{"id":1005736,"date":"2024-12-27T10:38:17","date_gmt":"2024-12-27T02:38:17","guid":{"rendered":""},"modified":"2024-12-27T10:38:23","modified_gmt":"2024-12-27T02:38:23","slug":"%e5%a6%82%e4%bd%95%e4%bd%bf%e7%94%a8python%e5%8f%a0%e5%8a%a0%e8%82%a1%e7%a5%a8","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1005736.html","title":{"rendered":"\u5982\u4f55\u4f7f\u7528python\u53e0\u52a0\u80a1\u7968"},"content":{"rendered":"

\"\u5982\u4f55\u4f7f\u7528python\u53e0\u52a0\u80a1\u7968\"<\/p>\n

\u4f7f\u7528Python\u53e0\u52a0\u80a1\u7968\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u83b7\u53d6\u80a1\u7968\u6570\u636e\u3001\u6570\u636e\u9884\u5904\u7406\u3001\u6570\u636e\u53e0\u52a0\u3001\u6570\u636e\u53ef\u89c6\u5316<\/strong>\u3002\u8fd9\u4e9b\u6b65\u9aa4\u6784\u6210\u4e86\u4e00\u4e2a\u5b8c\u6574\u7684\u6d41\u7a0b\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6295\u8d44\u8005\u5206\u6790\u591a\u53ea\u80a1\u7968\u7684\u8868\u73b0\u3002\u5728\u6b64\u57fa\u7840\u4e0a\uff0c\u6570\u636e\u53ef\u89c6\u5316\u662f\u6700\u4e3a\u5173\u952e\u7684\u4e00\u6b65\uff0c\u56e0\u4e3a\u5b83\u80fd\u591f\u76f4\u89c2\u5c55\u793a\u591a\u53ea\u80a1\u7968\u7684\u53e0\u52a0\u8d8b\u52bf\uff0c\u5e2e\u52a9\u6295\u8d44\u8005\u505a\u51fa\u66f4\u660e\u667a\u7684\u51b3\u7b56\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Python\u6765\u5b9e\u73b0\u8fd9\u4e9b\u6b65\u9aa4\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u83b7\u53d6\u80a1\u7968\u6570\u636e<\/h3>\n<\/p>\n

\u83b7\u53d6\u80a1\u7968\u6570\u636e\u662f\u5206\u6790\u7684\u7b2c\u4e00\u6b65\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528Python\u4e2d\u7684\u591a\u79cd\u5e93\u6765\u5b9e\u73b0\u8fd9\u4e00\u70b9\u3002\u5e38\u7528\u7684\u5e93\u5305\u62ecyfinance<\/code>\u3001pandas_datareader<\/code>\u548cAlpha Vantage<\/code>\u3002\u8fd9\u4e9b\u5e93\u63d0\u4f9b\u4e86\u4fbf\u6377\u7684\u63a5\u53e3\uff0c\u53ef\u4ee5\u4ece\u96c5\u864e\u8d22\u7ecf\u3001\u8c37\u6b4c\u8d22\u7ecf\u7b49\u6570\u636e\u6e90\u83b7\u53d6\u5386\u53f2\u80a1\u7968\u6570\u636e\u3002<\/p>\n<\/p>\n

    \n
  1. \n

    \u4f7f\u7528yfinance\u5e93<\/strong>
    yfinance<\/code>\u662f\u4e00\u4e2a\u5f3a\u5927\u7684Python\u5e93\uff0c\u7528\u4e8e\u4ece\u96c5\u864e\u8d22\u7ecf\u83b7\u53d6\u80a1\u7968\u6570\u636e\u3002\u5b83\u975e\u5e38\u6613\u4e8e\u4f7f\u7528\uff0c\u5e76\u4e14\u652f\u6301\u591a\u79cd\u6570\u636e\u683c\u5f0f\u3002<\/p>\n<\/p>\n

