{"id":1052403,"date":"2024-12-31T14:24:41","date_gmt":"2024-12-31T06:24:41","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1052403.html"},"modified":"2024-12-31T14:24:43","modified_gmt":"2024-12-31T06:24:43","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e6%8f%90%e5%8f%96%e7%9f%a9%e9%98%b5%e4%b8%ad%e7%9a%84%e5%85%83%e7%b4%a0","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1052403.html","title":{"rendered":"\u5982\u4f55\u7528python\u63d0\u53d6\u77e9\u9635\u4e2d\u7684\u5143\u7d20"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u63d0\u53d6\u77e9\u9635\u4e2d\u7684\u5143\u7d20\"<\/p>\n

\u4f7f\u7528Python\u63d0\u53d6\u77e9\u9635\u4e2d\u7684\u5143\u7d20\u9700\u8981\u4f7f\u7528\u5408\u9002\u7684\u5e93\u5e76\u638c\u63e1\u57fa\u672c\u7684\u7d22\u5f15\u65b9\u6cd5\u3001\u5207\u7247\u64cd\u4f5c\u3001\u5217\u8868\u63a8\u5bfc\u7b49\u6280\u5de7\u3002<\/strong> \u63a8\u8350\u4f7f\u7528NumPy\u5e93\uff0c\u56e0\u4e3a\u5b83\u5728\u5904\u7406\u77e9\u9635\u548c\u6570\u7ec4\u65b9\u9762\u529f\u80fd\u5f3a\u5927\u4e14\u9ad8\u6548\u3002\u4e0b\u9762\u6211\u4eec\u5c06\u8be6\u7ec6\u8bb2\u89e3\u5982\u4f55\u4f7f\u7528Python\u6765\u63d0\u53d6\u77e9\u9635\u4e2d\u7684\u5143\u7d20\u3002<\/p>\n<\/p>\n

\u4e00\u3001NUMPY\u5e93\u7684\u5b89\u88c5\u4e0e\u5bfc\u5165<\/p>\n<\/p>\n

\u9996\u5148\uff0c\u786e\u4fdd\u4f60\u5b89\u88c5\u4e86NumPy\u5e93\u3002\u5982\u679c\u4f60\u8fd8\u6ca1\u6709\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\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\u6211\u4eec\u9700\u8981\u5728Python\u811a\u672c\u4e2d\u5bfc\u5165NumPy\u5e93\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u521b\u5efa\u77e9\u9635<\/p>\n<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u77e9\u9635\u6709\u591a\u79cd\u65b9\u5f0f\uff0c\u6700\u7b80\u5355\u7684\u65b9\u5f0f\u662f\u4f7f\u7528NumPy\u7684array<\/code>\u51fd\u6570\u3002\u6211\u4eec\u53ef\u4ee5\u4ece\u4e00\u4e2a\u5d4c\u5957\u7684\u5217\u8868\u521b\u5efa\u4e00\u4e2a\u4e8c\u7ef4\u6570\u7ec4\uff08\u5373\u77e9\u9635\uff09\uff1a<\/p>\n<\/p>\n
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u5c06\u521b\u5efa\u4e00\u4e2a3×3\u7684\u77e9\u9635\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u7d22\u5f15\u548c\u5207\u7247<\/p>\n<\/p>\n
    \n
  1. \u5355\u4e2a\u5143\u7d20\u7684\u7d22\u5f15<\/strong><\/li>\n<\/ol>\n
    \u8981\u63d0\u53d6\u77e9\u9635\u4e2d\u7684\u5355\u4e2a\u5143\u7d20\uff0c\u53ef\u4ee5\u4f7f\u7528\u884c\u548c\u5217\u7684\u7d22\u5f15\u3002NumPy\u7684\u7d22\u5f15\u4ece0\u5f00\u59cb\uff1a<\/p>\n<\/p>\n
    element = matrix[1, 2]  # \u63d0\u53d6\u7b2c2\u884c\u7b2c3\u5217\u7684\u5143\u7d20\uff0c\u53736<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u63d0\u53d6\u4e00\u884c\u6216\u4e00\u5217<\/strong><\/li>\n<\/ol>\n
      \u63d0\u53d6\u7279\u5b9a\u7684\u884c\u6216\u5217\u53ef\u4ee5\u4f7f\u7528\u5207\u7247\u64cd\u4f5c\u3002\u4ee5\u4e0b\u662f\u63d0\u53d6\u7b2c2\u884c\u548c\u7b2c3\u5217\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
      row = matrix[1, :]  # \u63d0\u53d6\u7b2c2\u884c\uff1a[4, 5, 6]<\/p>\n
