{"id":949374,"date":"2024-12-27T00:12:20","date_gmt":"2024-12-26T16:12:20","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/949374.html"},"modified":"2024-12-27T00:12:21","modified_gmt":"2024-12-26T16:12:21","slug":"python%e5%a6%82%e4%bd%95%e6%b1%82%e6%96%b9%e5%b7%ae","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/949374.html","title":{"rendered":"python\u5982\u4f55\u6c42\u65b9\u5dee"},"content":{"rendered":"

\"python\u5982\u4f55\u6c42\u65b9\u5dee\"<\/p>\n

\u4e00\u3001\u65b9\u5dee\u7684\u8ba1\u7b97\u65b9\u6cd5\u6982\u8ff0<\/p>\n<\/p>\n

\u5728Python\u4e2d\uff0c\u8ba1\u7b97\u65b9\u5dee<\/strong>\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5305\u62ec\u4f7f\u7528\u5185\u7f6e\u51fd\u6570\u3001Numpy\u5e93\u548cPandas\u5e93\u7b49\u3002\u65b9\u5dee\u662f\u63cf\u8ff0\u6570\u636e\u96c6\u5206\u5e03\u7684\u91cd\u8981\u7edf\u8ba1\u91cf\uff0c\u8868\u793a\u6570\u636e\u70b9\u4e0e\u5747\u503c\u4e4b\u95f4\u7684\u504f\u79bb\u7a0b\u5ea6\u3002\u5e38\u7528\u7684\u8ba1\u7b97\u65b9\u6cd5\u6709\uff1a\u4f7f\u7528Python\u5185\u7f6e\u7684statistics<\/code>\u6a21\u5757\u3001\u4f7f\u7528Numpy\u5e93\u7684var()<\/code>\u51fd\u6570\u3001\u4f7f\u7528Pandas\u5e93\u7684var()<\/code>\u65b9\u6cd5\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5176\u4e2d\u4e00\u79cd\u65b9\u6cd5\uff1a\u4f7f\u7528Numpy\u5e93<\/strong>\u3002<\/p>\n<\/p>\n

Numpy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u503c\u8ba1\u7b97\u5e93\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u5b66\u548c\u7edf\u8ba1\u51fd\u6570\u3002\u8981\u4f7f\u7528Numpy\u8ba1\u7b97\u65b9\u5dee\uff0c\u9996\u5148\u9700\u8981\u5bfc\u5165\u8be5\u5e93\uff0c\u7136\u540e\u5c06\u6570\u636e\u8f6c\u6362\u4e3aNumpy\u6570\u7ec4\uff0c\u6700\u540e\u8c03\u7528var()<\/code>\u51fd\u6570\u3002Numpy\u7684var()<\/code>\u51fd\u6570\u53ef\u4ee5\u5904\u7406\u591a\u7ef4\u6570\u7ec4\uff0c\u4e14\u6548\u7387\u9ad8\uff0c\u9002\u7528\u4e8e\u5927\u89c4\u6a21\u6570\u636e\u5904\u7406\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n

import numpy as np<\/p>\n
data = [1, 2, 3, 4, 5]<\/p>\n
variance = np.var(data)<\/p>\n
print("\u65b9\u5dee\u4e3a:", variance)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528Python\u5185\u7f6e\u6a21\u5757\u8ba1\u7b97\u65b9\u5dee<\/p>\n<\/p>\n
Python\u63d0\u4f9b\u4e86\u4e00\u4e2a\u7edf\u8ba1\u6a21\u5757statistics<\/code>\uff0c\u5176\u4e2d\u5305\u542b\u4e86\u8ba1\u7b97\u65b9\u5dee\u7684\u51fd\u6570variance()<\/code>\u3002\u4f7f\u7528\u8be5\u6a21\u5757\u8ba1\u7b97\u65b9\u5dee\u7684\u6b65\u9aa4\u975e\u5e38\u7b80\u5355\uff0c\u53ea\u9700\u5c06\u6570\u636e\u4f20\u5165\u51fd\u6570\u5373\u53ef\u3002\u9700\u8981\u6ce8\u610f\u7684\u662f\uff0c\u8be5\u51fd\u6570\u8ba1\u7b97\u7684\u662f\u6837\u672c\u65b9\u5dee\uff0c\u800c\u4e0d\u662f\u603b\u4f53\u65b9\u5dee\u3002<\/p>\n<\/p>\n
