{"id":948631,"date":"2024-12-27T00:05:25","date_gmt":"2024-12-26T16:05:25","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/948631.html"},"modified":"2024-12-27T00:05:27","modified_gmt":"2024-12-26T16:05:27","slug":"python-%e6%95%b0%e7%bb%84%e5%a6%82%e4%bd%95%e4%bd%bf%e7%94%a8","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/948631.html","title":{"rendered":"python \u6570\u7ec4\u5982\u4f55\u4f7f\u7528"},"content":{"rendered":"

\"python<\/p>\n

\u5728Python\u4e2d\uff0c\u6570\u7ec4\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u4f7f\u7528\uff0c\u5305\u62ec\u5217\u8868\u3001\u5143\u7ec4\u548cNumPy\u6570\u7ec4\u7b49\u3002\u5217\u8868\u662f\u6700\u5e38\u7528\u7684\u65b9\u5f0f\uff0c\u56e0\u4e3a\u5b83\u4eec\u63d0\u4f9b\u4e86\u52a8\u6001\u5927\u5c0f\u3001\u7075\u6d3b\u6027\u548c\u5f3a\u5927\u7684\u5185\u7f6e\u65b9\u6cd5\uff0cNumPy\u6570\u7ec4\u5219\u9002\u7528\u4e8e\u9700\u8981\u5904\u7406\u5927\u91cf\u6570\u636e\u548c\u6267\u884c\u9ad8\u6548\u6570\u503c\u8ba1\u7b97\u7684\u573a\u5408\u3002<\/strong>\u5728Python\u4e2d\uff0c\u4f7f\u7528\u6570\u7ec4\u7684\u5173\u952e\u5728\u4e8e\u9009\u62e9\u9002\u5f53\u7684\u6570\u636e\u7ed3\u6784\u548c\u5de5\u5177\uff0c\u4ee5\u6ee1\u8db3\u5177\u4f53\u9700\u6c42\u3002\u4f8b\u5982\uff0c\u82e5\u9700\u8981\u5bf9\u6570\u7ec4\u8fdb\u884c\u590d\u6742\u7684\u6570\u5b66\u8fd0\u7b97\uff0cNumPy\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\u548c\u4f18\u5316\u7684\u6027\u80fd\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5217\u8868\u4e0e\u6570\u7ec4\u7684\u533a\u522b<\/p>\n<\/p>\n

\u5728Python\u4e2d\uff0c\u6570\u7ec4\u6700\u5e38\u7528\u7684\u5b9e\u73b0\u662f\u5217\u8868\u3002\u5217\u8868\u662f\u52a8\u6001\u7684\uff0c\u53ef\u4ee5\u5b58\u50a8\u4e0d\u540c\u7c7b\u578b\u7684\u5143\u7d20\u3002\u867d\u7136Python\u6ca1\u6709\u5185\u7f6e\u7684\u6570\u7ec4\u7c7b\u578b\uff0c\u4f46\u53ef\u4ee5\u901a\u8fc7\u5bfc\u5165array<\/code>\u6a21\u5757\u6765\u4f7f\u7528\u6570\u7ec4\u3002\u4e0e\u5217\u8868\u4e0d\u540c\uff0c\u6570\u7ec4\u4e2d\u7684\u6240\u6709\u5143\u7d20\u5fc5\u987b\u662f\u540c\u4e00\u7c7b\u578b\u3002<\/p>\n<\/p>\n

