{"id":925714,"date":"2024-12-26T15:35:51","date_gmt":"2024-12-26T07:35:51","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/925714.html"},"modified":"2024-12-26T15:35:53","modified_gmt":"2024-12-26T07:35:53","slug":"python-%e5%a6%82%e4%bd%95clean","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/925714.html","title":{"rendered":"python \u5982\u4f55clean"},"content":{"rendered":"

\"python<\/p>\n

\u5728Python\u4e2d\uff0c\u6570\u636e\u6e05\u6d17\u662f\u6570\u636e\u9884\u5904\u7406\u7684\u4e00\u4e2a\u91cd\u8981\u6b65\u9aa4\uff0c\u5bf9\u4e8e\u6570\u636e\u5206\u6790\u548c\u673a\u5668\u5b66\u4e60<\/a>\u9879\u76ee\u81f3\u5173\u91cd\u8981\u3002\u6570\u636e\u6e05\u6d17\u4e3b\u8981\u5305\u62ec\u5220\u9664\u7f3a\u5931\u503c\u3001\u5904\u7406\u91cd\u590d\u6570\u636e\u3001\u683c\u5f0f\u5316\u6570\u636e\u3001\u6807\u51c6\u5316\u6570\u636e\u548c\u53bb\u9664\u5f02\u5e38\u503c<\/strong>\u7b49\u6b65\u9aa4\u3002\u4ee5\u4e0b\u662f\u5bf9\u5176\u4e2d\u4e00\u4e2a\u6b65\u9aa4\u7684\u8be6\u7ec6\u63cf\u8ff0\uff1a\u5220\u9664\u7f3a\u5931\u503c<\/strong>\u3002\u7f3a\u5931\u503c\u5728\u6570\u636e\u96c6\u4e2d\u662f\u5e38\u89c1\u7684\uff0c\u53ef\u80fd\u4f1a\u5bfc\u81f4\u5206\u6790\u7ed3\u679c\u4e0d\u51c6\u786e\u3002\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u7684dropna()<\/code>\u51fd\u6570\u8f7b\u677e\u5220\u9664\u7f3a\u5931\u503c\uff0c\u786e\u4fdd\u6570\u636e\u5b8c\u6574\u6027\u3002\u6b64\u5916\uff0c\u8fd8\u53ef\u4ee5\u9009\u62e9\u4f7f\u7528\u63d2\u503c\u6cd5\u6216\u586b\u5145\u5747\u503c\u3001\u4f17\u6570\u7b49\u65b9\u6cd5\u6765\u66ff\u6362\u7f3a\u5931\u503c\uff0c\u4ee5\u51cf\u5c11\u6570\u636e\u635f\u5931\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5220\u9664\u7f3a\u5931\u503c\u3001\u5904\u7406\u7f3a\u5931\u503c<\/p>\n<\/p>\n

\u5728\u6570\u636e\u96c6\u4e2d\uff0c\u7f3a\u5931\u503c\u662f\u4e00\u4e2a\u5e38\u89c1\u7684\u95ee\u9898\u3002\u5904\u7406\u7f3a\u5931\u503c\u7684\u65b9\u5f0f\u591a\u79cd\u591a\u6837\uff0c\u6700\u5e38\u89c1\u7684\u65b9\u5f0f\u5305\u62ec\u5220\u9664\u542b\u6709\u7f3a\u5931\u503c\u7684\u884c\u6216\u5217\u3001\u7528\u7279\u5b9a\u7684\u503c\u8fdb\u884c\u586b\u8865\u7b49\u3002Pandas\u5e93\u63d0\u4f9b\u4e86\u975e\u5e38\u65b9\u4fbf\u7684\u65b9\u6cd5\u6765\u5904\u7406\u7f3a\u5931\u503c\u3002<\/p>\n<\/p>\n

    \n
  1. \n

    \u5220\u9664\u542b\u6709\u7f3a\u5931\u503c\u7684\u884c\u6216\u5217<\/strong><\/p>\n<\/p>\n

