{"id":1019775,"date":"2024-12-27T13:04:22","date_gmt":"2024-12-27T05:04:22","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1019775.html"},"modified":"2024-12-27T13:04:24","modified_gmt":"2024-12-27T05:04:24","slug":"python%e5%a6%82%e4%bd%95%e4%bd%bf%e7%94%a8%e6%95%b0%e5%ad%a6%e5%ba%93","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1019775.html","title":{"rendered":"python\u5982\u4f55\u4f7f\u7528\u6570\u5b66\u5e93"},"content":{"rendered":"

\"python\u5982\u4f55\u4f7f\u7528\u6570\u5b66\u5e93\"<\/p>\n

\u5728Python\u4e2d\u4f7f\u7528\u6570\u5b66\u5e93\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u5bfc\u5165Python\u7684\u5185\u7f6e\u6a21\u5757math\u3001\u4f7f\u7528NumPy\u5e93\u3001\u4ee5\u53ca\u4f7f\u7528SymPy\u5e93\u6765\u5b9e\u73b0\u5404\u79cd\u6570\u5b66\u8ba1\u7b97<\/strong>\u3002math\u6a21\u5757\u63d0\u4f9b\u4e86\u57fa\u672c\u7684\u6570\u5b66\u51fd\u6570\u548c\u5e38\u91cf<\/strong>\uff0c\u5982\u5e73\u65b9\u6839\u3001\u5bf9\u6570\u3001\u6b63\u5f26\u548c\u4f59\u5f26\uff1bNumPy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u5e93\uff0c\u7528\u4e8e\u5904\u7406\u6570\u7ec4\u548c\u77e9\u9635\u8fd0\u7b97<\/strong>\uff0c\u5e76\u4e14\u5177\u6709\u8bb8\u591a\u7528\u4e8e\u79d1\u5b66\u8ba1\u7b97\u7684\u9ad8\u7ea7\u529f\u80fd\uff1bSymPy\u5219\u7528\u4e8e\u7b26\u53f7\u6570\u5b66\u8ba1\u7b97<\/strong>\uff0c\u5982\u5fae\u79ef\u5206\u548c\u4ee3\u6570\u8868\u8fbe\u5f0f\u7684\u89e3\u6790\u3002\u4e0b\u9762\u6211\u4eec\u5c06\u8be6\u7ec6\u8ba8\u8bba\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u5e93\u6765\u8fdb\u884c\u5404\u79cd\u6570\u5b66\u8ba1\u7b97\u3002<\/p>\n<\/p>\n

\u4e00\u3001MATH\u6a21\u5757<\/p>\n<\/p>\n

math\u6a21\u5757\u662fPython\u7684\u6807\u51c6\u5e93\u4e4b\u4e00\uff0c\u63d0\u4f9b\u4e86\u4e00\u7cfb\u5217\u57fa\u672c\u7684\u6570\u5b66\u51fd\u6570\u548c\u5e38\u91cf\u3002\u5b83\u4e3b\u8981\u7528\u4e8e\u5b9e\u73b0\u4e00\u4e9b\u57fa\u7840\u7684\u6570\u5b66\u8fd0\u7b97\uff0c\u5bf9\u4e8e\u7b80\u5355\u7684\u6570\u5b66\u4efb\u52a1\uff0cmath\u6a21\u5757\u662f\u4e00\u4e2a\u65b9\u4fbf\u7684\u9009\u62e9\u3002<\/p>\n<\/p>\n

    \n
  1. \u5e38\u7528\u51fd\u6570<\/li>\n<\/ol>\n

    math\u6a21\u5757\u4e2d\u5305\u542b\u4e86\u4e00\u4e9b\u5e38\u7528\u7684\u6570\u5b66\u51fd\u6570\u3002\u6bd4\u5982\uff0cmath.sqrt(x)<\/code>\u7528\u4e8e\u8ba1\u7b97x\u7684\u5e73\u65b9\u6839\uff1bmath.log(x, base)<\/code>\u7528\u4e8e\u8ba1\u7b97\u4ee5base\u4e3a\u5e95x\u7684\u5bf9\u6570\uff0c\u5982\u679c\u4e0d\u6307\u5b9abase\uff0c\u5219\u9ed8\u8ba4\u4e3a\u81ea\u7136\u5bf9\u6570\uff1bmath.sin(x)<\/code>\u3001math.cos(x)<\/code>\u548cmath.tan(x)<\/code>\u7528\u4e8e\u8ba1\u7b97\u6b63\u5f26\u3001\u4f59\u5f26\u548c\u6b63\u5207\u7b49\u4e09\u89d2\u51fd\u6570\u3002<\/p>\n<\/p>\n

    import math<\/p>\n
    \u8ba1\u7b97\u5e73\u65b9\u6839<\/strong><\/h2>\n
    sqrt_value = math.sqrt(16)<\/p>\n