import statistics<\/p>\n
data = [1, 2, 3, 4, 5]<\/p>\n
variance = statistics.variance(data)<\/p>\n
print("\u6837\u672c\u65b9\u5dee\u4e3a:", variance)<\/p>\n
<\/code><\/pre>\n<\/p>\n
    \n
  1. \n
    \u6837\u672c\u65b9\u5dee\u4e0e\u603b\u4f53\u65b9\u5dee<\/strong><\/p>\n<\/p>\n
    \u6837\u672c\u65b9\u5dee\u548c\u603b\u4f53\u65b9\u5dee\u7684\u533a\u522b\u5728\u4e8e\u8ba1\u7b97\u65f6\u7684\u5206\u6bcd\u3002\u6837\u672c\u65b9\u5dee\u7684\u5206\u6bcd\u662f\u6837\u672c\u91cf\u51cf\u4e00\uff0c\u800c\u603b\u4f53\u65b9\u5dee\u7684\u5206\u6bcd\u662f\u6837\u672c\u91cf\u3002\u8fd9\u662f\u56e0\u4e3a\u6837\u672c\u65b9\u5dee\u662f\u5bf9\u603b\u4f53\u65b9\u5dee\u7684\u65e0\u504f\u4f30\u8ba1\u3002<\/p>\n<\/p>\n<\/li>\n
  2. \n
    \u9002\u7528\u573a\u666f<\/strong><\/p>\n<\/p>\n
    \u4f7f\u7528statistics<\/code>\u6a21\u5757\u9002\u5408\u4e8e\u5904\u7406\u7b80\u5355\u6570\u636e\u96c6\uff0c\u5c24\u5176\u662f\u9700\u8981\u8ba1\u7b97\u6837\u672c\u65b9\u5dee\u7684\u573a\u5408\u3002\u4f46\u5f53\u6570\u636e\u91cf\u8f83\u5927\u6216\u9700\u8981\u8fdb\u884c\u591a\u7ef4\u6570\u7ec4\u8ba1\u7b97\u65f6\uff0c\u5efa\u8bae\u4f7f\u7528Numpy\u5e93\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
    \u4e09\u3001\u4f7f\u7528Numpy\u5e93\u8ba1\u7b97\u65b9\u5dee<\/p>\n<\/p>\n
    Numpy\u5e93\u662fPython\u4e2d\u8fdb\u884c\u79d1\u5b66\u8ba1\u7b97\u7684\u57fa\u7840\u5e93\uff0c\u63d0\u4f9b\u4e86\u8ba1\u7b97\u65b9\u5dee\u7684var()<\/code>\u51fd\u6570\uff0c\u652f\u6301\u591a\u7ef4\u6570\u7ec4\u548c\u53c2\u6570\u8bbe\u7f6e\uff0c\u7075\u6d3b\u6027\u9ad8\u3002<\/p>\n<\/p>\n
      \n
    1. \n
      \u8ba1\u7b97\u603b\u4f53\u65b9\u5dee<\/strong><\/p>\n<\/p>\n
      \u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff0cvar()<\/code>\u51fd\u6570\u8ba1\u7b97\u7684\u662f\u603b\u4f53\u65b9\u5dee\u3002\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6eddof=1<\/code>\u6765\u8ba1\u7b97\u6837\u672c\u65b9\u5dee\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
      print("\u603b\u4f53\u65b9\u5dee\u4e3a:", variance)<\/p>\n
      <\/code><\/pre>\n<\/p>\n<\/li>\n
    2. \n
      \u8ba1\u7b97\u6837\u672c\u65b9\u5dee<\/strong><\/p>\n<\/p>\n
      \u5f53\u8ba1\u7b97\u6837\u672c\u65b9\u5dee\u65f6\uff0c\u9700\u8bbe\u7f6eddof=1<\/code>\u53c2\u6570\uff1a<\/p>\n<\/p>\n
      sample_variance = np.var(data, ddof=1)<\/p>\n
      print("\u6837\u672c\u65b9\u5dee\u4e3a:", sample_variance)<\/p>\n
      <\/code><\/pre>\n<\/p>\n<\/li>\n
    3. \n
      \u591a\u7ef4\u6570\u7ec4\u7684\u65b9\u5dee\u8ba1\u7b97<\/strong><\/p>\n<\/p>\n
