{"id":2263,"date":"2023-07-23T00:54:54","date_gmt":"2023-07-23T00:54:54","guid":{"rendered":"https:\/\/statorials.org\/cn\/numpy-float64-%e5%af%b9%e8%b1%a1%e6%97%a0%e6%b3%95%e8%a7%a3%e9%87%8a%e4%b8%ba%e6%95%b4%e6%95%b0\/"},"modified":"2023-07-23T00:54:54","modified_gmt":"2023-07-23T00:54:54","slug":"numpy-float64-%e5%af%b9%e8%b1%a1%e6%97%a0%e6%b3%95%e8%a7%a3%e9%87%8a%e4%b8%ba%e6%95%b4%e6%95%b0","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/numpy-float64-%e5%af%b9%e8%b1%a1%e6%97%a0%e6%b3%95%e8%a7%a3%e9%87%8a%e4%b8%ba%e6%95%b4%e6%95%b0\/","title":{"rendered":"\u5982\u4f55\u4fee\u590d\uff1a\u201cnumpy.float64\u201d\u5bf9\u8c61\u65e0\u6cd5\u89e3\u91ca\u4e3a int"},"content":{"rendered":"

<\/p>\n

\n

\u4f7f\u7528 NumPy \u65f6\u53ef\u80fd\u9047\u5230\u7684\u9519\u8bef\u662f\uff1a<\/span><\/p>\n

