{"id":254,"date":"2025-05-07T19:38:45","date_gmt":"2025-05-07T11:38:45","guid":{"rendered":"https:\/\/www.ddosidc.com\/wp\/?p=254"},"modified":"2025-05-07T19:38:45","modified_gmt":"2025-05-07T11:38:45","slug":"%e5%a6%82%e4%bd%95%e5%b0%86numpy%e7%9f%a9%e9%98%b5%e8%be%93%e5%87%ba%e4%b8%bacsv%e6%96%87%e4%bb%b6%e4%bb%a5%e4%be%bf%e4%ba%8e%e6%95%b0%e6%8d%ae%e7%ae%a1%e7%90%86%e5%92%8c%e5%88%86%e6%9e%90","status":"publish","type":"post","link":"https:\/\/www.ddosidc.com\/wp\/254.html","title":{"rendered":"\u5982\u4f55\u5c06numpy\u77e9\u9635\u8f93\u51fa\u4e3acsv\u6587\u4ef6\u4ee5\u4fbf\u4e8e\u6570\u636e\u7ba1\u7406\u548c\u5206\u6790"},"content":{"rendered":"

\"\u5982\u4f55\u5c06numpy\u77e9\u9635\u8f93\u51fa\u4e3acsv\u6587\u4ef6\u4ee5\u4fbf\u4e8e\u6570\u636e\u7ba1\u7406\u548c\u5206\u6790\"<\/p>\n

1. numpy\u77e9\u9635\u8f93\u51fa\u4e3acsv\u7684\u5185\u5bb9\u548c\u6570\u91cf<\/h3>\n<\/p>\n

numpy\u5e93\u662fPython\u4e2d\u4e00\u4e2a\u975e\u5e38\u5f3a\u5927\u7684\u6570\u5b66\u5e93\uff0c\u5e7f\u6cdb\u7528\u4e8e\u6570\u503c\u8ba1\u7b97\u548c\u6570\u636e\u5904\u7406\u3002\u5f53\u4f60\u6709\u4e00\u4e2anumpy\u77e9\u9635\u5e76\u5e0c\u671b\u5c06\u5176\u8f93\u51fa\u4e3acsv\u6587\u4ef6\u65f6\uff0c\u5b9e\u9645\u4e0a\u4f60\u53ef\u4ee5\u9009\u62e9\u8f93\u51fa\u7684\u77e9\u9635\u5185\u5bb9\u548c\u6570\u91cf\u3002\u901a\u5e38\uff0ccsv\uff08Comma-Separated Values\uff09\u683c\u5f0f\u4e3b\u8981\u7528\u4e8e\u5b58\u50a8\u8868\u683c\u6570\u636e\uff0c\u5982\u7535\u5b50\u8868\u683c\u6216\u6570\u636e\u5e93\u3002\u901a\u8fc7numpy\u7684`numpy.savetxt`\u51fd\u6570\uff0c\u4f60\u53ef\u4ee5\u5f88\u65b9\u4fbf\u5730\u5c06\u4e8c\u7ef4\u6570\u7ec4\u6216\u77e9\u9635\u4fdd\u5b58\u4e3acsv\u683c\u5f0f\u3002<\/p>\n

\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u4f60\u53ef\u4ee5\u8f93\u51fa\u6574\u4e2a\u77e9\u9635\uff0c\u6216\u8005\u53ea\u9009\u62e9\u7279\u5b9a\u7684\u884c\u548c\u5217\u3002\u8f93\u51fa\u7684\u77e9\u9635\u5185\u5bb9\u53ef\u4ee5\u5305\u62ec\u6d6e\u70b9\u6570\u3001\u6574\u6570\u548c\u5176\u4ed6\u6570\u5b57\u3002\u503c\u5f97\u4e00\u63d0\u7684\u662f\uff0c\u5728\u8f93\u51fa\u8fc7\u7a0b\u4e2d\u53ef\u4ee5\u8bbe\u7f6e\u5206\u9694\u7b26\uff0c\u6bd4\u5982\u6211\u4eec\u901a\u5e38\u7528\u9017\u53f7\u6216\u5236\u8868\u7b26\u3002<\/p>\n