      Numpy\u7684var()<\/code>\u51fd\u6570\u652f\u6301\u591a\u7ef4\u6570\u7ec4\uff0c\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6eaxis<\/code>\u53c2\u6570\u6765\u6307\u5b9a\u8ba1\u7b97\u65b9\u5dee\u7684\u7ef4\u5ea6\u3002<\/p>\n<\/p>\n
      multi_dim_data = np.array([[1, 2], [3, 4], [5, 6]])<\/p>\n
      variance_axis0 = np.var(multi_dim_data, axis=0)<\/p>\n
      variance_axis1 = np.var(multi_dim_data, axis=1)<\/p>\n
      print("\u6cbf\u8f740\u7684\u65b9\u5dee:", variance_axis0)<\/p>\n
      print("\u6cbf\u8f741\u7684\u65b9\u5dee:", variance_axis1)<\/p>\n
      <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
      \u56db\u3001\u4f7f\u7528Pandas\u5e93\u8ba1\u7b97\u65b9\u5dee<\/p>\n<\/p>\n
      Pandas\u662fPython\u4e2d\u5904\u7406\u6570\u636e\u5206\u6790\u7684\u5229\u5668\uff0c\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u7ed3\u6784\u548c\u5206\u6790\u5de5\u5177\u3002\u4f7f\u7528Pandas\u53ef\u4ee5\u65b9\u4fbf\u5730\u8ba1\u7b97\u6570\u636e\u6846\u6216\u7cfb\u5217\u7684\u65b9\u5dee\u3002<\/p>\n<\/p>\n
        \n
      1. \n
        \u8ba1\u7b97\u6570\u636e\u7cfb\u5217\u7684\u65b9\u5dee<\/strong><\/p>\n<\/p>\n
        \u4f7f\u7528Pandas\u8ba1\u7b97\u65b9\u5dee\u65f6\uff0c\u53ef\u4ee5\u5c06\u6570\u636e\u5b58\u50a8\u5728Series<\/code>\u4e2d\uff0c\u7136\u540e\u8c03\u7528var()<\/code>\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
        import pandas as pd<\/p>\n
        data_series = pd.Series([1, 2, 3, 4, 5])<\/p>\n
        series_variance = data_series.var()<\/p>\n
        print("\u6570\u636e\u7cfb\u5217\u7684\u65b9\u5dee\u4e3a:", series_variance)<\/p>\n
        <\/code><\/pre>\n<\/p>\n<\/li>\n
      2. \n
        \u8ba1\u7b97\u6570\u636e\u6846\u7684\u65b9\u5dee<\/strong><\/p>\n<\/p>\n
        \u5bf9\u4e8e\u6570\u636e\u6846\uff0c\u53ef\u4ee5\u8ba1\u7b97\u6bcf\u5217\u7684\u65b9\u5dee\uff0c\u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff0cvar()<\/code>\u65b9\u6cd5\u8ba1\u7b97\u7684\u662f\u6837\u672c\u65b9\u5dee\u3002<\/p>\n<\/p>\n
        data_frame = pd.DataFrame({<\/p>\n
            'A': [1, 2, 3],<\/p>\n
            'B': [4, 5, 6]<\/p>\n
        })<\/p>\n
        dataframe_variance = data_frame.var()<\/p>\n
        print("\u6570\u636e\u6846\u7684\u65b9\u5dee\u4e3a:\\n", dataframe_variance)<\/p>\n
        <\/code><\/pre>\n<\/p>\n<\/li>\n
      3. \n
        \u6307\u5b9a\u7ef4\u5ea6\u8ba1\u7b97\u65b9\u5dee<\/strong><\/p>\n<\/p>\n
        \u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6eaxis<\/code>\u53c2\u6570\u6765\u6307\u5b9a\u6cbf\u7740\u884c\u6216\u5217\u8ba1\u7b97\u65b9\u5dee\u3002<\/p>\n<\/p>\n
        row_variance = data_frame.var(axis=1)<\/p>\n