      column = matrix[:, 2]  # \u63d0\u53d6\u7b2c3\u5217\uff1a[3, 6, 9]<\/p>\n
      <\/code><\/pre>\n<\/p>\n
        \n
      1. \u63d0\u53d6\u5b50\u77e9\u9635<\/strong><\/li>\n<\/ol>\n
        \u53ef\u4ee5\u901a\u8fc7\u6307\u5b9a\u884c\u548c\u5217\u7684\u8303\u56f4\u6765\u63d0\u53d6\u5b50\u77e9\u9635\uff1a<\/p>\n<\/p>\n
        sub_matrix = matrix[0:2, 1:3]  # \u63d0\u53d6\u4e00\u4e2a2x2\u7684\u5b50\u77e9\u9635\uff0c\u5305\u542b\u7b2c1\u30012\u884c\u548c\u7b2c2\u30013\u5217<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        1. \u4f7f\u7528\u6761\u4ef6\u7d22\u5f15<\/strong><\/li>\n<\/ol>\n
          NumPy\u5141\u8bb8\u4f60\u4f7f\u7528\u5e03\u5c14\u6570\u7ec4\u8fdb\u884c\u6761\u4ef6\u7d22\u5f15\u3002\u4ee5\u4e0b\u662f\u63d0\u53d6\u6240\u6709\u5927\u4e8e5\u7684\u5143\u7d20\uff1a<\/p>\n<\/p>\n
          greater_than_five = matrix[matrix > 5]  # \u63d0\u53d6\u6240\u6709\u5927\u4e8e5\u7684\u5143\u7d20\uff1a[6, 7, 8, 9]<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u56db\u3001\u9ad8\u7ea7\u64cd\u4f5c<\/p>\n<\/p>\n
            \n
          1. \u4f7f\u7528numpy.where<\/code><\/strong><\/li>\n<\/ol>\n
            numpy.where<\/code>\u53ef\u4ee5\u6839\u636e\u6761\u4ef6\u8fd4\u56de\u7d22\u5f15\u6216\u66ff\u6362\u5143\u7d20\uff1a<\/p>\n<\/p>\n
            indices = np.where(matrix > 5)  # \u8fd4\u56de\u6ee1\u8db3\u6761\u4ef6\u7684\u5143\u7d20\u7684\u7d22\u5f15<\/p>\n
            print(indices)  # (array([1, 2, 2, 2]), array([2, 0, 1, 2]))<\/p>\n
            \u6216\u8005\u66ff\u6362\u5143\u7d20<\/strong><\/h2>\n
            modified_matrix = np.where(matrix > 5, 0, matrix)  # \u5c06\u6240\u6709\u5927\u4e8e5\u7684\u5143\u7d20\u66ff\u6362\u4e3a0<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. \u4f7f\u7528numpy.take<\/code><\/strong><\/li>\n<\/ol>\n
              numpy.take<\/code>\u53ef\u4ee5\u6839\u636e\u7d22\u5f15\u63d0\u53d6\u5143\u7d20\uff1a<\/p>\n<\/p>\n
              elements = np.take(matrix, [0, 4, 8])  # \u63d0\u53d6\u7b2c1\u30015\u30019\u4e2a\u5143\u7d20\uff1a[1, 5, 9]<\/p>\n
              <\/code><\/pre>\n<\/p>\n
                \n
              1. \u4f7f\u7528numpy.ravel<\/code><\/strong><\/li>\n<\/ol>\n
                numpy.ravel<\/code>\u5c06\u77e9\u9635\u5c55\u5e73\u4e3a\u4e00\u7ef4\u6570\u7ec4\uff0c\u4fbf\u4e8e\u8fdb\u4e00\u6b65\u64cd\u4f5c\uff1a<\/p>\n<\/p>\n
                flattened = matrix.ravel()  # \u5c55\u5e73\u7684\u6570\u7ec4\uff1a[1, 2, 3, 4, 5, 6, 7, 8, 9]<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                \u4e94\u3001\u5e94\u7528\u5b9e\u4f8b<\/p>\n<\/p>\n
                  \n
                1. \u7edf\u8ba1\u77e9\u9635\u4e2d\u7684\u5143\u7d20<\/strong><\/li>\n<\/ol>\n
                  \u53ef\u4ee5\u4f7f\u7528NumPy\u7684\u7edf\u8ba1\u51fd\u6570\u6765\u5206\u6790\u77e9\u9635\u4e2d\u7684\u5143\u7d20\uff1a<\/p>\n<\/p>\n
                  mean_value = np.mean(matrix)  # \u8ba1\u7b97\u77e9\u9635\u5143\u7d20\u7684\u5747\u503c<\/p>\n
                  sum_value = np.sum(matrix)  # \u8ba1\u7b97\u77e9\u9635\u5143\u7d20\u7684\u548c<\/p>\n