    \u4f7f\u7528Pandas\u5e93\u7684dropna()<\/code>\u51fd\u6570\u53ef\u4ee5\u5220\u9664\u542b\u6709\u7f3a\u5931\u503c\u7684\u884c\u6216\u5217\u3002\u4f8b\u5982\uff0cdf.dropna()<\/code>\u53ef\u4ee5\u5220\u9664\u6240\u6709\u542b\u6709\u7f3a\u5931\u503c\u7684\u884c\uff0c\u800cdf.dropna(axis=1)<\/code>\u5219\u53ef\u4ee5\u5220\u9664\u542b\u6709\u7f3a\u5931\u503c\u7684\u5217\u3002\u8fd9\u79cd\u65b9\u6cd5\u7b80\u5355\u76f4\u63a5\uff0c\u4f46\u53ef\u80fd\u4f1a\u4e22\u5931\u5927\u91cf\u6570\u636e\uff0c\u56e0\u6b64\u9700\u8981\u8c28\u614e\u4f7f\u7528\u3002<\/p>\n<\/p>\n<\/li>\n

  2. \n

    \u7528\u7279\u5b9a\u7684\u503c\u586b\u8865\u7f3a\u5931\u503c<\/strong><\/p>\n<\/p>\n

    \u53e6\u4e00\u79cd\u65b9\u6cd5\u662f\u7528\u7279\u5b9a\u7684\u503c\u6765\u586b\u8865\u7f3a\u5931\u503c\u3002\u53ef\u4ee5\u4f7f\u7528fillna()<\/code>\u51fd\u6570\u6307\u5b9a\u586b\u8865\u7684\u503c\u3002\u4f8b\u5982\uff0cdf.fillna(0)<\/code>\u53ef\u4ee5\u5c06\u6240\u6709\u7684\u7f3a\u5931\u503c\u66ff\u6362\u4e3a0\u3002\u6b64\u5916\uff0c\u8fd8\u53ef\u4ee5\u4f7f\u7528\u5217\u7684\u5747\u503c\u3001\u4f17\u6570\u6216\u4e2d\u4f4d\u6570\u6765\u586b\u8865\u3002\u4f8b\u5982\uff0cdf['column'].fillna(df['column'].mean())<\/code>\u53ef\u4ee5\u7528\u5217\u7684\u5747\u503c\u6765\u586b\u8865\u7f3a\u5931\u503c\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n

    \u4e8c\u3001\u5904\u7406\u91cd\u590d\u6570\u636e<\/p>\n<\/p>\n

    \u91cd\u590d\u6570\u636e\u4f1a\u5bfc\u81f4\u5206\u6790\u7ed3\u679c\u7684\u4e0d\u51c6\u786e\uff0c\u56e0\u6b64\u9700\u8981\u53ca\u65f6\u5220\u9664\u3002Pandas\u63d0\u4f9b\u4e86drop_duplicates()<\/code>\u51fd\u6570\u6765\u5904\u7406\u91cd\u590d\u6570\u636e\u3002<\/p>\n<\/p>\n

      \n
    1. \n

      \u8bc6\u522b\u548c\u5220\u9664\u91cd\u590d\u884c<\/strong><\/p>\n<\/p>\n

      \u53ef\u4ee5\u4f7f\u7528df.duplicated()<\/code>\u51fd\u6570\u6765\u6807\u8bb0\u91cd\u590d\u7684\u884c\uff0c\u8fd9\u4e2a\u51fd\u6570\u8fd4\u56de\u4e00\u4e2a\u5e03\u5c14\u503c\u6570\u7ec4\uff0c\u6807\u8bb0\u54ea\u4e9b\u884c\u662f\u91cd\u590d\u7684\u3002\u7136\u540e\u53ef\u4ee5\u4f7f\u7528df.drop_duplicates()<\/code>\u6765\u5220\u9664\u8fd9\u4e9b\u91cd\u590d\u884c\u3002<\/p>\n<\/p>\n<\/li>\n

    2. \n

      \u6839\u636e\u7279\u5b9a\u5217\u5220\u9664\u91cd\u590d\u6570\u636e<\/strong><\/p>\n<\/p>\n

