{"id":958704,"date":"2024-12-27T03:32:42","date_gmt":"2024-12-26T19:32:42","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/958704.html"},"modified":"2024-12-27T03:32:44","modified_gmt":"2024-12-26T19:32:44","slug":"python%e5%a6%82%e4%bd%95%e6%9e%84%e9%80%a0%e5%a4%9a%e7%bb%b4%e6%95%b0%e7%bb%84","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/958704.html","title":{"rendered":"python\u5982\u4f55\u6784\u9020\u591a\u7ef4\u6570\u7ec4"},"content":{"rendered":"

\"python\u5982\u4f55\u6784\u9020\u591a\u7ef4\u6570\u7ec4\"<\/p>\n

\u5728Python\u4e2d\u6784\u9020\u591a\u7ef4\u6570\u7ec4\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528\u5d4c\u5957\u5217\u8868\u3001NumPy\u5e93\u3001\u6570\u7ec4\u63a8\u5bfc\u7b49\u3002\u5d4c\u5957\u5217\u8868\u63d0\u4f9b\u4e86\u57fa\u672c\u7684\u591a\u7ef4\u6570\u7ec4\u7ed3\u6784\u3001NumPy\u5e93\u63d0\u4f9b\u4e86\u9ad8\u6548\u4e14\u529f\u80fd\u4e30\u5bcc\u7684\u591a\u7ef4\u6570\u7ec4\u652f\u6301\u3001\u6570\u7ec4\u63a8\u5bfc\u53ef\u4ee5\u7b80\u5316\u6570\u7ec4\u7684\u521b\u5efa\u8fc7\u7a0b\u3002<\/strong>\u5176\u4e2d\uff0cNumPy\u56e0\u5176\u5f3a\u5927\u7684\u529f\u80fd\u548c\u4e30\u5bcc\u7684\u64cd\u4f5c\u800c\u88ab\u5e7f\u6cdb\u4f7f\u7528\uff0c\u7279\u522b\u662f\u5728\u79d1\u5b66\u8ba1\u7b97\u548c\u6570\u636e\u5206\u6790\u9886\u57df\u3002\u4e0b\u9762\u6211\u4eec\u5c06\u8be6\u7ec6\u8ba8\u8bba\u8fd9\u4e9b\u65b9\u6cd5\u4ee5\u53ca\u5b83\u4eec\u7684\u4f18\u7f3a\u70b9\u548c\u4f7f\u7528\u573a\u666f\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528\u5d4c\u5957\u5217\u8868\u6784\u9020\u591a\u7ef4\u6570\u7ec4<\/p>\n<\/p>\n

\u5d4c\u5957\u5217\u8868\u662fPython\u4e2d\u6784\u9020\u591a\u7ef4\u6570\u7ec4\u6700\u76f4\u63a5\u7684\u65b9\u6cd5\u3002\u901a\u8fc7\u5c06\u5217\u8868\u5d4c\u5957\u5728\u53e6\u4e00\u4e2a\u5217\u8868\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u521b\u5efa\u4e8c\u7ef4\u6216\u66f4\u9ad8\u7ef4\u5ea6\u7684\u6570\u7ec4\u3002<\/p>\n<\/p>\n

    \n
  1. \u4e8c\u7ef4\u6570\u7ec4<\/strong><\/li>\n<\/ol>\n

    \u4e8c\u7ef4\u6570\u7ec4\u53ef\u4ee5\u770b\u4f5c\u662f\u5217\u8868\u7684\u5217\u8868\uff0c\u5176\u4e2d\u6bcf\u4e2a\u5b50\u5217\u8868\u4ee3\u8868\u6570\u7ec4\u7684\u4e00\u884c\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n

    # \u521b\u5efa\u4e00\u4e2a3x3\u7684\u4e8c\u7ef4\u6570\u7ec4<\/p>\n
    array_2d = [<\/p>\n
        [1, 2, 3],<\/p>\n
        [4, 5, 6],<\/p>\n
        [7, 8, 9]<\/p>\n
    ]<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u8fd9\u79cd\u65b9\u6cd5\u7b80\u5355\u6613\u61c2\uff0c\u4f46\u5728\u5904\u7406\u5927\u578b\u6570\u7ec4\u65f6\u6548\u7387\u8f83\u4f4e\uff0c\u56e0\u4e3aPython\u7684\u5217\u8868\u4e0d\u662f\u4e3a\u6570\u503c\u8ba1\u7b97\u800c\u4f18\u5316\u7684\u3002<\/p>\n<\/p>\n