    \u8ba1\u7b97\u81ea\u7136\u5bf9\u6570<\/strong><\/h2>\n
    log_value = math.log(10)<\/p>\n
    \u8ba1\u7b97\u6b63\u5f26<\/strong><\/h2>\n
    sin_value = math.sin(math.pi \/ 2)<\/p>\n
    print(f"Square root: {sqrt_value}, Log: {log_value}, Sine: {sin_value}")<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u5e38\u91cf<\/li>\n<\/ol>\n
      math\u6a21\u5757\u8fd8\u63d0\u4f9b\u4e86\u4e00\u4e9b\u6570\u5b66\u5e38\u91cf\uff0c\u5982math.pi<\/code>\u8868\u793a\u5706\u5468\u7387\u03c0\uff0cmath.e<\/code>\u8868\u793a\u81ea\u7136\u5bf9\u6570\u7684\u5e95e\u3002\u901a\u8fc7\u4f7f\u7528\u8fd9\u4e9b\u5e38\u91cf\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u5404\u79cd\u6570\u5b66\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
      # \u4f7f\u7528\u5e38\u91cfpi\u8ba1\u7b97\u5706\u7684\u9762\u79ef<\/p>\n
      radius = 5<\/p>\n
      area = math.pi * radius2<\/p>\n
      print(f"Area of circle: {area}")<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u4e8c\u3001NUMPY\u5e93<\/p>\n<\/p>\n
      NumPy\u662f\u4e00\u4e2a\u7528\u4e8e\u79d1\u5b66\u8ba1\u7b97\u7684\u5f00\u6e90\u5e93\uff0c\u63d0\u4f9b\u4e86\u652f\u6301\u5927\u578b\u591a\u7ef4\u6570\u7ec4\u548c\u77e9\u9635\u7684\u8fd0\u7b97\u3002\u5b83\u8fd8\u5305\u542b\u4e86\u5927\u91cf\u7684\u6570\u5b66\u51fd\u6570\uff0c\u7528\u4e8e\u5bf9\u6570\u7ec4\u8fdb\u884c\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
        \n
      1. \u6570\u7ec4\u64cd\u4f5c<\/li>\n<\/ol>\n
        NumPy\u6700\u5f3a\u5927\u7684\u7279\u6027\u4e4b\u4e00\u662f\u5176\u5bf9\u6570\u7ec4\u7684\u9ad8\u6548\u64cd\u4f5c\u3002\u53ef\u4ee5\u8f7b\u677e\u5730\u521b\u5efa\u6570\u7ec4\uff0c\u5e76\u6267\u884c\u5404\u79cd\u6570\u5b66\u8fd0\u7b97\u3002NumPy\u7684\u6570\u7ec4\u6bd4Python\u7684\u5217\u8868\u66f4\u52a0\u9ad8\u6548\uff0c\u5c24\u5176\u662f\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\u3002<\/p>\n<\/p>\n
        import numpy as np<\/p>\n
        \u521b\u5efa\u4e00\u7ef4\u6570\u7ec4<\/strong><\/h2>\n
        array_1d = np.array([1, 2, 3, 4, 5])<\/p>\n
        \u521b\u5efa\u4e8c\u7ef4\u6570\u7ec4<\/strong><\/h2>\n
        array_2d = np.array([[1, 2, 3], [4, 5, 6]])<\/p>\n
        \u6570\u7ec4\u52a0\u6cd5<\/strong><\/h2>\n
        sum_array = array_1d + array_1d<\/p>\n
        print(f"Sum of arrays: {sum_array}")<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        1. \u6570\u5b66\u51fd\u6570<\/li>\n<\/ol>\n
          NumPy\u63d0\u4f9b\u4e86\u4e00\u7cfb\u5217\u7528\u4e8e\u6570\u7ec4\u64cd\u4f5c\u7684\u6570\u5b66\u51fd\u6570\u3002\u6bd4\u5982\uff0cnp.sum()<\/code>\u7528\u4e8e\u8ba1\u7b97\u6570\u7ec4\u7684\u5143\u7d20\u548c\uff0cnp.mean()<\/code>\u7528\u4e8e\u8ba1\u7b97\u6570\u7ec4\u7684\u5e73\u5747\u503c\uff0cnp.std()<\/code>\u7528\u4e8e\u8ba1\u7b97\u6570\u7ec4\u7684\u6807\u51c6\u5dee\uff0c\u7b49\u7b49\u3002<\/p>\n<\/p>\n
