{"id":991361,"date":"2024-12-27T08:27:59","date_gmt":"2024-12-27T00:27:59","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/991361.html"},"modified":"2024-12-27T08:28:01","modified_gmt":"2024-12-27T00:28:01","slug":"%e5%a6%82%e4%bd%95%e8%bf%90%e7%94%a8python%e5%88%86%e6%9e%90%e5%9b%be%e5%bd%a2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/991361.html","title":{"rendered":"\u5982\u4f55\u8fd0\u7528python\u5206\u6790\u56fe\u5f62"},"content":{"rendered":"

\"\u5982\u4f55\u8fd0\u7528python\u5206\u6790\u56fe\u5f62\"<\/p>\n

\u8fd0\u7528Python\u5206\u6790\u56fe\u5f62\u53ef\u4ee5\u901a\u8fc7\uff1a\u4f7f\u7528\u6570\u636e\u53ef\u89c6\u5316\u5e93\u3001\u5229\u7528\u56fe\u5f62\u5904\u7406\u5e93\u3001\u7ed3\u5408\u7edf\u8ba1\u5206\u6790\u5de5\u5177\u3002Python\u63d0\u4f9b\u4e86\u4e00\u7cfb\u5217\u5f3a\u5927\u7684\u5e93\u548c\u5de5\u5177\uff0c\u4f7f\u5f97\u6570\u636e\u5206\u6790\u548c\u56fe\u5f62\u5904\u7406\u53d8\u5f97\u9ad8\u6548\u548c\u7b80\u5355\u3002<\/strong>\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u5229\u7528Python\u8fdb\u884c\u56fe\u5f62\u5206\u6790\uff0c\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u548c\u5e94\u7528\u8fd9\u4e9b\u5de5\u5177\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528\u6570\u636e\u53ef\u89c6\u5316\u5e93<\/p>\n<\/p>\n

Python\u6709\u8bb8\u591a\u4f18\u79c0\u7684\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u5982Matplotlib\u3001Seaborn\u548cPlotly\u3002\u8fd9\u4e9b\u5e93\u53ef\u4ee5\u5e2e\u52a9\u4f60\u5c06\u6570\u636e\u8f6c\u5316\u4e3a\u6613\u4e8e\u7406\u89e3\u7684\u56fe\u5f62\u3002<\/p>\n<\/p>\n

    \n
  1. \n

    Matplotlib<\/strong>
    Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u6570\u636e\u53ef\u89c6\u5316\u5e93\u4e4b\u4e00\u3002\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\uff0c\u53ef\u4ee5\u521b\u5efa\u9759\u6001\u3001\u52a8\u6001\u548c\u4ea4\u4e92\u5f0f\u7684\u56fe\u5f62\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528Matplotlib\u7ed8\u5236\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u6563\u70b9\u56fe\u7b49\u591a\u79cd\u7c7b\u578b\u7684\u56fe\u5f62\u3002\u901a\u8fc7\u8bbe\u7f6e\u56fe\u5f62\u7684\u6837\u5f0f\u3001\u989c\u8272\u548c\u6807\u7b7e\uff0c\u4f60\u53ef\u4ee5\u521b\u5efa\u51fa\u4e13\u4e1a\u6c34\u51c6\u7684\u56fe\u8868\u3002<\/p>\n<\/p>\n<\/li>\n

  2. \n

    Seaborn<\/strong>
    Seaborn\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\u548c\u66f4\u7b80\u5355\u7684API\u3002Seaborn\u64c5\u957f\u5904\u7406\u7edf\u8ba1\u6570\u636e\uff0c\u80fd\u591f\u8f7b\u677e\u521b\u5efa\u590d\u6742\u7684\u53ef\u89c6\u5316\u56fe\u8868\uff0c\u5982\u70ed\u529b\u56fe\u3001\u5206\u7c7b\u6563\u70b9\u56fe\u7b49\u3002\u901a\u8fc7Seaborn\uff0c\u4f60\u53ef\u4ee5\u5feb\u901f\u5730\u63a2\u7d22\u548c\u7406\u89e3\u6570\u636e\u4e4b\u95f4\u7684\u5173\u7cfb\u3002<\/p>\n<\/p>\n<\/li>\n

