{"id":937232,"date":"2024-12-26T19:47:58","date_gmt":"2024-12-26T11:47:58","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/937232.html"},"modified":"2024-12-26T19:48:01","modified_gmt":"2024-12-26T11:48:01","slug":"python-%e5%a6%82%e4%bd%95%e8%ae%a1%e7%ae%97%e6%96%b9%e5%b7%ae","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/937232.html","title":{"rendered":"python \u5982\u4f55\u8ba1\u7b97\u65b9\u5dee"},"content":{"rendered":"

\"python<\/p>\n

\u5f00\u5934\u6bb5\u843d\uff1a
Python \u8ba1\u7b97\u65b9\u5dee\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5305\u62ec\u4f7f\u7528\u5185\u7f6e\u51fd\u6570\u3001NumPy\u5e93\u3001Pandas\u5e93\u3001\u624b\u52a8\u5b9e\u73b0\u516c\u5f0f\u7b49\u3002<\/strong> \u5176\u4e2d\uff0c\u4f7f\u7528NumPy\u5e93\u8ba1\u7b97\u65b9\u5dee\u662f\u6700\u5e38\u7528\u7684\u65b9\u6cd5\uff0c\u56e0\u4e3a\u5b83\u63d0\u4f9b\u4e86\u9ad8\u6548\u4e14\u6613\u4e8e\u4f7f\u7528\u7684\u51fd\u6570\u3002\u65b9\u5dee\u662f\u7edf\u8ba1\u5b66\u4e2d\u7684\u4e00\u4e2a\u91cd\u8981\u6982\u5ff5\uff0c\u7528\u4e8e\u63cf\u8ff0\u6570\u636e\u96c6\u7684\u79bb\u6563\u7a0b\u5ea6\u3002\u8ba1\u7b97\u65b9\u5dee\u65f6\uff0c\u53ef\u4ee5\u9009\u62e9\u603b\u4f53\u65b9\u5dee\u6216\u6837\u672c\u65b9\u5dee\uff0c\u4e24\u8005\u7684\u533a\u522b\u5728\u4e8e\u5206\u6bcd\u7684\u53d6\u503c\u4e0d\u540c\u3002\u603b\u4f53\u65b9\u5dee\u4f7f\u7528\u7684\u662f\u6570\u636e\u603b\u6570N\uff0c\u800c\u6837\u672c\u65b9\u5dee\u5219\u4f7f\u7528N-1\u3002NumPy\u5e93\u7684var()<\/code>\u51fd\u6570\u53ef\u4ee5\u8f7b\u677e\u5730\u8ba1\u7b97\u65b9\u5dee\uff0c\u5e76\u4e14\u53ef\u4ee5\u6307\u5b9a\u662f\u5426\u4e3a\u6837\u672c\u65b9\u5dee\u3002<\/p>\n<\/p>\n

\u4e00\u3001PYTHON \u8ba1\u7b97\u65b9\u5dee\u7684\u57fa\u672c\u65b9\u6cd5<\/p>\n<\/p>\n

\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u8ba1\u7b97\u65b9\u5dee\u3002\u521d\u5b66\u8005\u53ef\u4ee5\u4ece\u7b80\u5355\u7684\u5185\u7f6e\u51fd\u6570\u5f00\u59cb\uff0c\u7136\u540e\u9010\u6b65\u8fc7\u6e21\u5230\u4f7f\u7528NumPy\u548cPandas\u7b49\u5e93\u3002<\/p>\n<\/p>\n

    \n
  1. \u4f7f\u7528\u5185\u7f6e\u51fd\u6570<\/li>\n<\/ol>\n

    Python\u7684\u5185\u7f6e\u51fd\u6570\u5e93\u63d0\u4f9b\u4e86\u4e00\u4e9b\u7b80\u5355\u7684\u65b9\u6cd5\u6765\u8ba1\u7b97\u65b9\u5dee\u3002\u6700\u57fa\u672c\u7684\u65b9\u6cd5\u662f\u4f7f\u7528\u5217\u8868\u63a8\u5bfc\u5f0f\u548cPython\u7684\u6570\u5b66\u8fd0\u7b97\u3002\u9996\u5148\uff0c\u8ba1\u7b97\u5e73\u5747\u503c\uff0c\u7136\u540e\u8ba1\u7b97\u6bcf\u4e2a\u6570\u636e\u70b9\u4e0e\u5e73\u5747\u503c\u7684\u5dee\u7684\u5e73\u65b9\uff0c\u6700\u540e\u6c42\u8fd9\u4e9b\u5e73\u65b9\u7684\u5e73\u5747\u503c\u3002\u867d\u7136\u8fd9\u79cd\u65b9\u6cd5\u662f\u76f4\u89c2\u7684\uff0c\u4f46\u5bf9\u4e8e\u5927\u6570\u636e\u96c6\u6765\u8bf4\u6548\u7387\u8f83\u4f4e\u3002<\/p>\n<\/p>\n

