355\u7684\u6838\u5fc3\u4fe1\u606f\uff08\u5982\u8ba2\u5355ID\u3001\u5546\u54c1\u540d\u79f0\uff09\u7f3a\u5931\uff0c\u8be5\u8ba2\u5355\u53ef\u80fd\u65e0\u6548\uff0c\u76f4\u63a5\u5220\u9664\u3002\ndf_dropna = df_dirty.dropna(subset=&#91;'product_name', 'order_id']) # \u5220\u9664product_name\u6216order_id\u6709\u7f3a\u5931\u7684\u884c\nprint(\"\\n\u5220\u9664\u7279\u5b9a\u5217\u7f3a\u5931\u503c\u540e\u7684\u6570\u636e\u884c\u6570:\", len(df_dropna))\n\n# 3. \u586b\u5145\u7f3a\u5931\u503c\n# \u7535\u5546\u573a\u666f\uff1a\u5546\u54c1\u8bc4\u5206(rating)\u7f3a\u5931\uff0c\u53ef\u4ee5\u7528\u5e73\u5747\u8bc4\u5206\u6216\u4e2d\u4f4d\u6570\u586b\u5145\uff0c\u6216\u8005\u75280\u8868\u793a\u672a\u8bc4\u5206\u3002\n# \u586b\u5145\u524d\uff0c\u5148\u770b\u770brating\u5217\u7684\u7edf\u8ba1\u4fe1\u606f\nprint(\"\\n\u586b\u5145\u524drating\u5217\u7684\u63cf\u8ff0\u6027\u7edf\u8ba1:\\n\", df_dirty&#91;'rating'].describe())\n\n# \u65b9\u6cd5\u4e00\uff1a\u7528\u5e73\u5747\u503c\u586b\u5145 'rating' \u5217\u7684\u7f3a\u5931\u503c\ndf_filled_mean = df_dirty.copy()\nmean_rating = df_filled_mean&#91;'rating'].mean()\ndf_filled_mean&#91;'rating'].fillna(mean_rating, inplace=True)\nprint(f\"\\n\u7528\u5e73\u5747\u503c ({mean_rating:.2f}) \u586b\u5145rating\u5217\u7f3a\u5931\u503c\u540e\u7684\u524d\u51e0\u884c:\\n\", df_filled_mean.head(8))\n\n# \u65b9\u6cd5\u4e8c\uff1a\u7528\u4e2d\u4f4d\u6570\u586b\u5145 'rating' \u5217\u7684\u7f3a\u5931\u503c\ndf_filled_median = df_dirty.copy()\nmedian_rating = df_filled_median&#91;'rating'].median()\ndf_filled_median&#91;'rating'].fillna(median_rating, inplace=True)\nprint(f\"\\n\u7528\u4e2d\u4f4d\u6570 ({median_rating:.2f}) \u586b\u5145rating\u5217\u7f3a\u5931\u503c\u540e\u7684\u524d\u51e0\u884c:\\n\", df_filled_median.head(8))\n\n# \u65b9\u6cd5\u4e09\uff1a\u7528\u56fa\u5b9a\u503c\u586b\u5145 (\u4f8b\u5982\uff0c0\u8868\u793a\u672a\u8bc4\u5206)\ndf_filled_zero = df_dirty.copy()\ndf_filled_zero&#91;'rating'].fillna(0, inplace=True)\nprint(\"\\n\u75280\u586b\u5145rating\u5217\u7f3a\u5931\u503c\u540e\u7684\u524d\u51e0\u884c:\\n\", df_filled_zero.head(8))\n\n# \u65b9\u6cd5\u56db\uff1a\u7528\u524d\u4e00\u4e2a\u6709\u6548\u503c\u586b\u5145 (forward fill)\ndf_filled_ffill = df_dirty.copy()\ndf_filled_ffill&#91;'rating'].fillna(method='ffill', inplace=True)\nprint(\"\\n\u7528\u524d\u4e00\u4e2a\u6709\u6548\u503c\u586b\u5145rating\u5217\u7f3a\u5931\u503c\u540e\u7684\u524d\u51e0\u884c:\\n\", df_filled_ffill.head(8))\n\n# \u65b9\u6cd5\u4e94\uff1a\u4f7f\u7528sklearn\u7684SimpleImputer (\u66f4\u901a\u7528\u7684\u65b9\u6cd5\uff0c\u5c24\u5176\u5728ML\u9884\u5904\u7406\u4e2d)\ndf_sklearn_impute = df_dirty.copy()\n# \u521b\u5efa\u4e00\u4e2aImputer\uff0c\u7528\u5217\u7684\u5747\u503c\u586b\u5145NaN\nimputer = SimpleImputer(missing_values=np.nan, strategy='mean')\n# fit_transform\u4f1a\u8fd4\u56deNumpy\u6570\u7ec4\uff0c\u9700\u8981\u8f6c\u6362\u56deDataFrame\ndf_sklearn_impute&#91;'rating'] = imputer.fit_transform(df_sklearn_impute&#91;&#91;'rating']])\nprint(\"\\n\u4f7f\u7528SimpleImputer\u7528\u5747\u503c\u586b\u5145rating\u5217\u7f3a\u5931\u503c\u540e\u7684\u524d\u51e0\u884c:\\n\", df_sklearn_impute.head(8))\n\n\n# --- \u5f02\u5e38\u503c\u5904\u7406 ---\nprint(\"\\n\\n--- \u5f02\u5e38\u503c\u5904\u7406 ---\")\n# \u5f02\u5e38\u503c\uff1a\u4ef7\u683c(price)\u5217\u4e2d\uff0cORD010\u76843500.0\u53ef\u80fd\u662f\u4e00\u4e2a\u5f02\u5e38\u503c\u3002\n# \u6211\u4eec\u4f7f\u7528Z-score\u65b9\u6cd5\u6765\u68c0\u6d4b\u3002Z-score &gt; 3 \u6216 &lt; -3 \u901a\u5e38\u88ab\u8ba4\u4e3a\u662f\u5f02\u5e38\u503c\u3002\n\n# \u8ba1\u7b97price\u5217\u7684Z-score\ndf_clean = df_filled_mean.copy() # \u57fa\u4e8e\u5df2\u7ecf\u586b\u5145\u597d\u7f3a\u5931\u503c\u7684DataFrame\u8fdb\u884c\u5f02\u5e38\u503c\u5904\u7406\ndf_clean&#91;'price_zscore'] = np.abs((df_clean&#91;'price'] - df_clean&#91;'price'].mean()) \/ df_clean&#91;'price'].std())\nprint(\"\\n\u4ef7\u683cZ-score:\\n\", df_clean&#91;&#91;'price', 'price_zscore']].sort_values(by='price_zscore', ascending=False).head())\n\n# \u7535\u5546\u573a\u666f\uff1a\u8bc6\u522b\u4ef7\u683cZ-score\u5927\u4e8e3\u7684\u5f02\u5e38\u8ba2\u5355\noutliers = df_clean&#91;df_clean&#91;'price_zscore'] &gt; 3]\nprint(\"\\n\u68c0\u6d4b\u5230\u7684\u4ef7\u683c\u5f02\u5e38\u503c\u8ba2\u5355:\\n\", outliers)\n\n# \u5904\u7406\u5f02\u5e38\u503c\uff1a\u5220\u9664\u5f02\u5e38\u503c\u6240\u5728\u7684\u884c\ndf_no_outliers = df_clean&#91;df_clean&#91;'price_zscore'] &lt;= 3].drop(columns=&#91;'price_zscore'])\nprint(\"\\n\u5220\u9664\u4ef7\u683c\u5f02\u5e38\u503c\u540e\u7684\u6570\u636e\u884c\u6570:\", len(df_no_outliers))\nprint(\"\u5220\u9664\u4ef7\u683c\u5f02\u5e38\u503c\u540e\u7684\u6570\u636e\u9884\u89c8:\\n\", df_no_outliers.sort_values(by='price', ascending=False).head())\n\n# \u53e6\u4e00\u4e2a\u5904\u7406\u5f02\u5e38\u503c\u7684\u65b9\u6cd5\uff1a\u66ff\u6362\u4e3a\u4e2d\u4f4d\u6570\u6216\u4e0a\u9650\/\u4e0b\u9650\n# \u4f8b\u5982\uff0c\u6211\u4eec\u5c06\u8d85\u8fc7\u67d0\u4e2a\u9608\u503c\u7684\u4ef7\u683c\u66ff\u6362\u4e3a\u8be5\u9608\u503c\n# price_upper_bound = df_clean&#91;'price'].quantile(0.99) # 99%\u5206\u4f4d\u6570\u4f5c\u4e3a\u4e0a\u9650\n# df_clean&#91;'price'] = np.where(df_clean&#91;'price'] &gt; price_upper_bound, price_upper_bound, df_clean&#91;'price'])\n\n\n# --- \u91cd\u590d\u503c\u5904\u7406 ---\nprint(\"\\n\\n--- \u91cd\u590d\u503c\u5904\u7406 ---\")\n# 1. \u68c0\u6d4b\u91cd\u590d\u503c\n# \u7535\u5546\u573a\u666f\uff1a\u5b8c\u5168\u76f8\u540c\u7684\u8ba2\u5355\u8bb0\u5f55\u53ef\u80fd\u662f\u91cd\u590d\u5f55\u5165\uff0c\u9700\u8981\u8bc6\u522b\u3002\n# ORD001 \u548c ORD021 \u5b8c\u5168\u4e00\u6837\uff0cORD005 \u548c ORD015 \u9664\u4e86ID\u548c\u65e5\u671f\uff0c\u5176\u4ed6\u4fe1\u606f\u4e5f\u9ad8\u5ea6\u76f8\u4f3c\u3002\n\nprint(\"\\n\u68c0\u6d4b\u5b8c\u5168\u91cd\u590d\u7684\u884c:\\n\", df_dirty.duplicated())\nprint(\"\\n\u5b8c\u5168\u91cd\u590d\u7684\u884c\u6570\u91cf:\", df_dirty.duplicated().sum())\n\n# 2. \u5220\u9664\u91cd\u590d\u503c\n# \u9ed8\u8ba4\u5220\u9664\u6240\u6709\u5217\u90fd\u91cd\u590d\u7684\u884c\uff0c\u53ea\u4fdd\u7559\u7b2c\u4e00\u6b21\u51fa\u73b0\u7684\ndf_no_duplicates_all = df_dirty.drop_duplicates()\nprint(\"\\n\u5220\u9664\u5b8c\u5168\u91cd\u590d\u884c\u540e\u7684\u6570\u636e\u884c\u6570:\", len(df_no_duplicates_all))\n\n# \u7535\u5546\u573a\u666f\uff1a\u53ef\u80fd\u8ba2\u5355ID\u4e0d\u540c\uff0c\u4f46\u5546\u54c1\u3001\u7528\u6237\u3001\u6570\u91cf\u3001\u4ef7\u683c\u90fd\u4e00\u6837\uff0c\u6211\u4eec\u8ba4\u4e3a\u8fd9\u7c7b\u662f\u91cd\u590d\u8ba2\u5355\u3002\n# \u6211\u4eec\u53ef\u4ee5\u6307\u5b9a\u68c0\u67e5\u54ea\u4e9b\u5217\u7684\u91cd\u590d\ndf_no_duplicates_subset = df_dirty.drop_duplicates(subset=&#91;'user_id', 'product_name', 'price', 'quantity'])\nprint(\"\\n\u5220\u9664\u6839\u636e\u7279\u5b9a\u5217\u5224\u65ad\u7684\u91cd\u590d\u884c\u540e\u7684\u6570\u636e\u884c\u6570:\", len(df_no_duplicates_subset))\nprint(\"\u7279\u5b9a\u5217\u53bb\u91cd\u540e\u7684\u6570\u636e\u9884\u89c8:\\n\", df_no_duplicates_subset)\n\n# --- \u6570\u636e\u7c7b\u578b\u8f6c\u6362 ---\nprint(\"\\n\\n--- \u6570\u636e\u7c7b\u578b\u8f6c\u6362 ---\")\n# \u7535\u5546\u573a\u666f\uff1aorder_date \u901a\u5e38\u662f\u5b57\u7b26\u4e32\uff0c\u9700\u8981\u8f6c\u6362\u4e3a\u65e5\u671f\u65f6\u95f4\u7c7b\u578b\u624d\u80fd\u8fdb\u884c\u65e5\u671f\u8ba1\u7b97\u3002\n\ndf_processed = df_no_outliers.copy() # \u57fa\u4e8e\u4e4b\u524d\u5904\u7406\u597d\u7684\u6570\u636e\nprint(\"\\n\u8f6c\u6362\u524d 'order_date' \u5217\u7684\u6570\u636e\u7c7b\u578b:\", df_processed&#91;'order_date'].dtype)\ndf_processed&#91;'order_date'] = pd.to_datetime(df_processed&#91;'order_date'])\nprint(\"\u8f6c\u6362\u540e 'order_date' \u5217\u7684\u6570\u636e\u7c7b\u578b:\", df_processed&#91;'order_date'].dtype)\nprint(\"\u8f6c\u6362\u540e 'order_date' \u5217\u7684\u524d\u51e0\u884c:\\n\", df_processed&#91;'order_date'].head())\n\n# \u53ef\u4ee5\u63d0\u53d6\u5e74\u4efd\u3001\u6708\u4efd\u3001\u661f\u671f\u51e0\u7b49\ndf_processed&#91;'order_year'] = df_processed&#91;'order_date'].dt.year\ndf_processed&#91;'order_month'] = df_processed&#91;'order_date'].dt.month\ndf_processed&#91;'order_day_of_week'] = df_processed&#91;'order_date'].dt.day_name()\nprint(\"\\n\u6dfb\u52a0\u65e5\u671f\u7279\u5f81\u540e\u7684\u6570\u636e\u9884\u89c8:\\n\", df_processed.head())<\/code><\/pre>\n\n\n\n<p><strong>\u3010\u4e92\u52a8\u95ee\u7b54\u3011<\/strong><\/p>\n\n\n\n<ul>\n<li>\u4e3a\u4ec0\u4e48\u5728\u5904\u7406\u7f3a\u5931\u503c\u4e4b\u524d\u8981\u5148\u67e5\u770b&nbsp;<code>df.info()<\/code>&nbsp;\u548c&nbsp;<code>df.isnull().sum()<\/code>\uff1f<\/li>\n\n\n\n<li>\u5728\u7535\u5546\u573a\u666f\u4e2d\uff0c\u4f60\u89c9\u5f97\u54ea\u4e9b\u6570\u636e\u5217\u7684\u7f3a\u5931\u503c\u53ef\u4ee5\u5220\u9664\uff0c\u54ea\u4e9b\u66f4\u9002\u5408\u586b\u5145\uff1f\u4e3a\u4ec0\u4e48\uff1f<\/li>\n\n\n\n<li>\u9664\u4e86Z-score\uff0c\u8fd8\u6709\u54ea\u4e9b\u65b9\u6cd5\u53ef\u4ee5\u68c0\u6d4b\u5f02\u5e38\u503c\uff1f\uff08\u63d0\u793a\uff1aIQR\uff0c\u7bb1\u7ebf\u56fe\uff09<\/li>\n\n\n\n<li><code>df.drop_duplicates()<\/code>&nbsp;\u9ed8\u8ba4\u7684\u884c\u4e3a\u662f\u4ec0\u4e48\uff1f\u5982\u4f55\u6539\u53d8\u5b83\u7684\u884c\u4e3a\uff1f<\/li>\n\n\n\n<li>\u4e3a\u4ec0\u4e48\u8981\u628a\u65e5\u671f\u5b57\u7b26\u4e32\u8f6c\u6362\u4e3a\u65e5\u671f\u65f6\u95f4\u7c7b\u578b\uff1f\u8f6c\u6362\u540e\u80fd\u505a\u4ec0\u4e48\uff1f<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"23_%E6%95%B0%E6%8D%AE%E5%90%88%E5%B9%B6%E4%B8%8E%E8%BF%9E%E6%8E%A5\"><\/span><strong>2.3 \u6570\u636e\u5408\u5e76\u4e0e\u8fde\u63a5<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p><strong>\u3010\u7406\u8bba\u8bb2\u89e3\u3011<\/strong><\/p>\n\n\n\n<p>\u5728\u5b9e\u9645\u7684\u7535\u5546\u8fd0\u8425\u4e2d\uff0c\u6570\u636e\u5f80\u5f80\u5206\u6563\u5728\u4e0d\u540c\u7684\u8868\u683c\u4e2d\uff0c\u6bd4\u5982\u8ba2\u5355\u6570\u636e\u3001\u5546\u54c1\u8be6\u60c5\u6570\u636e\u3001\u7528\u6237\u6570\u636e\u7b49\u3002\u6570\u636e\u5408\u5e76\u4e0e\u8fde\u63a5\u5c31\u662f\u5c06\u8fd9\u4e9b\u5206\u6563\u7684\u6570\u636e\u6574\u5408\u8d77\u6765\uff0c\u5f62\u6210\u4e00\u5f20\u66f4\u5b8c\u6574\u3001\u66f4\u6709\u7528\u7684\u8868\u683c\u3002<\/p>\n\n\n\n<ul>\n<li><strong>concat\uff1a<\/strong>&nbsp;\u7b80\u5355\u5730\u5c06DataFrame\u5806\u53e0\u5728\u4e00\u8d77\uff08\u884c\u5806\u53e0\u6216\u5217\u5806\u53e0\uff09\u3002<\/li>\n\n\n\n<li><strong>merge\uff1a<\/strong>&nbsp;\u57fa\u4e8e\u4e00\u4e2a\u6216\u591a\u4e2a\u5171\u540c\u7684\u201c\u952e\u201d\u5c06\u4e24\u4e2aDataFrame\u8fde\u63a5\u8d77\u6765\uff0c\u5c31\u50cf\u6570\u636e\u5e93\u4e2d\u7684JOIN\u64cd\u4f5c\u3002\n<ul>\n<li><strong>\u5185\u8fde\u63a5 (inner join)\uff1a<\/strong>&nbsp;\u53ea\u4fdd\u7559\u4e24\u4e2a\u8868\u4e2d\u90fd\u6709\u5339\u914d\u952e\u7684\u884c\u3002<\/li>\n\n\n\n<li><strong>\u5de6\u8fde\u63a5 (left join)\uff1a<\/strong>&nbsp;\u4fdd\u7559\u5de6\u8868\u7684\u6240\u6709\u884c\uff0c\u5e76\u5339\u914d\u53f3\u8868\u4e2d\u7684\u884c\uff0c\u5982\u679c\u53f3\u8868\u6ca1\u6709\u5339\u914d\u5219\u4e3a\u7f3a\u5931\u503c\u3002<\/li>\n\n\n\n<li><strong>\u53f3\u8fde\u63a5 (right join)\uff1a<\/strong>&nbsp;\u4fdd\u7559\u53f3\u8868\u7684\u6240\u6709\u884c\uff0c\u5e76\u5339\u914d\u5de6\u8868\u4e2d\u7684\u884c\uff0c\u5982\u679c\u5de6\u8868\u6ca1\u6709\u5339\u914d\u5219\u4e3a\u7f3a\u5931\u503c\u3002<\/li>\n\n\n\n<li><strong>\u5916\u8fde\u63a5 (outer join)\uff1a<\/strong>&nbsp;\u4fdd\u7559\u4e24\u4e2a\u8868\u7684\u6240\u6709\u884c\uff0c\u4e0d\u5339\u914d\u7684\u586b\u5145\u7f3a\u5931\u503c\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<p><strong>\u3010\u63d0\u4f9b\u6570\u636e\u96c6\u3011<\/strong><\/p>\n\n\n\n<p>\u9664\u4e86&nbsp;<code>ecommerce_orders.csv<\/code>\uff0c\u6211\u4eec\u518d\u6a21\u62df\u4e00\u4e2a&nbsp;<code>product_details.csv<\/code>&nbsp;\u6587\u4ef6\uff0c\u5305\u542b\u5546\u54c1\u7684\u66f4\u591a\u4fe1\u606f\u3002<\/p>\n\n\n\n<p><strong><code>product_details.csv<\/code>&nbsp;\u793a\u4f8b\u5185\u5bb9\uff1a<\/strong><\/p>\n\n\n\n<p>csv<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>product_id,product_description,brand,cost_price,supplier\nP101,Comfortable cotton T-shirt,FashionBrand,30.0,SupplierA\nP102,Classic straight-leg jeans,DenimStyle,70.0,SupplierB\nP103,High-performance running sneakers,SportGear,200.0,SupplierC\nP104,Stylish baseball cap,HeadWear,25.0,SupplierA\nP105,Durable waterproof backpack,OutdoorLife,120.0,SupplierD\nP106,Elegant smart watch,TechTime,400.0,SupplierE\nP107,Summer floral dress,FashionBrand,90.0,SupplierB\nP108,Noise-cancelling headphones,AudioPro,180.0,SupplierE\nP109,Mechanical gaming keyboard,GameTech,220.0,SupplierC\nP110,Ergonomic wireless mouse,GameTech,40.0,SupplierD\nP111,Comfortable cotton socks,FashionBrand,10.0,SupplierA<\/code><\/pre>\n\n\n\n<p><strong>\u3010\u4ee3\u7801\u5b9e\u4f8b\u4e0e\u7535\u5546\u573a\u666f\u6848\u4f8b\u3011<\/strong><\/p>\n\n\n\n<p>python<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>import pandas as pd\n\n# \u52a0\u8f7d\u8ba2\u5355\u6570\u636e\u548c\u5546\u54c1\u8be6\u60c5\u6570\u636e\ndf_orders = pd.read_csv('ecommerce_orders.csv')\ndf_products = pd.read_csv('product_details.csv')\n\nprint(\"\u8ba2\u5355\u6570\u636e\u9884\u89c8:\\n\", df_orders.head())\nprint(\"\\n\u5546\u54c1\u8be6\u60c5\u6570\u636e\u9884\u89c8:\\n\", df_products.head())\n\n# --- concat: \u7b80\u5355\u5806\u53e0 ---\nprint(\"\\n--- concat: \u7b80\u5355\u5806\u53e0 ---\")\n# \u7535\u5546\u573a\u666f\uff1a\u5982\u679c\u4f60\u6709\u6765\u81ea\u4e0d\u540c\u65f6\u95f4\u6bb5\u7684\u8ba2\u5355\u6570\u636e\uff0c\u4f46\u7ed3\u6784\u5b8c\u5168\u4e00\u6837\uff0c\u53ef\u4ee5\u7528concat\u5806\u53e0\u8d77\u6765\u3002\n# \u8fd9\u91cc\u6211\u4eec\u6a21\u62df\u4e24\u4e2a\u7ed3\u6784\u4e00\u6837\u7684\u8ba2\u5355\u5b50\u96c6\u8fdb\u884c\u5806\u53e0\ndf_orders_part1 = df_orders.iloc&#91;:5]\ndf_orders_part2 = df_orders.iloc&#91;5:10]\ndf_combined_orders = pd.concat(&#91;df_orders_part1, df_orders_part2])\nprint(\"\\n\u4f7f\u7528concat\u5806\u53e0\u540e\u7684\u8ba2\u5355\u6570\u636e:\\n\", df_combined_orders)\n\n# --- merge: \u57fa\u4e8e\u952e\u8fde\u63a5 ---\nprint(\"\\n\\n--- merge: \u57fa\u4e8e\u952e\u8fde\u63a5 ---\")\n# \u7535\u5546\u573a\u666f\uff1a\u5c06\u8ba2\u5355\u6570\u636e\u4e0e\u5546\u54c1\u8be6\u60c5\u6570\u636e\u5408\u5e76\uff0c\u4ee5\u4fbf\u5206\u6790\u8ba2\u5355\u4e2d\u5546\u54c1\u7684\u6210\u672c\u3001\u54c1\u724c\u7b49\u4fe1\u606f\u3002\n# \u5171\u540c\u7684\u952e\u662f 