          # \u8ba1\u7b97\u6570\u7ec4\u7684\u5143\u7d20\u548c<\/p>\n
          sum_value = np.sum(array_1d)<\/p>\n
          \u8ba1\u7b97\u6570\u7ec4\u7684\u5e73\u5747\u503c<\/strong><\/h2>\n
          mean_value = np.mean(array_1d)<\/p>\n
          \u8ba1\u7b97\u6570\u7ec4\u7684\u6807\u51c6\u5dee<\/strong><\/h2>\n
          std_value = np.std(array_1d)<\/p>\n
          print(f"Sum: {sum_value}, Mean: {mean_value}, Standard Deviation: {std_value}")<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u4e09\u3001SYMPY\u5e93<\/p>\n<\/p>\n
          SymPy\u662f\u4e00\u4e2a\u7528\u4e8e\u7b26\u53f7\u6570\u5b66\u8ba1\u7b97\u7684Python\u5e93\uff0c\u9002\u5408\u9700\u8981\u5904\u7406\u7b26\u53f7\u8868\u8fbe\u5f0f\u548c\u8fdb\u884c\u7b26\u53f7\u8ba1\u7b97\u7684\u573a\u5408\u3002\u5b83\u80fd\u591f\u6267\u884c\u4ee3\u6570\u8fd0\u7b97\u3001\u5fae\u79ef\u5206\u3001\u65b9\u7a0b\u6c42\u89e3\u7b49\u4efb\u52a1\u3002<\/p>\n<\/p>\n
            \n
          1. \u7b26\u53f7\u8fd0\u7b97<\/li>\n<\/ol>\n
            SymPy\u7684\u4e00\u4e2a\u91cd\u8981\u7279\u6027\u662f\u7b26\u53f7\u8fd0\u7b97\u3002\u901a\u8fc7\u5b9a\u4e49\u7b26\u53f7\u53d8\u91cf\uff0c\u53ef\u4ee5\u5bf9\u4ee3\u6570\u8868\u8fbe\u5f0f\u8fdb\u884c\u5404\u79cd\u8fd0\u7b97\uff0c\u6bd4\u5982\u7b80\u5316\u3001\u5c55\u5f00\u3001\u56e0\u5f0f\u5206\u89e3\u7b49\u3002<\/p>\n<\/p>\n
            from sympy import symbols, expand, simplify<\/p>\n
            \u5b9a\u4e49\u7b26\u53f7\u53d8\u91cf<\/strong><\/h2>\n
            x, y = symbols('x y')<\/p>\n
            \u5c55\u5f00\u8868\u8fbe\u5f0f<\/strong><\/h2>\n
            expanded_expr = expand((x + y)2)<\/p>\n
            \u7b80\u5316\u8868\u8fbe\u5f0f<\/strong><\/h2>\n
            simplified_expr = simplify(x<strong>2 + 2*x*y + y<\/strong>2)<\/p>\n
            print(f"Expanded: {expanded_expr}, Simplified: {simplified_expr}")<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. \u5fae\u79ef\u5206\u548c\u65b9\u7a0b\u6c42\u89e3<\/li>\n<\/ol>\n
              SymPy\u8fd8\u53ef\u4ee5\u7528\u4e8e\u6267\u884c\u5fae\u79ef\u5206\u8fd0\u7b97\u548c\u6c42\u89e3\u65b9\u7a0b\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u8ba1\u7b97\u51fd\u6570\u7684\u5bfc\u6570\u3001\u79ef\u5206\uff0c\u4ee5\u53ca\u6c42\u89e3\u4ee3\u6570\u65b9\u7a0b\u3002<\/p>\n<\/p>\n
              from sympy import diff, integrate, solve<\/p>\n
              \u8ba1\u7b97\u5bfc\u6570<\/strong><\/h2>\n
              derivative = diff(x2 + x, x)<\/p>\n
              \u8ba1\u7b97\u4e0d\u5b9a\u79ef\u5206<\/strong><\/h2>\n
              integral = integrate(x2 + x, x)<\/p>\n
              \u89e3\u65b9\u7a0b<\/strong><\/h2>\n
              solution = solve(x2 - 4, x)<\/p>\n
              print(f"Derivative: {derivative}, Integral: {integral}, Solution: {solution}")<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u56db\u3001\u4f7f\u7528SCIPY\u8fdb\u884c\u9ad8\u7ea7\u6570\u5b66\u8fd0\u7b97<\/p>\n<\/p>\n
