{"id":1009381,"date":"2024-12-27T11:11:18","date_gmt":"2024-12-27T03:11:18","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1009381.html"},"modified":"2024-12-27T11:11:23","modified_gmt":"2024-12-27T03:11:23","slug":"python%e4%b8%adpandas%e5%a6%82%e4%bd%95%e7%bb%9f%e8%ae%a1","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1009381.html","title":{"rendered":"python\u4e2dpandas\u5982\u4f55\u7edf\u8ba1"},"content":{"rendered":"

\"python\u4e2dpandas\u5982\u4f55\u7edf\u8ba1\"<\/p>\n

\u5728Python\u4e2d\uff0c\u4f7f\u7528Pandas\u8fdb\u884c\u7edf\u8ba1\u64cd\u4f5c\u7684\u6838\u5fc3\u5728\u4e8e\u5176\u5f3a\u5927\u7684\u6570\u636e\u64cd\u4f5c\u548c\u5206\u6790\u529f\u80fd\u3002\u4f60\u53ef\u4ee5\u901a\u8fc7DataFrame\u548cSeries\u5bf9\u8c61\u8fdb\u884c\u57fa\u672c\u7edf\u8ba1\u3001\u5206\u7ec4\u7edf\u8ba1\u3001\u805a\u5408\u7edf\u8ba1\u3001\u4ee5\u53ca\u9ad8\u7ea7\u7684\u6570\u636e\u5206\u6790\u64cd\u4f5c\u3002\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ec\uff1adescribe()\u65b9\u6cd5\u53ef\u4ee5\u5feb\u901f\u67e5\u770b\u6570\u636e\u7684\u57fa\u672c\u7edf\u8ba1\u4fe1\u606f\u3001groupby()\u65b9\u6cd5\u53ef\u4ee5\u8fdb\u884c\u5206\u7ec4\u7edf\u8ba1\u3001agg()\u65b9\u6cd5\u53ef\u4ee5\u8fdb\u884c\u7075\u6d3b\u7684\u805a\u5408\u64cd\u4f5c\u3001\u4ee5\u53ca\u4f7f\u7528pivot_table()\u521b\u5efa\u6570\u636e\u900f\u89c6\u8868\u7b49\u3002<\/strong>\u5176\u4e2d\uff0cgroupby()<\/code>\u65b9\u6cd5\u975e\u5e38\u5f3a\u5927\uff0c\u5b83\u5141\u8bb8\u4f60\u5728\u6570\u636e\u4e2d\u6839\u636e\u67d0\u4e00\u5217\u6216\u591a\u5217\u8fdb\u884c\u5206\u7ec4\uff0c\u7136\u540e\u5bf9\u6bcf\u4e2a\u7ec4\u8fdb\u884c\u5404\u79cd\u805a\u5408\u64cd\u4f5c\u3002\u4ee5\u4e0b\u5c06\u8be6\u7ec6\u4ecb\u7ecdPandas\u4e2d\u5982\u4f55\u8fdb\u884c\u5404\u7c7b\u7edf\u8ba1\u64cd\u4f5c\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u57fa\u672c\u7edf\u8ba1\u529f\u80fd<\/p>\n<\/p>\n

Pandas\u63d0\u4f9b\u4e86\u8bb8\u591a\u5185\u7f6e\u7684\u65b9\u6cd5\u6765\u8ba1\u7b97\u57fa\u672c\u7edf\u8ba1\u91cf\uff0c\u8fd9\u4e9b\u65b9\u6cd5\u53ef\u4ee5\u76f4\u63a5\u5728DataFrame\u6216Series\u5bf9\u8c61\u4e0a\u8c03\u7528\u3002<\/p>\n<\/p>\n

    \n
  1. \u63cf\u8ff0\u6027\u7edf\u8ba1<\/strong><\/li>\n<\/ol>\n

    \u4f7f\u7528describe()<\/code>\u65b9\u6cd5\u53ef\u4ee5\u5feb\u901f\u67e5\u770b\u6570\u636e\u7684\u4e3b\u8981\u7edf\u8ba1\u4fe1\u606f\uff0c\u5305\u62ec\u5747\u503c\u3001\u6807\u51c6\u5dee\u3001\u6700\u5c0f\u503c\u3001\u6700\u5927\u503c\u3001\u56db\u5206\u4f4d\u6570\u7b49\u3002\u8fd9\u662f\u8fdb\u884c\u6570\u636e\u521d\u6b65\u5206\u6790\u7684\u5e38\u7528\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

    import pandas as pd<\/p>\n
    data = {'A': [1, 2, 3, 4, 5],<\/p>\n
            'B': [5, 4, 3, 2, 1]}<\/p>\n
    df = pd.DataFrame(data)<\/p>\n
    \u8f93\u51fa\u6570\u636e\u7684\u63cf\u8ff0\u6027\u7edf\u8ba1\u4fe1\u606f<\/strong><\/h2>\n
    print(df.describe())<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u5355\u72ec\u7684\u7edf\u8ba1\u91cf<\/strong><\/li>\n<\/ol>\n