        print("\u6cbf\u884c\u8ba1\u7b97\u7684\u65b9\u5dee\u4e3a:", row_variance)<\/p>\n
        <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
        \u4e94\u3001\u4f7f\u7528Scipy\u5e93\u8ba1\u7b97\u65b9\u5dee<\/p>\n<\/p>\n
        Scipy\u662f\u4e00\u4e2a\u5f00\u6e90\u7684Python\u5e93\uff0c\u4e13\u4e3a\u6570\u5b66\u3001\u79d1\u5b66\u548c\u5de5\u7a0b\u8ba1\u7b97\u800c\u8bbe\u8ba1\u3002\u5b83\u63d0\u4f9b\u4e86\u66f4\u591a\u9ad8\u7ea7\u7684\u7edf\u8ba1\u529f\u80fd\uff0c\u5305\u62ec\u8ba1\u7b97\u65b9\u5dee\u3002<\/p>\n<\/p>\n
          \n
        1. \n
          \u57fa\u672c\u7528\u6cd5<\/strong><\/p>\n<\/p>\n
          Scipy\u7684stats<\/code>\u6a21\u5757\u4e2d\u5305\u542b\u4e86\u8ba1\u7b97\u65b9\u5dee\u7684\u51fd\u6570variation()<\/code>\uff0c\u53ef\u4ee5\u5904\u7406\u6807\u91cf\u548c\u591a\u7ef4\u6570\u7ec4\u3002<\/p>\n<\/p>\n
          from scipy import stats<\/p>\n
          data = [1, 2, 3, 4, 5]<\/p>\n
          variance = stats.tvar(data)<\/p>\n
          print("Scipy\u8ba1\u7b97\u7684\u65b9\u5dee\u4e3a:", variance)<\/p>\n
          <\/code><\/pre>\n<\/p>\n<\/li>\n
        2. \n
          \u591a\u7ef4\u6570\u7ec4\u7684\u65b9\u5dee\u8ba1\u7b97<\/strong><\/p>\n<\/p>\n
          Scipy\u540c\u6837\u652f\u6301\u5bf9\u591a\u7ef4\u6570\u7ec4\u8fdb\u884c\u65b9\u5dee\u8ba1\u7b97\uff0c\u4e14\u53ef\u4ee5\u6307\u5b9a\u8ba1\u7b97\u7684\u8f74\u3002<\/p>\n<\/p>\n
          multi_dim_data = [[1, 2], [3, 4], [5, 6]]<\/p>\n
          variance = stats.tvar(multi_dim_data, axis=0)<\/p>\n
          print("\u591a\u7ef4\u6570\u7ec4\u6cbf\u8f740\u7684\u65b9\u5dee\u4e3a:", variance)<\/p>\n
          <\/code><\/pre>\n<\/p>\n<\/li>\n
        3. \n
          \u4e0eNumpy\u7684\u6bd4\u8f83<\/strong><\/p>\n<\/p>\n
          Scipy\u4e0eNumpy\u7684\u65b9\u5dee\u8ba1\u7b97\u65b9\u6cd5\u7c7b\u4f3c\uff0c\u4f46Scipy\u63d0\u4f9b\u4e86\u66f4\u591a\u7684\u7edf\u8ba1\u529f\u80fd\uff0c\u9002\u5408\u9700\u8981\u8fdb\u884c\u590d\u6742\u7edf\u8ba1\u5206\u6790\u7684\u573a\u5408\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
          \u516d\u3001\u603b\u7ed3\u4e0e\u5e94\u7528\u573a\u666f<\/p>\n<\/p>\n
          \u5728Python\u4e2d\uff0c\u8ba1\u7b97\u65b9\u5dee\u7684\u65b9\u6cd5\u591a\u79cd\u591a\u6837\uff0c\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u53d6\u51b3\u4e8e\u6570\u636e\u7684\u89c4\u6a21\u548c\u5206\u6790\u7684\u590d\u6742\u6027\u3002<\/p>\n<\/p>\n
            \n
          1. \n
            \u5c0f\u89c4\u6a21\u6570\u636e<\/strong><\/p>\n<\/p>\n
            \u5bf9\u4e8e\u5c0f\u89c4\u6a21\u7684\u6570\u636e\u96c6\uff0cPython\u5185\u7f6e\u7684statistics<\/code>\u6a21\u5757\u8db3\u4ee5\u6ee1\u8db3\u9700\u6c42\uff0c\u4f7f\u7528\u7b80\u5355\uff0c\u4ee3\u7801\u6e05\u6670\u3002<\/p>\n<\/p>\n<\/li>\n