    data = [1, 2, 3, 4, 5]<\/p>\n
    mean = sum(data) \/ len(data)<\/p>\n
    variance = sum((x - mean)  2 for x in data) \/ len(data)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u4f7f\u7528NumPy\u5e93<\/li>\n<\/ol>\n
      NumPy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u7ec4\u8fd0\u7b97\u548c\u8bb8\u591a\u6570\u5b66\u51fd\u6570\u3002\u4f7f\u7528NumPy\u7684var()<\/code>\u51fd\u6570\u53ef\u4ee5\u8f7b\u677e\u8ba1\u7b97\u65b9\u5dee\u3002\u8fd9\u4e2a\u65b9\u6cd5\u4e0d\u4ec5\u66f4\u7b80\u6d01\uff0c\u800c\u4e14\u5728\u5904\u7406\u5927\u6570\u636e\u96c6\u65f6\u66f4\u9ad8\u6548\u3002<\/p>\n<\/p>\n
      import numpy as np<\/p>\n
      data = np.array([1, 2, 3, 4, 5])<\/p>\n
      variance = np.var(data)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u4e8c\u3001NUMPY\u8ba1\u7b97\u65b9\u5dee\u7684\u8be6\u7ec6\u8bf4\u660e<\/p>\n<\/p>\n
      NumPy\u5e93\u4e2d\u7684var()<\/code>\u51fd\u6570\u662f\u8ba1\u7b97\u65b9\u5dee\u7684\u9996\u9009\u65b9\u6cd5\u3002\u5b83\u4e0d\u4ec5\u5feb\u901f\uff0c\u800c\u4e14\u652f\u6301\u591a\u79cd\u53c2\u6570\u8bbe\u7f6e\uff0c\u9002\u5408\u4e0d\u540c\u7684\u8ba1\u7b97\u9700\u6c42\u3002<\/p>\n<\/p>\n
        \n
      1. \u57fa\u672c\u7528\u6cd5<\/li>\n<\/ol>\n
        \u4f7f\u7528np.var()<\/code>\u51fd\u6570\u53ef\u4ee5\u76f4\u63a5\u8ba1\u7b97\u6570\u7ec4\u7684\u65b9\u5dee\u3002\u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff0c\u5b83\u8ba1\u7b97\u7684\u662f\u603b\u4f53\u65b9\u5dee\u3002\u5982\u679c\u9700\u8981\u8ba1\u7b97\u6837\u672c\u65b9\u5dee\uff0c\u53ef\u4ee5\u8bbe\u7f6eddof=1<\/code>\uff08Delta Degrees of Freedom\uff09\u3002<\/p>\n<\/p>\n
        import numpy as np<\/p>\n
        data = np.array([1, 2, 3, 4, 5])<\/p>\n
        total_variance = np.var(data)  # \u603b\u4f53\u65b9\u5dee<\/p>\n
        sample_variance = np.var(data, ddof=1)  # \u6837\u672c\u65b9\u5dee<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        1. \u591a\u7ef4\u6570\u7ec4\u7684\u65b9\u5dee\u8ba1\u7b97<\/li>\n<\/ol>\n
          NumPy\u8fd8\u652f\u6301\u5bf9\u591a\u7ef4\u6570\u7ec4\u7684\u65b9\u5dee\u8ba1\u7b97\uff0c\u53ef\u4ee5\u6307\u5b9a\u8f74\u53c2\u6570\u6765\u8ba1\u7b97\u7279\u5b9a\u7ef4\u5ea6\u4e0a\u7684\u65b9\u5dee\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u8ba1\u7b97\u6bcf\u5217\u6216\u6bcf\u884c\u7684\u65b9\u5dee\u3002<\/p>\n<\/p>\n
