{"id":969627,"date":"2024-12-27T05:18:42","date_gmt":"2024-12-26T21:18:42","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/969627.html"},"modified":"2024-12-27T05:18:44","modified_gmt":"2024-12-26T21:18:44","slug":"%e7%94%a8python%e7%94%bb%e5%9b%be%e5%a6%82%e4%bd%95%e6%a3%80%e9%aa%8c","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/969627.html","title":{"rendered":"\u7528python\u753b\u56fe\u5982\u4f55\u68c0\u9a8c"},"content":{"rendered":"

\"\u7528python\u753b\u56fe\u5982\u4f55\u68c0\u9a8c\"<\/p>\n

\u7528Python\u753b\u56fe\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u5f0f\u68c0\u9a8c\uff1a\u68c0\u67e5\u6570\u636e\u7684\u51c6\u786e\u6027\u3001\u9a8c\u8bc1\u56fe\u5f62\u7684\u6b63\u786e\u6027\u3001\u8bc4\u4f30\u56fe\u5f62\u7684\u6e05\u6670\u5ea6\u3001\u786e\u4fdd\u56fe\u5f62\u7684\u53ef\u8bfb\u6027\u3002<\/strong>\u5176\u4e2d\uff0c\u68c0\u67e5\u6570\u636e\u7684\u51c6\u786e\u6027\u662f\u6700\u57fa\u7840\u4e5f\u662f\u6700\u5173\u952e\u7684\u4e00\u6b65\uff0c\u56e0\u4e3a\u6570\u636e\u7684\u8d28\u91cf\u76f4\u63a5\u5f71\u54cd\u5230\u56fe\u5f62\u7684\u6709\u6548\u6027\u3002\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u786e\u4fdd\u8f93\u5165\u7684\u6570\u636e\u662f\u51c6\u786e\u65e0\u8bef\u7684\uff0c\u53ef\u4ee5\u901a\u8fc7\u6570\u636e\u9884\u5904\u7406\u6b65\u9aa4\u8fdb\u884c\u6e05\u6d17\u548c\u8fc7\u6ee4\uff0c\u4ee5\u53bb\u9664\u5f02\u5e38\u503c\u548c\u7f3a\u5931\u503c\u3002\u53e6\u5916\uff0c\u53ef\u4ee5\u901a\u8fc7\u7edf\u8ba1\u5206\u6790\u7684\u65b9\u6cd5\u6765\u9a8c\u8bc1\u6570\u636e\u7684\u5408\u7406\u6027\uff0c\u6bd4\u5982\u8ba1\u7b97\u5e73\u5747\u503c\u3001\u6807\u51c6\u5dee\u7b49\u6307\u6807\u3002\u63a5\u4e0b\u6765\u5c06\u8be6\u7ec6\u63cf\u8ff0\u5982\u4f55\u5728Python\u4e2d\u5b9e\u73b0\u6570\u636e\u7684\u51c6\u786e\u6027\u68c0\u67e5\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u68c0\u67e5\u6570\u636e\u7684\u51c6\u786e\u6027<\/p>\n<\/p>\n

\u5728\u4f7f\u7528Python\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u4e4b\u524d\uff0c\u9996\u5148\u8981\u786e\u4fdd\u6570\u636e\u7684\u51c6\u786e\u6027\uff0c\u56e0\u4e3a\u6570\u636e\u7684\u9519\u8bef\u4f1a\u76f4\u63a5\u5bfc\u81f4\u56fe\u8868\u7ed3\u679c\u7684\u4e0d\u53ef\u9760\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u65b9\u6cd5\u6765\u68c0\u67e5\u6570\u636e\u7684\u51c6\u786e\u6027\uff1a<\/p>\n<\/p>\n

    \n
  1. \u6570\u636e\u9884\u5904\u7406<\/strong><\/li>\n<\/ol>\n

    \u6570\u636e\u9884\u5904\u7406\u662f\u4fdd\u8bc1\u6570\u636e\u51c6\u786e\u6027\u7684\u7b2c\u4e00\u6b65\u3002\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u6765\u5904\u7406\u6570\u636e\uff0c\u5305\u62ec\u5904\u7406\u7f3a\u5931\u503c\u3001\u53bb\u9664\u91cd\u590d\u9879\u3001\u6570\u636e\u7c7b\u578b\u8f6c\u6362\u7b49\u3002\u4f8b\u5982\uff0c\u901a\u8fc7dropna()<\/code>\u51fd\u6570\u53bb\u9664\u7f3a\u5931\u503c\uff0cdrop_duplicates()<\/code>\u51fd\u6570\u53bb\u9664\u91cd\u590d\u9879\u3002<\/p>\n<\/p>\n