 TypeError<\/span> : 'numpy.float64' object cannot be interpreted as an integer\n<\/strong><\/pre>\n
\u5f53\u60a8\u5411\u9700\u8981\u6574\u6570\u7684\u51fd\u6570\u63d0\u4f9b\u6d6e\u70b9\u6570\u65f6\uff0c\u4f1a\u53d1\u751f\u6b64\u9519\u8bef\u3002<\/span><\/p>\n
\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5b9e\u8df5\u4e2d\u7ea0\u6b63\u6b64\u9519\u8bef\u3002<\/span><\/p>\n
\u5982\u4f55\u91cd\u73b0\u9519\u8bef<\/strong><\/span><\/h3>\n
\u5047\u8bbe\u6211\u4eec\u5c1d\u8bd5\u4f7f\u7528\u4ee5\u4e0b for \u5faa\u73af\u5c06\u4e0d\u540c\u7684\u6570\u5b57\u6253\u5370\u5230 NumPy \u6570\u7ec4\u4e2d\uff1a<\/span><\/p>\n
 import<\/span> numpy as<\/span> np\n\n#define array of values<\/span>\ndata = np. array<\/span> ([3.3, 4.2, 5.1, 7.7, 10.8, 11.4])\n\n#use for loop to print out range of values at each index\n<\/span>for<\/span> i in<\/span> range(len(data)):\n    print<\/span> (range(data[i]))\n\nTypeError<\/span> : 'numpy.float64' object cannot be interpreted as an integer\n<\/span><\/span><\/strong><\/pre>\n
\u6211\u4eec\u6536\u5230\u4e00\u4e2a\u9519\u8bef\uff0c\u56e0\u4e3arange()<\/strong>\u51fd\u6570\u9700\u8981\u4e00\u4e2a\u6574\u6570\uff0c\u4f46 NumPy \u6570\u7ec4\u4e2d\u7684\u503c\u662f\u6d6e\u70b9\u6570\u3002<\/span><\/p>\n
\u5982\u4f55\u4fee\u590d\u9519\u8bef<\/strong><\/span><\/h3>\n
\u6709\u4e24\u79cd\u65b9\u6cd5\u53ef\u4ee5\u5feb\u901f\u4fee\u590d\u6b64\u9519\u8bef\uff1a<\/span><\/p>\n
\u65b9\u6cd5\u4e00\uff1a\u4f7f\u7528int()\u51fd\u6570<\/strong><\/span><\/p>\n
\u4fee\u590d\u6b64\u9519\u8bef\u7684\u4e00\u79cd\u65b9\u6cd5\u662f\u7b80\u5355\u5730\u7528int()<\/strong>\u5305\u88c5\u8c03\u7528\uff0c\u5982\u4e0b\u6240\u793a\uff1a<\/span><\/p>\n
 import<\/span> numpy as<\/span> np\n\n#define array of values<\/span>\ndata = np. array<\/span> ([3.3, 4.2, 5.1, 7.7, 10.8, 11.4])\n\n#use for loop to print out range of values at each index\n<\/span>for<\/span> i in<\/span> range(len(data)):\n    print<\/span> (range(int(data[i])))\n\nrange(0, 3)\nrange(0, 4)\nrange(0, 5)\nrange(0, 7)\nrange(0, 10)\nrange(0, 11)\n<\/span><\/span><\/strong><\/pre>\n
\u4f7f\u7528int()<\/strong>\u51fd\u6570\uff0c\u6211\u4eec\u5c06 NumPy \u6570\u7ec4\u4e2d\u7684\u6bcf\u4e2a\u6d6e\u70b9\u503c\u8f6c\u6362\u4e3a\u6574\u6570\uff0c\u4ee5\u907f\u514d\u6211\u4eec\u4e4b\u524d\u9047\u5230\u7684TypeError<\/strong> \u3002<\/span><\/p>\n
\u65b9\u6cd52\uff1a\u4f7f\u7528.astype(int)\u51fd\u6570<\/strong><\/span><\/p>\n
\u4fee\u590d\u6b64\u9519\u8bef\u7684\u53e6\u4e00\u79cd\u65b9\u6cd5\u662f\u9996\u5148\u5c06 NumPy \u6570\u7ec4\u503c\u8f6c\u6362\u4e3a\u6574\u6570\uff1a<\/span><\/p>\n
 import<\/span> numpy as<\/span> np\n\n#define array of values\n<\/span>data = np. array<\/span> ([3.3, 4.2, 5.1, 7.7, 10.8, 11.4])\n\n#convert array of floats to array of integers<\/span>\ndata_int = data. astype<\/span> (int)\n\n#use for loop to print out range of values at each index\n<\/span>for<\/span> i in<\/span> range(len(data)):\n    print<\/span> (range(data[i]))\n\nrange(0, 3)\nrange(0, 4)\nrange(0, 5)\nrange(0, 7)\nrange(0, 10)\nrange(0, 11)<\/span><\/span><\/strong><\/pre>\n
\u901a\u8fc7\u4f7f\u7528\u6b64\u65b9\u6cd5\uff0c\u6211\u4eec\u518d\u6b21\u907f\u514d\u4e86TypeError<\/strong> \u3002<\/span><\/p>\n
\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h3>\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\u4fee\u590d Pandas \u4e2d\u7684 KeyError<\/a>
 \u5982\u4f55\u4fee\u590d\uff1aValueError\uff1a\u65e0\u6cd5\u5c06 float NaN \u8f6c\u6362\u4e3a int<\/a>
 \u5982\u4f55\u4fee\u590d\uff1aValueError\uff1a\u64cd\u4f5c\u6570\u65e0\u6cd5\u4e0e\u5f62\u72b6\u4e00\u8d77\u5e7f\u64ad<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u4f7f\u7528 NumPy \u65f6\u53ef\u80fd\u9047\u5230\u7684\u9519\u8bef\u662f\uff1a TypeError : ‘numpy.float64’ object  […]<\/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-2263","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\n\u5982\u4f55\u4fee\u590d\uff1a\u5bf9\u8c61\u201cnumpy.float64\u201d\u65e0\u6cd5\u89e3\u91ca\u4e3a\u6574\u6570 - Statorials<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u4ecb\u7ecd\u5982\u4f55\u4fee\u590d Python \u4e2d\u7684\u4ee5\u4e0b\u9519\u8bef\uff1a\u5bf9\u8c61\u201cnumpy.float64\u201d\u65e0\u6cd5\u89e3\u91ca\u4e3a\u6574\u6570\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\/numpy-float64-\u5bf9\u8c61\u65e0\u6cd5\u89e3\u91ca\u4e3a\u6574\u6570\/\" \/>\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\u5bf9\u8c61\u201cnumpy.float64\u201d\u65e0\u6cd5\u89e3\u91ca\u4e3a\u6574\u6570 - 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