          import numpy as np<\/p>\n
          data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])<\/p>\n
          col_variance = np.var(data, axis=0)  # \u8ba1\u7b97\u6bcf\u5217\u7684\u65b9\u5dee<\/p>\n
          row_variance = np.var(data, axis=1)  # \u8ba1\u7b97\u6bcf\u884c\u7684\u65b9\u5dee<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u4e09\u3001PANDAS\u8ba1\u7b97\u65b9\u5dee\u7684\u65b9\u6cd5<\/p>\n<\/p>\n
          Pandas\u662f\u53e6\u4e00\u4e2a\u5e7f\u6cdb\u4f7f\u7528\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u7279\u522b\u9002\u5408\u5904\u7406\u8868\u683c\u6570\u636e\u3002Pandas\u7684DataFrame\u548cSeries\u5bf9\u8c61\u90fd\u652f\u6301\u76f4\u63a5\u8ba1\u7b97\u65b9\u5dee\u3002<\/p>\n<\/p>\n
            \n
          1. \u4f7f\u7528Series\u5bf9\u8c61<\/li>\n<\/ol>\n
            \u5bf9\u4e8e\u4e00\u7ef4\u6570\u636e\uff0cPandas\u7684Series\u5bf9\u8c61\u63d0\u4f9b\u4e86var()<\/code>\u65b9\u6cd5\u6765\u8ba1\u7b97\u65b9\u5dee\u3002\u9ed8\u8ba4\u8ba1\u7b97\u7684\u662f\u6837\u672c\u65b9\u5dee\u3002<\/p>\n<\/p>\n
            import pandas as pd<\/p>\n
            data = pd.Series([1, 2, 3, 4, 5])<\/p>\n
            variance = data.var()<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. \u4f7f\u7528DataFrame\u5bf9\u8c61<\/li>\n<\/ol>\n
              \u5bf9\u4e8e\u591a\u7ef4\u6570\u636e\uff0cPandas\u7684DataFrame\u5bf9\u8c61\u540c\u6837\u63d0\u4f9b\u4e86var()<\/code>\u65b9\u6cd5\uff0c\u53ef\u4ee5\u6307\u5b9a\u8f74\u53c2\u6570\u6765\u8ba1\u7b97\u7279\u5b9a\u7ef4\u5ea6\u4e0a\u7684\u65b9\u5dee\u3002<\/p>\n<\/p>\n
              import pandas as pd<\/p>\n
              data = pd.DataFrame({<\/p>\n
                  'A': [1, 2, 3],<\/p>\n
                  'B': [4, 5, 6],<\/p>\n
                  'C': [7, 8, 9]<\/p>\n
              })<\/p>\n
              col_variance = data.var(axis=0)  # \u8ba1\u7b97\u6bcf\u5217\u7684\u65b9\u5dee<\/p>\n
              row_variance = data.var(axis=1)  # \u8ba1\u7b97\u6bcf\u884c\u7684\u65b9\u5dee<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u56db\u3001\u624b\u52a8\u5b9e\u73b0\u65b9\u5dee\u8ba1\u7b97\u7684\u516c\u5f0f<\/p>\n<\/p>\n
              \u5728\u7406\u89e3\u4e86NumPy\u548cPandas\u7684\u7528\u6cd5\u540e\uff0c\u624b\u52a8\u5b9e\u73b0\u65b9\u5dee\u8ba1\u7b97\u53ef\u4ee5\u52a0\u6df1\u5bf9\u5176\u6570\u5b66\u539f\u7406\u7684\u7406\u89e3\u3002<\/p>\n<\/p>\n
                \n
              1. \u603b\u4f53\u65b9\u5dee\u516c\u5f0f<\/li>\n<\/ol>\n
