{"id":258093,"date":"2024-05-15T12:29:14","date_gmt":"2024-05-15T04:29:14","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/258093.html"},"modified":"2024-05-15T12:29:19","modified_gmt":"2024-05-15T04:29:19","slug":"%e4%b8%a4%e7%bb%84%e6%95%b0%e6%8d%ae%e7%9a%84%e5%b9%b3%e5%9d%87%e5%80%bc%e5%92%8c%e6%a0%87%e5%87%86%e5%b7%ae%e7%94%a8%e4%bb%80%e4%b9%88%e4%bb%a3%e7%a0%81","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/258093.html","title":{"rendered":"\u4e24\u7ec4\u6570\u636e\u7684\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\u7528\u4ec0\u4e48\u4ee3\u7801"},"content":{"rendered":"

\"\u4e24\u7ec4\u6570\u636e\u7684\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\u7528\u4ec0\u4e48\u4ee3\u7801\"<\/p>\n

\u5f53\u9700\u8981\u6c42\u89e3\u4e24\u7ec4\u6570\u636e\u7684\u5e73\u5747\u503c\u548c\u6807\u51cf\u5dee\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u7f16\u7a0b\u8bed\u8a00\u548c\u7edf\u8ba1\u8f6f\u4ef6\u6765\u5b9e\u73b0\uff0c\u4f8b\u5982Python\u7684NumPy\u5e93\u3001R\u8bed\u8a00\u6216Excel\u3002\u4e3a\u4e86\u56de\u7b54\u8fd9\u4e2a\u95ee\u9898\uff0c\u4e0b\u9762\u5c06\u91cd\u70b9\u4ecb\u7ecd\u5728Python\u73af\u5883\u4e0b\u4f7f\u7528NumPy\u5e93\u6765\u7f16\u5199\u4ee3\u7801\uff0c\u56e0\u4e3aPython\u7684\u4f7f\u7528\u5e7f\u6cdb\u4e14\u652f\u6301\u79d1\u5b66\u8ba1\u7b97\u3002<\/p>\n<\/p>\n

\u5728Python\u4e2d\u8ba1\u7b97\u4e24\u7ec4\u6570\u636e\u7684\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\uff0c\u4e3b\u8981\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u63d0\u4f9b\u7684mean\u548cstd\u51fd\u6570\u3002\u8fd9\u4e9b\u51fd\u6570\u975e\u5e38\u76f4\u63a5\uff1a\u8c03\u7528numpy.mean(data)\u6765\u8ba1\u7b97\u5e73\u5747\u503c\u3001\u4f7f\u7528numpy.std(data, ddof=1)\u6765\u8ba1\u7b97\u6837\u672c\u6807\u51c6\u5dee\u3002\u5176\u4e2d\uff0cddof\u53c2\u6570\u8868\u793a\u8d1d\u585e\u5c14\uff08\u65e0\u504f\u4f30\u8ba1\uff09\u6821\u6b63\u7cfb\u6570\uff0c\u5176\u9ed8\u8ba4\u503c\u4e3a0\uff0c\u4e3a\u4e86\u5f97\u5230\u6837\u672c\u6807\u51c6\u5dee\uff0c\u6211\u4eec\u901a\u5e38\u5c06\u5176\u8bbe\u7f6e\u4e3a1\u3002<\/strong><\/p>\n<\/p>\n

\u4e00\u3001\u5b89\u88c5\u548c\u5bfc\u5165NumPy\u5e93<\/strong><\/h3>\n<\/p>\n

\u5728\u7f16\u5199\u4ee3\u7801\u4e4b\u524d\uff0c\u786e\u4fddPython\u73af\u5883\u4e2d\u5df2\u5b89\u88c5NumPy\u5e93\u3002\u5982\u679c\u5c1a\u672a\u5b89\u88c5\uff0c\u53ef\u4ee5\u901a\u8fc7\u547d\u4ee4\u884c\u5de5\u5177\u4f7f\u7528pip\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install numpy<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u5bfc\u5165NumPy\u5e93\u4ee5\u4f7f\u7528\u5176\u63d0\u4f9b\u7684\u6570\u5b66\u51fd\u6570\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u51c6\u5907\u6570\u636e<\/strong><\/h3>\n<\/p>\n
\u9996\u5148\uff0c\u4f60\u9700\u8981\u51c6\u5907\u6216\u83b7\u53d6\u4f60\u7684\u6570\u636e\u96c6\u3002\u6211\u4eec\u5c06\u521b\u5efa\u4e24\u7ec4\u6837\u672c\u6570\u636e\uff0c\u4ee5\u5c55\u793a\u5982\u4f55\u8ba1\u7b97\u5b83\u4eec\u7684\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\uff1a<\/p>\n<\/p>\n