              SciPy\u662f\u57fa\u4e8eNumPy\u7684\u4e00\u4e2a\u7528\u4e8e\u79d1\u5b66\u8ba1\u7b97\u7684\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u66f4\u591a\u9ad8\u7ea7\u7684\u6570\u5b66\u51fd\u6570\u548c\u7b97\u6cd5\uff0c\u9002\u7528\u4e8e\u9700\u8981\u590d\u6742\u6570\u5b66\u8fd0\u7b97\u7684\u573a\u5408\u3002<\/p>\n<\/p>\n
                \n
              1. \u7ebf\u6027\u4ee3\u6570\u8fd0\u7b97<\/li>\n<\/ol>\n
                SciPy\u7684\u7ebf\u6027\u4ee3\u6570\u6a21\u5757scipy.linalg<\/code>\u63d0\u4f9b\u4e86\u4e00\u4e9b\u9ad8\u7ea7\u7ebf\u6027\u4ee3\u6570\u8fd0\u7b97\uff0c\u6bd4\u5982\u77e9\u9635\u5206\u89e3\u3001\u7279\u5f81\u503c\u8ba1\u7b97\u7b49\u3002<\/p>\n<\/p>\n
                import scipy.linalg as la<\/p>\n
                \u5b9a\u4e49\u77e9\u9635<\/strong><\/h2>\n
                matrix = np.array([[1, 2], [3, 4]])<\/p>\n
                \u8ba1\u7b97\u77e9\u9635\u7684\u884c\u5217\u5f0f<\/strong><\/h2>\n
                determinant = la.det(matrix)<\/p>\n
                \u8ba1\u7b97\u77e9\u9635\u7684\u9006<\/strong><\/h2>\n
                inverse = la.inv(matrix)<\/p>\n
                print(f"Determinant: {determinant}, Inverse: {inverse}")<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                  \n
                1. \u4f18\u5316\u548c\u65b9\u7a0b\u6c42\u89e3<\/li>\n<\/ol>\n
                  SciPy\u8fd8\u63d0\u4f9b\u4e86\u7528\u4e8e\u4f18\u5316\u548c\u65b9\u7a0b\u6c42\u89e3\u7684\u6a21\u5757\uff0c\u53ef\u4ee5\u7528\u4e8e\u6c42\u89e3\u975e\u7ebf\u6027\u65b9\u7a0b\u7ec4\u3001\u6267\u884c\u6700\u5c0f\u5316\u7b49\u4efb\u52a1\u3002<\/p>\n<\/p>\n
                  from scipy.optimize import minimize<\/p>\n
                  \u5b9a\u4e49\u76ee\u6807\u51fd\u6570<\/strong><\/h2>\n
                  def objective_function(x):<\/p>\n
                      return x2 + x + 2<\/p>\n
                  \u6267\u884c\u4f18\u5316<\/strong><\/h2>\n
                  result = minimize(objective_function, x0=0)<\/p>\n
                  optimal_value = result.x<\/p>\n
                  print(f"Optimal Value: {optimal_value}")<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u4e94\u3001MATPLOTLIB\u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316<\/p>\n<\/p>\n
                  \u867d\u7136Matplotlib\u5e76\u4e0d\u662f\u4e13\u95e8\u7528\u4e8e\u6570\u5b66\u8ba1\u7b97\u7684\u5e93\uff0c\u4f46\u5b83\u53ef\u4ee5\u7528\u4e8e\u53ef\u89c6\u5316\u6570\u5b66\u51fd\u6570\u548c\u6570\u636e\u3002\u901a\u8fc7\u56fe\u5f62\u5316\u8868\u793a\uff0c\u53ef\u4ee5\u66f4\u597d\u5730\u7406\u89e3\u6570\u5b66\u8fd0\u7b97\u7684\u7ed3\u679c\u3002<\/p>\n<\/p>\n
                    \n
                  1. \u7ed8\u5236\u57fa\u672c\u56fe\u5f62<\/li>\n<\/ol>\n