  3. \n

    Plotly<\/strong>
    Plotly\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u4ea4\u4e92\u5f0f\u6570\u636e\u53ef\u89c6\u5316\u5e93\u3002\u5b83\u652f\u6301\u591a\u79cd\u56fe\u8868\u7c7b\u578b\uff0c\u5e76\u4e14\u53ef\u4ee5\u751f\u6210\u4ea4\u4e92\u5f0f\u7684\u7f51\u9875\u56fe\u8868\uff0c\u975e\u5e38\u9002\u5408\u7528\u4e8e\u5c55\u793a\u548c\u5206\u4eab\u5206\u6790\u7ed3\u679c\u3002Plotly\u8fd8\u63d0\u4f9b\u4e86\u57fa\u4e8eDash\u7684\u5e94\u7528\u6846\u67b6\uff0c\u53ef\u4ee5\u5c06\u6570\u636e\u53ef\u89c6\u5316\u96c6\u6210\u5230\u7f51\u7edc\u5e94\u7528\u4e2d\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n

    \u4e8c\u3001\u5229\u7528\u56fe\u5f62\u5904\u7406\u5e93<\/p>\n<\/p>\n

    \u5728\u5904\u7406\u56fe\u5f62\u6570\u636e\u65f6\uff0cPython\u7684\u56fe\u5f62\u5904\u7406\u5e93\u5982OpenCV\u548cPillow\uff08PIL\uff09\u975e\u5e38\u6709\u7528\u3002<\/p>\n<\/p>\n

      \n
    1. \n

      OpenCV<\/strong>
      OpenCV\u662f\u4e00\u4e2a\u5f00\u6e90\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u56fe\u50cf\u5904\u7406\u529f\u80fd\u3002\u901a\u8fc7OpenCV\uff0c\u4f60\u53ef\u4ee5\u8fdb\u884c\u56fe\u50cf\u7684\u8bfb\u53d6\u3001\u663e\u793a\u3001\u7f16\u8f91\u3001\u53d8\u6362\u4ee5\u53ca\u7279\u5f81\u63d0\u53d6\u7b49\u64cd\u4f5c\u3002OpenCV\u8fd8\u652f\u6301\u89c6\u9891\u5904\u7406\u548c\u673a\u5668\u5b66\u4e60<\/a>\u7b97\u6cd5\uff0c\u4f7f\u5176\u6210\u4e3a\u56fe\u5f62\u5904\u7406\u7684\u5f3a\u5927\u5de5\u5177\u3002<\/p>\n<\/p>\n<\/li>\n

    2. \n

      Pillow (PIL)<\/strong>
      Pillow\u662fPython Imaging Library (PIL)\u7684\u4e00\u4e2a\u5206\u652f\uff0c\u5b83\u662fPython\u4e2d\u5904\u7406\u56fe\u50cf\u7684\u57fa\u7840\u5e93\u4e4b\u4e00\u3002Pillow\u63d0\u4f9b\u4e86\u7b80\u5355\u7684API\uff0c\u7528\u4e8e\u6253\u5f00\u3001\u64cd\u4f5c\u548c\u4fdd\u5b58\u56fe\u50cf\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528Pillow\u5bf9\u56fe\u50cf\u8fdb\u884c\u88c1\u526a\u3001\u65cb\u8f6c\u3001\u6ee4\u955c\u5e94\u7528\u7b49\u57fa\u672c\u5904\u7406\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n

      \u4e09\u3001\u7ed3\u5408\u7edf\u8ba1\u5206\u6790\u5de5\u5177<\/p>\n<\/p>\n

      \u5728\u8fdb\u884c\u56fe\u5f62\u5206\u6790\u65f6\uff0c\u7ed3\u5408\u7edf\u8ba1\u5206\u6790\u5de5\u5177\u53ef\u4ee5\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u3002<\/p>\n<\/p>\n

        \n
      1. \n

        Pandas<\/strong>
        Pandas\u662fPython\u4e2d\u6700\u6d41\u884c\u7684\u6570\u636e\u5206\u6790\u5e93\u4e4b\u4e00\u3002\u5b83\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u7ed3\u6784\u548c\u5206\u6790\u5de5\u5177\uff0c\u53ef\u4ee5\u8f7b\u677e\u5904\u7406\u590d\u6742\u7684\u6570\u636e\u96c6\u3002\u901a\u8fc7Pandas\uff0c\u4f60\u53ef\u4ee5\u5feb\u901f\u8fdb\u884c\u6570\u636e\u7684\u6e05\u6d17\u3001\u8f6c\u6362\u548c\u805a\u5408\uff0c\u5e76\u4e0e\u53ef\u89c6\u5316\u5e93\u7ed3\u5408\u4f7f\u7528\uff0c\u751f\u6210\u6709\u610f\u4e49\u7684\u56fe\u8868\u3002<\/p>\n<\/p>\n<\/li>\n

