{"id":895,"date":"2023-07-28T09:46:04","date_gmt":"2023-07-28T09:46:04","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e6%94%be%e4%b8%8b%e7%86%8a%e7%8c%ab\/"},"modified":"2023-07-28T09:46:04","modified_gmt":"2023-07-28T09:46:04","slug":"%e6%94%be%e4%b8%8b%e7%86%8a%e7%8c%ab","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e6%94%be%e4%b8%8b%e7%86%8a%e7%8c%ab\/","title":{"rendered":"\u5982\u4f55\u5220\u9664 pandas \u4e2d\u5305\u542b nan \u503c\u7684\u884c"},"content":{"rendered":"

<\/p>\n

\n

\u901a\u5e38\uff0c\u60a8\u53ef\u80fd\u5e0c\u671b\u5220\u9664 pandas DataFrame \u4e2d\u5305\u542b NaN \u503c\u7684\u884c\u3002\u5e78\u8fd0\u7684\u662f\uff0c\u4f7f\u7528 pandas dropna()<\/a>\u51fd\u6570\u53ef\u4ee5\u8f7b\u677e\u505a\u5230\u8fd9\u4e00\u70b9\u3002<\/span><\/p>\n

\u672c\u6559\u7a0b\u5c55\u793a\u4e86\u5728\u4ee5\u4e0b pandas DataFrame \u4e0a\u4f7f\u7528\u6b64\u51fd\u6570\u7684\u51e0\u4e2a\u793a\u4f8b\uff1a<\/span><\/p>\n