# \u793a\u4f8b\u6570\u636e<\/p>\n
data_group1 = np.array([4.0, 5.2, 7.1, 8.3, 5.6])<\/p>\n
data_group2 = np.array([3.5, 4.5, 6.7, 2.4, 4.1])<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u8ba1\u7b97\u5e73\u5747\u503c<\/strong><\/h3>\n<\/p>\n
\u4f7f\u7528NumPy\u7684mean\u51fd\u6570\u8ba1\u7b97\u6bcf\u7ec4\u6570\u636e\u7684\u5e73\u5747\u503c\uff1a<\/p>\n<\/p>\n
# \u8ba1\u7b97\u5e73\u5747\u503c<\/p>\n
mean_group1 = np.mean(data_group1)<\/p>\n
mean_group2 = np.mean(data_group2)<\/p>\n
print(f"The mean of the first group is: {mean_group1}")<\/p>\n
print(f"The mean of the second group is: {mean_group2}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u6bb5\u4ee3\u7801\u4f1a\u8ba1\u7b97\u51fa\u4e24\u7ec4\u6570\u636e\u5404\u81ea\u7684\u5e73\u5747\u503c\uff0c\u5e76\u6253\u5370\u51fa\u6765\u3002<\/p>\n<\/p>\n
\u56db\u3001\u8ba1\u7b97\u6807\u51c6\u5dee<\/strong><\/h3>\n<\/p>\n
\u4f7f\u7528NumPy\u7684std\u51fd\u6570\u8ba1\u7b97\u6bcf\u7ec4\u6570\u636e\u7684\u6807\u51c6\u5dee\u3002\u4fdd\u8bc1ddof\u53c2\u6570\u8bbe\u4e3a1\uff0c\u4ee5\u5f97\u5230\u6837\u672c\u6807\u51c6\u5dee\u800c\u975e\u603b\u4f53\u6807\u51c6\u5dee\uff1a<\/p>\n<\/p>\n
# \u8ba1\u7b97\u6807\u51c6\u5dee<\/p>\n
stddev_group1 = np.std(data_group1, ddof=1)<\/p>\n
stddev_group2 = np.std(data_group2, ddof=1)<\/p>\n
print(f"The standard deviation of the first group is: {stddev_group1}")<\/p>\n
print(f"The standard deviation of the second group is: {stddev_group2}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u5b8c\u6574\u7684\u793a\u4f8b\u4ee3\u7801<\/strong><\/h3>\n<\/p>\n
\u5c06\u6240\u6709\u6b65\u9aa4\u5408\u5e76\uff0c\u4ee5\u4e0b\u662f\u5b8c\u6574\u7684\u793a\u4f8b\u4ee3\u7801\uff0c\u7528\u4e8e\u8ba1\u7b97\u4e24\u7ec4\u6570\u636e\u7684\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
data_group1 = np.array([4.0, 5.2, 7.1, 8.3, 5.6])<\/p>\n
data_group2 = np.array([3.5, 4.5, 6.7, 2.4, 4.1])<\/p>\n
\u8ba1\u7b97\u5e73\u5747\u503c<\/strong><\/h2>\n
mean_group1 = np.mean(data_group1)<\/p>\n
mean_group2 = np.mean(data_group2)<\/p>\n
\u8ba1\u7b97\u6807\u51c6\u5dee<\/strong><\/h2>\n
stddev_group1 = np.std(data_group1, ddof=1)<\/p>\n
stddev_group2 = np.std(data_group2, ddof=1)<\/p>\n
\u6253\u5370\u7ed3\u679c<\/strong><\/h2>\n
print(f"The mean of the first group is: {mean_group1}")<\/p>\n
print(f"The mean of the second group is: {mean_group2}")<\/p>\n
print(f"The standard deviation of the first group is: {stddev_group1}")<\/p>\n
print(f"The standard deviation of the second group is: {stddev_group2}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u6267\u884c\u6b64\u4ee3\u7801\u5c06\u6253\u5370\u51fa\u4e24\u7ec4\u6570\u636e\u7684\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\u3002<\/p>\n<\/p>\n