                \u603b\u4f53\u65b9\u5dee\u7684\u516c\u5f0f\u4e3a\uff1a(\\sigma^2 = \\frac{1}{N} \\sum_{i=1}^{N} (x_i – \\mu)^2)\uff0c\u5176\u4e2d(N)\u662f\u6570\u636e\u603b\u6570\uff0c(\\mu)\u662f\u5e73\u5747\u503c\u3002<\/p>\n<\/p>\n
                def calculate_variance(data):<\/p>\n
                    mean = sum(data) \/ len(data)<\/p>\n
                    return sum((x - mean)  2 for x in data) \/ len(data)<\/p>\n
                data = [1, 2, 3, 4, 5]<\/p>\n
                variance = calculate_variance(data)<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                  \n
                1. \u6837\u672c\u65b9\u5dee\u516c\u5f0f<\/li>\n<\/ol>\n
                  \u6837\u672c\u65b9\u5dee\u7684\u516c\u5f0f\u4e3a\uff1a(s^2 = \\frac{1}{N-1} \\sum_{i=1}^{N} (x_i – \\bar{x})^2)\u3002\u8fd9\u91cc\u7684\u533a\u522b\u5728\u4e8e\u5206\u6bcd\u662f(N-1)\uff0c\u7528\u4e8e\u4fee\u6b63\u6837\u672c\u7684\u504f\u5dee\u3002<\/p>\n<\/p>\n
                  def calculate_sample_variance(data):<\/p>\n
                      mean = sum(data) \/ len(data)<\/p>\n
                      return sum((x - mean)  2 for x in data) \/ (len(data) - 1)<\/p>\n
                  data = [1, 2, 3, 4, 5]<\/p>\n
                  sample_variance = calculate_sample_variance(data)<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u4e94\u3001\u65b9\u5dee\u8ba1\u7b97\u4e2d\u7684\u6ce8\u610f\u4e8b\u9879<\/p>\n<\/p>\n
                  \u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u8ba1\u7b97\u65b9\u5dee\u65f6\u9700\u8981\u6ce8\u610f\u6570\u636e\u7684\u7c7b\u578b\u548c\u5206\u5e03\u60c5\u51b5\uff0c\u4ee5\u786e\u4fdd\u7ed3\u679c\u7684\u51c6\u786e\u6027\u548c\u610f\u4e49\u3002<\/p>\n<\/p>\n
                    \n
                  1. \u6570\u636e\u7c7b\u578b\u7684\u5f71\u54cd<\/li>\n<\/ol>\n
                    \u8ba1\u7b97\u65b9\u5dee\u65f6\uff0c\u6570\u636e\u7684\u7c7b\u578b\u4f1a\u5f71\u54cd\u7ed3\u679c\u7684\u7cbe\u5ea6\u3002\u6d6e\u70b9\u6570\u8ba1\u7b97\u53ef\u80fd\u4f1a\u5bfc\u81f4\u7cbe\u5ea6\u635f\u5931\uff0c\u5c24\u5176\u662f\u5728\u5904\u7406\u975e\u5e38\u5927\u7684\u6570\u636e\u96c6\u65f6\u3002\u4f7f\u7528NumPy\u548cPandas\u53ef\u4ee5\u5728\u4e00\u5b9a\u7a0b\u5ea6\u4e0a\u7f13\u89e3\u8fd9\u4e9b\u95ee\u9898\uff0c\u56e0\u4e3a\u5b83\u4eec\u4f7f\u7528\u7684\u662f\u4f18\u5316\u7684\u5e95\u5c42\u7b97\u6cd5\u3002<\/p>\n<\/p>\n
                      \n
                    1. \u6570\u636e\u5206\u5e03\u7684\u5f71\u54cd<\/li>\n<\/ol>\n