    import pandas as pd<\/p>\n
    \u8bfb\u53d6\u6570\u636e<\/strong><\/h2>\n
    data = pd.read_csv('data.csv')<\/p>\n
    \u53bb\u9664\u7f3a\u5931\u503c<\/strong><\/h2>\n
    data = data.dropna()<\/p>\n
    \u53bb\u9664\u91cd\u590d\u9879<\/strong><\/h2>\n
    data = data.drop_duplicates()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u6570\u636e\u7edf\u8ba1\u5206\u6790<\/strong><\/li>\n<\/ol>\n
      \u901a\u8fc7\u7edf\u8ba1\u5206\u6790\u7684\u65b9\u6cd5\u6765\u9a8c\u8bc1\u6570\u636e\u7684\u5408\u7406\u6027\u3002\u53ef\u4ee5\u4f7f\u7528\u63cf\u8ff0\u6027\u7edf\u8ba1\u6765\u67e5\u770b\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\uff0c\u6bd4\u5982\u5e73\u5747\u503c\u3001\u4e2d\u4f4d\u6570\u3001\u6807\u51c6\u5dee\u7b49\u6307\u6807\u3002Pandas\u5e93\u63d0\u4f9b\u4e86describe()<\/code>\u51fd\u6570\u6765\u83b7\u5f97\u6570\u636e\u7684\u57fa\u672c\u7edf\u8ba1\u4fe1\u606f\u3002<\/p>\n<\/p>\n
      # \u83b7\u53d6\u6570\u636e\u7684\u63cf\u8ff0\u6027\u7edf\u8ba1<\/p>\n
      print(data.describe())<\/p>\n
      <\/code><\/pre>\n<\/p>\n
        \n
      1. \u6570\u636e\u53ef\u89c6\u5316\u68c0\u67e5<\/strong><\/li>\n<\/ol>\n
        \u5728\u8fdb\u884c\u6b63\u5f0f\u7684\u56fe\u8868\u7ed8\u5236\u4e4b\u524d\uff0c\u53ef\u4ee5\u5148\u4f7f\u7528\u4e00\u4e9b\u7b80\u5355\u7684\u53ef\u89c6\u5316\u65b9\u6cd5\u6765\u68c0\u67e5\u6570\u636e\u662f\u5426\u5b58\u5728\u5f02\u5e38\u3002\u6bd4\u5982\u4f7f\u7528Matplotlib\u5e93\u7ed8\u5236\u76f4\u65b9\u56fe\u3001\u7bb1\u7ebf\u56fe\u6765\u67e5\u770b\u6570\u636e\u7684\u5206\u5e03\u548c\u5f02\u5e38\u503c\u3002<\/p>\n<\/p>\n
        import matplotlib.pyplot as plt<\/p>\n
        \u7ed8\u5236\u76f4\u65b9\u56fe<\/strong><\/h2>\n
        plt.hist(data['column_name'])<\/p>\n
        plt.show()<\/p>\n
        \u7ed8\u5236\u7bb1\u7ebf\u56fe<\/strong><\/h2>\n
        plt.boxplot(data['column_name'])<\/p>\n
        plt.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u4e8c\u3001\u9a8c\u8bc1\u56fe\u5f62\u7684\u6b63\u786e\u6027<\/p>\n<\/p>\n
        \u5728\u786e\u4fdd\u6570\u636e\u51c6\u786e\u7684\u57fa\u7840\u4e0a\uff0c\u63a5\u4e0b\u6765\u9700\u8981\u9a8c\u8bc1\u751f\u6210\u7684\u56fe\u5f62\u662f\u5426\u6b63\u786e\u3002\u8fd9\u5305\u62ec\u56fe\u5f62\u7684\u7c7b\u578b\u9009\u62e9\u662f\u5426\u5408\u9002\uff0c\u56fe\u5f62\u5143\u7d20\u662f\u5426\u6e05\u6670\u6613\u61c2\u7b49\u3002<\/p>\n<\/p>\n
          \n
        1. \u9009\u62e9\u5408\u9002\u7684\u56fe\u5f62\u7c7b\u578b<\/strong><\/li>\n<\/ol>\n
          \u6839\u636e\u6570\u636e\u7684\u7279\u6027\u548c\u5206\u6790\u76ee\u7684\u9009\u62e9\u5408\u9002\u7684\u56fe\u5f62\u7c7b\u578b\u3002\u4f8b\u5982\uff0c\u6298\u7ebf\u56fe\u9002\u5408\u5c55\u793a\u8d8b\u52bf\u53d8\u5316\uff0c\u67f1\u72b6\u56fe\u9002\u5408\u6bd4\u8f83\u4e0d\u540c\u7ec4\u7684\u6570\u636e\uff0c\u6563\u70b9\u56fe\u9002\u5408\u5c55\u793a\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u3002<\/p>\n<\/p>\n