                    Matplotlib\u53ef\u4ee5\u7528\u6765\u7ed8\u5236\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u5f62\uff0c\u6bd4\u5982\u6298\u7ebf\u56fe\u3001\u6563\u70b9\u56fe\u3001\u76f4\u65b9\u56fe\u7b49\u3002\u901a\u8fc7\u8fd9\u4e9b\u56fe\u5f62\uff0c\u53ef\u4ee5\u76f4\u89c2\u5730\u89c2\u5bdf\u6570\u636e\u7684\u5206\u5e03\u548c\u53d8\u5316\u8d8b\u52bf\u3002<\/p>\n<\/p>\n
                    import matplotlib.pyplot as plt<\/p>\n
                    \u5b9a\u4e49\u6570\u636e<\/strong><\/h2>\n
                    x = np.linspace(-10, 10, 100)<\/p>\n
                    y = x2<\/p>\n
                    \u7ed8\u5236\u6298\u7ebf\u56fe<\/strong><\/h2>\n
                    plt.plot(x, y, label='y = x^2')<\/p>\n
                    plt.xlabel('x')<\/p>\n
                    plt.ylabel('y')<\/p>\n
                    plt.title('Plot of y = x^2')<\/p>\n
                    plt.legend()<\/p>\n
                    plt.show()<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                      \n
                    1. \u9ad8\u7ea7\u56fe\u5f62\u529f\u80fd<\/li>\n<\/ol>\n
                      Matplotlib\u8fd8\u63d0\u4f9b\u4e86\u4e00\u4e9b\u9ad8\u7ea7\u56fe\u5f62\u529f\u80fd\uff0c\u6bd4\u59823D\u7ed8\u56fe\u3001\u70ed\u56fe\u7b49\uff0c\u53ef\u4ee5\u7528\u4e8e\u5c55\u793a\u590d\u6742\u7684\u6570\u636e\u7ed3\u6784\u548c\u6570\u5b66\u51fd\u6570\u3002<\/p>\n<\/p>\n
                      from mpl_toolkits.mplot3d import Axes3D<\/p>\n
                      \u521b\u5efa3D\u6570\u636e<\/strong><\/h2>\n
                      x = np.linspace(-5, 5, 100)<\/p>\n
                      y = np.linspace(-5, 5, 100)<\/p>\n
                      x, y = np.meshgrid(x, y)<\/p>\n
                      z = np.sin(np.sqrt(x<strong>2 + y<\/strong>2))<\/p>\n
                      \u7ed8\u52363D\u66f2\u9762<\/strong><\/h2>\n
                      fig = plt.figure()<\/p>\n
                      ax = fig.add_subplot(111, projection='3d')<\/p>\n
                      ax.plot_surface(x, y, z, cmap='viridis')<\/p>\n
                      plt.show()<\/p>\n
                      <\/code><\/pre>\n<\/p>\n
                      \u516d\u3001PANDAS\u7528\u4e8e\u6570\u636e\u5904\u7406\u548c\u5206\u6790<\/p>\n<\/p>\n
                      \u867d\u7136Pandas\u4e3b\u8981\u7528\u4e8e\u6570\u636e\u5904\u7406\u548c\u5206\u6790\uff0c\u4f46\u5b83\u4e5f\u652f\u6301\u4e00\u4e9b\u57fa\u672c\u7684\u6570\u5b66\u8fd0\u7b97\uff0c\u5c24\u5176\u662f\u5728\u5904\u7406\u6570\u636e\u6846\u548c\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u65f6\u3002<\/p>\n<\/p>\n
                        \n
                      1. \u6570\u636e\u6846\u8fd0\u7b97<\/li>\n<\/ol>\n