    \n
  1. \u5217\u8868\u7684\u7075\u6d3b\u6027<\/li>\n<\/ol>\n

    Python\u5217\u8868\u662f\u4e00\u79cd\u5185\u7f6e\u7684\u6570\u636e\u7ed3\u6784\uff0c\u652f\u6301\u52a8\u6001\u5927\u5c0f\u548c\u591a\u79cd\u6570\u636e\u7c7b\u578b\u3002\u5217\u8868\u53ef\u4ee5\u901a\u8fc7append()<\/code>\u65b9\u6cd5\u6765\u6dfb\u52a0\u5143\u7d20\uff0c\u901a\u8fc7remove()<\/code>\u65b9\u6cd5\u6765\u5220\u9664\u5143\u7d20\u3002\u5217\u8868\u7684\u8fd9\u79cd\u7075\u6d3b\u6027\u4f7f\u5176\u6210\u4e3a\u5904\u7406\u591a\u79cd\u6570\u636e\u7c7b\u578b\u7684\u7406\u60f3\u9009\u62e9\u3002<\/p>\n<\/p>\n

      \n
    1. \u6570\u7ec4\u7684\u6027\u80fd\u4f18\u52bf<\/li>\n<\/ol>\n

      \u867d\u7136\u5217\u8868\u5f88\u7075\u6d3b\uff0c\u4f46\u5728\u9700\u8981\u5904\u7406\u5927\u91cf\u540c\u7c7b\u578b\u6570\u636e\u65f6\uff0c\u4f7f\u7528\u6570\u7ec4\u53ef\u80fd\u4f1a\u66f4\u9ad8\u6548\u3002\u6570\u7ec4\u5728\u5185\u5b58\u4e2d\u4ee5\u8fde\u7eed\u5757\u5b58\u50a8\u6570\u636e\uff0c\u56e0\u800c\u53ef\u4ee5\u66f4\u5feb\u901f\u5730\u8bbf\u95ee\u5143\u7d20\u3002\u5bf9\u4e8e\u6570\u503c\u8ba1\u7b97\uff0c\u4f7f\u7528NumPy\u5e93\u7684\u6570\u7ec4\u7c7b\u578b\u4f1a\u66f4\u5177\u4f18\u52bf\u3002<\/p>\n<\/p>\n

      \u4e8c\u3001Python\u5217\u8868\u7684\u57fa\u672c\u64cd\u4f5c<\/p>\n<\/p>\n

      Python\u5217\u8868\u63d0\u4f9b\u4e86\u4e00\u7cfb\u5217\u5185\u7f6e\u65b9\u6cd5\uff0c\u5e2e\u52a9\u5f00\u53d1\u8005\u8f7b\u677e\u5730\u64cd\u4f5c\u6570\u636e\u3002\u8fd9\u4e9b\u65b9\u6cd5\u4f7f\u5f97\u5217\u8868\u5728\u6570\u636e\u5904\u7406\u4efb\u52a1\u4e2d\u6781\u4e3a\u65b9\u4fbf\u3002<\/p>\n<\/p>\n

        \n
      1. \u521b\u5efa\u548c\u8bbf\u95ee\u5217\u8868<\/li>\n<\/ol>\n

        \u521b\u5efa\u5217\u8868\u53ef\u4ee5\u901a\u8fc7\u65b9\u62ec\u53f7[]<\/code>\u76f4\u63a5\u5b9a\u4e49\uff0c\u6216\u8005\u4f7f\u7528list()<\/code>\u6784\u9020\u51fd\u6570\u3002\u8bbf\u95ee\u5217\u8868\u4e2d\u7684\u5143\u7d20\u53ef\u4ee5\u901a\u8fc7\u7d22\u5f15\u5b8c\u6210\u3002\u6ce8\u610f\uff0cPython\u7684\u7d22\u5f15\u662f\u4ece0\u5f00\u59cb\u7684\u3002<\/p>\n<\/p>\n