      Pandas\u8fd8\u63d0\u4f9b\u4e86\u5176\u4ed6\u5355\u72ec\u7684\u7edf\u8ba1\u65b9\u6cd5\uff0c\u5982mean()<\/code>\u3001median()<\/code>\u3001std()<\/code>\u3001var()<\/code>\u3001sum()<\/code>\u3001min()<\/code>\u3001max()<\/code>\u7b49\uff0c\u8fd9\u4e9b\u65b9\u6cd5\u53ef\u4ee5\u76f4\u63a5\u5728DataFrame\u6216Series\u5bf9\u8c61\u4e0a\u8c03\u7528\uff0c\u8ba1\u7b97\u51fa\u76f8\u5e94\u7684\u7edf\u8ba1\u91cf\u3002<\/p>\n<\/p>\n
      # \u8ba1\u7b97\u5217\u7684\u5747\u503c<\/p>\n
      mean_a = df['A'].mean()<\/p>\n
      mean_b = df['B'].mean()<\/p>\n
      \u8ba1\u7b97\u5217\u7684\u6807\u51c6\u5dee<\/strong><\/h2>\n
      std_a = df['A'].std()<\/p>\n
      std_b = df['B'].std()<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u4e8c\u3001\u5206\u7ec4\u7edf\u8ba1<\/p>\n<\/p>\n
      \u5206\u7ec4\u7edf\u8ba1\u662f\u6570\u636e\u5206\u6790\u4e2d\u975e\u5e38\u91cd\u8981\u7684\u4e00\u90e8\u5206\uff0c\u901a\u8fc7groupby()<\/code>\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u6839\u636e\u67d0\u4e00\u5217\u6216\u591a\u5217\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\uff0c\u7136\u540e\u5bf9\u6bcf\u4e2a\u7ec4\u8fdb\u884c\u805a\u5408\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
        \n
      1. \u4f7f\u7528groupby()\u8fdb\u884c\u5206\u7ec4<\/strong><\/li>\n<\/ol>\n
        groupby()<\/code>\u65b9\u6cd5\u8fd4\u56de\u4e00\u4e2aGroupBy\u5bf9\u8c61\uff0c\u6211\u4eec\u53ef\u4ee5\u5728\u8fd9\u4e2a\u5bf9\u8c61\u4e0a\u8c03\u7528\u5404\u79cd\u805a\u5408\u51fd\u6570\uff0c\u5982sum()<\/code>\u3001mean()<\/code>\u3001count()<\/code>\u7b49\u3002<\/p>\n<\/p>\n
        data = {'Category': ['A', 'A', 'B', 'B', 'C'],<\/p>\n
                'Values': [1, 2, 3, 4, 5]}<\/p>\n
        df = pd.DataFrame(data)<\/p>\n
        \u6309\u7167Category\u5217\u8fdb\u884c\u5206\u7ec4\uff0c\u5e76\u8ba1\u7b97\u6bcf\u7ec4\u7684\u5747\u503c<\/strong><\/h2>\n
        grouped = df.groupby('Category').mean()<\/p>\n
        print(grouped)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        1. \u5206\u7ec4\u540e\u7684\u591a\u79cd\u805a\u5408\u64cd\u4f5c<\/strong><\/li>\n<\/ol>\n
          \u9664\u4e86\u76f4\u63a5\u8c03\u7528\u5355\u4e00\u7684\u805a\u5408\u51fd\u6570\uff0c\u6211\u4eec\u8fd8\u53ef\u4ee5\u4f7f\u7528agg()<\/code>\u65b9\u6cd5\u5bf9\u6bcf\u4e2a\u7ec4\u6267\u884c\u591a\u79cd\u805a\u5408\u64cd\u4f5c\uff0c\u8fd9\u4f7f\u5f97\u7edf\u8ba1\u5206\u6790\u66f4\u52a0\u7075\u6d3b\u3002<\/p>\n<\/p>\n
          # \u5bf9\u6bcf\u4e2a\u7ec4\u6267\u884c\u591a\u79cd\u805a\u5408\u64cd\u4f5c<\/p>\n
          aggregated = df.groupby('Category').agg(['sum', 'mean', 'std'])<\/p>\n
          print(aggregated)<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u4e09\u3001\u6570\u636e\u900f\u89c6\u8868<\/p>\n<\/p>\n
          \u6570\u636e\u900f\u89c6\u8868\u662fExcel\u7528\u6237\u975e\u5e38\u719f\u6089\u7684\u529f\u80fd\uff0cPandas\u4e2d\u7684pivot_table()<\/code>\u65b9\u6cd5\u53ef\u4ee5\u5b9e\u73b0\u7c7b\u4f3c\u7684\u529f\u80fd\uff0c\u5141\u8bb8\u6839\u636e\u4e0d\u540c\u7684\u5206\u7c7b\u53d8\u91cf\u5bf9\u6570\u636e\u8fdb\u884c\u805a\u5408\u3002<\/p>\n<\/p>\n
            \n