      \n
    1. \u4e09\u7ef4\u53ca\u66f4\u9ad8\u7ef4\u5ea6\u6570\u7ec4<\/strong><\/li>\n<\/ol>\n
      \u540c\u6837\u7684\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u5d4c\u5957\u66f4\u591a\u5c42\u7684\u5217\u8868\u6765\u521b\u5efa\u4e09\u7ef4\u6216\u66f4\u9ad8\u7ef4\u5ea6\u7684\u6570\u7ec4\uff1a<\/p>\n<\/p>\n
      # \u521b\u5efa\u4e00\u4e2a2x2x2\u7684\u4e09\u7ef4\u6570\u7ec4<\/p>\n
      array_3d = [<\/p>\n
          [<\/p>\n
              [1, 2],<\/p>\n
              [3, 4]<\/p>\n
          ],<\/p>\n
          [<\/p>\n
              [5, 6],<\/p>\n
              [7, 8]<\/p>\n
          ]<\/p>\n
      ]<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u7136\u800c\uff0c\u968f\u7740\u7ef4\u5ea6\u7684\u589e\u52a0\uff0c\u7ba1\u7406\u548c\u64cd\u4f5c\u6570\u636e\u53d8\u5f97\u590d\u6742\uff0c\u5efa\u8bae\u4f7f\u7528NumPy\u5e93\u6765\u5904\u7406\u66f4\u9ad8\u7ef4\u5ea6\u7684\u6570\u7ec4\u3002<\/p>\n<\/p>\n
      \u4e8c\u3001\u4f7f\u7528NumPy\u5e93\u6784\u9020\u591a\u7ef4\u6570\u7ec4<\/p>\n<\/p>\n
      NumPy\u662fPython\u4e2d\u5904\u7406\u591a\u7ef4\u6570\u7ec4\u7684\u6807\u51c6\u5e93\u3002\u5b83\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u529f\u80fd\u548c\u6781\u9ad8\u7684\u6548\u7387\uff0c\u9002\u5408\u9700\u8981\u5927\u91cf\u6570\u636e\u8ba1\u7b97\u7684\u5e94\u7528\u3002<\/p>\n<\/p>\n
        \n
      1. \u5b89\u88c5NumPy<\/strong><\/li>\n<\/ol>\n
        \u5728\u4f7f\u7528NumPy\u4e4b\u524d\uff0c\u786e\u4fdd\u5df2\u901a\u8fc7pip\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
        pip install numpy<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        1. \u521b\u5efa\u6570\u7ec4<\/strong><\/li>\n<\/ol>\n
          NumPy\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u521b\u5efa\u6570\u7ec4\uff0c\u5305\u62ec\u4ece\u5217\u8868\u521b\u5efa\u3001\u4f7f\u7528\u5185\u7f6e\u51fd\u6570\u521b\u5efa\u7b49\u3002<\/p>\n<\/p>\n
          import numpy as np<\/p>\n
          \u4ece\u5217\u8868\u521b\u5efa\u4e8c\u7ef4\u6570\u7ec4<\/strong><\/h2>\n
          array_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])<\/p>\n
          \u4f7f\u7528\u5185\u7f6e\u51fd\u6570\u521b\u5efa\u6570\u7ec4<\/strong><\/h2>\n
          zeros_array = np.zeros((3, 3))  # \u521b\u5efa\u4e00\u4e2a3x3\u7684\u96f6\u77e9\u9635<\/p>\n
          ones_array = np.ones((2, 2, 2))  # \u521b\u5efa\u4e00\u4e2a2x2x2\u7684\u51681\u6570\u7ec4<\/p>\n
          <\/code><\/pre>\n<\/p>\n
            \n
          1. \u6570\u7ec4\u5c5e\u6027\u548c\u64cd\u4f5c<\/strong><\/li>\n<\/ol>\n
            NumPy\u6570\u7ec4\u5177\u6709\u4e30\u5bcc\u7684\u5c5e\u6027\u548c\u65b9\u6cd5\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u7ec4\u64cd\u4f5c\uff1a<\/p>\n<\/p>\n