      \u5982\u679c\u53ea\u9700\u8981\u5220\u9664\u7279\u5b9a\u5217\u4e2d\u91cd\u590d\u7684\u6570\u636e\uff0c\u53ef\u4ee5\u5728drop_duplicates()<\/code>\u4e2d\u6307\u5b9a\u5217\u540d\u3002\u4f8b\u5982\uff0cdf.drop_duplicates(subset=['column'])<\/code>\u53ef\u4ee5\u5220\u9664\u7279\u5b9a\u5217\u4e2d\u91cd\u590d\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n

      \u4e09\u3001\u6570\u636e\u683c\u5f0f\u5316<\/p>\n<\/p>\n

      \u6570\u636e\u683c\u5f0f\u5316\u662f\u4e3a\u4e86\u786e\u4fdd\u6570\u636e\u7684\u4e00\u81f4\u6027\u548c\u53ef\u8bfb\u6027\u3002\u5e38\u89c1\u7684\u683c\u5f0f\u5316\u4efb\u52a1\u5305\u62ec\u8f6c\u6362\u6570\u636e\u7c7b\u578b\u3001\u5904\u7406\u65e5\u671f\u65f6\u95f4\u683c\u5f0f\u7b49\u3002<\/p>\n<\/p>\n

        \n
      1. \n

        \u8f6c\u6362\u6570\u636e\u7c7b\u578b<\/strong><\/p>\n<\/p>\n

        \u5728\u6570\u636e\u5206\u6790\u4e2d\uff0c\u4e0d\u540c\u7684\u6570\u636e\u7c7b\u578b\u5bf9\u5206\u6790\u7ed3\u679c\u6709\u7740\u91cd\u8981\u7684\u5f71\u54cd\u3002\u53ef\u4ee5\u4f7f\u7528Pandas\u7684astype()<\/code>\u65b9\u6cd5\u8f6c\u6362\u6570\u636e\u7c7b\u578b\u3002\u4f8b\u5982\uff0c\u5c06\u6574\u6570\u7c7b\u578b\u8f6c\u6362\u4e3a\u6d6e\u70b9\u6570\u7c7b\u578b\u53ef\u4ee5\u4f7f\u7528df['column'].astype(float)<\/code>\u3002<\/p>\n<\/p>\n<\/li>\n

      2. \n

        \u5904\u7406\u65e5\u671f\u65f6\u95f4\u683c\u5f0f<\/strong><\/p>\n<\/p>\n

        \u65e5\u671f\u548c\u65f6\u95f4\u683c\u5f0f\u7684\u5904\u7406\u5728\u6570\u636e\u5206\u6790\u4e2d\u975e\u5e38\u91cd\u8981\u3002Pandas\u63d0\u4f9b\u4e86to_datetime()<\/code>\u51fd\u6570\u6765\u5c06\u5b57\u7b26\u4e32\u8f6c\u6362\u4e3a\u65e5\u671f\u65f6\u95f4\u683c\u5f0f\u3002\u4f8b\u5982\uff0cdf['date'] = pd.to_datetime(df['date'])<\/code>\u53ef\u4ee5\u5c06\u5b57\u7b26\u4e32\u683c\u5f0f\u7684\u65e5\u671f\u8f6c\u6362\u4e3a\u65e5\u671f\u65f6\u95f4\u683c\u5f0f\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n

        \u56db\u3001\u6807\u51c6\u5316\u6570\u636e<\/p>\n<\/p>\n

        \u6807\u51c6\u5316\u662f\u6570\u636e\u9884\u5904\u7406\u4e2d\u91cd\u8981\u7684\u4e00\u6b65\uff0c\u5e38\u7528\u4e8e\u7279\u5f81\u7f29\u653e\uff0c\u4f7f\u5f97\u4e0d\u540c\u7279\u5f81\u7684\u6570\u636e\u5728\u540c\u4e00\u5c3a\u5ea6\u4e0a\u3002<\/p>\n<\/p>\n

