{"id":3559,"date":"2023-07-16T20:39:21","date_gmt":"2023-07-16T20:39:21","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e8%be%93%e5%85%a5%e5%8c%85%e5%90%ab%e6%97%a0%e9%99%90-nan-%e6%88%96%e5%80%bc%e5%af%b9%e4%ba%8e-dtype-%e6%9d%a5%e8%af%b4%e5%a4%aa%e5%a4%a7\/"},"modified":"2023-07-16T20:39:21","modified_gmt":"2023-07-16T20:39:21","slug":"%e8%be%93%e5%85%a5%e5%8c%85%e5%90%ab%e6%97%a0%e9%99%90-nan-%e6%88%96%e5%80%bc%e5%af%b9%e4%ba%8e-dtype-%e6%9d%a5%e8%af%b4%e5%a4%aa%e5%a4%a7","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e8%be%93%e5%85%a5%e5%8c%85%e5%90%ab%e6%97%a0%e9%99%90-nan-%e6%88%96%e5%80%bc%e5%af%b9%e4%ba%8e-dtype-%e6%9d%a5%e8%af%b4%e5%a4%aa%e5%a4%a7\/","title":{"rendered":"\u5982\u4f55\u4fee\u590d\uff1a\u8f93\u5165\u5305\u542b nan\u3001\u65e0\u7a77\u5927\u6216\u5bf9\u4e8e dtype \u6765\u8bf4\u592a\u5927\u7684\u503c\uff08\u201cfloat64\u201d\uff09"},"content":{"rendered":"

<\/p>\n

\n

\u4f7f\u7528 Python \u65f6\u53ef\u80fd\u9047\u5230\u7684\u4e00\u4e2a\u5e38\u89c1\u9519\u8bef\u662f\uff1a<\/span><\/p>\n