            # \u83b7\u53d6\u6570\u7ec4\u7684\u5f62\u72b6<\/p>\n
            shape = array_2d.shape<\/p>\n
            \u6570\u7ec4\u7684\u57fa\u672c\u8fd0\u7b97<\/strong><\/h2>\n
            sum_array = array_2d + 1<\/p>\n
            product_array = array_2d * 2<\/p>\n
            \u8bbf\u95ee\u548c\u4fee\u6539\u5143\u7d20<\/strong><\/h2>\n
            element = array_2d[1, 2]  # \u8bbf\u95ee\u7b2c\u4e8c\u884c\u7b2c\u4e09\u5217\u7684\u5143\u7d20<\/p>\n
            array_2d[0, 0] = 10  # \u4fee\u6539\u7b2c\u4e00\u884c\u7b2c\u4e00\u5217\u7684\u5143\u7d20<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u4e09\u3001\u4f7f\u7528\u6570\u7ec4\u63a8\u5bfc\u7b80\u5316\u6570\u7ec4\u521b\u5efa<\/p>\n<\/p>\n
            \u6570\u7ec4\u63a8\u5bfc\u53ef\u4ee5\u7528\u4e8e\u7b80\u5316\u6570\u7ec4\u7684\u521b\u5efa\u8fc7\u7a0b\uff0c\u7279\u522b\u662f\u5728\u9700\u8981\u521d\u59cb\u5316\u7279\u5b9a\u6a21\u5f0f\u7684\u6570\u7ec4\u65f6\u3002<\/p>\n<\/p>\n
              \n
            1. \u57fa\u672c\u6570\u7ec4\u63a8\u5bfc<\/strong><\/li>\n<\/ol>\n
              \u4f7f\u7528\u6570\u7ec4\u63a8\u5bfc\u53ef\u4ee5\u5feb\u901f\u521b\u5efa\u4e00\u4e2a\u6ee1\u8db3\u7279\u5b9a\u6761\u4ef6\u7684\u6570\u7ec4\uff1a<\/p>\n<\/p>\n
              # \u521b\u5efa\u4e00\u4e2a\u5305\u542b0\u52309\u7684\u5e73\u65b9\u6570\u7684\u6570\u7ec4<\/p>\n
              squared_array = [x2 for x in range(10)]<\/p>\n
              <\/code><\/pre>\n<\/p>\n
                \n
              1. \u591a\u7ef4\u6570\u7ec4\u63a8\u5bfc<\/strong><\/li>\n<\/ol>\n
                \u5bf9\u4e8e\u591a\u7ef4\u6570\u7ec4\u63a8\u5bfc\uff0c\u6211\u4eec\u53ef\u4ee5\u5d4c\u5957\u4f7f\u7528\u63a8\u5bfc\u5f0f\uff1a<\/p>\n<\/p>\n
                # \u521b\u5efa\u4e00\u4e2a3x3\u7684\u4e8c\u7ef4\u6570\u7ec4\uff0c\u5143\u7d20\u4e3a\u884c\u7d22\u5f15\u548c\u5217\u7d22\u5f15\u4e4b\u548c<\/p>\n
                array_2d = [[i + j for j in range(3)] for i in range(3)]<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                \u56db\u3001NumPy\u7684\u9ad8\u7ea7\u529f\u80fd<\/p>\n<\/p>\n
                NumPy\u4e0d\u4ec5\u652f\u6301\u57fa\u672c\u7684\u6570\u7ec4\u521b\u5efa\u548c\u64cd\u4f5c\uff0c\u8fd8\u63d0\u4f9b\u4e86\u4e00\u4e9b\u9ad8\u7ea7\u529f\u80fd\uff0c\u5982\u5e7f\u64ad\u3001\u77e9\u9635\u8fd0\u7b97\u3001\u7ebf\u6027\u4ee3\u6570\u7b49\u3002<\/p>\n<\/p>\n
                  \n
                1. \u5e7f\u64ad<\/strong><\/li>\n<\/ol>\n
                  \u5e7f\u64ad\u662fNumPy\u4e2d\u4e00\u4e2a\u5f3a\u5927\u7684\u529f\u80fd\uff0c\u5b83\u5141\u8bb8\u4e0d\u540c\u5f62\u72b6\u7684\u6570\u7ec4\u8fdb\u884c\u8fd0\u7b97\uff1a<\/p>\n<\/p>\n
                  a = np.array([1, 2, 3])<\/p>\n
                  b = np.array([[1], [2], [3]])<\/p>\n
