{"id":4316,"date":"2023-07-12T01:56:40","date_gmt":"2023-07-12T01:56:40","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e5%a4%a7%e7%86%8a%e7%8c%ab%e6%8c%89%e8%8c%83%e5%9b%b4%e5%88%86%e7%bb%84\/"},"modified":"2023-07-12T01:56:40","modified_gmt":"2023-07-12T01:56:40","slug":"%e5%a4%a7%e7%86%8a%e7%8c%ab%e6%8c%89%e8%8c%83%e5%9b%b4%e5%88%86%e7%bb%84","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e5%a4%a7%e7%86%8a%e7%8c%ab%e6%8c%89%e8%8c%83%e5%9b%b4%e5%88%86%e7%bb%84\/","title":{"rendered":"Pandas\uff1a\u5982\u4f55\u6309\u503c\u8303\u56f4\u5206\u7ec4"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\">\u5728\u6267\u884c\u805a\u5408\u4e4b\u524d\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u4f7f\u7528 pandas \u4e2d\u7684<strong>groupby()<\/strong>\u51fd\u6570\u6309\u503c\u8303\u56f4\u5bf9\u5217\u8fdb\u884c\u5206\u7ec4\uff1a<\/span><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df. <span style=\"color: #3366ff;\">groupby<\/span> (pd. <span style=\"color: #3366ff;\">cut<\/span> (df[' <span style=\"color: #ff0000;\">my_column<\/span> '], [0, 25, 50, 75, 100])). <span style=\"color: #3366ff;\">sum<\/span> ()\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6b64\u7279\u5b9a\u793a\u4f8b\u5c06\u6839\u636e\u540d\u4e3a<strong>my_column<\/strong>\u7684\u5217\u4e2d\u7684\u4ee5\u4e0b\u503c\u8303\u56f4\u5bf9 DataFrame \u7684\u884c\u8fdb\u884c\u5206\u7ec4\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">(0.25]<\/span><\/li>\n<li> <span style=\"color: #000000;\">(25, 50]<\/span><\/li>\n<li> <span style=\"color: #000000;\">(50, 75]<\/span><\/li>\n<li> <span style=\"color: #000000;\">(75, 100]<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u7136\u540e\uff0c\u5b83\u5c06\u4f7f\u7528\u8fd9\u4e9b\u503c\u8303\u56f4\u4f5c\u4e3a\u7ec4\u6765\u8ba1\u7b97 DataFrame \u6240\u6709\u5217\u4e2d\u7684\u503c\u7684\u603b\u548c\u3002<\/span><\/p>\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<h2><span style=\"color: #000000;\"><strong>\u793a\u4f8b\uff1a\u5982\u4f55\u5728 Pandas \u4e2d\u6309\u503c\u8303\u56f4\u8fdb\u884c\u5206\u7ec4<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u5047\u8bbe\u6211\u4eec\u6709\u4ee5\u4e0b pandas DataFrame\uff0c\u5176\u4e2d\u5305\u542b\u6709\u5173\u4e0d\u540c\u96f6\u552e\u5546\u5e97\u7684\u89c4\u6a21\u53ca\u5176\u603b\u9500\u552e\u989d\u7684\u4fe1\u606f\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#createDataFrame\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">store_size<\/span> ': [14, 25, 26, 29, 45, 58, 67, 81, 90, 98],\n                   ' <span style=\"color: #ff0000;\">sales<\/span> ': [15, 18, 24, 25, 20, 35, 34, 49, 44, 49]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n   store_size sales\n0 14 15\n1 25 18\n2 26 24\n3 29 25\n4 45 20\n5 58 35\n6 67 34\n7 81 49\n8 90 44\n9 98 49\n<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u6839\u636e<strong>store_size<\/strong>\u5217\u7684\u7279\u5b9a\u8303\u56f4\u5bf9 DataFrame \u8fdb\u884c\u5206\u7ec4\uff0c\u7136\u540e\u4f7f\u7528\u8303\u56f4\u4f5c\u4e3a\u7ec4\u6765\u8ba1\u7b97 DataFrame \u4e2d\u6240\u6709\u5176\u4ed6\u5217\u7684\u603b\u548c\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;\">#group by ranges of store_size and calculate sum of all columns\n<\/span>df. <span style=\"color: #3366ff;\">groupby<\/span> (pd. <span style=\"color: #3366ff;\">cut<\/span> (df[' <span style=\"color: #ff0000;\">store_size<\/span> '], [0, 25, 50, 75, 100])). <span style=\"color: #3366ff;\">sum<\/span> ()\n\n\t store_size sales\nstore_size\t\t\n(0.25] 39 33\n(25, 50] 100 69\n(50, 75] 125 69\n(75, 100] 269 142\n<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\">\u4ece\u7ed3\u679c\u6211\u4eec\u53ef\u4ee5\u770b\u51fa\uff1a<\/span><\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u5bf9\u4e8e store_size \u503c\u5728 0 \u5230 25 \u4e4b\u95f4\u7684\u884c\uff0cstore_size \u7684\u603b\u548c\u4e3a<strong>39<\/strong> \uff0c\u9500\u552e\u989d\u7684\u603b\u548c\u4e3a<strong>33<\/strong> \u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5bf9\u4e8e