 ValueError: Input contains infinity or a value too large for dtype('float64').\n<\/strong><\/span><\/pre>\n
\u5f53\u60a8\u5c1d\u8bd5\u4f7f\u7528 scikit-learn \u6a21\u5757\u4e2d\u7684\u51fd\u6570\uff0c\u4f46\u7528\u4f5c\u8f93\u5165\u7684 DataFrame \u6216\u77e9\u9635\u5177\u6709 NaN \u503c\u6216\u65e0\u9650\u503c\u65f6\uff0c\u901a\u5e38\u4f1a\u53d1\u751f\u6b64\u9519\u8bef\u3002<\/span><\/p>\n
\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5b9e\u8df5\u4e2d\u89e3\u51b3\u6b64\u9519\u8bef\u3002<\/span><\/p>\n
\u5982\u4f55\u91cd\u73b0\u9519\u8bef<\/strong><\/span><\/h2>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4ee5\u4e0b pandas DataFrame\uff1a<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\nimport<\/span> numpy as<\/span> np\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' x1<\/span> ': [1, 2, 2, 4, 2, 1, 5, 4, 2, 4, 4],\n                   ' x2<\/span> ': [1, 3, 3, 5, 2, 2, 1, np.inf, 0, 3, 4],\n                   ' y<\/span> ': [np.nan, 78, 85, 88, 72, 69, 94, 94, 88, 92, 90]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n    x1 x2 y\n0 1 1.0 NaN\n1 2 3.0 78.0\n2 2 3.0 85.0\n3 4 5.0 88.0\n4 2 2.0 72.0\n5 1 2.0 69.0\n6 5 1.0 94.0\n7 4 lower 94.0\n8 2 0.0 88.0\n9 4 3.0 92.0\n10 4 4.0 90.0<\/span><\/span><\/strong><\/pre>\n
\u73b0\u5728\u5047\u8bbe\u6211\u4eec\u5c1d\u8bd5\u4f7f\u7528scikit-learn<\/a>\u51fd\u6570\u62df\u5408\u591a\u5143\u7ebf\u6027\u56de\u5f52\u6a21\u578b<\/a>\uff1a<\/span><\/p>\n
 from<\/span> sklearn. linear_model<\/span> import<\/span> LinearRegression\n\n#initiate linear regression model\n<\/span>model = LinearRegression()\n\n#define predictor and response variables\n<\/span>x, y = df[[' x1<\/span> ', ' x2<\/span> ']], df. y<\/span>\n\n#fit regression model\n<\/span>model. fit<\/span> (x,y)\n\n#print model intercept and coefficients\n<\/span>print<\/span> (model. intercept_<\/span> , model. coef_<\/span> )\n\n<\/span>ValueError: Input contains infinity or a value too large for dtype('float64').<\/span>\n<\/span><\/strong><\/pre>\n
\u6211\u4eec\u6536\u5230\u9519\u8bef\uff0c\u56e0\u4e3a\u6211\u4eec\u4f7f\u7528\u7684 DataFrame \u5177\u6709\u65e0\u9650\u503c\u548c NaN \u503c\u3002<\/span><\/p>\n
\u5982\u4f55\u4fee\u590d\u9519\u8bef<\/strong><\/span><\/h2>\n
\u89e3\u51b3\u6b64\u9519\u8bef\u7684\u65b9\u6cd5\u662f\u9996\u5148\u4ece DataFrame \u4e2d\u5220\u9664\u5305\u542b\u65e0\u9650\u6216 NaN \u503c\u7684\u6240\u6709\u884c\uff1a<\/span><\/p>\n
 #remove rows with any values that are not finite\n<\/span>df_new = df[np. isfinite<\/span> (df). all<\/span> ( 1<\/span> )]\n\n#view updated DataFrame\n<\/span>print<\/span> (df_new)\n\n    x1 x2 y\n1 2 3.0 78.0\n2 2 3.0 85.0\n3 4 5.0 88.0\n4 2 2.0 72.0\n5 1 2.0 69.0\n6 5 1.0 94.0\n8 2 0.0 88.0\n9 4 3.0 92.0\n10 4 4.0 90.0\n<\/span><\/span><\/strong><\/pre>\n
\u5177\u6709\u65e0\u9650\u6216 NaN \u503c\u7684\u4e24\u6761\u7ebf\u5df2\u88ab\u5220\u9664\u3002<\/span><\/p>\n
\u6211\u4eec\u73b0\u5728\u53ef\u4ee5\u7ee7\u7eed\u62df\u5408\u6211\u4eec\u7684\u7ebf\u6027\u56de\u5f52\u6a21\u578b\uff1a<\/span><\/p>\n
 from<\/span> sklearn. linear_model<\/span> import<\/span> LinearRegression\n\n#initiate linear regression model\n<\/span>model = LinearRegression()\n\n#define predictor and response variables\n<\/span>x, y = df_new[[' x1<\/span> ', ' x2<\/span> ']], df_new. y<\/span>\n\n#fit regression model\n<\/span>model. fit<\/span> (x,y)\n\n#print model intercept and coefficients\n<\/span>print<\/span> (model. intercept_<\/span> , model. coef_<\/span> )\n\n69.85144124168515 [5.72727273 -0.93791574]\n<\/span><\/span><\/strong><\/pre>\n
\u8bf7\u6ce8\u610f\uff0c\u8fd9\u6b21\u6211\u4eec\u6ca1\u6709\u6536\u5230\u4efb\u4f55\u9519\u8bef\uff0c\u56e0\u4e3a\u6211\u4eec\u9996\u5148\u4ece DataFrame \u4e2d\u5220\u9664\u4e86\u5177\u6709\u65e0\u9650\u6216 NaN \u503c\u7684\u884c\u3002<\/span><\/p>\n
\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h2>\n
\u4ee5\u4e0b\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u4fee\u590d Python \u4e2d\u7684\u5176\u4ed6\u5e38\u89c1\u9519\u8bef\uff1a<\/span><\/p>\n
 \u5982\u4f55\u5728 Python \u4e2d\u4fee\u590d\uff1a\u5bf9\u8c61\u201cnumpy.ndarray\u201d\u4e0d\u53ef\u8c03\u7528<\/a>
\u5982\u4f55\u4fee\u590d\uff1a\u7c7b\u578b\u9519\u8bef\uff1a\u5bf9\u8c61\u201cnumpy.float64\u201d\u4e0d\u53ef\u8c03\u7528<\/a>
\u5982\u4f55\u4fee\u590d\uff1a\u7c7b\u578b\u9519\u8bef\uff1a\u9884\u671f\u5b57\u7b26\u4e32\u6216\u5b57\u8282\u5bf9\u8c61<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u4f7f\u7528 Python \u65f6\u53ef\u80fd\u9047\u5230\u7684\u4e00\u4e2a\u5e38\u89c1\u9519\u8bef\u662f\uff1a ValueError: Input contains inf […]<\/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-3559","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\n\u5982\u4f55\u4fee\u590d\uff1a\u8f93\u5165\u5305\u542b NaN\u3001\u65e0\u7a77\u5927\u6216 dtype('float64') \u7684\u503c\u592a\u5927 - Statorials<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u4ecb\u7ecd\u5982\u4f55\u4fee\u590d Python \u4e2d\u7684\u4ee5\u4e0b\u9519\u8bef\uff1a\u8f93\u5165\u5305\u542b NaN\u3001\u65e0\u7a77\u5927\u6216\u5bf9\u4e8e dtype('float64') \u6765\u8bf4\u592a\u5927\u7684\u503c\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\/\u8f93\u5165\u5305\u542b\u65e0\u9650-nan-\u6216\u503c\u5bf9\u4e8e-dtype-\u6765\u8bf4\u592a\u5927\/\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u5982\u4f55\u4fee\u590d\uff1a\u8f93\u5165\u5305\u542b NaN\u3001\u65e0\u7a77\u5927\u6216 dtype('float64') \u7684\u503c\u592a\u5927 - 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