          1. \u521b\u5efa\u7b80\u5355\u7684\u6570\u636e\u900f\u89c6\u8868<\/strong><\/li>\n<\/ol>\n
            \u4f7f\u7528pivot_table()<\/code>\u65b9\u6cd5\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a\u6570\u636e\u900f\u89c6\u8868\uff0c\u6307\u5b9a\u7d22\u5f15\u3001\u5217\u548c\u503c\u5b57\u6bb5\uff0c\u8fdb\u884c\u805a\u5408\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
            data = {'Category': ['A', 'A', 'B', 'B', 'C'],<\/p>\n
                    'Subcategory': ['X', 'Y', 'X', 'Y', 'X'],<\/p>\n
                    'Values': [1, 2, 3, 4, 5]}<\/p>\n
            df = pd.DataFrame(data)<\/p>\n
            \u521b\u5efa\u6570\u636e\u900f\u89c6\u8868<\/strong><\/h2>\n
            pivot_table = df.pivot_table(values='Values', index='Category', columns='Subcategory', aggfunc='sum')<\/p>\n
            print(pivot_table)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. \u9ad8\u7ea7\u7684\u6570\u636e\u900f\u89c6\u8868<\/strong><\/li>\n<\/ol>\n
              \u53ef\u4ee5\u901a\u8fc7\u4f20\u9012\u591a\u4e2a\u805a\u5408\u51fd\u6570\u6765\u521b\u5efa\u66f4\u590d\u6742\u7684\u6570\u636e\u900f\u89c6\u8868\uff0c\u4ee5\u663e\u793a\u6570\u636e\u7684\u591a\u4e2a\u7ef4\u5ea6\u3002<\/p>\n<\/p>\n
              # \u4f7f\u7528\u591a\u4e2a\u805a\u5408\u51fd\u6570\u521b\u5efa\u6570\u636e\u900f\u89c6\u8868<\/p>\n
              pivot_table_multi = df.pivot_table(values='Values', index='Category', columns='Subcategory', aggfunc=['sum', 'mean'])<\/p>\n
              print(pivot_table_multi)<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u56db\u3001\u65f6\u95f4\u5e8f\u5217\u7edf\u8ba1<\/p>\n<\/p>\n
              Pandas\u5bf9\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u6709\u5f88\u5f3a\u7684\u652f\u6301\uff0c\u53ef\u4ee5\u5f88\u65b9\u4fbf\u5730\u8fdb\u884c\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u7edf\u8ba1\u548c\u5206\u6790\u3002<\/p>\n<\/p>\n
                \n
              1. \u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u8bfb\u53d6\u548c\u89e3\u6790<\/strong><\/li>\n<\/ol>\n
                Pandas\u53ef\u4ee5\u8f7b\u677e\u5730\u8bfb\u53d6\u548c\u89e3\u6790\u65f6\u95f4\u5e8f\u5217\u6570\u636e\uff0c\u5c24\u5176\u662f\u901a\u8fc7read_csv()<\/code>\u65b9\u6cd5\u4e0eparse_dates<\/code>\u53c2\u6570\u7ed3\u5408\u4f7f\u7528\u3002<\/p>\n<\/p>\n
                # \u8bfb\u53d6\u65f6\u95f4\u5e8f\u5217\u6570\u636e<\/p>\n
                df = pd.read_csv('timeseries_data.csv', parse_dates=['Date'], index_col='Date')<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                  \n
                1. \u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u91cd\u91c7\u6837<\/strong><\/li>\n<\/ol>\n
                  \u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u91cd\u91c7\u6837\u662f\u5c06\u6570\u636e\u4ece\u4e00\u4e2a\u9891\u7387\u8f6c\u6362\u4e3a\u53e6\u4e00\u4e2a\u9891\u7387\u7684\u8fc7\u7a0b\uff0cPandas\u63d0\u4f9b\u4e86resample()<\/code>\u65b9\u6cd5\u8fdb\u884c\u91cd\u91c7\u6837\u3002<\/p>\n<\/p>\n
                  # \u5c06\u6570\u636e\u4ece\u65e5\u9891\u7387\u91cd\u91c7\u6837\u4e3a\u6708\u9891\u7387\uff0c\u5e76\u8ba1\u7b97\u6bcf\u6708\u7684\u5747\u503c<\/p>\n