                        Pandas\u7684\u6570\u636e\u6846\u7c7b\u4f3c\u4e8e\u6570\u636e\u5e93\u4e2d\u7684\u8868\u683c\uff0c\u53ef\u4ee5\u7528\u4e8e\u5b58\u50a8\u548c\u64cd\u4f5c\u7ed3\u6784\u5316\u6570\u636e\u3002\u652f\u6301\u5404\u79cd\u6570\u5b66\u8fd0\u7b97\uff0c\u6bd4\u5982\u52a0\u51cf\u4e58\u9664\u3001\u7edf\u8ba1\u5206\u6790\u7b49\u3002<\/p>\n<\/p>\n
                        import pandas as pd<\/p>\n
                        \u521b\u5efa\u6570\u636e\u6846<\/strong><\/h2>\n
                        data = {'A': [1, 2, 3], 'B': [4, 5, 6]}<\/p>\n
                        df = pd.DataFrame(data)<\/p>\n
                        \u8ba1\u7b97\u5217\u7684\u548c<\/strong><\/h2>\n
                        sum_A = df['A'].sum()<\/p>\n
                        \u8ba1\u7b97\u5217\u7684\u5e73\u5747\u503c<\/strong><\/h2>\n
                        mean_B = df['B'].mean()<\/p>\n
                        print(f"Sum of A: {sum_A}, Mean of B: {mean_B}")<\/p>\n
                        <\/code><\/pre>\n<\/p>\n
                          \n
                        1. \u65f6\u95f4\u5e8f\u5217\u5206\u6790<\/li>\n<\/ol>\n
                          Pandas\u8fd8\u652f\u6301\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u5904\u7406\uff0c\u53ef\u4ee5\u8fdb\u884c\u65f6\u95f4\u5e8f\u5217\u7684\u6ed1\u52a8\u7a97\u53e3\u8ba1\u7b97\u3001\u6307\u6570\u5e73\u6ed1\u7b49\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
                          # \u521b\u5efa\u65f6\u95f4\u5e8f\u5217<\/p>\n
                          dates = pd.date_range('20230101', periods=6)<\/p>\n
                          ts = pd.Series([1, 2, 3, 4, 5, 6], index=dates)<\/p>\n
                          \u6ed1\u52a8\u7a97\u53e3\u5e73\u5747\u503c<\/strong><\/h2>\n
                          rolling_mean = ts.rolling(window=3).mean()<\/p>\n
                          print(f"Rolling Mean:\\n{rolling_mean}")<\/p>\n
                          <\/code><\/pre>\n<\/p>\n
                          \u4e03\u3001\u4f7f\u7528SCIKIT-LEARN\u8fdb\u884c\u673a\u5668\u5b66\u4e60<\/a><\/p>\n<\/p>\n
                          Scikit-learn\u662f\u4e00\u4e2a\u7528\u4e8e\u673a\u5668\u5b66\u4e60\u7684Python\u5e93\uff0c\u63d0\u4f9b\u4e86\u4e00\u7cfb\u5217\u7528\u4e8e\u5206\u7c7b\u3001\u56de\u5f52\u3001\u805a\u7c7b\u7b49\u4efb\u52a1\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u3002<\/p>\n<\/p>\n
                            \n
                          1. \u7ebf\u6027\u56de\u5f52<\/li>\n<\/ol>\n
                            Scikit-learn\u7684\u7ebf\u6027\u6a21\u578b\u6a21\u5757sklearn.linear_model<\/code>\u53ef\u4ee5\u7528\u4e8e\u6267\u884c\u7ebf\u6027\u56de\u5f52\u5206\u6790\uff0c\u9002\u7528\u4e8e\u7814\u7a76\u81ea\u53d8\u91cf\u548c\u56e0\u53d8\u91cf\u4e4b\u95f4\u7684\u7ebf\u6027\u5173\u7cfb\u3002<\/p>\n<\/p>\n
                            from sklearn.linear_model import LinearRegression<\/p>\n
                            \u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
