{"id":4311,"date":"2023-07-12T02:48:52","date_gmt":"2023-07-12T02:48:52","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e5%b8%a6%e5%8f%98%e9%87%8f%e7%9a%84-pandas-%e6%9f%a5%e8%af%a2\/"},"modified":"2023-07-12T02:48:52","modified_gmt":"2023-07-12T02:48:52","slug":"%e5%b8%a6%e5%8f%98%e9%87%8f%e7%9a%84-pandas-%e6%9f%a5%e8%af%a2","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e5%b8%a6%e5%8f%98%e9%87%8f%e7%9a%84-pandas-%e6%9f%a5%e8%af%a2\/","title":{"rendered":"Pandas\uff1a\u5982\u4f55\u5728 query() \u51fd\u6570\u4e2d\u4f7f\u7528\u53d8\u91cf"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u5728 pandas \u4e2d\u4f7f\u7528<strong>query()<\/strong>\u51fd\u6570\u5e76\u5f15\u7528\u53d8\u91cf\u540d\u79f0\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df. <span style=\"color: #3366ff;\">query<\/span> (' <span style=\"color: #ff0000;\">team == @team_name<\/span> ')<\/strong><\/pre>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\">\u6b64\u7279\u5b9a\u67e5\u8be2\u5728 pandas DataFrame \u4e2d\u641c\u7d22\u884c\uff0c\u5176\u4e2d<strong>team<\/strong>\u5217\u7b49\u4e8e\u5b58\u50a8\u5728\u540d\u4e3a<strong>team_name<\/strong>\u7684\u53d8\u91cf\u4e2d\u7684\u503c\u3002<\/span><\/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 \u67e5\u8be2\u4e2d\u4f7f\u7528\u53d8\u91cf<\/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\u5404\u79cd\u7bee\u7403\u8fd0\u52a8\u5458\u7684\u4fe1\u606f\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">import<\/span> pandas <span style=\"color: #107d3f;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#createDataFrame<\/span>\ndf = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">team<\/span> ': ['A', 'A', 'A', 'B', 'B', 'B', 'B', 'C', 'C', 'C', 'C '],\n                   ' <span style=\"color: #ff0000;\">position<\/span> ':['G', 'G', 'F', 'G', 'F', 'F', 'F', 'G', 'G', 'F', 'F'],\n                   ' <span style=\"color: #ff0000;\">points<\/span> ': [22, 25, 24, 39, 34, 20, 18, 17, 20, 19, 22]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n   team position points\n0 AG 22\n1 AG 25\n2 AF 24\n3 BG 39\n4 BF 34\n5 BF 20\n6 BF 18\n7 GC 17\n8 GC 20\n9 CF 19\n10 CF 22\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u73b0\u5728\u5047\u8bbe\u6211\u4eec\u60f3\u8981\u67e5\u627e<strong>team<\/strong>\u5217\u4e2d\u7684\u503c\u7b49\u4e8e C \u7684\u884c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u521b\u5efa\u4e00\u4e2a\u540d\u4e3a<strong>team_name<\/strong>\u4e14\u7b49\u4e8e\u201cC\u201d\u7684\u53d8\u91cf\uff0c\u7136\u540e\u5728<strong>query()<\/strong>\u51fd\u6570\u4e2d\u5f15\u7528\u8be5\u53d8\u91cf\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#specify team name to search for\n<\/span>team_name = ' <span style=\"color: #ff0000;\">C<\/span> '\n\n<span style=\"color: #008080;\">#query for rows where team is equal to team_name\n<\/span>df. <span style=\"color: #3366ff;\">query<\/span> (' <span style=\"color: #ff0000;\">team == @team_name<\/span> ')\n\n        team position points\n7 C G 17\n8 C G 20\n9 C F 19\n10 C F 22<\/strong><\/pre>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\">\u8bf7\u6ce8\u610f\uff0c <strong>query()<\/strong>\u51fd\u6570\u8fd4\u56de<strong>team<\/strong>\u5217\u4e2d\u7684\u503c\u7b49\u4e8e C \u7684\u6240\u6709\u884c\u3002<\/span><\/span><\/p>\n<p><span style=\"color: #000000;\">\u53e6\u8bf7\u6ce8\u610f\uff0c\u5982\u679c\u9700\u8981\uff0c\u6211\u4eec\u53ef\u4ee5\u5728<strong>query()<\/strong>\u51fd\u6570\u4e2d\u5f15\u7528\u591a\u4e2a\u53d8\u91cf\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4f8b\u5982\uff0c\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u5982\u4f55\u4f7f\u7528<strong>query()<\/strong>\u51fd\u6570\u8fd4\u56de<strong>team<\/strong>\u5217\u4e2d\u7684\u503c\u7b49\u4e8e\u540d\u4e3a<strong>team_A<\/strong>\u7684\u53d8\u91cf<em>\u6216<\/em>\u540d\u4e3a<strong>team_C<\/strong>\u7684\u53d8\u91cf\u7684\u503c\u7684\u6240\u6709\u884c\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create two variables\n<span style=\"color: #000000;\">team_A = ' <span style=\"color: #ff0000;\">A<\/span> ' \n<\/span><\/span>team_C = ' <span style=\"color: #ff0000;\">C<\/span> '\n\n<span style=\"color: #008080;\">#query for rows where team is equal to either of the two variables\n<\/span>df. <span style=\"color: #3366ff;\">query<\/span> (' <span style=\"color: #ff0000;\">team == @team_A | team == @team_C<\/span> ')\n\n        team position points\n0 A G 22\n1 A G 25\n2 A F 24\n7 C G 17\n8 C G 20\n9 C F 19\n10 C F 22\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u8be5\u67e5\u8be2\u8fd4\u56de DataFrame \u4e2d\u7684\u6240\u6709\u884c\uff0c\u5176\u4e2d<strong>team<\/strong>\u7b49\u4e8e\u6211\u4eec\u6307\u5b9a\u7684\u4e24\u4e2a\u53d8\u91cf\u4e4b\u4e00\u4e2d\u5b58\u50a8\u7684\u503c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u6ce8\u610f<\/strong>\uff1a\u60a8\u53ef\u4ee5<a href=\"https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.query.html\" target=\"_blank\" rel=\"noopener\">\u5728\u6b64\u5904<\/a>\u627e\u5230 pandas <strong>query()<\/strong>\u51fd\u6570\u7684\u5b8c\u6574\u6587\u6863\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\u4efb\u52a1\uff1a<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/cn\/pandas-\u6309\u5b57\u7b26\u4e32\u957f\u5ea6\u8fc7\u6ee4\u884c\/\" target=\"_blank\" rel=\"noopener\">Pandas\uff1a\u5982\u4f55\u6839\u636e\u5b57\u7b26\u4e32\u957f\u5ea6\u8fc7\u6ee4\u884c<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/pandas-\u6839\u636e\u6761\u4ef6\u5220\u9664\u884c\/\" target=\"_blank\" rel=\"noopener\">Pandas\uff1a\u5982\u4f55\u6839\u636e\u6761\u4ef6\u5220\u9664\u884c<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u7f3a\u5e2d\u7684\u718a\u732b\/\" target=\"_blank\" rel=\"noopener\">Pandas\uff1a\u5982\u4f55\u4f7f\u7528\u201cNO IN\u201d\u8fc7\u6ee4\u5668<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u5728 pandas \u4e2d\u4f7f\u7528query()\u51fd\u6570\u5e76\u5f15\u7528\u53d8\u91cf\u540d\u79f0\uff1a df. query (&#8216; tea [&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-4311","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\u5728 query() \u51fd\u6570\u4e2d\u4f7f\u7528\u53d8\u91cf - Statorials<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u901a\u8fc7\u4e00\u4e2a\u793a\u4f8b\u89e3\u91ca\u4e86\u5982\u4f55\u5728 pandas query() 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