{"id":2827,"date":"2023-07-20T12:48:52","date_gmt":"2023-07-20T12:48:52","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e7%86%8a%e7%8c%ab%e6%a1%88%e4%be%8b%e9%99%88%e8%bf%b0\/"},"modified":"2023-07-20T12:48:52","modified_gmt":"2023-07-20T12:48:52","slug":"%e7%86%8a%e7%8c%ab%e6%a1%88%e4%be%8b%e9%99%88%e8%bf%b0","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e7%86%8a%e7%8c%ab%e6%a1%88%e4%be%8b%e9%99%88%e8%bf%b0\/","title":{"rendered":"\u5982\u4f55\u7528 pandas \u7f16\u5199\u6848\u4f8b\u9648\u8ff0\uff08\u9644\u793a\u4f8b\uff09"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>case \u8bed\u53e5<\/strong>\u662f\u4e00\u79cd\u5faa\u73af\u6761\u4ef6\u5e76\u5728\u6ee1\u8db3\u7b2c\u4e00\u4e2a\u6761\u4ef6\u65f6\u8fd4\u56de\u503c\u7684\u8bed\u53e5\u7c7b\u578b\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5728 Pandas DataFrame \u4e2d\u5b9e\u73b0 case \u8bed\u53e5\u7684\u6700\u7b80\u5355\u65b9\u6cd5\u662f\u4f7f\u7528<strong>NumPywhere()<\/strong>\u51fd\u6570\uff0c\u8be5\u51fd\u6570\u4f7f\u7528\u4ee5\u4e0b\u57fa\u672c\u8bed\u6cd5\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df[' <span style=\"color: #ff0000;\">new_column<\/span> '] = np. <span style=\"color: #3366ff;\">where<\/span> (df[' <span style=\"color: #ff0000;\">col2<\/span> ']&lt;9, 'value1',\n                   n.p. <span style=\"color: #3366ff;\">where<\/span> (df[' <span style=\"color: #ff0000;\">col2<\/span> ']&lt;12, 'value2',\n                   n.p. <span style=\"color: #3366ff;\">where<\/span> (df[' <span style=\"color: #ff0000;\">col2<\/span> ']&lt;15, 'value3', 'value4')))\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u8fd9\u4e2a\u7279\u5b9a\u7684\u51fd\u6570\u67e5\u770b\u540d\u4e3a<strong>col2<\/strong>\u7684\u5217\u4e2d\u7684\u503c\u5e76\u8fd4\u56de\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\u201c <strong>value1<\/strong> \u201d \u5982\u679c col2 \u4e2d\u7684\u503c\u5c0f\u4e8e 9<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u201c <strong>value2<\/strong> \u201d \u5982\u679c col2 \u4e2d\u7684\u503c\u5c0f\u4e8e 12<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u201c <strong>value3<\/strong> \u201d \u5982\u679c col2 \u4e2d\u7684\u503c\u5c0f\u4e8e 15<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u201c <strong>value4<\/strong> \u201d \u5982\u679c\u524d\u9762\u7684\u6761\u4ef6\u90fd\u4e0d\u6210\u7acb<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u4e0b\u9762\u7684\u4f8b\u5b50\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5b9e\u9645\u4e2d\u4f7f\u7528\u8fd9\u4e2a\u529f\u80fd\u3002<\/span><\/p>\n<h3><strong><span style=\"color: #000000;\">\u793a\u4f8b\uff1aPandas \u4e2d\u7684 case \u8bed\u53e5<\/span><\/strong><\/h3>\n<p><span style=\"color: #000000;\">\u5047\u8bbe\u6211\u4eec\u6709\u4ee5\u4e0b pandas DataFrame\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<span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n\n<span style=\"color: #008080;\">#createDataFrame\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">player<\/span> ': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                   ' <span style=\"color: #ff0000;\">points<\/span> ': [6, 8, 9, 9, 12, 14, 15, 17, 19, 22]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span>df\n\n\tplayer points\n0 1 6\n1 2 8\n2 3 9\n3 4 9\n4 5 12\n5 6 14\n6 7 15\n7 8 17\n8 9 19\n9 10 