\u4f60\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u5c06numpy\u77e9\u9635\u4fdd\u5b58\u4e3acsv\u6587\u4ef6\uff1a<\/p>\n

<\/p>\r\n
import numpy as np<\/p>\r\n\r\n
# \u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u77e9\u9635<\/p>\r\n
matrix = np.array([[1.1, 2.2, 3.3], [4.4, 5.5, 6.6]])<\/p>\r\n\r\n
# \u8f93\u51fa\u4e3acsv<\/p>\r\n
np.savetxt('output.csv', matrix, delimiter=',')<\/p>\r\n
<\/code><\/pre>\n<\/p>\n
\n
2. numpy.matrix\u4e0enumpy.ndarray\u7684\u533a\u522b<\/h3>\n<\/p>\n
\u5728\u8ba8\u8bbanumpy\u77e9\u9635\u8f93\u51fa\u4e3acsv\u65f6\uff0c\u9996\u5148\u8981\u660e\u767dnumpy\u6709\u4e24\u79cd\u4e3b\u8981\u7684\u6570\u636e\u7ed3\u6784\uff1anumpy.matrix\u548cnumpy.ndarray\u3002\u867d\u7136\u8fd9\u4e24\u8005\u5728\u8868\u9762\u4e0a\u770b\u8d77\u6765\u5f88\u76f8\u4f3c\uff0c\u4f46\u5b83\u4eec\u7684\u884c\u4e3a\u548c\u7528\u6cd5\u6709\u4e9b\u4e0d\u540c\u3002<\/p>\n
numpy.matrix\u662f\u4e00\u4e2a\u4e13\u95e8\u7528\u4e8e\u7ebf\u6027\u4ee3\u6570\u7684\u4e8c\u7ef4\u77e9\u9635\u7c7b\u578b\uff0c\u652f\u6301\u4e00\u4e9b\u9644\u52a0\u7684\u670d\u52a1\uff0c\u6bd4\u5982\u77e9\u9635\u4e58\u6cd5\u3002\u800cnumpy.ndarray\u662f\u4e00\u4e2a\u66f4\u901a\u7528\u7684\u591a\u7ef4\u6570\u7ec4\u7c7b\u578b\uff0c\u652f\u6301\u66f4\u5e7f\u6cdb\u7684\u64cd\u4f5c\u3002\u5982\u679c\u4f60\u4e0d\u9700\u8981\u4f7f\u7528\u7279\u522b\u7684\u7ebf\u6027\u4ee3\u6570\u529f\u80fd\uff0c\u901a\u5e38\u63a8\u8350\u4f7f\u7528ndarray\uff0c\u56e0\u4e3a\u5b83\u7684\u64cd\u4f5c\u66f4\u7075\u6d3b\uff0c\u6027\u80fd\u4e5f\u66f4\u597d\u3002<\/p>\n
\u56e0\u6b64\uff0c\u5728\u5904\u7406\u8981\u4fdd\u5b58\u4e3acsv\u7684\u6570\u636e\u65f6\uff0c\u4f7f\u7528numpy.ndarray\u65e0\u7591\u66f4\u52a0\u6070\u5f53\u3002\u53ef\u4ee5\u4f7f\u7528`np.array()`\u5c06\u6570\u636e\u8f6c\u6362\u4e3andarray\u683c\u5f0f\u3002\u5982\u679c\u4ecd\u9700\u4f7f\u7528matrix\u7c7b\u578b\uff0c\u5219\u53ef\u4ee5\u4f7f\u7528`np.matrix()`\u3002<\/p>\n
\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n
<\/p>\r\n
import numpy as np<\/p>\r\n\r\n
# \u4f7f\u7528ndarray<\/p>\r\n
data = np.array([[1, 2, 3], [4, 5, 6]])<\/p>\r\n\r\n
# \u4f7f\u7528matrix<\/p>\r\n
data_matrix = np.matrix([[1, 2, 3], [4, 5, 6]])<\/p>\r\n\r\n