                  max_value = np.max(matrix)  # \u8ba1\u7b97\u77e9\u9635\u4e2d\u7684\u6700\u5927\u503c<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                    \n
                  1. \u77e9\u9635\u7684\u8f6c\u7f6e<\/strong><\/li>\n<\/ol>\n
                    \u77e9\u9635\u7684\u8f6c\u7f6e\u662f\u5c06\u77e9\u9635\u7684\u884c\u548c\u5217\u4e92\u6362\uff1a<\/p>\n<\/p>\n
                    transposed_matrix = np.transpose(matrix)  # \u8f6c\u7f6e\u540e\u7684\u77e9\u9635<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                      \n
                    1. \u77e9\u9635\u7684\u4e58\u79ef<\/strong><\/li>\n<\/ol>\n
                      \u53ef\u4ee5\u4f7f\u7528numpy.dot<\/code>\u51fd\u6570\u8fdb\u884c\u77e9\u9635\u4e58\u6cd5\uff1a<\/p>\n<\/p>\n
                      matrix_a = np.array([[1, 2], [3, 4]])<\/p>\n
                      matrix_b = np.array([[5, 6], [7, 8]])<\/p>\n
                      product = np.dot(matrix_a, matrix_b)  # \u77e9\u9635\u4e58\u6cd5<\/p>\n
                      <\/code><\/pre>\n<\/p>\n
                        \n
                      1. \u63d0\u53d6\u5bf9\u89d2\u7ebf\u5143\u7d20<\/strong><\/li>\n<\/ol>\n
                        \u4f7f\u7528numpy.diag<\/code>\u51fd\u6570\u53ef\u4ee5\u63d0\u53d6\u77e9\u9635\u7684\u5bf9\u89d2\u7ebf\u5143\u7d20\uff1a<\/p>\n<\/p>\n
                        diagonal = np.diag(matrix)  # \u63d0\u53d6\u5bf9\u89d2\u7ebf\u5143\u7d20\uff1a[1, 5, 9]<\/p>\n
                        <\/code><\/pre>\n<\/p>\n
                        \u516d\u3001\u5b9e\u6218\u6848\u4f8b<\/p>\n<\/p>\n
                          \n
                        1. \u56fe\u50cf\u5904\u7406<\/strong><\/li>\n<\/ol>\n
                          \u5728\u56fe\u50cf\u5904\u7406\u4e2d\uff0c\u6bcf\u4e2a\u56fe\u50cf\u53ef\u4ee5\u8868\u793a\u4e3a\u4e00\u4e2a\u77e9\u9635\uff0c\u5176\u4e2d\u6bcf\u4e2a\u5143\u7d20\u8868\u793a\u50cf\u7d20\u503c\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4e0a\u8ff0\u65b9\u6cd5\u6765\u63d0\u53d6\u548c\u4fee\u6539\u56fe\u50cf\u4e2d\u7684\u50cf\u7d20\u3002<\/p>\n<\/p>\n
                          import matplotlib.pyplot as plt<\/p>\n
                          \u521b\u5efa\u4e00\u4e2a\u968f\u673a\u56fe\u50cf\u77e9\u9635<\/strong><\/h2>\n
                          image_matrix = np.random.rand(100, 100)<\/p>\n
                          \u63d0\u53d6\u7279\u5b9a\u533a\u57df<\/strong><\/h2>\n
                          region = image_matrix[20:50, 30:60]<\/p>\n
                          \u663e\u793a\u56fe\u50cf<\/strong><\/h2>\n
                          plt.imshow(image_matrix, cmap='gray')<\/p>\n
                          plt.show()<\/p>\n
                          <\/code><\/pre>\n<\/p>\n
                            \n
                          1. \u6570\u636e\u5206\u6790<\/strong><\/li>\n<\/ol>\n
                            \u5728\u6570\u636e\u5206\u6790\u4e2d\uff0c\u6211\u4eec\u901a\u5e38\u9700\u8981\u4ece\u6570\u636e\u77e9\u9635\u4e2d\u63d0\u53d6\u7279\u5b9a\u7684\u884c\u6216\u5217\u4ee5\u8fdb\u884c\u5206\u6790\u3002\u4f8b\u5982\uff0c\u63d0\u53d6\u6240\u6709\u5e74\u9f84\u5927\u4e8e30\u7684\u8bb0\u5f55\uff1a<\/p>\n<\/p>\n