                  result = a + b<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u5728\u8fd9\u91cc\uff0ca<\/code>\u548cb<\/code>\u7684\u5f62\u72b6\u4e0d\u540c\uff0c\u4f46NumPy\u901a\u8fc7\u5e7f\u64ad\u673a\u5236\u81ea\u52a8\u6269\u5c55\u5b83\u4eec\u4ee5\u8fdb\u884c\u52a0\u6cd5\u8fd0\u7b97\u3002<\/p>\n<\/p>\n
                    \n
                  1. \u77e9\u9635\u8fd0\u7b97<\/strong><\/li>\n<\/ol>\n
                    NumPy\u652f\u6301\u591a\u79cd\u77e9\u9635\u8fd0\u7b97\uff0c\u5305\u62ec\u70b9\u79ef\u3001\u77e9\u9635\u4e58\u6cd5\u7b49\uff1a<\/p>\n<\/p>\n
                    a = np.array([[1, 2], [3, 4]])<\/p>\n
                    b = np.array([[5, 6], [7, 8]])<\/p>\n
                    \u77e9\u9635\u4e58\u6cd5<\/strong><\/h2>\n
                    product = np.dot(a, b)<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                      \n
                    1. \u7ebf\u6027\u4ee3\u6570<\/strong><\/li>\n<\/ol>\n
                      NumPy\u7684\u7ebf\u6027\u4ee3\u6570\u6a21\u5757\u63d0\u4f9b\u4e86\u6c42\u89e3\u7ebf\u6027\u65b9\u7a0b\u7ec4\u3001\u77e9\u9635\u5206\u89e3\u7b49\u529f\u80fd\uff1a<\/p>\n<\/p>\n
                      # \u6c42\u89e3\u7ebf\u6027\u65b9\u7a0b\u7ec4Ax = b<\/p>\n
                      A = np.array([[1, 2], [3, 4]])<\/p>\n
                      b = np.array([5, 11])<\/p>\n
                      x = np.linalg.solve(A, b)<\/p>\n
                      <\/code><\/pre>\n<\/p>\n
                      \u4e94\u3001NumPy\u4e0e\u5176\u4ed6\u5e93\u7684\u96c6\u6210<\/p>\n<\/p>\n
                      NumPy\u5e38\u4e0e\u5176\u4ed6\u79d1\u5b66\u8ba1\u7b97\u5e93\u96c6\u6210\u4f7f\u7528\uff0c\u4f8b\u5982SciPy\u3001Pandas\u3001Matplotlib\u7b49\uff0c\u4ee5\u63d0\u4f9b\u66f4\u4e30\u5bcc\u7684\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\u529f\u80fd\u3002<\/p>\n<\/p>\n
                        \n
                      1. \u4e0eSciPy\u7ed3\u5408<\/strong><\/li>\n<\/ol>\n
                        SciPy\u5efa\u7acb\u5728NumPy\u4e4b\u4e0a\uff0c\u63d0\u4f9b\u4e86\u66f4\u591a\u7684\u79d1\u5b66\u8ba1\u7b97\u529f\u80fd\uff0c\u5982\u4f18\u5316\u3001\u4fe1\u53f7\u5904\u7406\u7b49\uff1a<\/p>\n<\/p>\n
                        from scipy.optimize import minimize<\/p>\n
                        \u4f7f\u7528SciPy\u8fdb\u884c\u51fd\u6570\u6700\u5c0f\u5316<\/strong><\/h2>\n
                        result = minimize(lambda x: x2, 0)<\/p>\n
                        <\/code><\/pre>\n<\/p>\n
                          \n
                        1. \u4e0ePandas\u7ed3\u5408<\/strong><\/li>\n<\/ol>\n
                          Pandas\u4f7f\u7528NumPy\u6570\u7ec4\u4f5c\u4e3a\u5176\u6570\u636e\u7ed3\u6784\u7684\u57fa\u7840\uff0c\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u64cd\u4f5c\u548c\u5206\u6790\u529f\u80fd\uff1a<\/p>\n<\/p>\n