\u9700\u8981\u6ce8\u610f\u7684\u662f\uff0c\u8fd9\u4e2a\u4f8b\u5b50\u5047\u8bbe\u4f60\u5df2\u7ecf\u6709\u4e86\u4e24\u7ec4\u6570\u636e\u3002\u5728\u5b9e\u9645\u60c5\u51b5\u4e2d\uff0c\u6570\u636e\u53ef\u80fd\u6765\u81ea\u4e8e\u6587\u4ef6\u3001\u7f51\u7edc\u6216\u5176\u4ed6\u6570\u636e\u6e90\uff0c\u4f60\u9700\u8981\u5148\u7528\u5408\u9002\u7684\u65b9\u6cd5\u8bfb\u53d6\u6570\u636e\uff0c\u518d\u8fdb\u884c\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
\u8ba1\u7b97\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\u662f\u7edf\u8ba1\u5206\u6790\u4e2d\u7684\u57fa\u7840\u64cd\u4f5c\uff0c\u5728\u6570\u636e\u79d1\u5b66\u3001\u673a\u5668\u5b66\u4e60<\/a>\u3001\u91d1\u878d\u5206\u6790\u7b49\u4f17\u591a\u9886\u57df\u90fd\u6709\u5e7f\u6cdb\u5e94\u7528\u3002<\/strong> \u4f7f\u7528\u8bf8\u5982NumPy\u8fd9\u6837\u7684\u4e13\u4e1a\u5de5\u5177\u53ef\u4ee5\u7b80\u5316\u8fd9\u4e9b\u8ba1\u7b97\uff0c\u540c\u65f6\u4fdd\u8bc1\u4e86\u8ba1\u7b97\u7684\u51c6\u786e\u6027\u548c\u6548\u7387\u3002\u5728\u5904\u7406\u5927\u578b\u6570\u636e\u96c6\u6216\u8fdb\u884c\u66f4\u590d\u6742\u7684\u7edf\u8ba1\u5206\u6790\u65f6\uff0c\u4f7f\u7528\u8fd9\u4e9b\u5de5\u5177\u53ef\u4ee5\u663e\u8457\u63d0\u9ad8\u5de5\u4f5c\u6548\u7387\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
1.\u5982\u4f55\u7528\u4ee3\u7801\u8ba1\u7b97\u4e24\u7ec4\u6570\u636e\u7684\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\uff1f<\/strong><\/p>\n
\u95ee\u9898\uff1a<\/strong>\u5982\u4f55\u4f7f\u7528\u4ee3\u7801\u8ba1\u7b97\u4e24\u7ec4\u6570\u636e\u7684\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\uff1f<\/p>\n
\u56de\u7b54\uff1a<\/strong>\u4f60\u53ef\u4ee5\u4f7f\u7528\u7f16\u7a0b\u8bed\u8a00\u4e2d\u7684\u7edf\u8ba1\u51fd\u6570\u6765\u8ba1\u7b97\u4e24\u7ec4\u6570\u636e\u7684\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\u3002\u4f8b\u5982\uff0c\u5728Python\u4e2d\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u4e2d\u7684mean()<\/code>\u51fd\u6570\u6765\u8ba1\u7b97\u5e73\u5747\u503c\uff0c\u4f7f\u7528std()<\/code>\u51fd\u6570\u6765\u8ba1\u7b97\u6807\u51c6\u5dee\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n
import numpy as np\n\n# \u7b2c\u4e00\u7ec4\u6570\u636e\ndata1 = [1, 2, 3, 4, 5]\n\n# \u7b2c\u4e8c\u7ec4\u6570\u636e\ndata2 = [6, 7, 8, 9, 10]\n\n# \u8ba1\u7b97\u5e73\u5747\u503c\nmean1 = np.mean(data1)\nmean2 = np.mean(data2)\n\n# \u8ba1\u7b97\u6807\u51c6\u5dee\nstd1 = np.std(data1)\nstd2 = np.std(data2)\n\nprint("\u7b2c\u4e00\u7ec4\u6570\u636e\u7684\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\u5206\u522b\u4e3a\uff1a", mean1, std1)\nprint("\u7b2c\u4e8c\u7ec4\u6570\u636e\u7684\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\u5206\u522b\u4e3a\uff1a", mean2, std2)\n<\/code><\/pre>\n
\u8fd9\u6bb5\u4ee3\u7801\u5c06\u8f93\u51fa\u4e24\u7ec4\u6570\u636e\u7684\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\u3002<\/p>\n