        my_list = [1, 2, 3, 4, 5]<\/p>\n
        print(my_list[0])  # \u8f93\u51fa: 1<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        1. \u5217\u8868\u7684\u5e38\u7528\u65b9\u6cd5<\/li>\n<\/ol>\n
          Python\u5217\u8868\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\uff0c\u5982append()<\/code>, insert()<\/code>, remove()<\/code>, pop()<\/code>, sort()<\/code>, \u548creverse()<\/code>\u7b49\u3002\u8fd9\u4e9b\u65b9\u6cd5\u4f7f\u5f97\u64cd\u4f5c\u5217\u8868\u53d8\u5f97\u7b80\u5355\u3002<\/p>\n<\/p>\n
          my_list.append(6)      # \u6dfb\u52a0\u5143\u7d206\u5230\u5217\u8868\u672b\u5c3e<\/p>\n
          my_list.insert(0, 0)   # \u5728\u7d22\u5f150\u5904\u63d2\u5165\u5143\u7d200<\/p>\n
          my_list.remove(3)      # \u5220\u9664\u5217\u8868\u4e2d\u7b2c\u4e00\u4e2a\u51fa\u73b0\u7684\u5143\u7d203<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u4e09\u3001\u4f7f\u7528NumPy\u5e93\u8fdb\u884c\u6570\u7ec4\u64cd\u4f5c<\/p>\n<\/p>\n
          NumPy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u7ec4\u64cd\u4f5c\u529f\u80fd\u3002\u5b83\u7684\u6570\u7ec4\u5bf9\u8c61ndarray<\/code>\u662f\u4e00\u4e2a\u591a\u7ef4\u6570\u7ec4\uff0c\u53ef\u4ee5\u5b58\u50a8\u540c\u7c7b\u578b\u5143\u7d20\u3002<\/p>\n<\/p>\n
            \n
          1. NumPy\u6570\u7ec4\u7684\u521b\u5efa<\/li>\n<\/ol>\n
            NumPy\u6570\u7ec4\u53ef\u4ee5\u901a\u8fc7\u5217\u8868\u8f6c\u6362\u3001\u4f7f\u7528arange()<\/code>\u3001zeros()<\/code>\u3001ones()<\/code>\u7b49\u51fd\u6570\u521b\u5efa\u3002NumPy\u6570\u7ec4\u652f\u6301\u591a\u7ef4\u5ea6\uff0c\u56e0\u6b64\u53ef\u4ee5\u65b9\u4fbf\u5730\u8868\u793a\u77e9\u9635\u6216\u5f20\u91cf\u3002<\/p>\n<\/p>\n
            import numpy as np<\/p>\n
            np_array = np.array([1, 2, 3, 4, 5])<\/p>\n
            zero_array = np.zeros((3, 3))  # \u521b\u5efa\u4e00\u4e2a3x3\u7684\u96f6\u6570\u7ec4<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. \u6570\u7ec4\u7684\u57fa\u672c\u64cd\u4f5c<\/li>\n<\/ol>\n
              NumPy\u6570\u7ec4\u652f\u6301\u5404\u79cd\u6570\u5b66\u8fd0\u7b97\uff0c\u5982\u52a0\u51cf\u4e58\u9664\u3002\u5b83\u8fd8\u63d0\u4f9b\u4e86\u9ad8\u7ea7\u7684\u51fd\u6570\uff0c\u5982\u77e9\u9635\u4e58\u6cd5\u3001\u8f6c\u7f6e\u3001\u6c42\u9006\u7b49\u3002<\/p>\n<\/p>\n
              sum_array = np_array + 5      # \u6bcf\u4e2a\u5143\u7d20\u52a05<\/p>\n
              multiplied = np_array * 2     # \u6bcf\u4e2a\u5143\u7d20\u4e582<\/p>\n
              dot_product = np.dot(np_array, np_array)  # \u70b9\u79ef<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u56db\u3001\u6570\u7ec4\u7684\u9ad8\u7ea7\u64cd\u4f5c\u6280\u5de7<\/p>\n<\/p>\n