22\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u7f16\u5199\u4e00\u4e2a case \u8bed\u53e5\uff0c\u8be5\u8bed\u53e5\u521b\u5efa\u4e00\u4e2a\u540d\u4e3a<strong>class<\/strong>\u7684\u65b0\u5217\uff0c\u5176\u503c\u7531<strong>Points<\/strong>\u5217\u4e2d\u7684\u503c\u786e\u5b9a\uff1a<\/span><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#add 'class' column using case-statement logic\n<\/span>df[' <span style=\"color: #ff0000;\">class<\/span> '] = np. <span style=\"color: #3366ff;\">where<\/span> (df[' <span style=\"color: #ff0000;\">points<\/span> ']&lt;9, 'Bad',\n              n.p. <span style=\"color: #3366ff;\">where<\/span> (df[' <span style=\"color: #ff0000;\">points<\/span> ']&lt;12, 'OK',\n              n.p. <span style=\"color: #3366ff;\">where<\/span> (df[' <span style=\"color: #ff0000;\">points<\/span> ']&lt;15, 'Good', 'Great')))\n\n<span style=\"color: #008080;\">#view updated DataFrame\n<\/span>df\n\n\tplayer points class\n0 1 6 Bad\n1 2 8 Bad\n2 3 9 OK\n3 4 9 OK\n4 5 12 Good\n5 6 14 Good\n6 7 15 Great\n7 8 17 Great\n8 9 19 Great\n9 10 22 Great\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">case \u8bed\u53e5\u67e5\u770b<strong>\u70b9<\/strong>\u5217\u4e2d\u7684\u503c\u5e76\u8fd4\u56de\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u5982\u679c\u5206\u6570\u680f\u4e2d\u7684\u503c\u5c0f\u4e8e 9\uff0c\u5219\u4e3a\u201c<strong>\u574f<\/strong>\u201d<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5982\u679c\u70b9\u5217\u4e2d\u7684\u503c\u5c0f\u4e8e 12\uff0c\u5219\u201c<strong>\u786e\u5b9a<\/strong>\u201d<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5982\u679c\u5206\u6570\u680f\u4e2d\u7684\u503c\u5c0f\u4e8e 15\uff0c\u5219\u4e3a\u201c<strong>\u597d<\/strong>\u201d<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5982\u679c\u524d\u9762\u7684\u6761\u4ef6\u90fd\u4e0d\u6210\u7acb\uff0c\u5219\u201c<strong>\u5f88\u597d<\/strong>\u201d<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\"><strong>\u6ce8\u610f<\/strong>\uff1a\u60a8\u53ef\u4ee5<a href=\"https:\/\/numpy.org\/doc\/stable\/reference\/generated\/numpy.where.html\" target=\"_blank\" rel=\"noopener\">\u5728\u6b64\u5904<\/a>\u627e\u5230<strong>NumPywhere()<\/strong>\u51fd\u6570\u7684\u5b8c\u6574\u6587\u6863\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 Pandas \u4e2d\u6267\u884c\u5176\u4ed6\u5e38\u89c1\u4efb\u52a1\uff1a<\/span><\/p>\n<p><a href=\"https:\/\/statorials.org\/cn\/\u57fa\u4e8e\u5217\u521b\u5efa-pandas\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u6839\u636e Pandas \u4e2d\u7684\u6761\u4ef6\u521b\u5efa\u65b0\u5217<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/numpy-\u6216\u591a\u4e2a\u6761\u4ef6\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728\u591a\u4e2a\u6761\u4ef6\u4e0b\u4f7f\u7528 NumPyWhere() \u51fd\u6570<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>case \u8bed\u53e5\u662f\u4e00\u79cd\u5faa\u73af\u6761\u4ef6\u5e76\u5728\u6ee1\u8db3\u7b2c\u4e00\u4e2a\u6761\u4ef6\u65f6\u8fd4\u56de\u503c\u7684\u8bed\u53e5\u7c7b\u578b\u3002 \u5728 Pandas DataFrame \u4e2d\u5b9e [&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-2827","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\u5728 Pandas \u64b0\u5199\u6848\u4f8b\u9648\u8ff0\uff08\u9644\u793a\u4f8b\uff09-Statorials<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u5728 pandas \u4e2d\u7f16\u5199 case 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