      2. \n

        NumPy<\/strong>
        NumPy\u662fPython\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u591a\u7ef4\u6570\u7ec4\u5bf9\u8c61\u548c\u5404\u79cd\u6570\u5b66\u51fd\u6570\u3002NumPy\u5728\u6570\u503c\u8ba1\u7b97\u65b9\u9762\u975e\u5e38\u9ad8\u6548\uff0c\u53ef\u4ee5\u52a0\u901f\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u8fc7\u7a0b\u3002\u7ed3\u5408Matplotlib\u548cPandas\uff0cNumPy\u53ef\u4ee5\u5e2e\u52a9\u4f60\u8fdb\u884c\u590d\u6742\u7684\u7edf\u8ba1\u5206\u6790\u3002<\/p>\n<\/p>\n<\/li>\n

      3. \n

        SciPy<\/strong>
        SciPy\u662f\u4e00\u4e2a\u7528\u4e8e\u79d1\u5b66\u548c\u5de5\u7a0b\u8ba1\u7b97\u7684Python\u5e93\u3002\u5b83\u57fa\u4e8eNumPy\u6784\u5efa\uff0c\u63d0\u4f9b\u4e86\u5927\u91cf\u7684\u6570\u5b66\u3001\u79d1\u5b66\u548c\u5de5\u7a0b\u51fd\u6570\u3002SciPy\u5728\u56fe\u50cf\u5904\u7406\u3001\u4f18\u5316\u3001\u4fe1\u53f7\u5904\u7406\u548c\u7edf\u8ba1\u5206\u6790\u65b9\u9762\u6709\u7740\u5e7f\u6cdb\u7684\u5e94\u7528\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n

        \u56db\u3001\u7efc\u5408\u5e94\u7528\u6848\u4f8b<\/p>\n<\/p>\n

        \u4e3a\u4e86\u66f4\u597d\u5730\u7406\u89e3\u5982\u4f55\u8fd0\u7528Python\u5206\u6790\u56fe\u5f62\uff0c\u4e0b\u9762\u662f\u4e00\u4e2a\u7efc\u5408\u5e94\u7528\u6848\u4f8b\u3002<\/p>\n<\/p>\n

        \u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u5305\u542b\u591a\u4e2a\u57ce\u5e02\u6c14\u6e29\u6570\u636e\u7684CSV\u6587\u4ef6\uff0c\u6211\u4eec\u5e0c\u671b\u5206\u6790\u4e0d\u540c\u57ce\u5e02\u7684\u6c14\u6e29\u53d8\u5316\u8d8b\u52bf\uff0c\u5e76\u5c06\u7ed3\u679c\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n

          \n
        1. \u6570\u636e\u8bfb\u53d6\u4e0e\u5904\u7406<\/strong>
          \u9996\u5148\uff0c\u6211\u4eec\u4f7f\u7528Pandas\u8bfb\u53d6CSV\u6587\u4ef6\uff0c\u5e76\u5bf9\u6570\u636e\u8fdb\u884c\u6e05\u6d17\u548c\u6574\u7406\u3002\u53ef\u4ee5\u901a\u8fc7Pandas\u7684\u5404\u79cd\u65b9\u6cd5\uff0c\u5982fillna()<\/code>\u3001dropna()<\/code>\u7b49\u5904\u7406\u7f3a\u5931\u6570\u636e\u3002<\/li>\n<\/ol>\n

