{"id":2028,"date":"2023-07-24T00:47:19","date_gmt":"2023-07-24T00:47:19","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e7%86%8a%e7%8c%ab%e5%a1%ab%e5%85%85\/"},"modified":"2023-07-24T00:47:19","modified_gmt":"2023-07-24T00:47:19","slug":"%e7%86%8a%e7%8c%ab%e5%a1%ab%e5%85%85","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e7%86%8a%e7%8c%ab%e5%a1%ab%e5%85%85\/","title":{"rendered":"\u5982\u4f55\u4f7f\u7528 pandas fillna() \u66ff\u6362 nan \u503c"},"content":{"rendered":"

<\/p>\n

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\u60a8\u53ef\u4ee5\u4f7f\u7528fillna()<\/strong>\u51fd\u6570\u6765\u66ff\u6362 pandas DataFrame \u4e2d\u7684 NaN \u503c\u3002<\/span><\/p>\n

\u8be5\u51fd\u6570\u4f7f\u7528\u4ee5\u4e0b\u57fa\u672c\u8bed\u6cd5\uff1a<\/span><\/p>\n

 #replace NaN values in one column<\/span>\ndf[' col1<\/span> '] = df[' col1<\/span> ']. fillna<\/span> (0)\n\n#replace NaN values in multiple columns\n<\/span>df[[' col1<\/span> ', ' col2<\/span> ']] = df[[' col1<\/span> ', ' col2<\/span> ']]. fillna<\/span> (0) \n\n#replace NaN values in all columns<\/span>\ndf = df. fillna<\/span> (0)<\/strong><\/pre>\n
\u672c\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u5c06\u6b64\u51fd\u6570\u4e0e\u4ee5\u4e0b pandas DataFrame \u4e00\u8d77\u4f7f\u7528\uff1a<\/span><\/p>\n
 import<\/span> numpy as<\/span> np\nimport<\/span> pandas as<\/span> pd\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': [25, np.nan, 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<\/span>\ndf\n\n        rating points assists rebounds\n0 NaN 25.0 5.0 11\n1 85.0 NaN 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\u4f8b1\uff1a\u66ff\u6362\u5217\u4e2d\u7684NaN\u503c<\/strong><\/span><\/h3>\n
\u4e0b\u9762\u7684\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u5728\u201cnote\u201d\u5217\u4e2d\u7528\u96f6\u66ff\u6362 NaN \u503c\uff1a<\/span><\/p>\n
 #replace NaNs with zeros in 'rating' column<\/span>\ndf[' rating<\/span> '] = df[' rating<\/span> ']. fillna<\/span> (0)\n\n#view DataFrame<\/span>\ndf\n\n\trating points assists rebounds\n0 0.0 25.0 5.0 11\n1 85.0 NaN 7.0 8\n2 0.0 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\u4f8b2\uff1a\u66ff\u6362\u591a\u5217\u4e2d\u7684NaN\u503c<\/strong><\/h3>\n
\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u5728\u201cgrade\u201d\u548c\u201cpoints\u201d\u5217\u4e2d\u7528\u96f6\u66ff\u6362 NaN \u503c\uff1a<\/span><\/p>\n
 #replace NaNs with zeros in 'rating' and 'points' columns<\/span>\ndf[[' rating<\/span> ', ' points<\/span> ']] = df[[' rating<\/span> ', ' points<\/span> ']]. fillna<\/span> (0)\n\n#view DataFrame\n<\/span>df\n\n\trating points assists rebounds\n0 0.0 25.0 5.0 11\n1 85.0 0.0 7.0 8\n2 0.0 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\u4f8b3\uff1a\u66ff\u6362\u6240\u6709\u5217\u4e2d\u7684NaN\u503c<\/strong><\/h3>\n
\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u5c06\u6bcf\u5217\u4e2d\u7684 NaN \u503c\u66ff\u6362\u4e3a\u96f6\uff1a<\/span><\/p>\n
 #replace NaNs with zeros in all columns<\/span> \ndf = df. fillna<\/span> (0)\n\n#view DataFrame\n<\/span>df\n\n        rating points assists rebounds\n0 0.0 25.0 5.0 11\n1 85.0 0.0 7.0 8\n2 0.0 14.0 7.0 10\n3 88.0 16.0 0.0 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
\u60a8\u53ef\u4ee5\u5728\u6b64\u5904<\/a>\u627e\u5230fillna()<\/strong>\u51fd\u6570\u7684\u5b8c\u6574\u5728\u7ebf\u6587\u6863\u3002<\/span><\/p>\n
\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h3>\n
\u4ee5\u4e0b\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u5728 pandas \u4e2d\u6267\u884c\u5176\u4ed6\u5e38\u89c1\u64cd\u4f5c\uff1a<\/span><\/p>\n
\u5982\u4f55\u7edf\u8ba1pandas\u4e2d\u7684\u7f3a\u5931\u503c<\/a>
\u5982\u4f55\u5220\u9664 Pandas \u4e2d\u5305\u542b NaN \u503c\u7684\u884c<\/a>
\u5982\u4f55\u5220\u9664 Pandas \u4e2d\u5305\u542b\u7279\u5b9a\u503c\u7684\u884c<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u60a8\u53ef\u4ee5\u4f7f\u7528fillna()\u51fd\u6570\u6765\u66ff\u6362 pandas DataFrame \u4e2d\u7684 NaN \u503c\u3002 \u8be5\u51fd\u6570\u4f7f\u7528\u4ee5\u4e0b\u57fa\u672c […]<\/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-2028","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\n\u5982\u4f55\u4f7f\u7528 Pandas fillna() \u66ff\u6362 NaN \u503c \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u901a\u8fc7\u793a\u4f8b\u89e3\u91ca\u4e86\u5982\u4f55\u4f7f\u7528 pandas fillna() \u51fd\u6570\u66ff\u6362 DataFrame \u4e2d\u7684 NaN \u503c\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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