 import<\/span> numpy as<\/span> np\nimport<\/span> scipy.stats as<\/span> stats\n\n#create DataFrame with some NaN values<\/span>\ndf = pd.DataFrame({'rating': [np.nan, 85, np.nan, 88, 94, 90, 76, 75, 87, 86],\n                   'points': [np.nan, 25, 14, 16, 27, 20, 12, 15, 14, 19],\n                   'assists': [5, 7, 7, np.nan, 5, 7, 6, 9, 9, 5],\n                   'rebounds': [11, 8, 10, 6, 6, 9, 6, 10, 10, 7]})\n\n#view DataFrame\n<\/span>df\n\n\n        rating points assists rebounds\n0 NaN NaN 5.0 11\n1 85.0 25.0 7.0 8\n2 NaN 14.0 7.0 10\n3 88.0 16.0 NaN 6\n4 94.0 27.0 5.0 6\n5 90.0 20.0 7.0 9\n6 76.0 12.0 6.0 6\n7 75.0 15.0 9.0 10\n8 87.0 14.0 9.0 10\n9 86.0 19.0 5.0 7\n<\/strong><\/pre>\n
\u793a\u4f8b 1\uff1a\u5220\u9664\u5177\u6709 NaN \u503c\u7684\u884c<\/strong><\/span><\/h3>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u5220\u9664\u6240\u6709\u5305\u542b NaN\u503c<\/em>\u7684\u884c\uff1a<\/span><\/p>\n
 df. dropna<\/span> ()\n\n\trating points assists rebounds\n1 85.0 25.0 7.0 8\n4 94.0 27.0 5.0 6\n5 90.0 20.0 7.0 9\n6 76.0 12.0 6.0 6\n7 75.0 15.0 9.0 10\n8 87.0 14.0 9.0 10\n9 86.0 19.0 5.0 7\n<\/strong><\/pre>\n
\u793a\u4f8b 2\uff1a\u5220\u9664\u5305\u542b\u6240\u6709 NaN \u503c\u7684\u884c<\/strong><\/span><\/h3>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u5220\u9664\u6bcf\u5217\u4e2d\u5305\u542b\u6240\u6709<\/em>NaN \u503c\u7684\u6240\u6709\u884c\uff1a<\/span><\/p>\n
 df. dropna<\/span> (how=' all<\/span> ') \n\n        rating points assists rebounds\n0 NaN NaN 5.0 11\n1 85.0 25.0 7.0 8\n2 NaN 14.0 7.0 10\n3 88.0 16.0 NaN 6\n4 94.0 27.0 5.0 6\n5 90.0 20.0 7.0 9\n6 76.0 12.0 6.0 6\n7 75.0 15.0 9.0 10\n8 87.0 14.0 9.0 10\n9 86.0 19.0 5.0 7\n<\/strong><\/pre>\n
\u5728\u8fd9\u4e2a\u7279\u5b9a\u7684 DataFrame \u4e2d\u4e0d\u5b58\u5728\u5168\u90e8\u4e3a NaN \u503c\u7684\u884c\uff0c\u56e0\u6b64\u6ca1\u6709\u4efb\u4f55\u884c\u88ab\u5220\u9664\u3002<\/span><\/p>\n
\u793a\u4f8b 3\uff1a\u5220\u9664\u4f4e\u4e8e\u7279\u5b9a\u9608\u503c\u7684\u884c<\/strong><\/span><\/h3>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u6765\u5220\u9664\u6240\u6709\u4e0d\u5177\u6709\u81f3\u5c11<\/em>\u4e00\u5b9a\u6570\u91cf\u7684\u975e NaN \u503c\u7684\u884c\uff1a<\/span><\/p>\n
 df. dropna<\/span> (thresh= 3<\/span> ) \n\n\trating points assists rebounds\n1 85.0 25.0 7.0 8\n2 NaN 14.0 7.0 10\n3 88.0 16.0 NaN 6\n4 94.0 27.0 5.0 6\n5 90.0 20.0 7.0 9\n6 76.0 12.0 6.0 6\n7 75.0 15.0 9.0 10\n8 87.0 14.0 9.0 10\n9 86.0 19.0 5.0 7\n<\/strong><\/pre>\n
\u539f\u59cb DataFrame \u7684\u7b2c\u4e00\u884c\u4e0d\u5305\u542b\u81f3\u5c11 3 \u4e2a\u975e NaN \u503c\uff0c\u56e0\u6b64\u8fd9\u662f\u552f\u4e00\u88ab\u5220\u9664\u7684\u884c\u3002<\/span><\/p>\n
\u793a\u4f8b4\uff1a\u5220\u9664\u7279\u5b9a\u5217\u4e2d\u5177\u6709Nan\u503c\u7684\u884c<\/strong><\/span><\/h3>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u6765\u5220\u9664\u7279\u5b9a\u5217\u4e2d\u5177\u6709 NaN \u503c\u7684\u6240\u6709\u884c\uff1a<\/span><\/p>\n
 df. dropna<\/span> (subset=[' assists<\/span> '])\n\n\trating points assists rebounds\n0 NaN NaN 5.0 11\n1 85.0 25.0 7.0 8\n2 NaN 14.0 7.0 10\n4 94.0 27.0 5.0 6\n5 90.0 20.0 7.0 9\n6 76.0 12.0 6.0 6\n7 75.0 15.0 9.0 10\n8 87.0 14.0 9.0 10\n9 86.0 19.0 5.0 7<\/strong><\/pre>\n
\u793a\u4f8b 5\uff1a\u5220\u9664\u5305\u542b NaN \u7684\u884c\u540e\u91cd\u7f6e\u7d22\u5f15<\/strong><\/span><\/h3>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u5728\u5220\u9664\u5177\u6709 NaN \u503c\u7684\u884c\u540e\u91cd\u7f6e DataFrame \u7684\u7d22\u5f15\uff1a<\/span><\/p>\n
 #drop all rows that have any NaN values<\/span>\ndf = df. dropna<\/span> ()\n\n#reset index of DataFrame<\/span>\ndf = df. reset_index<\/span> (drop=True)\n\n#view DataFrame<\/span>\ndf\n\n        rating points assists rebounds\n0 85.0 25.0 7.0 8\n1 94.0 27.0 5.0 6\n2 90.0 20.0 7.0 9\n3 76.0 12.0 6.0 6\n4 75.0 15.0 9.0 10\n5 87.0 14.0 9.0 10\n6 86.0 19.0 5.0 77<\/strong><\/pre>\n
\u60a8\u53ef\u4ee5\u5728\u6b64\u5904<\/a>\u627e\u5230 dropna() \u51fd\u6570\u7684\u5b8c\u6574\u6587\u6863\u3002<\/em><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"
\u901a\u5e38\uff0c\u60a8\u53ef\u80fd\u5e0c\u671b\u5220\u9664 pandas DataFrame \u4e2d\u5305\u542b NaN \u503c\u7684\u884c\u3002\u5e78\u8fd0\u7684\u662f\uff0c\u4f7f\u7528 pandas  […]<\/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-895","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\n\u5982\u4f55\u5220\u9664 Pandas \u4e2d\u5305\u542b NaN \u503c\u7684\u884c<\/title>\n<meta name=\"description\" content=\"\u5173\u4e8e\u5982\u4f55\u5220\u9664 pandas DataFrame \u4e2d\u5177\u6709 NaN \u503c\u7684\u884c\u7684\u7b80\u5355\u8bf4\u660e\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\/\u653e\u4e0b\u718a\u732b\/\" \/>\n<meta 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