{"id":3267,"date":"2023-07-18T09:31:53","date_gmt":"2023-07-18T09:31:53","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e5%92%8c-pandas-%e4%b8%ad%e7%9a%84%e8%bf%90%e7%ae%97%e7%ac%a6\/"},"modified":"2023-07-18T09:31:53","modified_gmt":"2023-07-18T09:31:53","slug":"%e5%92%8c-pandas-%e4%b8%ad%e7%9a%84%e8%bf%90%e7%ae%97%e7%ac%a6","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e5%92%8c-pandas-%e4%b8%ad%e7%9a%84%e8%bf%90%e7%ae%97%e7%ac%a6\/","title":{"rendered":"\u5982\u4f55\u5728 pandas \u4e2d\u4f7f\u7528\u201cand\u201d\u8fd0\u7b97\u7b26\uff08\u9644\u793a\u4f8b\uff09"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u60a8\u53ef\u4ee5\u5728 pandas \u4e2d\u4f7f\u7528\u7b26\u53f7<strong>&amp;<\/strong>\u4f5c\u4e3a\u201cAND\u201d\u8fd0\u7b97\u7b26\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u57fa\u672c\u8bed\u6cd5\u6765\u8fc7\u6ee4 pandas DataFrame \u4e2d\u6ee1\u8db3\u6761\u4ef6 1<b>\u548c<\/b>2 \u7684\u884c\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df[(condition1) &amp; (condition2)]\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728\u4e0d\u540c\u573a\u666f\u4e2d\u4f7f\u7528\u201cAND\u201d\u8fd0\u7b97\u7b26\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u793a\u4f8b1\uff1a\u5728Pandas\u4e2d\u4f7f\u7528\u201cAND\u201d\u8fd0\u7b97\u7b26\u6839\u636e\u6570\u503c\u8fc7\u6ee4\u884c<\/strong><\/span><\/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\n<span style=\"color: #008080;\">#createDataFrame\n<span style=\"color: #000000;\">df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">team<\/span> ': ['A', 'A', 'B', 'B', 'B', 'B', 'C', 'C'],\n                   ' <span style=\"color: #ff0000;\">points<\/span> ': [25, 12, 15, 14, 19, 23, 25, 29],\n                   ' <span style=\"color: #ff0000;\">assists<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' <span style=\"color: #ff0000;\">rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n  team points assists rebounds\n0 to 25 5 11\n1 to 12 7 8\n2 B 15 7 10\n3 B 14 9 6\n4 B 19 12 6\n5 B 23 9 5\n6 C 25 9 9\n7 C 29 4 12<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u6765\u8fc7\u6ee4 DataFrame \u4e2d\u7684\u70b9\u5217\u4e2d\u7684\u503c\u5927\u4e8e 20<b>\u5e76\u4e14<\/b>\u8f85\u52a9\u5217\u4e2d\u7684\u503c\u7b49\u4e8e 9 \u7684\u884c\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#filter rows where points &gt; 20 and assists = 9<\/span>\n<span style=\"color: #000000;\">df[(df. <span style=\"color: #3366ff;\">points<\/span> &gt; 20) &amp; (df. <span style=\"color: #3366ff;\">assists<\/span> == 9)]\n\n        team points assists rebounds\n5 B 23 9 5\n6 C 25 9 9<\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u8fd4\u56de\u7684\u552f\u4e00\u884c\u662f\u90a3\u4e9b\u5f97\u5206\u503c\u5927\u4e8e 20<b>\u5e76\u4e14<\/b>\u52a9\u653b\u503c\u7b49\u4e8e 9 \u7684\u884c\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u793a\u4f8b2\uff1a\u5728Pandas\u4e2d\u4f7f\u7528\u201cAND\u201d\u8fd0\u7b97\u7b26\u6839\u636e\u5b57\u7b26\u4e32\u503c\u8fc7\u6ee4\u884c<\/strong><\/span><\/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\n<span style=\"color: #008080;\">#createDataFrame\n<span style=\"color: #000000;\">df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">team<\/span> ': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'],\n                   ' <span style=\"color: #ff0000;\">position<\/span> ': ['G', 'G', 'F', 'F', 'C', 'F', 'C', 'C'],\n                   ' <span style=\"color: #ff0000;\">conference<\/span> ': ['W', 'W', 'W', 'W', 'E', 'E', 'E', 'E'],\n                   ' <span style=\"color: #ff0000;\">points<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12]})\n\n#view DataFrame\n<span style=\"color: #008000;\">print<\/span> (df)\n\n  team position conference points\n0 AGW 11\n1 BGW 8\n2 CFW 10\n3DFW 6\n4 ECE 6\n5 FFE 5\n6 GCE 9\n7 HCE 12<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u6765\u8fc7\u6ee4 DataFrame \u4e2d\u4f4d\u7f6e\u5217\u4e2d\u7684\u503c\u7b49\u4e8e G<strong>\u4e14<\/strong>\u4f1a\u8bae\u5217\u4e2d\u7684\u503c\u7b49\u4e8e W \u7684\u884c\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#filter rows based on string values<\/span>\n<span style=\"color: #000000;\">df[(df.<\/span> <span style=\"color: #000000;\"><span style=\"color: #3366ff;\">position<\/span> <span style=\"color: #000000;\">== ' <span style=\"color: #ff0000;\">G<\/span><\/span> <span style=\"color: #000000;\">') &amp; (df.<\/span> <span style=\"color: #3366ff;\">conference<\/span> <span style=\"color: #000000;\">== '<\/span> <span style=\"color: #ff0000;\">W<\/span> <span style=\"color: #000000;\">')<\/span> <span style=\"color: #000000;\">]\n\n<\/span><span style=\"color: #000000;\">team position<\/span> <span style=\"color: #000000;\">conference<\/span> <span style=\"color: #000000;\">points\n0 A G<\/span> <span style=\"color: #000000;\">W<\/span> <span style=\"color: #000000;\">11\n1 B G<\/span> <span style=\"color: #000000;\">W<\/span> <span style=\"color: #000000;\">8<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u8fd4\u56de\u7684\u552f\u4e00\u884c\u662f\u4f4d\u7f6e\u5217\u7b49\u4e8e G<strong>\u4e14<\/strong>\u4f1a\u8bae\u5217\u7b49\u4e8e W \u7684\u884c\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\/\u6216-pandas-\u4e2d\u7684\u8fd0\u7b97\u7b26\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 Pandas \u4e2d\u4f7f\u7528\u201cOR\u201d\u8fd0\u7b97\u7b26<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u7edf\u8ba1\u5b66\u4ee5\u7b80\u5355\u76f4\u63a5\u7684\u65b9\u5f0f\u89e3\u91ca\u6982\u5ff5\uff0c\u6211\u4eec\u4f7f\u5b66\u4e60\u7edf\u8ba1\u53d8\u5f97\u66f4\u5bb9\u6613\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u6309\u65e5\u671f\u8fc7\u6ee4 Pandas DataFrame \u884c<\/a><br \/>\u5982\u4f55\u6309\u5217\u503c\u8fc7\u6ee4 Pandas DataFrame<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u60a8\u53ef\u4ee5\u5728 pandas \u4e2d\u4f7f\u7528\u7b26\u53f7&amp;\u4f5c\u4e3a\u201cAND\u201d\u8fd0\u7b97\u7b26\u3002 \u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u57fa\u672c\u8bed\u6cd5\u6765\u8fc7\u6ee4 pan [&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-3267","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- 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