              \u65e0\u8bba\u662f\u5217\u8868\u8fd8\u662fNumPy\u6570\u7ec4\uff0cPython\u90fd\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\u6765\u6ee1\u8db3\u590d\u6742\u7684\u6570\u636e\u5904\u7406\u9700\u6c42\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u9ad8\u7ea7\u64cd\u4f5c\u6280\u5de7\u3002<\/p>\n<\/p>\n
                \n
              1. \u5217\u8868\u63a8\u5bfc\u5f0f<\/li>\n<\/ol>\n
                \u5217\u8868\u63a8\u5bfc\u5f0f\u662f\u4e00\u79cd\u7b80\u6d01\u7684\u8bed\u6cd5\uff0c\u7528\u4e8e\u521b\u5efa\u548c\u64cd\u4f5c\u5217\u8868\u3002\u5b83\u53ef\u4ee5\u5728\u4e00\u884c\u4ee3\u7801\u4e2d\u5b8c\u6210\u5faa\u73af\u548c\u6761\u4ef6\u5224\u65ad\u3002<\/p>\n<\/p>\n
                squared = [x2 for x in range(10)]  # \u751f\u6210\u5e73\u65b9\u6570\u5217\u8868<\/p>\n
                even_numbers = [x for x in range(20) if x % 2 == 0]  # \u751f\u6210\u5076\u6570\u5217\u8868<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                  \n
                1. NumPy\u7684\u9ad8\u7ea7\u529f\u80fd<\/li>\n<\/ol>\n
                  NumPy\u4e0d\u4ec5\u652f\u6301\u57fa\u672c\u7684\u6570\u7ec4\u64cd\u4f5c\uff0c\u8fd8\u63d0\u4f9b\u4e86\u8bb8\u591a\u9ad8\u7ea7\u529f\u80fd\uff0c\u5982\u5e7f\u64ad\uff08broadcasting\uff09\u3001\u5411\u91cf\u5316\u8fd0\u7b97\u548c\u9ad8\u7ea7\u7d22\u5f15\u7b49\u3002<\/p>\n<\/p>\n
                  broadcasted = np_array + np.ones(5)  # \u5e7f\u64ad\u52a0\u6cd5<\/p>\n
                  vectorized_addition = np_array + np_array  # \u5411\u91cf\u5316\u8fd0\u7b97<\/p>\n
                  sliced_array = np_array[1:4]  # \u9ad8\u7ea7\u7d22\u5f15<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u4e94\u3001\u6570\u7ec4\u5728\u6570\u636e\u79d1\u5b66\u4e2d\u7684\u5e94\u7528<\/p>\n<\/p>\n
                  \u5728\u6570\u636e\u79d1\u5b66\u9886\u57df\uff0c\u6570\u7ec4\u662f\u6570\u636e\u8868\u793a\u548c\u5904\u7406\u7684\u6838\u5fc3\u3002\u65e0\u8bba\u662f\u6570\u636e\u5206\u6790\u3001\u673a\u5668\u5b66\u4e60<\/a>\u8fd8\u662f\u6df1\u5ea6\u5b66\u4e60\uff0c\u6570\u7ec4\u90fd\u626e\u6f14\u7740\u91cd\u8981\u89d2\u8272\u3002<\/p>\n<\/p>\n
                    \n
                  1. \u6570\u636e\u5206\u6790\u4e2d\u7684\u6570\u7ec4<\/li>\n<\/ol>\n
                    \u5728\u6570\u636e\u5206\u6790\u4e2d\uff0cNumPy\u6570\u7ec4\u5e38\u7528\u4e8e\u5b58\u50a8\u548c\u5904\u7406\u5927\u578b\u6570\u636e\u96c6\u3002Pandas\u5e93\u57fa\u4e8eNumPy\u6784\u5efa\uff0c\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u6570\u636e\u5206\u6790\u529f\u80fd\u3002<\/p>\n<\/p>\n
                    import pandas as pd<\/p>\n
                    data = np.random.rand(10, 4)<\/p>\n
                    df = pd.DataFrame(data, columns=['A', 'B', 'C', 'D'])<\/p>\n
                    print(df.describe())<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                      \n
                    1. \u673a\u5668\u5b66\u4e60\u4e2d\u7684\u6570\u7ec4<\/li>\n<\/ol>\n