                            X = np.array([[1], [2], [3], [4], [5]])<\/p>\n
                            y = np.array([1, 2, 3, 4, 5])<\/p>\n
                            \u521b\u5efa\u5e76\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
                            model = LinearRegression()<\/p>\n
                            model.fit(X, y)<\/p>\n
                            \u9884\u6d4b<\/strong><\/h2>\n
                            predictions = model.predict(X)<\/p>\n
                            print(f"Predictions: {predictions}")<\/p>\n
                            <\/code><\/pre>\n<\/p>\n
                              \n
                            1. \u805a\u7c7b\u5206\u6790<\/li>\n<\/ol>\n
                              Scikit-learn\u7684\u805a\u7c7b\u6a21\u5757sklearn.cluster<\/code>\u63d0\u4f9b\u4e86\u5404\u79cd\u805a\u7c7b\u7b97\u6cd5\uff0c\u6bd4\u5982K\u5747\u503c\u805a\u7c7b\uff0c\u53ef\u4ee5\u7528\u4e8e\u5c06\u6570\u636e\u5206\u4e3a\u591a\u4e2a\u7c7b\u522b\u3002<\/p>\n<\/p>\n
                              from sklearn.cluster import KMeans<\/p>\n
                              \u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
                              data = np.array([[1, 2], [1, 4], [1, 0],<\/p>\n
                                               [4, 2], [4, 4], [4, 0]])<\/p>\n
                              \u6267\u884cK\u5747\u503c\u805a\u7c7b<\/strong><\/h2>\n
                              kmeans = KMeans(n_clusters=2, random_state=0)<\/p>\n
                              kmeans.fit(data)<\/p>\n
                              \u83b7\u53d6\u805a\u7c7b\u7ed3\u679c<\/strong><\/h2>\n
                              labels = kmeans.labels_<\/p>\n
                              print(f"Cluster Labels: {labels}")<\/p>\n
                              <\/code><\/pre>\n<\/p>\n
                              \u901a\u8fc7\u8fd9\u4e9bPython\u6570\u5b66\u5e93\u548c\u5de5\u5177\uff0c\u60a8\u53ef\u4ee5\u6267\u884c\u5404\u79cd\u6570\u5b66\u8fd0\u7b97\u548c\u6570\u636e\u5206\u6790\u4efb\u52a1\u3002\u65e0\u8bba\u662f\u57fa\u672c\u7684\u6570\u5b66\u8ba1\u7b97\u3001\u9ad8\u7ea7\u7684\u79d1\u5b66\u8ba1\u7b97\uff0c\u8fd8\u662f\u6570\u636e\u53ef\u89c6\u5316\u548c\u673a\u5668\u5b66\u4e60\uff0cPython\u90fd\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u529f\u80fd\u548c\u4fbf\u6377\u7684\u64cd\u4f5c\u3002\u5bf9\u4e8e\u9700\u8981\u8fdb\u884c\u590d\u6742\u6570\u5b66\u8fd0\u7b97\u548c\u5206\u6790\u7684\u573a\u5408\uff0c\u8fd9\u4e9b\u5e93\u548c\u5de5\u5177\u90fd\u662f\u4e0d\u53ef\u6216\u7f3a\u7684\u3002<\/p>\n<\/p>\n
                              \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                               \u5728Python\u4e2d\u5982\u4f55\u5bfc\u5165\u6570\u5b66\u5e93\uff1f<\/strong>