'product_id'\n\n# 1. \u5185\u8fde\u63a5 (inner merge)\n# \u53ea\u4fdd\u7559\u5728 df_orders \u548c df_products \u4e2d\u90fd\u6709 product_id \u5339\u914d\u7684\u884c\ndf_inner_merged = pd.merge(df_orders, df_products, on='product_id', how='inner')\nprint(\"\\n\u5185\u8fde\u63a5 (inner merge) \u540e\u7684\u6570\u636e\u9884\u89c8 (\u8ba2\u5355\u4e0e\u5546\u54c1\u8be6\u60c5):\\n\", df_inner_merged.head())\nprint(\"\u5185\u8fde\u63a5\u540e\u7684\u6570\u636e\u884c\u6570:\", len(df_inner_merged))\n\n# 2. \u5de6\u8fde\u63a5 (left merge)\n# \u4fdd\u7559 df_orders \u7684\u6240\u6709\u884c\uff0c\u5e76\u5339\u914d df_products \u4e2d\u7684\u4fe1\u606f\u3002\n# \u5982\u679c df_products \u4e2d\u6ca1\u6709\u5bf9\u5e94\u7684 product_id\uff0c\u5219\u5546\u54c1\u8be6\u60c5\u5217\u4e3a NaN\u3002\ndf_left_merged = pd.merge(df_orders, df_products, on='product_id', how='left')\nprint(\"\\n\u5de6\u8fde\u63a5 (left merge) \u540e\u7684\u6570\u636e\u9884\u89c8:\\n\", df_left_merged.head())\nprint(\"\u5de6\u8fde\u63a5\u540e\u7684\u6570\u636e\u884c\u6570:\", len(df_left_merged))\n# \u68c0\u67e5\u662f\u5426\u6709\u672a\u5339\u914d\u7684\u5546\u54c1\uff08\u5373 product_details \u4e2d\u7684\u5217\u51fa\u73b0 NaN\uff09\nprint(\"\\n\u5de6\u8fde\u63a5\u540e\uff0c\u68c0\u67e5\u662f\u5426\u6709\u672a\u5339\u914d\u7684\u5546\u54c1\uff08NaN\u503c\uff09:\\n\", df_left_merged&#91;df_left_merged&#91;'brand'].isnull()])\n\n\n# 3. \u53f3\u8fde\u63a5 (right merge)\n# \u4fdd\u7559 df_products \u7684\u6240\u6709\u884c\uff0c\u5e76\u5339\u914d df_orders \u4e2d\u7684\u4fe1\u606f\u3002\n# \u5982\u679c df_orders \u4e2d\u6ca1\u6709\u5bf9\u5e94\u7684 product_id\uff0c\u5219\u8ba2\u5355\u8be6\u60c5\u5217\u4e3a NaN\u3002\ndf_right_merged = pd.merge(df_orders, df_products, on='product_id', how='right')\nprint(\"\\n\u53f3\u8fde\u63a5 (right merge) \u540e\u7684\u6570\u636e\u9884\u89c8:\\n\", df_right_merged.head())\nprint(\"\u53f3\u8fde\u63a5\u540e\u7684\u6570\u636e\u884c\u6570:\", len(df_right_merged))\n# \u68c0\u67e5\u662f\u5426\u6709\u672a\u88ab\u8d2d\u4e70\u7684\u5546\u54c1\nprint(\"\\n\u53f3\u8fde\u63a5\u540e\uff0c\u68c0\u67e5\u662f\u5426\u6709\u672a\u88ab\u8d2d\u4e70\u7684\u5546\u54c1\uff08NaN\u503c\uff09:\\n\", df_right_merged&#91;df_right_merged&#91;'order_id'].isnull()])\n\n\n# 4. \u5916\u8fde\u63a5 (outer merge)\n# \u4fdd\u7559 df_orders \u548c df_products \u7684\u6240\u6709\u884c\uff0c\u4e0d\u5339\u914d\u7684\u586b\u5145 NaN\u3002\ndf_outer_merged = pd.merge(df_orders, df_products, on='product_id', how='outer')\nprint(\"\\n\u5916\u8fde\u63a5 (outer merge) \u540e\u7684\u6570\u636e\u9884\u89c8:\\n\", df_outer_merged.head())\nprint(\"\u5916\u8fde\u63a5\u540e\u7684\u6570\u636e\u884c\u6570:\", len(df_outer_merged))\n\n# \u7535\u5546\u573a\u666f\uff1a\u8ba1\u7b97\u6bcf\u4e2a\u8ba2\u5355\u7684\u6bdb\u5229\u6da6\n# \u5047\u8bbe\u9500\u552e\u4ef7\u683c\u662f df_orders&#91;'price']\uff0c\u6210\u672c\u4ef7\u683c\u662f df_products&#91;'cost_price']\n# \u6211\u4eec\u9700\u8981\u5148\u5408\u5e76\u6570\u636e\ndf_merged_for_profit = pd.merge(df_orders, df_products&#91;&#91;'product_id', 'cost_price']], on='product_id', how='left')\ndf_merged_for_profit&#91;'total_revenue'] = df_merged_for_profit&#91;'price'] * df_merged_for_profit&#91;'quantity']\ndf_merged_for_profit&#91;'total_cost'] = df_merged_for_profit&#91;'cost_price'] * df_merged_for_profit&#91;'quantity']\ndf_merged_for_profit&#91;'gross_profit'] = df_merged_for_profit&#91;'total_revenue'] - df_merged_for_profit&#91;'total_cost']\nprint(\"\\n\u8ba1\u7b97\u6bdb\u5229\u6da6\u540e\u7684\u6570\u636e\u9884\u89c8 (\u65b0\u589e\u603b\u6536\u5165\u3001\u603b\u6210\u672c\u3001\u6bdb\u5229\u6da6):\\n\", df_merged_for_profit&#91;&#91;'order_id', 'product_name', 'price', 'quantity', 'cost_price', 'total_revenue', 'total_cost', 'gross_profit']].head())<\/code><\/pre>\n\n\n\n<p><strong>\u3010\u4e92\u52a8\u95ee\u7b54\u3011<\/strong><\/p>\n\n\n\n<ul>\n<li><code>pd.concat()<\/code>&nbsp;\u548c&nbsp;<code>pd.merge()<\/code>&nbsp;\u7684\u4e3b\u8981\u533a\u522b\u662f\u4ec0\u4e48\uff1f\u4ec0\u4e48\u65f6\u5019\u7528\u54ea\u4e2a\uff1f<\/li>\n\n\n\n<li><code>merge()<\/code>&nbsp;\u51fd\u6570\u4e2d\u7684&nbsp;<code>on<\/code>&nbsp;\u53c2\u6570\u6709\u4ec0\u4e48\u4f5c\u7528\uff1f<\/li>\n\n\n\n<li><code>how<\/code>&nbsp;\u53c2\u6570\u4e2d\u7684&nbsp;<code>'inner'<\/code>,&nbsp;<code>'left'<\/code>,&nbsp;<code>'right'<\/code>,&nbsp;<code>'outer'<\/code>&nbsp;\u5206\u522b\u4ee3\u8868\u4ec0\u4e48\u610f\u601d\uff1f\u5728\u7535\u5546\u573a\u666f\u4e2d\uff0c\u4f60\u4f1a\u5728\u4ec0\u4e48\u60c5\u51b5\u4e0b\u9009\u62e9\u54ea\u79cd\u8fde\u63a5\u65b9\u5f0f\uff1f<\/li>\n\n\n\n<li>\u5982\u4f55\u5904\u7406\u5408\u5e76\u540e\u51fa\u73b0\u7684\u91cd\u590d\u5217\u540d\uff08\u5982\u679c\u4e24\u4e2a\u8868\u4e2d\u90fd\u6709\u540c\u540d\u4f46\u542b\u4e49\u4e0d\u540c\u7684\u5217\uff09\uff1f\uff08\u63d0\u793a\uff1a<code>suffixes<\/code>&nbsp;\u53c2\u6570\uff09<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"24_%E6%95%B0%E6%8D%AE%E5%88%86%E7%BB%84%E4%B8%8E%E8%81%9A%E5%90%88\"><\/span><strong>2.4 \u6570\u636e\u5206\u7ec4\u4e0e\u805a\u5408<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p><strong>\u3010\u7406\u8bba\u8bb2\u89e3\u3011<\/strong><\/p>\n\n\n\n<p>\u6570\u636e\u5206\u7ec4\u4e0e\u805a\u5408\u662f\u7535\u5546\u6570\u636e\u5206\u6790\u4e2d\u975e\u5e38\u91cd\u8981\u7684\u6280\u80fd\u3002\u5b83\u80fd\u5e2e\u52a9\u6211\u4eec\u4ece\u6d77\u91cf\u6570\u636e\u4e2d\u63d0\u53d6\u51fa\u6709\u610f\u4e49\u7684\u7edf\u8ba1\u4fe1\u606f\uff0c\u6bd4\u5982\uff1a\u54ea\u4e2a\u5546\u54c1\u7c7b\u522b\u5356\u5f97\u6700\u597d\uff1f\u54ea\u4e2a\u57ce\u5e02\u7684\u9500\u552e\u989d\u6700\u9ad8\uff1f\u54ea\u4e2a\u7528\u6237\u6d88\u8d39\u91d1\u989d\u6700\u591a\uff1f<\/p>\n\n\n\n<ul>\n<li><strong>groupby\uff1a<\/strong>&nbsp;\u6309\u7167\u4e00\u4e2a\u6216\u591a\u4e2a\u5217\u7684\u503c\u5c06DataFrame\u5206\u6210\u82e5\u5e72\u4e2a\u7ec4\u3002<\/li>\n\n\n\n<li><strong>agg\uff1a<\/strong>&nbsp;\u5bf9\u6bcf\u4e2a\u7ec4\u5e94\u7528\u4e00\u4e2a\u6216\u591a\u4e2a\u805a\u5408\u51fd\u6570\uff08\u5982\u6c42\u548c\u3001\u5e73\u5747\u503c\u3001\u8ba1\u6570\u3001\u6700\u5927\u503c\u3001\u6700\u5c0f\u503c\u7b49\uff09\u3002<\/li>\n<\/ul>\n\n\n\n<p><strong>\u3010\u4ee3\u7801\u5b9e\u4f8b\u4e0e\u7535\u5546\u573a\u666f\u6848\u4f8b\u3011<\/strong><\/p>\n\n\n\n<p>\u6211\u4eec\u5c06\u7ee7\u7eed\u4f7f\u7528\u5408\u5e76\u540e\u7684\u7535\u5546\u8ba2\u5355\u6570\u636e\u3002<\/p>\n\n\n\n<p>python<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>import pandas as pd\n\n# \u52a0\u8f7d\u7535\u5546\u8ba2\u5355\u6570\u636e\ndf_orders = pd.read_csv('ecommerce_orders.csv')\n\n# \u786e\u4fddorder_date\u662f\u65e5\u671f\u65f6\u95f4\u7c7b\u578b\uff0c\u4ee5\u4fbf\u540e\u7eed\u6309\u65e5\u671f\u5206\u7ec4\ndf_orders&#91;'order_date'] = pd.to_datetime(df_orders&#91;'order_date'])\n\n# \u8ba1\u7b97\u6bcf\u4e2a\u8ba2\u5355\u7684\u603b\u91d1\u989d\ndf_orders&#91;'total_amount'] = df_orders&#91;'price'] * df_orders&#91;'quantity']\n\nprint(\"\u5904\u7406\u540e\u7684\u8ba2\u5355\u6570\u636e\u9884\u89c8:\\n\", df_orders.head())\n\n# --- groupby: \u5206\u7ec4 ---\nprint(\"\\n--- groupby: \u5206\u7ec4\u4e0e\u805a\u5408 ---\")\n\n# \u7535\u5546\u573a\u666f\uff1a\u7edf\u8ba1\u6bcf\u4e2a\u5546\u54c1\u7c7b\u522b\u7684\u603b\u9500\u552e\u989d\nsales_by_category = df_orders.groupby('category')&#91;'total_amount'].sum()\nprint(\"\\n\u6bcf\u4e2a\u5546\u54c1\u7c7b\u522b\u7684\u603b\u9500\u552e\u989d:\\n\", sales_by_category.sort_values(ascending=False))\n\n# \u7535\u5546\u573a\u666f\uff1a\u7edf\u8ba1\u6bcf\u4e2a\u7528\u6237\u7684\u8ba2\u5355\u6570\u91cf\u548c\u603b\u6d88\u8d39\u91d1\u989d\nuser_stats = df_orders.groupby('user_id').agg(\n    order_count=('order_id', 'count'), # \u8ba1\u7b97\u8ba2\u5355\u6570\u91cf\n    total_spent=('total_amount', 'sum'), # \u8ba1\u7b97\u603b\u6d88\u8d39\u91d1\u989d\n    avg_price_per_item=('price', 'mean') # \u8ba1\u7b97\u5e73\u5747\u5546\u54c1\u5355\u4ef7\n)\nprint(\"\\n\u6bcf\u4e2a\u7528\u6237\u7684\u8ba2\u5355\u6570\u91cf\u548c\u603b\u6d88\u8d39\u91d1\u989d:\\n\", user_stats.sort_values(by='total_spent', ascending=False).head())\n\n# \u7535\u5546\u573a\u666f\uff1a\u6309\u57ce\u5e02\u7edf\u8ba1\u5e73\u5747\u8ba2\u5355\u91d1\u989d\navg_order_by_city = df_orders.groupby('shipping_city')&#91;'total_amount'].mean()\nprint(\"\\n\u6309\u57ce\u5e02\u7edf\u8ba1\u7684\u5e73\u5747\u8ba2\u5355\u91d1\u989d:\\n\", avg_order_by_city.sort_values(ascending=False))\n\n\n# \u7535\u5546\u573a\u666f\uff1a\u6309\u65e5\u671f\u7edf\u8ba1\u6bcf\u65e5\u9500\u552e\u989d\u548c\u8ba2\u5355\u6570\u91cf\ndaily_sales = df_orders.groupby('order_date').agg(\n    daily_revenue=('total_amount', 'sum'),\n    daily_orders=('order_id', 'count')\n)\nprint(\"\\n\u6bcf\u65e5\u9500\u552e\u989d\u548c\u8ba2\u5355\u6570\u91cf:\\n\", daily_sales.head())\n\n\n# \u591a\u5217\u5206\u7ec4\n# \u7535\u5546\u573a\u666f\uff1a\u7edf\u8ba1\u6bcf\u4e2a\u57ce\u5e02\u4e0d\u540c\u652f\u4ed8\u65b9\u5f0f\u7684\u9500\u552e\u989d\nsales_by_city_payment = df_orders.groupby(&#91;'shipping_city', 'payment_method'])&#91;'total_amount'].sum().unstack()\nprint(\"\\n\u6bcf\u4e2a\u57ce\u5e02\u4e0d\u540c\u652f\u4ed8\u65b9\u5f0f\u7684\u9500\u552e\u989d:\\n\", sales_by_city_payment)\n\n\n# --- apply: \u5e94\u7528\u81ea\u5b9a\u4e49\u51fd\u6570 ---\nprint(\"\\n\\n--- apply: \u5e94\u7528\u81ea\u5b9a\u4e49\u51fd\u6570 ---\")\n# \u7535\u5546\u573a\u666f\uff1a\u4e3a\u6bcf\u4e2a\u7528\u6237\u6253\u4e0a\u6d88\u8d39\u7b49\u7ea7\u6807\u7b7e\uff08\u4f8b\u5982\uff0c\u603b\u6d88\u8d39\u91d1\u989d\u524d20%\u4e3a\u201c\u9ad8\u4ef7\u503c\u7528\u6237\u201d\uff09\n\n# \u6b65\u9aa41: \u5b9a\u4e49\u4e00\u4e2a\u51fd\u6570\u6765\u5224\u65ad\u6d88\u8d39\u7b49\u7ea7\ndef assign_customer_segment(total_spent):\n    if total_spent &gt;= user_stats&#91;'total_spent'].quantile(0.8): # \u8d85\u8fc780%\u5206\u4f4d\u6570\n        return '\u9ad8\u4ef7\u503c\u7528\u6237'\n    elif total_spent &gt;= user_stats&#91;'total_spent'].quantile(0.5): # \u8d85\u8fc750%\u5206\u4f4d\u6570\n        return '\u4e2d\u4ef7\u503c\u7528\u6237'\n    else:\n        return '\u666e\u901a\u7528\u6237'\n\n# \u6b65\u9aa42: \u5c06\u51fd\u6570\u5e94\u7528\u5230\u6bcf\u4e2a\u7528\u6237\u7684\u603b\u6d88\u8d39\u91d1\u989d\u4e0a\nuser_stats&#91;'customer_segment'] = user_stats&#91;'total_spent'].apply(assign_customer_segment)\nprint(\"\\n\u6dfb\u52a0\u7528\u6237\u6d88\u8d39\u7b49\u7ea7\u540e\u7684\u7528\u6237\u7edf\u8ba1:\\n\", user_stats.sort_values(by='total_spent', ascending=False).head())\n\n# \u7535\u5546\u573a\u666f\uff1a\u8ba1\u7b97\u6bcf\u4e2a\u8ba2\u5355\u4e2d\uff0c\u5546\u54c1\u5355\u4ef7\u662f\u5426\u9ad8\u4e8e\u8be5\u7c7b\u522b\u5e73\u5747\u5355\u4ef7\n# \u5148\u8ba1\u7b97\u6bcf\u4e2a\u7c7b\u522b\u7684\u5e73\u5747\u5355\u4ef7\navg_price_by_category = df_orders.groupby('category')&#91;'price'].mean().rename('avg_category_price')\n# \u5c06\u5e73\u5747\u5355\u4ef7\u5408\u5e76\u56de\u539f\u59cb\u8ba2\u5355\u6570\u636e\ndf_merged_avg_price = pd.merge(df_orders, avg_price_by_category, on='category', how='left')\ndf_merged_avg_price&#91;'is_above_category_avg'] = df_merged_avg_price&#91;'price'] &gt; df_merged_avg_price&#91;'avg_category_price']\nprint(\"\\n\u5224\u65ad\u5546\u54c1\u5355\u4ef7\u662f\u5426\u9ad8\u4e8e\u5176\u7c7b\u522b\u5e73\u5747\u5355\u4ef7:\\n\", df_merged_avg_price&#91;&#91;'product_name', 'category', 'price', 'avg_category_price', 'is_above_category_avg']].head())<\/code><\/pre>\n\n\n\n<p><strong>\u3010\u4e92\u52a8\u95ee\u7b54\u3011<\/strong><\/p>\n\n\n\n<ul>\n<li><code>groupby()<\/code>&nbsp;\u540e\u76f4\u63a5\u8ddf&nbsp;<code>sum()<\/code>&nbsp;\u548c&nbsp;<code>agg()<\/code>&nbsp;\u6709\u4ec0\u4e48\u533a\u522b\uff1f\u4ec0\u4e48\u65f6\u5019\u7528&nbsp;<code>agg()<\/code>&nbsp;\u66f4\u6709\u4f18\u52bf\uff1f<\/li>\n\n\n\n<li>\u5982\u4f55\u7edf\u8ba1\u6bcf\u4e2a\u5546\u54c1\u7c7b\u522b\u7684\u72ec\u7acb\u7528\u6237\u6570\u91cf\uff1f<\/li>\n\n\n\n<li><code>unstack()<\/code>&nbsp;\u51fd\u6570\u6709\u4ec0\u4e48\u4f5c\u7528\uff1f\u5728\u4ec0\u4e48\u573a\u666f\u4e0b\u4f1a\u4f7f\u7528\u5b83\uff1f<\/li>\n\n\n\n<li><code>apply()<\/code>&nbsp;\u51fd\u6570\u5728\u6570\u636e\u5904\u7406\u4e2d\u6709\u4ec0\u4e48\u5f3a\u5927\u7684\u4f5c\u7528\uff1f\u4f60\u80fd\u60f3\u51fa\u5176\u4ed6\u7535\u5546\u573a\u666f\u4e2d\u4f7f\u7528&nbsp;<code>apply()<\/code>&nbsp;\u7684\u4f8b\u5b50\u5417\uff1f<\/li>\n\n\n\n<li>\u5982\u4f55\u8ba1\u7b97\u6bcf\u4e2a\u7528\u6237\u5728\u7b2c\u4e00\u6b21\u8d2d\u4e70\u540e\uff0c\u7b2c\u4e8c\u6b21\u8d2d\u4e70\u7684\u95f4\u9694\u65f6\u95f4\uff1f\uff08\u8fd9\u9700\u8981\u7ed3\u5408\u65e5\u671f\u65f6\u95f4\u64cd\u4f5c\u548c\u5206\u7ec4\uff09<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>\u3010\u7406\u8bba\u8bb2\u89e3\u3011 Pandas\u662f\u6570\u636e\u5206\u6790\u7684\u745e\u58eb\u519b\u5200\uff0c\u5b83\u7684\u5f3a\u5927\u4e4b\u5904\u5728\u4e8e\u80fd\u9ad8\u6548\u5730\u5904\u7406\u8868\u683c\u6570\u636e\u3002\u672c\u7ae0\u6211\u4eec\u5c06\u5b66\u4e60&hellip; <a href=\"http:\/\/viplao.com\/index.php\/2025\/10\/17\/%e3%80%90%e8%bf%90%e8%90%a5%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90-%e5%9f%ba%e7%a1%80%e7%af%87%e3%80%91pandas%e6%95%b0%e6%8d%ae%e5%a4%84%e7%90%86%e6%a0%b8%e5%bf%83%e6%8a%80%e8%83%bd\/\" class=\"more-link read-more\" rel=\"bookmark\">\u7ee7\u7eed\u9605\u8bfb <span class=\"screen-reader-text\">\u3010\u8fd0\u8425\u6570\u636e\u5206\u6790-\u57fa\u7840\u7bc7\u3011Pandas\u6570\u636e\u5904\u7406\u6838\u5fc3\u6280\u80fd<\/span><i class=\"fa fa-arrow-right\"><\/i><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[28],"views":627,"_links":{"self":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts\/4069"}],"collection":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/comments?post=4069"}],"version-history":[{"count":3,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts\/4069\/revisions"}],"predecessor-version":[{"id":4094,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts\/4069\/revisions\/4094"}],"wp:attachment":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/media?parent=4069"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/categories?post=4069"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/tags?post=4069"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}