          import pandas as pd<\/p>\n
          data = pd.read_csv('temperature_data.csv')<\/p>\n
          data = data.dropna()  # \u5220\u9664\u7f3a\u5931\u6570\u636e<\/p>\n
          <\/code><\/pre>\n<\/p>\n
            \n
          1. \u6570\u636e\u53ef\u89c6\u5316<\/strong>
            \u63a5\u4e0b\u6765\uff0c\u4f7f\u7528Matplotlib\u548cSeaborn\u5bf9\u6570\u636e\u8fdb\u884c\u53ef\u89c6\u5316\uff0c\u7ed8\u5236\u4e0d\u540c\u57ce\u5e02\u7684\u6c14\u6e29\u53d8\u5316\u8d8b\u52bf\u56fe\u3002<\/li>\n<\/ol>\n
            import matplotlib.pyplot as plt<\/p>\n
            import seaborn as sns<\/p>\n
            plt.figure(figsize=(10, 6))<\/p>\n
            sns.lineplot(data=data, x='Date', y='Temperature', hue='City')<\/p>\n
            plt.title('Temperature Trends of Different Cities')<\/p>\n
            plt.xlabel('Date')<\/p>\n
            plt.ylabel('Temperature')<\/p>\n
            plt.show()<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. \u6570\u636e\u5206\u6790<\/strong>
              \u7ed3\u5408NumPy\u548cSciPy\u8fdb\u884c\u7edf\u8ba1\u5206\u6790\uff0c\u6bd4\u5982\u8ba1\u7b97\u4e0d\u540c\u57ce\u5e02\u7684\u5e73\u5747\u6c14\u6e29\u548c\u6e29\u5ea6\u6ce2\u52a8\u3002<\/li>\n<\/ol>\n
              import numpy as np<\/p>\n
              from scipy import stats<\/p>\n
              city_avg_temp = data.groupby('City')['Temperature'].mean()<\/p>\n
              city_temp_variance = data.groupby('City')['Temperature'].var()<\/p>\n
              print("Average Temperature by City:")<\/p>\n
              print(city_avg_temp)<\/p>\n
              print("\\nTemperature Variance by City:")<\/p>\n
              print(city_temp_variance)<\/p>\n
              <\/code><\/pre>\n<\/p>\n
                \n
              1. \u7ed3\u679c\u89e3\u91ca\u4e0e\u5e94\u7528<\/strong>
                \u901a\u8fc7\u53ef\u89c6\u5316\u56fe\u8868\u548c\u7edf\u8ba1\u5206\u6790\u7ed3\u679c\uff0c\u6211\u4eec\u53ef\u4ee5\u76f4\u89c2\u5730\u770b\u5230\u4e0d\u540c\u57ce\u5e02\u7684\u6c14\u6e29\u53d8\u5316\u8d8b\u52bf\u548c\u7279\u5f81\u3002\u7136\u540e\uff0c\u53ef\u4ee5\u6839\u636e\u8fd9\u4e9b\u7ed3\u679c\u8fdb\u884c\u66f4\u6df1\u5165\u7684\u6c14\u5019\u7814\u7a76\u6216\u51b3\u7b56\u652f\u6301\u3002<\/li>\n<\/ol>\n
                \u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u4f60\u53ef\u4ee5\u5168\u9762\u5730\u8fd0\u7528Python\u8fdb\u884c\u56fe\u5f62\u6570\u636e\u7684\u5206\u6790\u548c\u53ef\u89c6\u5316\u3002Python\u7684\u4e30\u5bcc\u751f\u6001\u7cfb\u7edf\u4f7f\u5f97\u6570\u636e\u5206\u6790\u53d8\u5f97\u66f4\u52a0\u9ad8\u6548\u548c\u7075\u6d3b\u3002\u5e0c\u671b\u901a\u8fc7\u8fd9\u7bc7\u6587\u7ae0\uff0c\u4f60\u80fd\u591f\u638c\u63e1Python\u5728\u56fe\u5f62\u5206\u6790\u4e2d\u7684\u5e94\u7528\uff0c\u5e76\u5c06\u5176\u5e94\u7528\u4e8e\u5b9e\u9645\u9879\u76ee\u4e2d\u3002<\/p>\n<\/p>\n
                \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                 \u5982\u4f55\u4f7f\u7528Python\u8fdb\u884c\u56fe\u5f62\u5206\u6790\u7684\u57fa\u672c\u6b65\u9aa4\u662f\u4ec0\u4e48\uff1f<\/strong>