                          import pandas as pd<\/p>\n
                          \u521b\u5efa\u4e00\u4e2aDataFrame<\/strong><\/h2>\n
                          data = np.array([[1, 2], [3, 4]])<\/p>\n
                          df = pd.DataFrame(data, columns=['A', 'B'])<\/p>\n
                          <\/code><\/pre>\n<\/p>\n
                            \n
                          1. \u4e0eMatplotlib\u7ed3\u5408<\/strong><\/li>\n<\/ol>\n
                            Matplotlib\u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\uff0c\u53ef\u4ee5\u76f4\u63a5\u4f7f\u7528NumPy\u6570\u7ec4\u751f\u6210\u56fe\u5f62\uff1a<\/p>\n<\/p>\n
                            import matplotlib.pyplot as plt<\/p>\n
                            \u7ed8\u5236\u7b80\u5355\u7684\u6298\u7ebf\u56fe<\/strong><\/h2>\n
                            x = np.linspace(0, 10, 100)<\/p>\n
                            y = np.sin(x)<\/p>\n
                            plt.plot(x, y)<\/p>\n
                            plt.show()<\/p>\n
                            <\/code><\/pre>\n<\/p>\n
                            \u603b\u7ed3\uff1aPython\u63d0\u4f9b\u4e86\u591a\u79cd\u6784\u9020\u591a\u7ef4\u6570\u7ec4\u7684\u65b9\u6cd5\uff0c\u5d4c\u5957\u5217\u8868\u9002\u5408\u5c0f\u578b\u548c\u7b80\u5355\u7684\u6570\u636e\u7ed3\u6784\uff0cNumPy\u5219\u662f\u5904\u7406\u5927\u578b\u6570\u7ec4\u548c\u590d\u6742\u8fd0\u7b97\u7684\u9996\u9009\u3002\u901a\u8fc7\u7406\u89e3\u548c\u7ed3\u5408\u4e0d\u540c\u5de5\u5177\u7684\u4f18\u52bf\uff0c\u53ef\u4ee5\u5728\u6570\u636e\u5904\u7406\u548c\u79d1\u5b66\u8ba1\u7b97\u4e2d\u5b9e\u73b0\u9ad8\u6548\u548c\u7075\u6d3b\u7684\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
                            \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                             \u5982\u4f55\u5728Python\u4e2d\u521b\u5efa\u591a\u7ef4\u6570\u7ec4\uff1f<\/strong>
                            \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u6765\u521b\u5efa\u591a\u7ef4\u6570\u7ec4\u3002NumPy\u63d0\u4f9b\u4e86\u4e00\u4e2a\u5f3a\u5927\u7684\u529f\u80fd\u6765\u5904\u7406\u6570\u7ec4\u548c\u77e9\u9635\u8fd0\u7b97\u3002\u53ef\u4ee5\u4f7f\u7528numpy.array()<\/code>\u51fd\u6570\u5c06\u5d4c\u5957\u5217\u8868\u8f6c\u6362\u4e3a\u591a\u7ef4\u6570\u7ec4\u3002\u4f8b\u5982\uff0cnumpy.array([[1, 2, 3], [4, 5, 6]])<\/code>\u4f1a\u521b\u5efa\u4e00\u4e2a\u4e8c\u7ef4\u6570\u7ec4\u3002\u786e\u4fdd\u5728\u4f7f\u7528NumPy\u4e4b\u524d\u5b89\u88c5\u8be5\u5e93\uff0c\u53ef\u4ee5\u901a\u8fc7\u547d\u4ee4pip install numpy<\/code>\u8fdb\u884c\u5b89\u88c5\u3002<\/p>\n
                            \u591a\u7ef4\u6570\u7ec4\u7684\u7d22\u5f15\u548c\u5207\u7247\u662f\u5982\u4f55\u5de5\u4f5c\u7684\uff1f<\/strong>