          2. \n
            \u5927\u89c4\u6a21\u6570\u636e<\/strong><\/p>\n<\/p>\n
            \u5f53\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u6216\u9700\u8981\u8fdb\u884c\u591a\u7ef4\u6570\u7ec4\u8ba1\u7b97\u65f6\uff0cNumpy\u5e93\u662f\u6700\u4f73\u9009\u62e9\u3002\u5176\u9ad8\u6548\u7684\u7b97\u6cd5\u548c\u7075\u6d3b\u7684\u53c2\u6570\u8bbe\u7f6e\uff0c\u4f7f\u5f97\u8ba1\u7b97\u66f4\u52a0\u5feb\u901f\u548c\u7cbe\u786e\u3002<\/p>\n<\/p>\n<\/li>\n
          3. \n
            \u6570\u636e\u5206\u6790<\/strong><\/p>\n<\/p>\n
            \u5728\u8fdb\u884c\u6570\u636e\u5206\u6790\u65f6\uff0cPandas\u5e93\u53ef\u4ee5\u7b80\u5316\u6570\u636e\u7684\u5904\u7406\u8fc7\u7a0b\uff0c\u5176\u6570\u636e\u7ed3\u6784\u548c\u65b9\u6cd5\u4e0e\u6570\u636e\u6846\u67b6\u7d27\u5bc6\u7ed3\u5408\uff0c\u9002\u5408\u5728\u6570\u636e\u5206\u6790\u548c\u5904\u7406\u9636\u6bb5\u4f7f\u7528\u3002<\/p>\n<\/p>\n<\/li>\n
          4. \n
            \u590d\u6742\u7edf\u8ba1\u5206\u6790<\/strong><\/p>\n<\/p>\n
            \u5bf9\u4e8e\u590d\u6742\u7684\u7edf\u8ba1\u5206\u6790\u4efb\u52a1\uff0cScipy\u5e93\u63d0\u4f9b\u4e86\u66f4\u4e3a\u4e30\u5bcc\u7684\u7edf\u8ba1\u51fd\u6570\u548c\u5de5\u5177\uff0c\u53ef\u4ee5\u6ee1\u8db3\u5404\u79cd\u9ad8\u7ea7\u7edf\u8ba1\u9700\u6c42\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
            \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
             1. \u5982\u4f55\u4f7f\u7528Python\u4e2d\u7684NumPy\u5e93\u6765\u8ba1\u7b97\u65b9\u5dee\uff1f<\/strong>
            \u5728Python\u4e2d\uff0c\u4f7f\u7528NumPy\u5e93\u53ef\u4ee5\u65b9\u4fbf\u5730\u8ba1\u7b97\u65b9\u5dee\u3002\u9996\u5148\uff0c\u60a8\u9700\u8981\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86NumPy\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7pip install numpy<\/code>\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\u3002\u63a5\u4e0b\u6765\uff0c\u60a8\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a\u6570\u7ec4\u5e76\u4f7f\u7528numpy.var()<\/code>\u51fd\u6570\u6765\u8ba1\u7b97\u65b9\u5dee\u3002\u4f8b\u5982\uff1a<\/p>\n
            import numpy as np\n\ndata = [1, 2, 3, 4, 5]\nvariance = np.var(data)\nprint("\u65b9\u5dee\u4e3a:", variance)\n<\/code><\/pre>\n
            \u8fd9\u4e2a\u65b9\u6cd5\u4f1a\u8ba1\u7b97\u6574\u4e2a\u6570\u636e\u96c6\u7684\u65b9\u5dee\u3002\u5982\u679c\u9700\u8981\u8ba1\u7b97\u6837\u672c\u65b9\u5dee\uff0c\u53ef\u4ee5\u5c06ddof<\/code>\u53c2\u6570\u8bbe\u7f6e\u4e3a1\uff0c\u5982np.var(data, ddof=1)<\/code>\u3002<\/p>\n
            2. Python\u7684\u5185\u7f6e\u51fd\u6570\u662f\u5426\u53ef\u4ee5\u7528\u6765\u8ba1\u7b97\u65b9\u5dee\uff1f<\/strong>