          # \u7ed8\u5236\u6298\u7ebf\u56fe<\/p>\n
          plt.plot(data['x'], data['y'])<\/p>\n
          plt.title('Line Chart')<\/p>\n
          plt.xlabel('X-axis')<\/p>\n
          plt.ylabel('Y-axis')<\/p>\n
          plt.show()<\/p>\n
          <\/code><\/pre>\n<\/p>\n
            \n
          1. \u68c0\u67e5\u56fe\u5f62\u5143\u7d20<\/strong><\/li>\n<\/ol>\n
            \u786e\u4fdd\u56fe\u5f62\u4e2d\u7684\u5143\u7d20\uff08\u5982\u6807\u9898\u3001\u5750\u6807\u8f74\u6807\u7b7e\u3001\u56fe\u4f8b\u7b49\uff09\u6e05\u6670\u6613\u61c2\u3002\u4f7f\u7528Matplotlib\u7684title()<\/code>\u3001xlabel()<\/code>\u3001ylabel()<\/code>\u51fd\u6570\u8bbe\u7f6e\u6807\u9898\u548c\u6807\u7b7e\uff1b\u4f7f\u7528legend()<\/code>\u8bbe\u7f6e\u56fe\u4f8b\u3002<\/p>\n<\/p>\n
            # \u8bbe\u7f6e\u6807\u9898\u548c\u6807\u7b7e<\/p>\n
            plt.title('Sample Chart')<\/p>\n
            plt.xlabel('X-axis')<\/p>\n
            plt.ylabel('Y-axis')<\/p>\n
            \u6dfb\u52a0\u56fe\u4f8b<\/strong><\/h2>\n
            plt.legend(['Sample Legend'])<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u4e09\u3001\u8bc4\u4f30\u56fe\u5f62\u7684\u6e05\u6670\u5ea6<\/p>\n<\/p>\n
            \u56fe\u5f62\u7684\u6e05\u6670\u5ea6\u662f\u786e\u4fdd\u56fe\u5f62\u4fe1\u606f\u4f20\u9012\u6709\u6548\u6027\u7684\u5173\u952e\u3002\u6e05\u6670\u7684\u56fe\u5f62\u80fd\u591f\u5e2e\u52a9\u89c2\u4f17\u5feb\u901f\u7406\u89e3\u6570\u636e\u7684\u542b\u4e49\u3002<\/p>\n<\/p>\n
              \n
            1. \u8c03\u6574\u56fe\u5f62\u5927\u5c0f\u548c\u5206\u8fa8\u7387<\/strong><\/li>\n<\/ol>\n
              \u53ef\u4ee5\u901a\u8fc7Matplotlib\u7684figure()<\/code>\u51fd\u6570\u6765\u8bbe\u7f6e\u56fe\u5f62\u7684\u5927\u5c0f\u548c\u5206\u8fa8\u7387\uff0c\u4ee5\u786e\u4fdd\u56fe\u5f62\u5728\u4e0d\u540c\u8bbe\u5907\u4e0a\u7684\u6e05\u6670\u5ea6\u3002<\/p>\n<\/p>\n
              plt.figure(figsize=(10, 6), dpi=100)<\/p>\n
              <\/code><\/pre>\n<\/p>\n
                \n
              1. \u9009\u62e9\u5408\u9002\u7684\u989c\u8272\u548c\u6837\u5f0f<\/strong><\/li>\n<\/ol>\n
                \u4f7f\u7528\u989c\u8272\u548c\u6837\u5f0f\u6765\u589e\u5f3a\u56fe\u5f62\u7684\u53ef\u8bfb\u6027\u3002\u907f\u514d\u4f7f\u7528\u8fc7\u591a\u7684\u989c\u8272\u548c\u590d\u6742\u7684\u6837\u5f0f\uff0c\u4ee5\u514d\u5e72\u6270\u89c2\u4f17\u7684\u6ce8\u610f\u529b\u3002<\/p>\n<\/p>\n
                # \u8bbe\u7f6e\u7ebf\u6761\u989c\u8272\u548c\u6837\u5f0f<\/p>\n
                plt.plot(data['x'], data['y'], color='blue', linestyle='--')<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                \u56db\u3001\u786e\u4fdd\u56fe\u5f62\u7684\u53ef\u8bfb\u6027<\/p>\n<\/p>\n
                \u56fe\u5f62\u7684\u53ef\u8bfb\u6027\u5305\u62ec\u6587\u672c\u7684\u6e05\u6670\u5ea6\u3001\u56fe\u5f62\u7684\u5e03\u5c40\u7b49\u3002\u786e\u4fdd\u56fe\u5f62\u7684\u6bcf\u4e2a\u90e8\u5206\u90fd\u80fd\u591f\u88ab\u6e05\u6670\u5730\u7406\u89e3\u3002<\/p>\n<\/p>\n