2.\u8bf7\u95ee\u6709\u54ea\u4e9b\u7f16\u7a0b\u8bed\u8a00\u53ef\u4ee5\u7528\u6765\u8ba1\u7b97\u4e24\u7ec4\u6570\u636e\u7684\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\uff1f<\/strong><\/p>\n
\u95ee\u9898\uff1a<\/strong>\u6709\u54ea\u4e9b\u7f16\u7a0b\u8bed\u8a00\u53ef\u4ee5\u7528\u6765\u8ba1\u7b97\u4e24\u7ec4\u6570\u636e\u7684\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\uff1f<\/p>\n
\u56de\u7b54\uff1a<\/strong>\u8bb8\u591a\u7f16\u7a0b\u8bed\u8a00\u90fd\u63d0\u4f9b\u4e86\u7528\u4e8e\u8ba1\u7b97\u7edf\u8ba1\u91cf\u7684\u51fd\u6570\u6216\u5e93\u3002\u4e00\u4e9b\u5e38\u7528\u7684\u7f16\u7a0b\u8bed\u8a00\u5305\u62ecPython\u3001R\u3001MATLAB\u3001Java\u548cC ++\u7b49\u3002<\/p>\n
    \n
  • \n
    Python\uff1aPython\u662f\u4e00\u79cd\u975e\u5e38\u53d7\u6b22\u8fce\u7684\u7f16\u7a0b\u8bed\u8a00\uff0c\u6709\u8bb8\u591a\u7528\u4e8e\u6570\u636e\u5206\u6790\u548c\u7edf\u8ba1\u7684\u5f3a\u5927\u5e93\uff0c\u5982NumPy\u3001Pandas\u548cSciPy\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528\u8fd9\u4e9b\u5e93\u6765\u8ba1\u7b97\u4e24\u7ec4\u6570\u636e\u7684\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\u3002<\/p>\n<\/li>\n
  • \n
    R\uff1aR\u662f\u4e00\u79cd\u4e13\u95e8\u7528\u4e8e\u6570\u636e\u5206\u6790\u548c\u7edf\u8ba1\u7684\u7f16\u7a0b\u8bed\u8a00\u3002\u5b83\u5177\u6709\u8bb8\u591a\u5185\u7f6e\u7684\u51fd\u6570\u548c\u5305\u7528\u4e8e\u8ba1\u7b97\u7edf\u8ba1\u91cf\uff0c\u5305\u62ec\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\u3002<\/p>\n<\/li>\n
  • \n
    MATLAB\uff1aMATLAB\u662f\u4e00\u79cd\u7528\u4e8e\u79d1\u5b66\u8ba1\u7b97\u548c\u6570\u636e\u5206\u6790\u7684\u4e13\u4e1a\u8f6f\u4ef6\u3002\u5b83\u63d0\u4f9b\u4e86\u8bb8\u591a\u529f\u80fd\u5f3a\u5927\u7684\u5de5\u5177\u7bb1\uff0c\u5176\u4e2d\u5305\u62ec\u7528\u4e8e\u8ba1\u7b97\u7edf\u8ba1\u91cf\u7684\u51fd\u6570\u3002<\/p>\n<\/li>\n
  • \n
    Java\u548cC++\uff1aJava\u548cC ++\u662f\u901a\u7528\u7684\u7f16\u7a0b\u8bed\u8a00\uff0c\u5b83\u4eec\u4e5f\u63d0\u4f9b\u4e86\u5404\u79cd\u6570\u5b66\u5e93\u548c\u51fd\u6570\uff0c\u53ef\u4ee5\u7528\u4e8e\u8ba1\u7b97\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\u3002<\/p>\n<\/li>\n<\/ul>\n
    \u65e0\u8bba\u4f60\u9009\u62e9\u54ea\u79cd\u7f16\u7a0b\u8bed\u8a00\uff0c\u90fd\u53ef\u4ee5\u6839\u636e\u7279\u5b9a\u7684\u8bed\u6cd5\u548c\u51fd\u6570\u6765\u8ba1\u7b97\u4e24\u7ec4\u6570\u636e\u7684\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\u3002<\/p>\n
    3.\u662f\u5426\u6709\u5176\u4ed6\u65b9\u6cd5\u53ef\u4ee5\u8ba1\u7b97\u4e24\u7ec4\u6570\u636e\u7684\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\uff0c\u800c\u4e0d\u4f7f\u7528\u7f16\u7a0b\u8bed\u8a00\uff1f<\/strong><\/p>\n
    \u95ee\u9898\uff1a<\/strong>\u9664\u4e86\u4f7f\u7528\u7f16\u7a0b\u8bed\u8a00\uff0c\u662f\u5426\u8fd8\u6709\u5176\u4ed6\u65b9\u6cd5\u53ef\u4ee5\u8ba1\u7b97\u4e24\u7ec4\u6570\u636e\u7684\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\uff1f<\/p>\n