                              \u8981\u4f7f\u7528Python\u7684\u6570\u5b66\u5e93\uff0c\u60a8\u9700\u8981\u5728\u4ee3\u7801\u7684\u5f00\u5934\u5bfc\u5165\u5b83\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u53e5\uff1aimport math<\/code>\u3002\u5bfc\u5165\u540e\uff0c\u60a8\u5c31\u53ef\u4ee5\u8bbf\u95ee\u5e93\u4e2d\u7684\u5404\u79cd\u6570\u5b66\u51fd\u6570\u548c\u5e38\u91cf\uff0c\u4f8b\u5982math.pi<\/code>\u548cmath.sqrt()<\/code>\u7b49\u3002<\/p>\n
                              Python\u6570\u5b66\u5e93\u63d0\u4f9b\u4e86\u54ea\u4e9b\u5e38\u7528\u7684\u6570\u5b66\u51fd\u6570\uff1f<\/strong>
                              Python\u7684\u6570\u5b66\u5e93\u5305\u542b\u4e86\u8bb8\u591a\u5e38\u7528\u7684\u6570\u5b66\u51fd\u6570\uff0c\u5982\u4e09\u89d2\u51fd\u6570\uff08math.sin()<\/code>\u3001math.cos()<\/code>\u3001math.tan()<\/code>\uff09\u3001\u5bf9\u6570\u51fd\u6570\uff08math.log()<\/code>\uff09\u3001\u5e73\u65b9\u6839\uff08math.sqrt()<\/code>\uff09\u4ee5\u53ca\u5e38\u6570\uff08\u5982math.pi<\/code>\u3001math.e<\/code>\uff09\u3002\u8fd9\u4e9b\u51fd\u6570\u53ef\u4ee5\u5e2e\u52a9\u7528\u6237\u8fdb\u884c\u590d\u6742\u7684\u6570\u5b66\u8ba1\u7b97\u3002<\/p>\n
                              \u5982\u4f55\u4f7f\u7528Python\u6570\u5b66\u5e93\u8fdb\u884c\u7edf\u8ba1\u8ba1\u7b97\uff1f<\/strong>
                              \u867d\u7136Python\u7684\u6570\u5b66\u5e93\u4e3b\u8981\u4fa7\u91cd\u4e8e\u57fa\u7840\u6570\u5b66\u8ba1\u7b97\uff0c\u4f46\u60a8\u53ef\u4ee5\u7ed3\u5408\u4f7f\u7528math<\/code>\u5e93\u548c\u5176\u4ed6\u5e93\uff08\u5982statistics<\/code>\u6216numpy<\/code>\uff09\u6765\u5b8c\u6210\u7edf\u8ba1\u8ba1\u7b97\u3002\u6bd4\u5982\uff0c\u4f7f\u7528numpy.mean()<\/code>\u53ef\u4ee5\u8ba1\u7b97\u5e73\u5747\u503c\uff0cnumpy.std()<\/code>\u53ef\u4ee5\u8ba1\u7b97\u6807\u51c6\u5dee\uff0c\u8fd9\u4e9b\u529f\u80fd\u589e\u5f3a\u4e86Python\u5728\u6570\u636e\u5206\u6790\u548c\u7edf\u8ba1\u9886\u57df\u7684\u5e94\u7528\u80fd\u529b\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u4f7f\u7528\u6570\u5b66\u5e93\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u5bfc\u5165Python\u7684\u5185\u7f6e\u6a21\u5757math\u3001\u4f7f\u7528NumPy\u5e93\u3001\u4ee5\u53ca\u4f7f\u7528SymPy […]","protected":false},"author":3,"featured_media":1019778,"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\/1019775"}],"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=1019775"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1019775\/revisions"}],"predecessor-version":[{"id":1019779,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1019775\/revisions\/1019779"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1019778"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1019775"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1019775"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1019775"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}