                  \n
                1. \u6587\u672c\u7684\u6e05\u6670\u5ea6<\/strong><\/li>\n<\/ol>\n
                  \u5728\u56fe\u5f62\u4e2d\u6dfb\u52a0\u6587\u672c\u65f6\uff0c\u786e\u4fdd\u5b57\u4f53\u5927\u5c0f\u9002\u4e2d\uff0c\u989c\u8272\u4e0e\u80cc\u666f\u5f62\u6210\u5bf9\u6bd4\u3002\u53ef\u4ee5\u4f7f\u7528Matplotlib\u7684text()<\/code>\u51fd\u6570\u6dfb\u52a0\u6587\u672c\u3002<\/p>\n<\/p>\n
                  plt.text(x=1, y=2, s='Sample Text', fontsize=12, color='black')<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                    \n
                  1. \u56fe\u5f62\u5e03\u5c40<\/strong><\/li>\n<\/ol>\n
                    \u4f7f\u7528\u5b50\u56fe\u548c\u7f51\u683c\u6765\u7ec4\u7ec7\u590d\u6742\u7684\u56fe\u5f62\u5e03\u5c40\u3002Matplotlib\u7684subplot()<\/code>\u548cgrid()<\/code>\u51fd\u6570\u53ef\u4ee5\u5e2e\u52a9\u521b\u5efa\u6709\u5e8f\u7684\u56fe\u5f62\u5e03\u5c40\u3002<\/p>\n<\/p>\n
                    # \u521b\u5efa\u5b50\u56fe<\/p>\n
                    plt.subplot(1, 2, 1)<\/p>\n
                    plt.plot(data['x1'], data['y1'])<\/p>\n
                    plt.subplot(1, 2, 2)<\/p>\n
                    plt.plot(data['x2'], data['y2'])<\/p>\n
                    plt.tight_layout()  # \u81ea\u52a8\u8c03\u6574\u5b50\u56fe\u4e4b\u95f4\u7684\u95f4\u8ddd<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                    \u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u53ef\u4ee5\u6709\u6548\u5730\u68c0\u9a8cPython\u7ed8\u5236\u7684\u56fe\u5f62\uff0c\u786e\u4fdd\u5176\u51c6\u786e\u6027\u3001\u6b63\u786e\u6027\u3001\u6e05\u6670\u5ea6\u548c\u53ef\u8bfb\u6027\uff0c\u4ece\u800c\u4e3a\u6570\u636e\u5206\u6790\u548c\u51b3\u7b56\u63d0\u4f9b\u53ef\u9760\u7684\u652f\u6301\u3002<\/p>\n<\/p>\n
                    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                     \u5982\u4f55\u786e\u8ba4\u4f7f\u7528Python\u7ed8\u5236\u7684\u56fe\u5f62\u662f\u5426\u6b63\u786e\uff1f<\/strong>
                    \u5728\u4f7f\u7528Python\u7ed8\u56fe\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u6765\u9a8c\u8bc1\u56fe\u5f62\u7684\u51c6\u786e\u6027\u3002\u9996\u5148\uff0c\u6bd4\u8f83\u751f\u6210\u7684\u56fe\u5f62\u4e0e\u9884\u671f\u7ed3\u679c\u3002\u53ef\u4ee5\u4f7f\u7528\u5df2\u77e5\u6570\u636e\u96c6\u8fdb\u884c\u6d4b\u8bd5\uff0c\u786e\u4fdd\u8f93\u51fa\u4e0e\u6570\u5b66\u7406\u8bba\u6216\u6570\u636e\u5206\u6790\u7ed3\u679c\u4e00\u81f4\u3002\u6b64\u5916\uff0c\u4f7f\u7528\u56fe\u5f62\u7684\u63cf\u8ff0\u6027\u7edf\u8ba1\u6570\u636e\uff08\u5982\u5747\u503c\u3001\u65b9\u5dee\u7b49\uff09\u6765\u9a8c\u8bc1\u56fe\u5f62\u7684\u903b\u8f91\u6027\u4e5f\u662f\u4e00\u4e2a\u6709\u6548\u7684\u65b9\u6cd5\u3002<\/p>\n