                      \u65b9\u5dee\u662f\u5bf9\u6570\u636e\u79bb\u6563\u7a0b\u5ea6\u7684\u5ea6\u91cf\uff0c\u56e0\u6b64\u5728\u6570\u636e\u5206\u5e03\u4e0d\u5747\u5300\u6216\u5b58\u5728\u5f02\u5e38\u503c\u65f6\uff0c\u65b9\u5dee\u53ef\u80fd\u4e0d\u80fd\u51c6\u786e\u53cd\u6620\u6570\u636e\u7684\u5b9e\u9645\u60c5\u51b5\u3002\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u53ef\u4ee5\u8003\u8651\u4f7f\u7528\u4e2d\u4f4d\u6570\u7edd\u5bf9\u504f\u5dee\uff08MAD\uff09\u7b49\u5176\u4ed6\u7edf\u8ba1\u91cf\u3002<\/p>\n<\/p>\n
                      \u603b\u4e4b\uff0cPython\u63d0\u4f9b\u4e86\u591a\u79cd\u8ba1\u7b97\u65b9\u5dee\u7684\u65b9\u6cd5\uff0c\u4ece\u5185\u7f6e\u51fd\u6570\u5230\u5f3a\u5927\u7684NumPy\u548cPandas\u5e93\uff0c\u90fd\u53ef\u4ee5\u5e2e\u52a9\u6570\u636e\u5206\u6790\u4eba\u5458\u548c\u79d1\u5b66\u5bb6\u9ad8\u6548\u5730\u5206\u6790\u6570\u636e\u7684\u79bb\u6563\u7a0b\u5ea6\u3002\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u4e0d\u4ec5\u53ef\u4ee5\u63d0\u9ad8\u8ba1\u7b97\u6548\u7387\uff0c\u8fd8\u80fd\u786e\u4fdd\u7ed3\u679c\u7684\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n
                      \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                       \u65b9\u5dee\u7684\u5b9a\u4e49\u662f\u4ec0\u4e48\uff1f<\/strong>
                      \u65b9\u5dee\u662f\u7528\u6765\u8861\u91cf\u6570\u636e\u96c6\u7684\u79bb\u6563\u7a0b\u5ea6\u7684\u4e00\u79cd\u7edf\u8ba1\u6307\u6807\uff0c\u5b83\u8868\u793a\u6570\u636e\u70b9\u4e0e\u5747\u503c\u4e4b\u95f4\u7684\u5dee\u5f02\u7a0b\u5ea6\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u65b9\u5dee\u8d8a\u5927\uff0c\u6570\u636e\u70b9\u5206\u5e03\u8d8a\u5206\u6563\uff1b\u65b9\u5dee\u8d8a\u5c0f\uff0c\u6570\u636e\u70b9\u66f4\u96c6\u4e2d\u4e8e\u5747\u503c\u9644\u8fd1\u3002\u8ba1\u7b97\u65b9\u5dee\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u7406\u89e3\u6570\u636e\u7684\u6ce2\u52a8\u6027\u3002<\/p>\n
                      \u5728Python\u4e2d\u8ba1\u7b97\u65b9\u5dee\u6709\u54ea\u4e9b\u65b9\u6cd5\uff1f<\/strong>
                      \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u65b9\u6cd5\u6765\u8ba1\u7b97\u65b9\u5dee\u3002\u6700\u5e38\u7528\u7684\u662f\u4f7f\u7528NumPy\u5e93\u4e2d\u7684var()<\/code>\u51fd\u6570\uff0c\u5b83\u80fd\u591f\u9ad8\u6548\u5730\u5904\u7406\u6570\u7ec4\u6570\u636e\u3002\u6b64\u5916\uff0cPython\u7684\u6807\u51c6\u5e93\u7edf\u8ba1\u6a21\u5757\u4e5f\u63d0\u4f9b\u4e86variance()<\/code>\u51fd\u6570\u3002\u4f60\u8fd8\u53ef\u4ee5\u624b\u52a8\u5b9e\u73b0\u65b9\u5dee\u7684\u8ba1\u7b97\uff0c\u5177\u4f53\u65b9\u6cd5\u5305\u62ec\u8ba1\u7b97\u5747\u503c\u3001\u6c42\u6bcf\u4e2a\u6570\u636e\u70b9\u4e0e\u5747\u503c\u7684\u5dee\u7684\u5e73\u65b9\uff0c\u5e76\u53d6\u5176\u5e73\u5747\u503c\u3002<\/p>\n