                            data_matrix = np.array([[25, 30, 35], [40, 45, 50], [20, 25, 30]])<\/p>\n
                            age_column = data_matrix[:, 0]<\/p>\n
                            filtered_data = data_matrix[age_column > 30, :]<\/p>\n
                            <\/code><\/pre>\n<\/p>\n
                              \n
                            1. \u673a\u5668\u5b66\u4e60<\/a><\/strong><\/li>\n<\/ol>\n
                              \u5728\u673a\u5668\u5b66\u4e60\u4e2d\uff0c\u7279\u5f81\u77e9\u9635\u548c\u6807\u7b7e\u77e9\u9635\u662f\u5e38\u89c1\u7684\u7ed3\u6784\u3002\u6211\u4eec\u901a\u5e38\u9700\u8981\u5bf9\u8fd9\u4e9b\u77e9\u9635\u8fdb\u884c\u64cd\u4f5c\u4ee5\u8fdb\u884c\u8bad\u7ec3\u548c\u9884\u6d4b\u3002<\/p>\n<\/p>\n
                              from sklearn.preprocessing import StandardScaler<\/p>\n
                              \u521b\u5efa\u7279\u5f81\u77e9\u9635<\/strong><\/h2>\n
                              features = np.array([[1.5, 2.3], [3.1, 4.7], [5.6, 6.8]])<\/p>\n
                              \u6807\u51c6\u5316\u7279\u5f81<\/strong><\/h2>\n
                              scaler = StandardScaler()<\/p>\n
                              standardized_features = scaler.fit_transform(features)<\/p>\n
                              <\/code><\/pre>\n<\/p>\n
                              \u4e03\u3001\u603b\u7ed3<\/p>\n<\/p>\n
                              \u901a\u8fc7\u4ee5\u4e0a\u5185\u5bb9\uff0c\u6211\u4eec\u8be6\u7ec6\u8bb2\u89e3\u4e86\u5982\u4f55\u4f7f\u7528Python\u63d0\u53d6\u77e9\u9635\u4e2d\u7684\u5143\u7d20\u3002\u65e0\u8bba\u662f\u57fa\u7840\u7684\u7d22\u5f15\u548c\u5207\u7247\uff0c\u8fd8\u662f\u9ad8\u7ea7\u7684\u64cd\u4f5c\u548c\u5e94\u7528\u5b9e\u4f8b\uff0c\u638c\u63e1\u8fd9\u4e9b\u6280\u5de7\u5c06\u4f7f\u4f60\u5728\u6570\u636e\u5904\u7406\u3001\u56fe\u50cf\u5904\u7406\u3001\u6570\u636e\u5206\u6790\u548c\u673a\u5668\u5b66\u4e60\u4e2d\u66f4\u52a0\u5f97\u5fc3\u5e94\u624b<\/strong>\u3002\u5e0c\u671b\u8fd9\u7bc7\u6587\u7ae0\u5bf9\u4f60\u6709\u6240\u5e2e\u52a9\uff0c\u5e76\u80fd\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u4e3a\u4f60\u63d0\u4f9b\u6307\u5bfc\u3002<\/p>\n<\/p>\n
                              \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                               \u5982\u4f55\u4f7f\u7528Python\u63d0\u53d6\u7279\u5b9a\u6761\u4ef6\u4e0b\u7684\u77e9\u9635\u5143\u7d20\uff1f<\/strong>
                              \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u6765\u5904\u7406\u77e9\u9635\uff0c\u5e76\u901a\u8fc7\u5e03\u5c14\u7d22\u5f15\u63d0\u53d6\u7b26\u5408\u7279\u5b9a\u6761\u4ef6\u7684\u5143\u7d20\u3002\u4f8b\u5982\uff0c\u82e5\u8981\u63d0\u53d6\u5927\u4e8e\u67d0\u4e2a\u503c\u7684\u6240\u6709\u5143\u7d20\uff0c\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a\u5e03\u5c14\u6570\u7ec4\uff0c\u7136\u540e\u5c06\u5176\u5e94\u7528\u4e8e\u539f\u59cb\u77e9\u9635\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a  <\/p>\n