                            \u5728\u591a\u7ef4\u6570\u7ec4\u4e2d\uff0c\u7d22\u5f15\u548c\u5207\u7247\u7684\u65b9\u5f0f\u4e0e\u4e00\u7ef4\u6570\u7ec4\u7c7b\u4f3c\uff0c\u4f46\u9700\u8981\u4e3a\u6bcf\u4e2a\u7ef4\u5ea6\u63d0\u4f9b\u7d22\u5f15\u3002\u4f8b\u5982\uff0c\u5bf9\u4e8e\u4e00\u4e2a\u4e8c\u7ef4\u6570\u7ec4arr<\/code>\uff0c\u8bbf\u95ee\u7b2c\u4e00\u4e2a\u5143\u7d20\u53ef\u4ee5\u4f7f\u7528arr[0, 0]<\/code>\u3002\u5207\u7247\u64cd\u4f5c\u4e5f\u9002\u7528\uff0c\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528arr[:, 1]<\/code>\u6765\u83b7\u53d6\u6240\u6709\u884c\u7684\u7b2c\u4e8c\u5217\u6570\u636e\u3002\u8fd9\u79cd\u7075\u6d3b\u6027\u4f7f\u5f97\u5bf9\u6570\u636e\u8fdb\u884c\u64cd\u4f5c\u548c\u5206\u6790\u53d8\u5f97\u66f4\u52a0\u7b80\u4fbf\u3002<\/p>\n
                            \u5982\u4f55\u5bf9\u591a\u7ef4\u6570\u7ec4\u8fdb\u884c\u6570\u5b66\u8fd0\u7b97\u548c\u64cd\u4f5c\uff1f<\/strong>
                            NumPy\u5e93\u652f\u6301\u5bf9\u591a\u7ef4\u6570\u7ec4\u8fdb\u884c\u5404\u79cd\u6570\u5b66\u8fd0\u7b97\uff0c\u4f8b\u5982\u52a0\u6cd5\u3001\u51cf\u6cd5\u3001\u4e58\u6cd5\u548c\u9664\u6cd5\u7b49\u3002\u8fd9\u4e9b\u8fd0\u7b97\u53ef\u4ee5\u76f4\u63a5\u5728\u6570\u7ec4\u5bf9\u8c61\u4e0a\u8fdb\u884c\uff0c\u6bd4\u5982array1 + array2<\/code>\u4f1a\u5bf9\u4e24\u4e2a\u6570\u7ec4\u7684\u6bcf\u4e2a\u5bf9\u5e94\u5143\u7d20\u8fdb\u884c\u52a0\u6cd5\u3002\u5982\u679c\u9700\u8981\u8fdb\u884c\u66f4\u590d\u6742\u7684\u64cd\u4f5c\uff0c\u5982\u77e9\u9635\u4e58\u6cd5\uff0c\u53ef\u4ee5\u4f7f\u7528numpy.dot()<\/code>\u6216@<\/code>\u8fd0\u7b97\u7b26\u3002\u8fd9\u4e9b\u529f\u80fd\u6781\u5927\u5730\u63d0\u9ad8\u4e86\u6570\u636e\u5904\u7406\u7684\u6548\u7387\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u6784\u9020\u591a\u7ef4\u6570\u7ec4\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528\u5d4c\u5957\u5217\u8868\u3001NumPy\u5e93\u3001\u6570\u7ec4\u63a8\u5bfc\u7b49\u3002\u5d4c\u5957\u5217\u8868\u63d0\u4f9b\u4e86\u57fa\u672c\u7684\u591a\u7ef4\u6570\u7ec4\u7ed3\u6784 […]","protected":false},"author":3,"featured_media":958710,"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\/958704"}],"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=958704"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/958704\/revisions"}],"predecessor-version":[{"id":958711,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/958704\/revisions\/958711"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/958710"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=958704"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=958704"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=958704"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}