                    \u4f7f\u7528\u54ea\u4e9b\u5e93\u53ef\u4ee5\u5e2e\u52a9\u6211\u5728Python\u4e2d\u8fdb\u884c\u56fe\u5f62\u9a8c\u8bc1\uff1f<\/strong>
                    Python\u4e2d\u6709\u591a\u4e2a\u5e93\u53ef\u4ee5\u5e2e\u52a9\u9a8c\u8bc1\u56fe\u5f62\u7684\u51c6\u786e\u6027\u3002\u5e38\u7528\u7684\u6709Matplotlib\u3001Seaborn\u548cPlotly\u7b49\u3002Matplotlib\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7ed8\u56fe\u529f\u80fd\uff0cSeaborn\u5219\u589e\u5f3a\u4e86\u6570\u636e\u53ef\u89c6\u5316\u7684\u7f8e\u89c2\u6027\u548c\u6613\u7528\u6027\uff0c\u800cPlotly\u5219\u9002\u5408\u7528\u4e8e\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u5f62\u3002\u901a\u8fc7\u8fd9\u4e9b\u5e93\u7684\u5185\u7f6e\u51fd\u6570\uff0c\u53ef\u4ee5\u66f4\u5bb9\u6613\u5730\u5bf9\u751f\u6210\u7684\u56fe\u5f62\u8fdb\u884c\u68c0\u67e5\u548c\u8c03\u6574\u3002<\/p>\n
                    \u5728\u7ed8\u5236\u56fe\u5f62\u65f6\u9047\u5230\u95ee\u9898\uff0c\u5982\u4f55\u8fdb\u884c\u8c03\u8bd5\uff1f<\/strong>
                    \u8c03\u8bd5Python\u7ed8\u56fe\u4ee3\u7801\u65f6\uff0c\u53ef\u4ee5\u4ece\u591a\u4e2a\u65b9\u9762\u5165\u624b\u3002\u9996\u5148\uff0c\u68c0\u67e5\u6570\u636e\u8f93\u5165\u662f\u5426\u6b63\u786e\uff0c\u786e\u4fdd\u6570\u636e\u683c\u5f0f\u7b26\u5408\u7ed8\u56fe\u8981\u6c42\u3002\u5176\u6b21\uff0c\u53ef\u4ee5\u9010\u6b65\u8fd0\u884c\u4ee3\u7801\uff0c\u89c2\u5bdf\u6bcf\u4e00\u6b65\u7684\u8f93\u51fa\uff0c\u4f7f\u7528print()<\/code>\u51fd\u6570\u67e5\u770b\u53d8\u91cf\u7684\u503c\u548c\u72b6\u6001\u3002\u6b64\u5916\uff0c\u67e5\u9605\u5e93\u7684\u6587\u6863\uff0c\u4e86\u89e3\u51fd\u6570\u7684\u53c2\u6570\u548c\u7528\u6cd5\uff0c\u4e5f\u80fd\u5e2e\u52a9\u89e3\u51b3\u95ee\u9898\u3002\u4f7f\u7528\u56fe\u5f62\u7684\u4fdd\u5b58\u529f\u80fd\u53ef\u4ee5\u5c06\u4e2d\u95f4\u7ed3\u679c\u4fdd\u5b58\u4e0b\u6765\uff0c\u65b9\u4fbf\u540e\u7eed\u5206\u6790\u548c\u6bd4\u8f83\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u7528Python\u753b\u56fe\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u5f0f\u68c0\u9a8c\uff1a\u68c0\u67e5\u6570\u636e\u7684\u51c6\u786e\u6027\u3001\u9a8c\u8bc1\u56fe\u5f62\u7684\u6b63\u786e\u6027\u3001\u8bc4\u4f30\u56fe\u5f62\u7684\u6e05\u6670\u5ea6\u3001\u786e\u4fdd\u56fe\u5f62\u7684\u53ef\u8bfb\u6027 […]","protected":false},"author":3,"featured_media":969633,"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\/969627"}],"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=969627"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/969627\/revisions"}],"predecessor-version":[{"id":969634,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/969627\/revisions\/969634"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/969633"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=969627"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=969627"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=969627"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}