          \n
        1. \n

          \u5f52\u4e00\u5316<\/strong><\/p>\n<\/p>\n

          \u5f52\u4e00\u5316\u662f\u5c06\u6570\u636e\u7f29\u653e\u5230\u7279\u5b9a\u7684\u533a\u95f4\uff08\u901a\u5e38\u662f[0, 1]\uff09\u3002\u5e38\u7528\u7684\u65b9\u6cd5\u662f\u6700\u5c0f-\u6700\u5927\u5f52\u4e00\u5316\u3002\u53ef\u4ee5\u4f7f\u7528sklearn.preprocessing<\/code>\u6a21\u5757\u4e2d\u7684MinMaxScaler<\/code>\u6765\u8fdb\u884c\u5f52\u4e00\u5316\u3002<\/p>\n<\/p>\n

          from sklearn.preprocessing import MinMaxScaler<\/p>\n
          scaler = MinMaxScaler()<\/p>\n
          df_scaled = scaler.fit_transform(df)<\/p>\n
          <\/code><\/pre>\n<\/p>\n<\/li>\n
        2. \n
          \u6807\u51c6\u5316<\/strong><\/p>\n<\/p>\n
          \u6807\u51c6\u5316\u662f\u5c06\u6570\u636e\u8c03\u6574\u4e3a\u5747\u503c\u4e3a0\uff0c\u65b9\u5dee\u4e3a1\u7684\u6b63\u6001\u5206\u5e03\u3002\u53ef\u4ee5\u4f7f\u7528StandardScaler<\/code>\u6765\u8fdb\u884c\u6807\u51c6\u5316\u3002<\/p>\n<\/p>\n
          from sklearn.preprocessing import StandardScaler<\/p>\n
          scaler = StandardScaler()<\/p>\n
          df_standardized = scaler.fit_transform(df)<\/p>\n
          <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
          \u4e94\u3001\u53bb\u9664\u5f02\u5e38\u503c<\/p>\n<\/p>\n
          \u5f02\u5e38\u503c\u662f\u504f\u79bb\u6570\u636e\u96c6\u5176\u4ed6\u503c\u7684\u89c2\u6d4b\u503c\uff0c\u53ef\u80fd\u662f\u7531\u566a\u58f0\u6216\u9519\u8bef\u6570\u636e\u5f15\u8d77\u7684\u3002\u53bb\u9664\u5f02\u5e38\u503c\u662f\u6570\u636e\u6e05\u6d17\u4e2d\u7684\u4e00\u9879\u91cd\u8981\u4efb\u52a1\u3002<\/p>\n<\/p>\n
            \n
          1. \n
            \u4f7f\u7528\u7edf\u8ba1\u65b9\u6cd5\u8bc6\u522b\u5f02\u5e38\u503c<\/strong><\/p>\n<\/p>\n
            \u5e38\u7528\u7684\u65b9\u6cd5\u6709\u6807\u51c6\u5dee\u6cd5\u548c\u7bb1\u5f62\u56fe\u6cd5\u3002\u6807\u51c6\u5dee\u6cd5\u662f\u6307\u5728\u5747\u503c\u7684\u57fa\u7840\u4e0a\uff0c\u901a\u8fc7\u6807\u51c6\u5dee\u7684\u500d\u6570\u6765\u8bc6\u522b\u5f02\u5e38\u503c\u3002\u7bb1\u5f62\u56fe\u6cd5\u5219\u662f\u901a\u8fc7\u56db\u5206\u4f4d\u6570\u8ba1\u7b97\u5f02\u5e38\u503c\u3002<\/p>\n<\/p>\n
            # \u6807\u51c6\u5dee\u6cd5<\/p>\n
            mean = df['column'].mean()<\/p>\n
            std_dev = df['column'].std()<\/p>\n
            df_no_outliers = df[(df['column'] > mean - 3 * std_dev) & (df['column'] < mean + 3 * std_dev)]<\/p>\n
            \u7bb1\u5f62\u56fe\u6cd5<\/strong><\/h2>\n
            Q1 = df['column'].quantile(0.25)<\/p>\n
            Q3 = df['column'].quantile(0.75)<\/p>\n
            IQR = Q3 - Q1<\/p>\n