                  monthly_data = df.resample('M').mean()<\/p>\n
                  print(monthly_data)<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u4e94\u3001\u8fdb\u9636\u7edf\u8ba1\u5206\u6790<\/p>\n<\/p>\n
                  \u9664\u4e86\u57fa\u672c\u7684\u7edf\u8ba1\u64cd\u4f5c\uff0cPandas\u8fd8\u53ef\u4ee5\u4e0e\u5176\u4ed6\u79d1\u5b66\u8ba1\u7b97\u5e93\u7ed3\u5408\u4f7f\u7528\uff0c\u8fdb\u884c\u66f4\u9ad8\u7ea7\u7684\u7edf\u8ba1\u5206\u6790\u3002<\/p>\n<\/p>\n
                    \n
                  1. \u534f\u65b9\u5dee\u548c\u76f8\u5173\u6027<\/strong><\/li>\n<\/ol>\n
                    Pandas\u63d0\u4f9b\u4e86cov()<\/code>\u548ccorr()<\/code>\u65b9\u6cd5\u8ba1\u7b97\u534f\u65b9\u5dee\u548c\u76f8\u5173\u6027\u77e9\u9635\uff0c\u7528\u4e8e\u5206\u6790\u6570\u636e\u4e4b\u95f4\u7684\u5173\u7cfb\u3002<\/p>\n<\/p>\n
                    # \u8ba1\u7b97\u534f\u65b9\u5dee\u77e9\u9635<\/p>\n
                    cov_matrix = df.cov()<\/p>\n
                    print(cov_matrix)<\/p>\n
                    \u8ba1\u7b97\u76f8\u5173\u6027\u77e9\u9635<\/strong><\/h2>\n
                    corr_matrix = df.corr()<\/p>\n
                    print(corr_matrix)<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                      \n
                    1. \u4e0eScipy\u7ed3\u5408\u4f7f\u7528<\/strong><\/li>\n<\/ol>\n
                      Pandas\u53ef\u4ee5\u4e0eScipy\u5e93\u7ed3\u5408\uff0c\u8fdb\u884c\u66f4\u590d\u6742\u7684\u7edf\u8ba1\u5206\u6790\uff0c\u5982\u56de\u5f52\u5206\u6790\u3001\u5047\u8bbe\u68c0\u9a8c\u7b49\u3002<\/p>\n<\/p>\n
                      from scipy import stats<\/p>\n
                      \u6267\u884c\u7ebf\u6027\u56de\u5f52\u5206\u6790<\/strong><\/h2>\n
                      slope, intercept, r_value, p_value, std_err = stats.linregress(df['A'], df['B'])<\/p>\n
                      print(f'Slope: {slope}, Intercept: {intercept}')<\/p>\n
                      <\/code><\/pre>\n<\/p>\n
                      \u901a\u8fc7\u4ee5\u4e0a\u5404\u4e2a\u65b9\u9762\u7684\u4ecb\u7ecd\uff0c\u53ef\u4ee5\u770b\u51faPandas\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u5de5\u5177\uff0c\u5b83\u4e0d\u4ec5\u53ef\u4ee5\u8fdb\u884c\u57fa\u672c\u7684\u7edf\u8ba1\u64cd\u4f5c\uff0c\u8fd8\u53ef\u4ee5\u8fdb\u884c\u590d\u6742\u7684\u6570\u636e\u5206\u6790\u548c\u5efa\u6a21\u3002\u65e0\u8bba\u662f\u6570\u636e\u7684\u6e05\u6d17\u3001\u8f6c\u6362\u3001\u805a\u5408\uff0c\u8fd8\u662f\u65f6\u95f4\u5e8f\u5217\u5206\u6790\uff0cPandas\u90fd\u80fd\u63d0\u4f9b\u9ad8\u6548\u7684\u89e3\u51b3\u65b9\u6848\u3002\u7ed3\u5408\u5176\u4ed6Python\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0cPandas\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u5b8c\u6210\u4ece\u6570\u636e\u51c6\u5907\u5230\u6a21\u578b\u6784\u5efa\u7684\u6574\u4e2a\u6570\u636e\u5206\u6790\u6d41\u7a0b\u3002<\/p>\n<\/p>\n
                      \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                       \u5982\u4f55\u4f7f\u7528Pandas\u8fdb\u884c\u6570\u636e\u7edf\u8ba1\uff1f<\/strong>