    \u56de\u7b54\uff1a<\/strong>\u662f\u7684\uff0c\u9664\u4e86\u4f7f\u7528\u7f16\u7a0b\u8bed\u8a00\uff0c\u8fd8\u6709\u5176\u4ed6\u65b9\u6cd5\u53ef\u4ee5\u8ba1\u7b97\u4e24\u7ec4\u6570\u636e\u7684\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\u3002<\/p>\n
      \n
    • \n
      \u4f7f\u7528\u7535\u5b50\u8868\u683c\u8f6f\u4ef6\uff1a\u4f60\u53ef\u4ee5\u4f7f\u7528\u7535\u5b50\u8868\u683c\u8f6f\u4ef6\u5982Excel\u6216Google Sheets\u6765\u8ba1\u7b97\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\u3002\u8fd9\u4e9b\u8f6f\u4ef6\u901a\u5e38\u63d0\u4f9b\u5185\u7f6e\u7684\u51fd\u6570\uff0c\u6bd4\u5982AVERAGE()<\/code>\u548cSTDEV()<\/code>\u3002<\/p>\n<\/li>\n
    • \n
      \u4f7f\u7528\u5728\u7ebf\u7edf\u8ba1\u8ba1\u7b97\u5668\uff1a\u6709\u8bb8\u591a\u5728\u7ebf\u7edf\u8ba1\u8ba1\u7b97\u5668\u53ef\u4f9b\u4f7f\u7528\uff0c\u4f60\u53ea\u9700\u8f93\u5165\u6570\u636e\u5e76\u9009\u62e9\u6240\u9700\u7684\u7edf\u8ba1\u91cf\uff0c\u8ba1\u7b97\u5668\u5c06\u5e2e\u52a9\u4f60\u8ba1\u7b97\u51fa\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\u3002<\/p>\n<\/li>\n
    • \n
      \u624b\u52a8\u8ba1\u7b97\uff1a\u5982\u679c\u6570\u636e\u7684\u6570\u91cf\u8f83\u5c0f\uff0c\u4f60\u4e5f\u53ef\u4ee5\u624b\u52a8\u8ba1\u7b97\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\u3002\u5e73\u5747\u503c\u53ef\u4ee5\u901a\u8fc7\u5c06\u6240\u6709\u6570\u636e\u76f8\u52a0\u5e76\u9664\u4ee5\u6570\u636e\u7684\u6570\u91cf\u6765\u8ba1\u7b97\u3002\u6807\u51c6\u5dee\u53ef\u4ee5\u901a\u8fc7\u8ba1\u7b97\u6bcf\u4e2a\u6570\u636e\u4e0e\u5e73\u5747\u503c\u7684\u5dee\u7684\u5e73\u65b9\uff0c\u5e76\u5c06\u6240\u6709\u5dee\u7684\u5e73\u65b9\u76f8\u52a0\uff0c\u7136\u540e\u53d6\u5e73\u5747\u503c\u7684\u5e73\u65b9\u6839\u6765\u8ba1\u7b97\u3002<\/p>\n<\/li>\n<\/ul>\n
      \u4ee5\u4e0a\u662f\u4e00\u4e9b\u4e0d\u4f7f\u7528\u7f16\u7a0b\u8bed\u8a00\u7684\u8ba1\u7b97\u65b9\u6cd5\uff0c\u4f60\u53ef\u4ee5\u6839\u636e\u81ea\u5df1\u7684\u9700\u6c42\u9009\u62e9\u6700\u5408\u9002\u7684\u65b9\u6cd5\u6765\u8ba1\u7b97\u4e24\u7ec4\u6570\u636e\u7684\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5f53\u9700\u8981\u6c42\u89e3\u4e24\u7ec4\u6570\u636e\u7684\u5e73\u5747\u503c\u548c\u6807\u51cf\u5dee\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u7f16\u7a0b\u8bed\u8a00\u548c\u7edf\u8ba1\u8f6f\u4ef6\u6765\u5b9e\u73b0\uff0c\u4f8b\u5982Python\u7684NumPy\u5e93\u3001R […]","protected":false},"author":3,"featured_media":258095,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/258093"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=258093"}],"version-history":[{"count":0,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/258093\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/258095"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=258093"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=258093"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=258093"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}