            df_no_outliers = df[(df['column'] >= Q1 - 1.5 * IQR) & (df['column'] <= Q3 + 1.5 * IQR)]<\/p>\n
            <\/code><\/pre>\n<\/p>\n<\/li>\n
          2. \n
            \u4f7f\u7528\u673a\u5668\u5b66\u4e60\u65b9\u6cd5\u8bc6\u522b\u5f02\u5e38\u503c<\/strong><\/p>\n<\/p>\n
            \u673a\u5668\u5b66\u4e60\u65b9\u6cd5\u5982\u5b64\u7acb\u68ee\u6797\uff08Isolation Forest\uff09\u548c\u5c40\u90e8\u5f02\u5e38\u56e0\u5b50\uff08Local Outlier Factor\uff09\u4e5f\u53ef\u4ee5\u7528\u4e8e\u8bc6\u522b\u5f02\u5e38\u503c\u3002\u8fd9\u4e9b\u65b9\u6cd5\u80fd\u591f\u81ea\u52a8\u8bc6\u522b\u6570\u636e\u4e2d\u7684\u5f02\u5e38\u6a21\u5f0f\u3002<\/p>\n<\/p>\n
            from sklearn.ensemble import IsolationForest<\/p>\n
            iso_forest = IsolationForest(contamination=0.1)<\/p>\n
            anomalies = iso_forest.fit_predict(df)<\/p>\n
            df_no_anomalies = df[anomalies != -1]<\/p>\n
            <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
            \u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\u7684\u5b9e\u65bd\uff0c\u53ef\u4ee5\u6709\u6548\u5730\u6e05\u6d17\u6570\u636e\uff0c\u786e\u4fdd\u6570\u636e\u7684\u51c6\u786e\u6027\u548c\u4e00\u81f4\u6027\uff0c\u4ece\u800c\u4e3a\u540e\u7eed\u7684\u6570\u636e\u5206\u6790\u548c\u5efa\u6a21\u63d0\u4f9b\u575a\u5b9e\u7684\u57fa\u7840\u3002\u6570\u636e\u6e05\u6d17\u662f\u4e00\u4e2a\u8fed\u4ee3\u7684\u8fc7\u7a0b\uff0c\u9700\u8981\u4e0d\u65ad\u5730\u8fdb\u884c\u68c0\u67e5\u548c\u8c03\u6574\uff0c\u4ee5\u9002\u5e94\u4e0d\u540c\u7684\u6570\u636e\u96c6\u548c\u5206\u6790\u9700\u6c42\u3002<\/p>\n<\/p>\n
            \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
             \u5982\u4f55\u4f7f\u7528Python\u8fdb\u884c\u6570\u636e\u6e05\u6d17\uff1f<\/strong>
            Python\u63d0\u4f9b\u4e86\u591a\u79cd\u5e93\u6765\u5e2e\u52a9\u7528\u6237\u8fdb\u884c\u6570\u636e\u6e05\u6d17\u3002\u6700\u5e38\u7528\u7684\u5e93\u5305\u62ecPandas\u548cNumPy\u3002\u901a\u8fc7\u8fd9\u4e9b\u5e93\uff0c\u7528\u6237\u53ef\u4ee5\u8f7b\u677e\u5904\u7406\u7f3a\u5931\u503c\u3001\u91cd\u590d\u6570\u636e\u3001\u683c\u5f0f\u4e0d\u7edf\u4e00\u7684\u5b57\u6bb5\u7b49\u3002\u4f7f\u7528Pandas\u7684dropna()<\/code>\u548cfillna()<\/code>\u51fd\u6570\u53ef\u4ee5\u6709\u6548\u5730\u5220\u9664\u6216\u586b\u5145\u7f3a\u5931\u6570\u636e\uff0c\u800cdrop_duplicates()<\/code>\u5219\u53ef\u4ee5\u53bb\u9664\u91cd\u590d\u8bb0\u5f55\u3002<\/p>\n
            \u5728\u6570\u636e\u6e05\u6d17\u8fc7\u7a0b\u4e2d\uff0c\u5982\u4f55\u5904\u7406\u7f3a\u5931\u503c\uff1f<\/strong>