# \u8f93\u51fandarray\u4e3acsv<\/p>\r\n
np.savetxt('data.csv', data, delimiter=',')<\/p>\r\n
# \u8f93\u51famatrix\u4e3acsv<\/p>\r\n
np.savetxt('data_matrix.csv', data_matrix, delimiter=',')<\/p>\r\n
<\/code><\/pre>\n<\/p>\n
\n
3. \u5982\u4f55\u5904\u7406\u7279\u5b9a\u683c\u5f0f\u548c\u7cbe\u5ea6<\/h3>\n<\/p>\n
\u5728\u5c06numpy\u77e9\u9635\u8f93\u51fa\u4e3acsv\u65f6\uff0c\u6709\u65f6\u6211\u4eec\u5bf9\u6570\u636e\u7684\u683c\u5f0f\u548c\u7cbe\u5ea6\u6709\u7279\u5b9a\u8981\u6c42\u3002\u4f8b\u5982\uff0c\u91d1\u878d\u6570\u636e\u6216\u5b9e\u9a8c\u6570\u636e\u901a\u5e38\u9700\u8981\u7279\u5b9a\u7684\u6d6e\u70b9\u6570\u7cbe\u5ea6\u3002\u5728\u6b64\u60c5\u51b5\u4e0b\uff0cnumpy.savetxt\u51fd\u6570\u540c\u6837\u53ef\u4ee5\u505a\u5230\u8fd9\u4e00\u70b9\u3002<\/p>\n
\u4f7f\u7528`fmt`\u53c2\u6570\u53ef\u4ee5\u5b9a\u4e49\u8f93\u51fa\u683c\u5f0f\uff0c\u6bd4\u5982\u8981\u9650\u5236\u5c0f\u6570\u70b9\u540e\u7684\u4f4d\u6570\u3002\u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff0cnumpy\u4f1a\u6309\u7167\u6d6e\u70b9\u6570\u7684\u9ed8\u8ba4\u683c\u5f0f\u8f93\u51fa\u6570\u636e\uff0c\u4f46\u4f60\u53ef\u4ee5\u81ea\u5b9a\u4e49\u3002\u4f8b\u5982\uff0c\u5982\u679c\u53ea\u60f3\u4fdd\u7559\u4e24\u4f4d\u5c0f\u6570\uff0c\u53ef\u4ee5\u8fd9\u6837\u505a\uff1a<\/p>\n
<\/p>\r\n
np.savetxt('output_formatted.csv', matrix, delimiter=',', fmt='%.2f')<\/p>\r\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u6837\uff0c\u6240\u6709\u8f93\u51fa\u7684\u6d6e\u70b9\u6570\u5c06\u4ec5\u4fdd\u7559\u4e24\u4f4d\u5c0f\u6570\u3002\u8fd9\u5bf9\u4e8e\u4fdd\u6301\u8f93\u51fa\u6570\u636e\u7684\u6574\u6d01\u548c\u53ef\u8bfb\u6027\u662f\u76f8\u5f53\u6709\u7528\u7684\u3002<\/p>\n
\u6b64\u65f6\uff0c\u8f93\u51fa\u7684csv\u6587\u4ef6\u770b\u8d77\u6765\u4f1a\u66f4\u52a0\u6f02\u4eae\uff0c\u5c24\u5176\u662f\u5728\u9700\u8981\u5c06\u6587\u4ef6\u5206\u4eab\u7ed9\u5176\u4ed6\u4eba\u7684\u60c5\u51b5\u4e0b\u3002\u6b64\u5916\uff0c\u5bf9\u683c\u5f0f\u7684\u63a7\u5236\u8fd8\u53ef\u4ee5\u65b9\u4fbf\u4f60\u540e\u7eed\u7684\u6570\u636e\u5904\u7406\uff0c\u786e\u4fdd\u6570\u636e\u7684\u4e00\u81f4\u6027\u548c\u7cbe\u786e\u6027\u3002<\/p>\n
\u901a\u8fc7\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\uff0c\u4f60\u53ef\u4ee5\u8f93\u51fa\u4e3a\u6307\u5b9a\u683c\u5f0f\uff0c\u5e76\u8f7b\u677e\u8c03\u6574\u6570\u636e\u7684\u53ef\u8bfb\u6027\uff1a<\/p>\n