                      \u5728\u673a\u5668\u5b66\u4e60\u4e2d\uff0c\u6570\u636e\u901a\u5e38\u4ee5\u6570\u7ec4\u5f62\u5f0f\u4f20\u9012\u7ed9\u6a21\u578b\u3002scikit-learn\u7b49\u5e93\u5229\u7528NumPy\u6570\u7ec4\u8fdb\u884c\u6570\u636e\u9884\u5904\u7406\u3001\u6a21\u578b\u8bad\u7ec3\u548c\u9884\u6d4b\u3002<\/p>\n<\/p>\n
                      from sklearn.linear_model import LinearRegression<\/p>\n
                      X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])<\/p>\n
                      y = np.dot(X, np.array([1, 2])) + 3<\/p>\n
                      model = LinearRegression().fit(X, y)<\/p>\n
                      predictions = model.predict(X)<\/p>\n
                      <\/code><\/pre>\n<\/p>\n
                      \u7efc\u4e0a\u6240\u8ff0\uff0cPython\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u5f0f\u6765\u4f7f\u7528\u6570\u7ec4\uff0c\u6ee1\u8db3\u4e0d\u540c\u573a\u666f\u4e0b\u7684\u6570\u636e\u5904\u7406\u9700\u6c42\u3002\u4ece\u5217\u8868\u7684\u7075\u6d3b\u6027\u5230NumPy\u7684\u9ad8\u6548\u8ba1\u7b97\u80fd\u529b\uff0c\u9009\u62e9\u5408\u9002\u7684\u5de5\u5177\u53ef\u4ee5\u663e\u8457\u63d0\u5347\u7a0b\u5e8f\u7684\u6027\u80fd\u548c\u53ef\u8bfb\u6027\u3002\u65e0\u8bba\u662f\u65e5\u5e38\u6570\u636e\u5904\u7406\u8fd8\u662f\u590d\u6742\u7684\u6570\u636e\u79d1\u5b66\u4efb\u52a1\uff0c\u638c\u63e1\u8fd9\u4e9b\u6280\u5de7\u5c06\u4e3a\u60a8\u5e26\u6765\u6781\u5927\u7684\u4fbf\u5229\u3002<\/p>\n<\/p>\n
                      \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                       \u5982\u4f55\u5728Python\u4e2d\u521b\u5efa\u6570\u7ec4\uff1f<\/strong>
                      \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u5217\u8868\u6765\u521b\u5efa\u6570\u7ec4\u3002\u5217\u8868\u662f\u4e00\u4e2a\u53ef\u53d8\u7684\u5e8f\u5217\uff0c\u53ef\u4ee5\u5305\u542b\u4e0d\u540c\u7c7b\u578b\u7684\u5143\u7d20\u3002\u4f60\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u5f0f\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u6570\u7ec4\uff1a  <\/p>\n
                      my_array = [1, 2, 3, 4, 5]\n<\/code><\/pre>\n
                      \u53e6\u5916\uff0c\u4f7f\u7528NumPy\u5e93\u4e5f\u53ef\u4ee5\u521b\u5efa\u66f4\u590d\u6742\u7684\u6570\u7ec4\uff0c\u5c24\u5176\u662f\u5728\u9700\u8981\u8fdb\u884c\u6570\u503c\u8ba1\u7b97\u65f6\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528NumPy\u7684array<\/code>\u51fd\u6570\uff1a<\/p>\n
                      import numpy as np\nmy_array = np.array([1, 2, 3, 4, 5])\n<\/code><\/pre>\n
                      Python\u6570\u7ec4\u53ef\u4ee5\u5b58\u50a8\u54ea\u4e9b\u7c7b\u578b\u7684\u6570\u636e\uff1f<\/strong>