            \u5904\u7406\u7f3a\u5931\u503c\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5177\u4f53\u9009\u62e9\u53d6\u51b3\u4e8e\u6570\u636e\u7684\u6027\u8d28\u548c\u5206\u6790\u76ee\u6807\u3002\u5e38\u89c1\u7684\u5904\u7406\u65b9\u5f0f\u5305\u62ec\u5220\u9664\u7f3a\u5931\u503c\u3001\u7528\u5747\u503c\u3001\u4e2d\u4f4d\u6570\u6216\u4f17\u6570\u66ff\u4ee3\u7f3a\u5931\u503c\uff0c\u6216\u8005\u4f7f\u7528\u63d2\u503c\u6cd5\u8fdb\u884c\u586b\u5145\u3002Pandas\u7684fillna()<\/code>\u65b9\u6cd5\u53ef\u4ee5\u5b9e\u73b0\u8fd9\u4e9b\u64cd\u4f5c\uff0c\u7528\u6237\u53ef\u4ee5\u6839\u636e\u6570\u636e\u7684\u5177\u4f53\u60c5\u51b5\u9009\u62e9\u6700\u5408\u9002\u7684\u65b9\u6848\u3002<\/p>\n
            \u4f7f\u7528Python\u6e05\u6d17\u6570\u636e\u65f6\uff0c\u5982\u4f55\u786e\u4fdd\u6570\u636e\u7684\u4e00\u81f4\u6027\uff1f<\/strong>
            \u786e\u4fdd\u6570\u636e\u4e00\u81f4\u6027\u662f\u6570\u636e\u6e05\u6d17\u7684\u91cd\u8981\u73af\u8282\u3002\u7528\u6237\u53ef\u4ee5\u901a\u8fc7\u6807\u51c6\u5316\u6570\u636e\u683c\u5f0f\uff08\u5982\u65e5\u671f\u683c\u5f0f\u3001\u6587\u672c\u5927\u5c0f\u5199\u7b49\uff09\u6765\u5b9e\u73b0\u4e00\u81f4\u6027\u3002\u4f8b\u5982\uff0c\u4f7f\u7528Pandas\u7684str.lower()<\/code>\u65b9\u6cd5\u5c06\u6240\u6709\u6587\u672c\u8f6c\u6362\u4e3a\u5c0f\u5199\uff0c\u6216\u8005\u4f7f\u7528pd.to_datetime()<\/code>\u5c06\u65e5\u671f\u5b57\u7b26\u4e32\u8f6c\u6362\u4e3a\u65e5\u671f\u5bf9\u8c61\u3002\u6b64\u5916\uff0c\u7528\u6237\u8fd8\u53ef\u4ee5\u4f7f\u7528\u6b63\u5219\u8868\u8fbe\u5f0f\u6e05\u7406\u4e0d\u89c4\u8303\u7684\u6587\u672c\u6570\u636e\uff0c\u786e\u4fdd\u6570\u636e\u7684\u6574\u6d01\u548c\u53ef\u7528\u6027\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\uff0c\u6570\u636e\u6e05\u6d17\u662f\u6570\u636e\u9884\u5904\u7406\u7684\u4e00\u4e2a\u91cd\u8981\u6b65\u9aa4\uff0c\u5bf9\u4e8e\u6570\u636e\u5206\u6790\u548c\u673a\u5668\u5b66\u4e60\u9879\u76ee\u81f3\u5173\u91cd\u8981\u3002\u6570\u636e\u6e05\u6d17\u4e3b\u8981\u5305\u62ec\u5220\u9664 […]","protected":false},"author":3,"featured_media":925717,"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\/925714"}],"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=925714"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/925714\/revisions"}],"predecessor-version":[{"id":925718,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/925714\/revisions\/925718"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/925717"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=925714"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=925714"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=925714"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}