<\/p>\r\n
# \u6307\u5b9a\u683c\u5f0f<\/p>\r\n
np.savetxt('formatted_output.csv', matrix, delimiter=',', fmt='%.4f')<\/p>\r\n
<\/code><\/pre>\n<\/p>\n
\n
4. \u5982\u4f55\u8bfb\u53d6csv\u6587\u4ef6\u4e2d\u7684\u6570\u636e\uff1f<\/b><\/h3>\n<\/p>\n
\u5c06numpy\u77e9\u9635\u4fdd\u5b58\u4e3acsv\u540e\uff0c\u6709\u65f6\u6211\u4eec\u9700\u8981\u5c06\u8be5\u6587\u4ef6\u518d\u6b21\u8bfb\u53d6\u3002numpy\u63d0\u4f9b\u4e86`numpy.loadtxt`\u548c`numpy.genfromtxt`\u4e24\u4e2a\u51fd\u6570\uff0c\u5e38\u7528\u6765\u8bfb\u53d6csv\u683c\u5f0f\u7684\u6570\u636e\u3002loadtxt\u9002\u5408\u4e8e\u7b80\u5355\u7684\u6570\u636e\u6587\u4ef6\uff0c\u800cgenfromtxt\u5219\u66f4\u4e3a\u7075\u6d3b\uff0c\u53ef\u4ee5\u5904\u7406\u7f3a\u5931\u503c\u548c\u5404\u79cd\u7c7b\u578b\u7684\u6570\u636e\u3002<\/p>\n
\u5982\u679ccsv\u6587\u4ef6\u5b58\u50a8\u4e86\u6d6e\u70b9\u6570\u7c7b\u578b\u7684\u6570\u636e\uff0c\u8bfb\u53d6\u6b65\u9aa4\u975e\u5e38\u7b80\u5355\u3002\u4f60\u53ea\u9700\u6307\u5b9a\u6587\u4ef6\u8def\u5f84\uff1a<\/p>\n
<\/p>\r\n
data_loaded = np.loadtxt('output.csv', delimiter=',')<\/p>\r\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u6837\uff0c\u6570\u636e\u5c06\u4f1a\u88ab\u52a0\u8f7d\u4e3a\u4e00\u4e2anumpy ndarray\u3002\u4f60\u8fd8\u53ef\u4ee5\u8fdb\u4e00\u6b65\u5bf9\u52a0\u8f7d\u7684\u6570\u636e\u6267\u884c\u5404\u79cd\u64cd\u4f5c\uff0c\u6bd4\u5982\u7edf\u8ba1\u5206\u6790\u6216\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
\n
5. csv\u6587\u4ef6\u4e2d\u7684\u6570\u636e\u5982\u4f55\u8fdb\u884c\u6570\u636e\u5904\u7406\uff1f<\/b><\/h3>\n<\/p>\n
\u8bf4\u5230\u6570\u636e\u5904\u7406\uff0c\u8bfb\u53d6csv\u6587\u4ef6\u540e\uff0c\u4f60\u53ef\u4ee5\u5229\u7528numpy\u7684\u4e00\u7cfb\u5217\u51fd\u6570\u8fdb\u884c\u6570\u636e\u5206\u6790\u3002\u4f8b\u5982\uff0c\u5982\u679c\u4f60\u5904\u7406\u7684\u662f\u5b9e\u9a8c\u6570\u636e\uff0c\u53ef\u4ee5\u8ba1\u7b97\u5747\u503c\u3001\u65b9\u5dee\u7b49\u7edf\u8ba1\u91cf\u3002\u8fd9\u6837\u53ef\u4ee5\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u3002<\/p>\n