store_size \u503c\u5728 25 \u5230 50 \u4e4b\u95f4\u7684\u884c\uff0cstore_size \u7684\u603b\u548c\u4e3a<strong>100<\/strong> \uff0c\u9500\u552e\u989d\u7684\u603b\u548c\u4e3a<strong>69<\/strong> \u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u7b49\u7b49\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\">\u5982\u679c\u9700\u8981\uff0c\u60a8\u8fd8\u53ef\u4ee5\u53ea\u8ba1\u7b97\u6bcf\u4e2a<strong>store_size<\/strong>\u8303\u56f4\u7684<strong>\u9500\u552e\u989d<\/strong>\u603b\u548c\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;\">#group by ranges of store_size and calculate sum of sales\n<\/span>df. <span style=\"color: #3366ff;\">groupby<\/span> (pd. <span style=\"color: #3366ff;\">cut<\/span> (df[' <span style=\"color: #ff0000;\">store_size<\/span> '], [0, 25, 50, 75, 100]))[' <span style=\"color: #ff0000;\">sales<\/span> ']. <span style=\"color: #3366ff;\">sum<\/span> ()\n\nstore_size\n(0.25] 33\n(25, 50] 69\n(50, 75] 69\n(75, 100] 142\nName: sales, dtype: int64<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u60a8\u8fd8\u53ef\u4ee5\u4f7f\u7528 NumPy <strong>arange()<\/strong>\u51fd\u6570\u5c06\u53d8\u91cf\u5206\u89e3\u4e3a\u8303\u56f4\uff0c\u800c\u65e0\u9700\u624b\u52a8\u6307\u5b9a\u6bcf\u4e2a\u5206\u5272\u70b9\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np<\/span>\n\n#group by ranges of store_size and calculate sum of sales\n<\/span>df. <span style=\"color: #3366ff;\">groupby<\/span> (pd. <span style=\"color: #3366ff;\">cut<\/span> (df[' <span style=\"color: #ff0000;\">store_size<\/span> '], np. <span style=\"color: #3366ff;\">arange<\/span> (0, 101, 25)))[' <span style=\"color: #ff0000;\">sales<\/span> ']. <span style=\"color: #3366ff;\">sum<\/span> ()\n\nstore_size\n(0.25] 33\n(25, 50] 69\n(50, 75] 69\n(75, 100] 142\nName: sales, dtype: int64<\/strong><\/pre>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\">\u8bf7\u6ce8\u610f\uff0c\u8fd9\u4e9b\u7ed3\u679c\u4e0e\u524d\u9762\u7684\u793a\u4f8b\u76f8\u5339\u914d\u3002<\/span><\/span><\/p>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\"><strong>\u6ce8\u610f<\/strong>\uff1a\u60a8\u53ef\u4ee5<a href=\"https:\/\/numpy.org\/doc\/stable\/reference\/generated\/numpy.arange.html\" target=\"_blank\" rel=\"noopener\">\u5728\u6b64\u5904<\/a>\u627e\u5230 NumPy <strong>arange()<\/strong>\u51fd\u6570\u7684\u5b8c\u6574\u6587\u6863\u3002<\/span><\/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\u4efb\u52a1\uff1a<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/cn\/pandas-groupby-\u8ba1\u6570\u552f\u4e00\/\" target=\"_blank\" rel=\"noopener\">Pandas\uff1a\u5982\u4f55\u4f7f\u7528groupby\u8ba1\u7b97\u552f\u4e00\u503c<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/pandas-groupby-\u5747\u503c\u548c\u6807\u51c6\u5dee\/\" target=\"_blank\" rel=\"noopener\">Pandas\uff1a\u5982\u4f55\u8ba1\u7b97groupby\u4e2d\u5217\u7684\u5e73\u5747\u503c\u548c\u8303\u6570<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/pandas-groupby-as_index\/\" target=\"_blank\" rel=\"noopener\">Pandas\uff1a\u5982\u4f55\u5728 groupby \u4e2d\u4f7f\u7528 as_index<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u5728\u6267\u884c\u805a\u5408\u4e4b\u524d\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u4f7f\u7528 pandas \u4e2d\u7684groupby()\u51fd\u6570\u6309\u503c\u8303\u56f4\u5bf9\u5217\u8fdb\u884c\u5206\u7ec4\uff1a df. [&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-4316","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\u6309\u503c\u8303\u56f4\u5206\u7ec4- Statorials<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u4ecb\u7ecd\u5982\u4f55\u5728 pandas \u4e2d\u4f7f\u7528\u5e26\u6709\u4e00\u7cfb\u5217\u503c\u7684 groupby() \u51fd\u6570\uff0c\u5305\u62ec\u4e00\u4e2a\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\" href=\"https:\/\/statorials.org\/cn\/\u5927\u718a\u732b\u6309\u8303\u56f4\u5206\u7ec4\/\" 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