                \u5728\u4f7f\u7528Python\u8fdb\u884c\u56fe\u5f62\u5206\u6790\u65f6\uff0c\u901a\u5e38\u53ef\u4ee5\u9075\u5faa\u4ee5\u4e0b\u51e0\u4e2a\u6b65\u9aa4\uff1a\u9996\u5148\uff0c\u5b89\u88c5\u6240\u9700\u7684\u5e93\uff0c\u4f8b\u5982Matplotlib\u3001NumPy\u548cPandas\u3002\u63a5\u4e0b\u6765\uff0c\u52a0\u8f7d\u6570\u636e\uff0c\u53ef\u80fd\u662f\u901a\u8fc7CSV\u6587\u4ef6\u6216\u6570\u636e\u5e93\u3002\u7136\u540e\uff0c\u4f7f\u7528Matplotlib\u6216Seaborn\u7b49\u53ef\u89c6\u5316\u5e93\u521b\u5efa\u56fe\u5f62\uff0c\u6bd4\u5982\u6298\u7ebf\u56fe\u3001\u6563\u70b9\u56fe\u6216\u67f1\u72b6\u56fe\u3002\u6700\u540e\uff0c\u5206\u6790\u751f\u6210\u7684\u56fe\u5f62\uff0c\u63d0\u53d6\u6709\u4ef7\u503c\u7684\u4fe1\u606f\u5e76\u8fdb\u884c\u6570\u636e\u89e3\u8bfb\u3002<\/p>\n
                Python\u4e2d\u7684\u54ea\u4e9b\u5e93\u6700\u9002\u5408\u8fdb\u884c\u56fe\u5f62\u5206\u6790\uff1f<\/strong>
                Python\u4e2d\u6709\u591a\u4e2a\u5e93\u9002\u5408\u56fe\u5f62\u5206\u6790\uff0c\u5176\u4e2d\u6700\u5e38\u7528\u7684\u662fMatplotlib\u548cSeaborn\uff0c\u5b83\u4eec\u53ef\u4ee5\u521b\u5efa\u5404\u79cd\u9759\u6001\u3001\u52a8\u6001\u548c\u4ea4\u4e92\u5f0f\u56fe\u5f62\u3002NumPy\u548cPandas\u5219\u7528\u4e8e\u6570\u636e\u5904\u7406\u548c\u7ba1\u7406\uff0c\u800cPlotly\u548cBokeh\u5219\u9002\u5408\u5236\u4f5c\u4ea4\u4e92\u5f0f\u56fe\u5f62\u3002\u8fd9\u4e9b\u5e93\u5404\u5177\u7279\u8272\uff0c\u53ef\u4ee5\u6839\u636e\u4e0d\u540c\u7684\u9700\u6c42\u9009\u62e9\u4f7f\u7528\u3002<\/p>\n
                \u5982\u4f55\u63d0\u9ad8Python\u56fe\u5f62\u5206\u6790\u7684\u6548\u7387\u548c\u53ef\u8bfb\u6027\uff1f<\/strong>
                \u63d0\u9ad8Python\u56fe\u5f62\u5206\u6790\u7684\u6548\u7387\u548c\u53ef\u8bfb\u6027\u53ef\u4ee5\u901a\u8fc7\u591a\u4e2a\u65b9\u5f0f\u5b9e\u73b0\u3002\u9996\u5148\uff0c\u4f7f\u7528\u6e05\u6670\u7684\u53d8\u91cf\u547d\u540d\u548c\u6ce8\u91ca\uff0c\u4f7f\u4ee3\u7801\u6613\u4e8e\u7406\u89e3\u3002\u5176\u6b21\uff0c\u5229\u7528\u51fd\u6570\u548c\u6a21\u5757\u5316\u7f16\u7a0b\u6765\u7ec4\u7ec7\u4ee3\u7801\uff0c\u51cf\u5c11\u91cd\u590d\u3002\u6b64\u5916\uff0c\u9009\u62e9\u5408\u9002\u7684\u56fe\u5f62\u7c7b\u578b\u6765\u51c6\u786e\u4f20\u8fbe\u6570\u636e\uff0c\u4ece\u800c\u63d0\u9ad8\u53ef\u8bfb\u6027\u3002\u6700\u540e\uff0c\u5b9a\u671f\u91cd\u6784\u4ee3\u7801\u4ee5\u4fdd\u6301\u5176\u7b80\u6d01\u548c\u9ad8\u6548\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u8fd0\u7528Python\u5206\u6790\u56fe\u5f62\u53ef\u4ee5\u901a\u8fc7\uff1a\u4f7f\u7528\u6570\u636e\u53ef\u89c6\u5316\u5e93\u3001\u5229\u7528\u56fe\u5f62\u5904\u7406\u5e93\u3001\u7ed3\u5408\u7edf\u8ba1\u5206\u6790\u5de5\u5177\u3002Python\u63d0\u4f9b\u4e86\u4e00\u7cfb\u5217 […]","protected":false},"author":3,"featured_media":991372,"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\/991361"}],"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=991361"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/991361\/revisions"}],"predecessor-version":[{"id":991373,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/991361\/revisions\/991373"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/991372"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=991361"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=991361"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=991361"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}