\u4e00\u65e6\u6570\u636e\u88ab\u52a0\u8f7d\u4e3anumpy\u6570\u7ec4\uff0c\u4f8b\u5982\u8fdb\u884c\u77e9\u9635\u76f8\u4e58\u3001\u57fa\u7840\u7684\u6570\u5b66\u8fd0\u7b97\u6216\u6761\u4ef6\u7b5b\u9009\u90fd\u5f88\u65b9\u4fbf\u3002\u5047\u8bbe\u4f60\u60f3\u901a\u8fc7\u8ba1\u7b97\u5217\u7684\u5747\u503c\u6765\u83b7\u5f97\u6bcf\u5217\u7684\u57fa\u672c\u7edf\u8ba1\u4fe1\u606f\uff0c\u53ef\u4ee5\u8fd9\u6837\u505a\uff1a<\/p>\n
<\/p>\r\n
column_means = np.mean(data_loaded, axis=0)<\/p>\r\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u5c06\u8fd4\u56de\u4e00\u4e2anumpy\u6570\u7ec4\uff0c\u5305\u542b\u6bcf\u4e00\u5217\u7684\u5747\u503c\u3002\u5229\u7528\u8fd9\u6837\u7684\u529f\u80fd\uff0c\u80fd\u591f\u4f7f\u6570\u636e\u5904\u7406\u66f4\u9ad8\u6548\u4e14\u4fbf\u6377\u3002<\/p>\n<\/p>\n
\n
6. \u4e3a\u4ec0\u4e48\u4f7f\u7528numpy\u8f93\u51fa\u4e3acsv\u66f4\u65b9\u4fbf\uff1f<\/b><\/h3>\n<\/p>\n
\u4f7f\u7528numpy\u5c06\u6570\u636e\u8f93\u51fa\u4e3acsv\u6587\u4ef6\u7684\u539f\u56e0\u6709\u5f88\u591a\u3002\u9996\u5148\uff0ccsv\u683c\u5f0f\u5177\u6709\u901a\u7528\u6027\uff0c\u51e0\u4e4e\u6240\u6709\u7684\u6570\u636e\u5904\u7406\u8f6f\u4ef6\u548c\u7f16\u7a0b\u8bed\u8a00\u90fd\u53ef\u4ee5\u8bfb\u53d6\u5b83\u3002\u5176\u6b21\uff0c\u4f7f\u7528numpy\u5e93\u53ef\u4ee5\u65b9\u4fbf\u5730\u5904\u7406\u5927\u91cf\u6570\u636e\uff0c\u63d0\u4f9b\u4e86\u9ad8\u6027\u80fd\u548c\u9ad8\u7075\u6d3b\u6027\u3002<\/p>\n
\u53e6\u5916\uff0cnumpy\u5bf9\u6587\u4ef6\u64cd\u4f5c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684API\uff0c\u4f7f\u5f97\u6570\u636e\u4fdd\u5b58\u548c\u52a0\u8f7d\u7684\u8fc7\u7a0b\u975e\u5e38\u7b80\u5355\u3002\u5728\u5f88\u591a\u60c5\u51b5\u4e0b\uff0c\u76f8\u6bd4\u76f4\u63a5\u64cd\u4f5c\u6587\u672c\u6587\u4ef6\u6216\u5176\u4ed6\u683c\u5f0f\uff0c\u4f7f\u7528numpy\u5e93\u8282\u7701\u4e86\u65f6\u95f4\u548c\u7cbe\u529b\u3002\u7279\u522b\u662f\u5bf9\u4e8e\u9700\u8981\u9891\u7e41\u5904\u7406\u7684\u79d1\u5b66\u8ba1\u7b97\u548c\u6570\u636e\u5206\u6790\u4efb\u52a1\uff0cnumpy\u7684\u6570\u636e\u8f93\u51fa\u7279\u6027\u4f7f\u6d41\u7a0b\u66f4\u52a0\u9ad8\u6548\u3002<\/p>\n