                      Python\u4e2d\u7684\u6570\u7ec4\uff08\u6216\u5217\u8868\uff09\u53ef\u4ee5\u5b58\u50a8\u591a\u79cd\u7c7b\u578b\u7684\u6570\u636e\uff0c\u5305\u62ec\u6574\u6570\u3001\u6d6e\u70b9\u6570\u3001\u5b57\u7b26\u4e32\u4ee5\u53ca\u5176\u4ed6\u5217\u8868\u6216\u5bf9\u8c61\u3002NumPy\u6570\u7ec4\u5219\u901a\u5e38\u7528\u4e8e\u5b58\u50a8\u540c\u79cd\u6570\u636e\u7c7b\u578b\u7684\u5143\u7d20\uff0c\u8fd9\u6837\u53ef\u4ee5\u63d0\u9ad8\u8ba1\u7b97\u6548\u7387\u3002\u4f8b\u5982\uff1a<\/p>\n
                      mixed_array = [1, 'text', 3.14]  # \u5217\u8868\u53ef\u4ee5\u5305\u542b\u4e0d\u540c\u7c7b\u578b\nnumpy_array = np.array([1, 2, 3])  # NumPy\u6570\u7ec4\u6700\u597d\u53ea\u5b58\u50a8\u540c\u79cd\u7c7b\u578b\n<\/code><\/pre>\n
                      \u5982\u4f55\u5728Python\u4e2d\u8bbf\u95ee\u548c\u4fee\u6539\u6570\u7ec4\u4e2d\u7684\u5143\u7d20\uff1f<\/strong>
                      \u8bbf\u95eePython\u6570\u7ec4\u4e2d\u7684\u5143\u7d20\u53ef\u4ee5\u901a\u8fc7\u7d22\u5f15\u5b8c\u6210\uff0c\u7d22\u5f15\u4ece0\u5f00\u59cb\u3002\u4f8b\u5982\uff0c\u8981\u8bbf\u95ee\u4e0a\u9762\u521b\u5efa\u7684\u5217\u8868\u4e2d\u7684\u7b2c\u4e00\u4e2a\u5143\u7d20\uff0c\u53ef\u4ee5\u4f7f\u7528\uff1a<\/p>\n
                      first_element = my_array[0]  # \u8bbf\u95ee\u7b2c\u4e00\u4e2a\u5143\u7d20\n<\/code><\/pre>\n
                      \u4fee\u6539\u6570\u7ec4\u4e2d\u7684\u5143\u7d20\u540c\u6837\u7b80\u5355\uff0c\u53ef\u4ee5\u901a\u8fc7\u7d22\u5f15\u76f4\u63a5\u8d4b\u503c\uff1a<\/p>\n
                      my_array[0] = 10  # \u5c06\u7b2c\u4e00\u4e2a\u5143\u7d20\u4fee\u6539\u4e3a10\n<\/code><\/pre>\n
                      \u5bf9\u4e8eNumPy\u6570\u7ec4\uff0c\u8bbf\u95ee\u548c\u4fee\u6539\u65b9\u5f0f\u4e5f\u662f\u7c7b\u4f3c\u7684\uff0c\u4f46\u8fd8\u53ef\u4ee5\u5229\u7528\u5207\u7247\u548c\u9ad8\u7ea7\u7d22\u5f15\u8fdb\u884c\u66f4\u590d\u6742\u7684\u64cd\u4f5c\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\uff0c\u6570\u7ec4\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u4f7f\u7528\uff0c\u5305\u62ec\u5217\u8868\u3001\u5143\u7ec4\u548cNumPy\u6570\u7ec4\u7b49\u3002\u5217\u8868\u662f\u6700\u5e38\u7528\u7684\u65b9\u5f0f\uff0c\u56e0\u4e3a\u5b83\u4eec\u63d0\u4f9b […]","protected":false},"author":3,"featured_media":948638,"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\/948631"}],"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=948631"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/948631\/revisions"}],"predecessor-version":[{"id":948639,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/948631\/revisions\/948639"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/948638"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=948631"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=948631"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=948631"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}