            \u867d\u7136Python\u7684\u6807\u51c6\u5e93\u6ca1\u6709\u76f4\u63a5\u63d0\u4f9b\u8ba1\u7b97\u65b9\u5dee\u7684\u51fd\u6570\uff0c\u4f46\u53ef\u4ee5\u901a\u8fc7\u81ea\u5b9a\u4e49\u51fd\u6570\u6765\u5b9e\u73b0\u3002\u60a8\u53ef\u4ee5\u4f7f\u7528\u5185\u7f6e\u7684statistics<\/code>\u6a21\u5757\u4e2d\u7684variance()<\/code>\u51fd\u6570\u6765\u8ba1\u7b97\u6837\u672c\u65b9\u5dee\u3002\u793a\u4f8b\u5982\u4e0b\uff1a<\/p>\n
            import statistics\n\ndata = [1, 2, 3, 4, 5]\nsample_variance = statistics.variance(data)\nprint("\u6837\u672c\u65b9\u5dee\u4e3a:", sample_variance)\n<\/code><\/pre>\n
            \u8fd9\u6bb5\u4ee3\u7801\u4f1a\u81ea\u52a8\u5904\u7406\u6570\u636e\u96c6\uff0c\u5e76\u8fd4\u56de\u6837\u672c\u65b9\u5dee\u3002<\/p>\n
            3. \u5728\u8ba1\u7b97\u65b9\u5dee\u65f6\u9700\u8981\u6ce8\u610f\u54ea\u4e9b\u4e8b\u9879\uff1f<\/strong>
            \u5728\u8ba1\u7b97\u65b9\u5dee\u65f6\uff0c\u9700\u8981\u8003\u8651\u6570\u636e\u7684\u6027\u8d28\u548c\u9009\u62e9\u5408\u9002\u7684\u65b9\u5dee\u7c7b\u578b\u3002\u4f8b\u5982\uff0c\u82e5\u6570\u636e\u662f\u6837\u672c\u800c\u975e\u603b\u4f53\uff0c\u5219\u5e94\u4f7f\u7528\u6837\u672c\u65b9\u5dee\uff0c\u8fd9\u6837\u4f1a\u66f4\u52a0\u51c6\u786e\u3002\u8fd8\u9700\u6ce8\u610f\u6570\u636e\u4e2d\u662f\u5426\u5b58\u5728\u5f02\u5e38\u503c\uff0c\u56e0\u4e3a\u5f02\u5e38\u503c\u4f1a\u663e\u8457\u5f71\u54cd\u65b9\u5dee\u7684\u8ba1\u7b97\u7ed3\u679c\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u5efa\u8bae\u5728\u8ba1\u7b97\u65b9\u5dee\u524d\u5148\u8fdb\u884c\u6570\u636e\u6e05\u6d17\uff0c\u4ee5\u786e\u4fdd\u7ed3\u679c\u7684\u53ef\u9760\u6027\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u4e00\u3001\u65b9\u5dee\u7684\u8ba1\u7b97\u65b9\u6cd5\u6982\u8ff0 \u5728Python\u4e2d\uff0c\u8ba1\u7b97\u65b9\u5dee\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5305\u62ec\u4f7f\u7528\u5185\u7f6e\u51fd\u6570\u3001Numpy\u5e93\u548cPandas\u5e93 […]","protected":false},"author":3,"featured_media":949380,"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\/949374"}],"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=949374"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/949374\/revisions"}],"predecessor-version":[{"id":949382,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/949374\/revisions\/949382"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/949380"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=949374"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=949374"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=949374"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}