\u56e0\u6b64\uff0c\u5982\u4f55\u5229\u7528numpy\u5c06\u77e9\u9635\u8f93\u51fa\u4e3acsv\uff0c\u662f\u6570\u636e\u5206\u6790\u5de5\u4f5c\u6d41\u7a0b\u4e2d\u4e00\u4e2a\u81f3\u5173\u91cd\u8981\u7684\u73af\u8282\u3002\u901a\u8fc7\u8fd9\u4e9b\u6b65\u9aa4\uff0c\u4f60\u53ef\u4ee5\u8f7b\u677e\u7ba1\u7406\u3001\u64cd\u4f5c\u548c\u5206\u6790\u6570\u636e\uff0c\u4ece\u800c\u63d0\u5347\u6574\u4f53\u5de5\u4f5c\u6548\u7387\uff0c\u5f97\u5230\u66f4\u6709\u4ef7\u503c\u7684\u89c1\u89e3\u3002<\/p><\/p>\n","protected":false},"excerpt":{"rendered":"
1. numpy\u77e9\u9635\u8f93\u51fa\u4e3acsv\u7684\u5185\u5bb9\u548c\u6570\u91cf numpy\u5e93\u662fPython\u4e2d\u4e00\u4e2a\u975e\u5e38\u5f3a\u5927\u7684\u6570\u5b66\u5e93\uff0c\u5e7f\u6cdb\u7528\u4e8e\u6570\u503c\u8ba1\u7b97\u548c\u6570\u636e\u5904\u7406\u3002\u5f53\u4f60\u6709\u4e00\u4e2anumpy\u77e9\u9635\u5e76\u5e0c\u671b\u5c06\u5176\u8f93\u51fa\u4e3acsv\u6587\u4ef6\u65f6\uff0c\u5b9e\u9645\u4e0a\u4f60\u53ef\u4ee5\u9009\u62e9\u8f93\u51fa\u7684\u77e9\u9635\u5185\u5bb9\u548c\u6570\u91cf\u3002\u901a\u5e38\uff0ccsv\uff08Comma…<\/p>\n","protected":false},"author":1,"featured_media":255,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"topic":[],"class_list":["post-254","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-baike"],"_links":{"self":[{"href":"https:\/\/www.ddosidc.com\/wp\/wp-json\/wp\/v2\/posts\/254","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.ddosidc.com\/wp\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.ddosidc.com\/wp\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.ddosidc.com\/wp\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.ddosidc.com\/wp\/wp-json\/wp\/v2\/comments?post=254"}],"version-history":[{"count":1,"href":"https:\/\/www.ddosidc.com\/wp\/wp-json\/wp\/v2\/posts\/254\/revisions"}],"predecessor-version":[{"id":257,"href":"https:\/\/www.ddosidc.com\/wp\/wp-json\/wp\/v2\/posts\/254\/revisions\/257"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.ddosidc.com\/wp\/wp-json\/wp\/v2\/media\/255"}],"wp:attachment":[{"href":"https:\/\/www.ddosidc.com\/wp\/wp-json\/wp\/v2\/media?parent=254"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.ddosidc.com\/wp\/wp-json\/wp\/v2\/categories?post=254"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.ddosidc.com\/wp\/wp-json\/wp\/v2\/tags?post=254"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/www.ddosidc.com\/wp\/wp-json\/wp\/v2\/topic?post=254"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}