                              import numpy as np\n\nmatrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\nresult = matrix[matrix > 5]\nprint(result)  # \u8f93\u51fa: [6 7 8 9]\n<\/code><\/pre>\n
                              \u5728Python\u4e2d\uff0c\u5982\u4f55\u63d0\u53d6\u77e9\u9635\u7684\u884c\u6216\u5217\uff1f<\/strong>
                              \u63d0\u53d6\u77e9\u9635\u7684\u884c\u6216\u5217\u53ef\u4ee5\u901a\u8fc7\u5207\u7247\u64cd\u4f5c\u6765\u5b9e\u73b0\u3002\u4f8b\u5982\uff0c\u5982\u679c\u4f60\u5e0c\u671b\u63d0\u53d6\u7b2c\u4e8c\u884c\u6216\u7b2c\u4e09\u5217\uff0c\u53ef\u4ee5\u4f7f\u7528\u5982\u4e0b\u4ee3\u7801\uff1a  <\/p>\n
                              # \u63d0\u53d6\u7b2c\u4e8c\u884c\nsecond_row = matrix[1, :]\n# \u63d0\u53d6\u7b2c\u4e09\u5217\nthird_column = matrix[:, 2]\n<\/code><\/pre>\n
                              \u8fd9\u79cd\u65b9\u5f0f\u4f7f\u5f97\u8bbf\u95ee\u548c\u64cd\u4f5c\u77e9\u9635\u7684\u7279\u5b9a\u90e8\u5206\u53d8\u5f97\u975e\u5e38\u7b80\u5355\u3002<\/p>\n
                              \u5728Python\u4e2d\uff0c\u5982\u4f55\u7528\u7d22\u5f15\u63d0\u53d6\u77e9\u9635\u4e2d\u7684\u591a\u4e2a\u5143\u7d20\uff1f<\/strong>
                              \u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528\u6570\u7ec4\u7d22\u5f15\u76f4\u63a5\u63d0\u53d6\u591a\u4e2a\u5143\u7d20\u3002\u53ef\u4ee5\u5b9a\u4e49\u4e00\u4e2a\u5305\u542b\u7d22\u5f15\u7684\u5217\u8868\u6216\u6570\u7ec4\uff0c\u7136\u540e\u5229\u7528\u8fd9\u4e9b\u7d22\u5f15\u6765\u63d0\u53d6\u6240\u9700\u7684\u5143\u7d20\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a  <\/p>\n
                              indices = [0, 2]  # \u63d0\u53d6\u7b2c\u4e00\u884c\u7684\u7b2c\u4e00\u4e2a\u548c\u7b2c\u4e09\u4e2a\u5143\u7d20\nselected_elements = matrix[0, indices]\nprint(selected_elements)  # \u8f93\u51fa: [1 3]\n<\/code><\/pre>\n
                              \u8fd9\u79cd\u65b9\u6cd5\u975e\u5e38\u7075\u6d3b\uff0c\u9002\u7528\u4e8e\u63d0\u53d6\u591a\u4e2a\u975e\u8fde\u7eed\u5143\u7d20\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u4f7f\u7528Python\u63d0\u53d6\u77e9\u9635\u4e2d\u7684\u5143\u7d20\u9700\u8981\u4f7f\u7528\u5408\u9002\u7684\u5e93\u5e76\u638c\u63e1\u57fa\u672c\u7684\u7d22\u5f15\u65b9\u6cd5\u3001\u5207\u7247\u64cd\u4f5c\u3001\u5217\u8868\u63a8\u5bfc\u7b49\u6280\u5de7\u3002 \u63a8\u8350\u4f7f\u7528Nu […]","protected":false},"author":3,"featured_media":1052409,"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\/1052403"}],"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=1052403"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1052403\/revisions"}],"predecessor-version":[{"id":1052411,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1052403\/revisions\/1052411"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1052409"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1052403"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1052403"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1052403"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}