                      \u4f7f\u7528NumPy\u8ba1\u7b97\u65b9\u5dee\u65f6\u9700\u8981\u6ce8\u610f\u54ea\u4e9b\u53c2\u6570\uff1f<\/strong>
                      \u5728\u4f7f\u7528NumPy\u7684var()<\/code>\u51fd\u6570\u65f6\uff0c\u53ef\u4ee5\u8bbe\u7f6eddof<\/code>\u53c2\u6570\u3002\u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff0cddof<\/code>\u4e3a0\uff0c\u8868\u793a\u8ba1\u7b97\u603b\u4f53\u65b9\u5dee\uff1b\u5982\u679c\u5c06ddof<\/code>\u8bbe\u4e3a1\uff0c\u5219\u8ba1\u7b97\u6837\u672c\u65b9\u5dee\u3002\u9009\u62e9\u6b63\u786e\u7684ddof<\/code>\u503c\u5bf9\u4e8e\u7edf\u8ba1\u5206\u6790\u7684\u51c6\u786e\u6027\u81f3\u5173\u91cd\u8981\uff0c\u5177\u4f53\u53d6\u51b3\u4e8e\u4f60\u7684\u6570\u636e\u96c6\u662f\u6837\u672c\u8fd8\u662f\u603b\u4f53\u3002<\/p>\n
                      \u5982\u4f55\u5728Pandas\u4e2d\u8ba1\u7b97\u65b9\u5dee\uff1f<\/strong>
                      \u5728Pandas\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528DataFrame\u6216Series\u5bf9\u8c61\u7684var()<\/code>\u65b9\u6cd5\u6765\u8ba1\u7b97\u65b9\u5dee\u3002\u6b64\u65b9\u6cd5\u540c\u6837\u652f\u6301ddof<\/code>\u53c2\u6570\uff0c\u5141\u8bb8\u7528\u6237\u6839\u636e\u9700\u8981\u9009\u62e9\u6837\u672c\u65b9\u5dee\u6216\u603b\u4f53\u65b9\u5dee\u3002\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u53ef\u4ee5\u4fbf\u6377\u5730\u5bf9\u6570\u636e\u6846\u4e2d\u7684\u7279\u5b9a\u5217\u8fdb\u884c\u65b9\u5dee\u8ba1\u7b97\uff0c\u9002\u5408\u5904\u7406\u590d\u6742\u7684\u6570\u636e\u96c6\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5f00\u5934\u6bb5\u843d\uff1aPython \u8ba1\u7b97\u65b9\u5dee\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5305\u62ec\u4f7f\u7528\u5185\u7f6e\u51fd\u6570\u3001NumPy\u5e93\u3001Pandas\u5e93\u3001\u624b\u52a8\u5b9e\u73b0\u516c\u5f0f\u7b49\u3002 […]","protected":false},"author":3,"featured_media":937236,"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\/937232"}],"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=937232"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/937232\/revisions"}],"predecessor-version":[{"id":937237,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/937232\/revisions\/937237"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/937236"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=937232"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=937232"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=937232"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}