                      Pandas\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u8fdb\u884c\u6570\u636e\u7edf\u8ba1\u5206\u6790\u3002\u5e38\u7528\u7684\u7edf\u8ba1\u65b9\u6cd5\u5305\u62ecdescribe()<\/code>\u7528\u4e8e\u751f\u6210\u63cf\u8ff0\u6027\u7edf\u8ba1\u6570\u636e\uff0cmean()<\/code>\u8ba1\u7b97\u5747\u503c\uff0cmedian()<\/code>\u8ba1\u7b97\u4e2d\u4f4d\u6570\uff0cstd()<\/code>\u8ba1\u7b97\u6807\u51c6\u5dee\u7b49\u3002\u901a\u8fc7\u8fd9\u4e9b\u51fd\u6570\uff0c\u7528\u6237\u53ef\u4ee5\u5feb\u901f\u4e86\u89e3\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u548c\u57fa\u672c\u7279\u5f81\u3002<\/p>\n
                      Pandas\u53ef\u4ee5\u5904\u7406\u54ea\u4e9b\u7c7b\u578b\u7684\u6570\u636e\u7edf\u8ba1\uff1f<\/strong>
                      Pandas\u80fd\u591f\u5904\u7406\u591a\u79cd\u6570\u636e\u7edf\u8ba1\u7c7b\u578b\uff0c\u5305\u62ec\u4f46\u4e0d\u9650\u4e8e\u8ba1\u6570\u7edf\u8ba1\u3001\u5747\u503c\u3001\u6807\u51c6\u5dee\u3001\u6700\u5927\u503c\u3001\u6700\u5c0f\u503c\u3001\u5206\u4f4d\u6570\u7b49\u3002\u7528\u6237\u53ef\u4ee5\u5229\u7528groupby()<\/code>\u65b9\u6cd5\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\u7edf\u8ba1\uff0c\u6216\u8005\u4f7f\u7528pivot_table()<\/code>\u521b\u5efa\u900f\u89c6\u8868\uff0c\u4ee5\u4fbf\u5bf9\u590d\u6742\u6570\u636e\u8fdb\u884c\u66f4\u6df1\u5165\u7684\u5206\u6790\u3002<\/p>\n
                      \u5982\u4f55\u5728Pandas\u4e2d\u5904\u7406\u7f3a\u5931\u6570\u636e\u4ee5\u8fdb\u884c\u6709\u6548\u7684\u7edf\u8ba1\uff1f<\/strong>
                      \u5728\u8fdb\u884c\u7edf\u8ba1\u4e4b\u524d\uff0c\u5904\u7406\u7f3a\u5931\u6570\u636e\u662f\u975e\u5e38\u91cd\u8981\u7684\u3002Pandas\u63d0\u4f9b\u4e86dropna()<\/code>\u65b9\u6cd5\u6765\u5220\u9664\u7f3a\u5931\u503c\uff0c\u6216\u8005\u4f7f\u7528fillna()<\/code>\u65b9\u6cd5\u586b\u5145\u7f3a\u5931\u503c\u3002\u5408\u7406\u5904\u7406\u7f3a\u5931\u6570\u636e\u53ef\u4ee5\u786e\u4fdd\u7edf\u8ba1\u7ed3\u679c\u7684\u51c6\u786e\u6027\uff0c\u5e2e\u52a9\u7528\u6237\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u96c6\u7684\u6574\u4f53\u60c5\u51b5\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\uff0c\u4f7f\u7528Pandas\u8fdb\u884c\u7edf\u8ba1\u64cd\u4f5c\u7684\u6838\u5fc3\u5728\u4e8e\u5176\u5f3a\u5927\u7684\u6570\u636e\u64cd\u4f5c\u548c\u5206\u6790\u529f\u80fd\u3002\u4f60\u53ef\u4ee5\u901a\u8fc7DataFram […]","protected":false},"author":3,"featured_media":1009393,"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\/1009381"}],"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=1009381"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1009381\/revisions"}],"predecessor-version":[{"id":1009402,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1009381\/revisions\/1009402"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1009393"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1009381"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1009381"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1009381"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}