    import yfinance as yf<\/p>\n
    \u83b7\u53d6\u7279\u5b9a\u80a1\u7968\u7684\u5386\u53f2\u6570\u636e<\/strong><\/h2>\n
    stock = yf.Ticker("AAPL")<\/p>\n
    data = stock.history(period="1y")<\/p>\n
    <\/code><\/pre>\n<\/p>\n<\/li>\n
  2. \n
    \u4f7f\u7528pandas_datareader<\/strong>
    pandas_datareader<\/code>\u662f\u53e6\u4e00\u4e2a\u6d41\u884c\u7684\u9009\u62e9\u3002\u5b83\u53ef\u4ee5\u4ece\u591a\u79cd\u6765\u6e90\u83b7\u53d6\u6570\u636e\uff0c\u5305\u62ec\u96c5\u864e\u8d22\u7ecf\u3001\u8c37\u6b4c\u8d22\u7ecf\u548cFRED\u3002<\/p>\n<\/p>\n
    import pandas_datareader as pdr<\/p>\n
    from datetime import datetime<\/p>\n
    \u83b7\u53d6\u7279\u5b9a\u65f6\u95f4\u6bb5\u7684\u80a1\u7968\u6570\u636e<\/strong><\/h2>\n
    start = datetime(2022, 1, 1)<\/p>\n
    end = datetime(2023, 1, 1)<\/p>\n
    data = pdr.get_data_yahoo("AAPL", start, end)<\/p>\n
    <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
    \u4e8c\u3001\u6570\u636e\u9884\u5904\u7406<\/h3>\n<\/p>\n
    \u5728\u83b7\u53d6\u5230\u539f\u59cb\u6570\u636e\u540e\uff0c\u6570\u636e\u9884\u5904\u7406\u662f\u5fc5\u4e0d\u53ef\u5c11\u7684\u4e00\u6b65\u3002\u4e3b\u8981\u5305\u62ec\u6570\u636e\u6e05\u6d17\u3001\u7f3a\u5931\u503c\u5904\u7406\u548c\u683c\u5f0f\u8f6c\u6362\u3002<\/p>\n<\/p>\n
      \n
    1. \n
      \u6570\u636e\u6e05\u6d17<\/strong>
      \u786e\u4fdd\u6570\u636e\u4e2d\u6ca1\u6709\u91cd\u590d\u884c\u6216\u65e0\u6548\u6570\u636e\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7Pandas\u5e93\u7684\u5185\u7f6e\u51fd\u6570\u8f7b\u677e\u5b9e\u73b0\u3002<\/p>\n<\/p>\n
      # \u79fb\u9664\u91cd\u590d\u884c<\/p>\n
      data.drop_duplicates(inplace=True)<\/p>\n
      <\/code><\/pre>\n<\/p>\n<\/li>\n
    2. \n
      \u7f3a\u5931\u503c\u5904\u7406<\/strong>
      \u7f3a\u5931\u503c\u53ef\u80fd\u4f1a\u5bfc\u81f4\u8ba1\u7b97\u9519\u8bef\uff0c\u56e0\u6b64\u9700\u8981\u5904\u7406\u3002\u5e38\u89c1\u7684\u65b9\u6cd5\u5305\u62ec\u5220\u9664\u7f3a\u5931\u503c\u548c\u7528\u5e73\u5747\u503c\u586b\u5145\u3002<\/p>\n<\/p>\n
      # \u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c<\/p>\n
      data.dropna(inplace=True)<\/p>\n
      <\/code><\/pre>\n<\/p>\n<\/li>\n
    3. \n
      \u683c\u5f0f\u8f6c\u6362<\/strong>
      \u5c06\u65e5\u671f\u5217\u8bbe\u7f6e\u4e3a\u7d22\u5f15\uff0c\u4ee5\u4fbf\u4e8e\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u3002<\/p>\n<\/p>\n
      # \u5c06\u65e5\u671f\u5217\u8f6c\u6362\u4e3aDatetime\u683c\u5f0f<\/p>\n
      data.index = pd.to_datetime(data.index)<\/p>\n
      <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
      \u4e09\u3001\u6570\u636e\u53e0\u52a0<\/h3>\n<\/p>\n
      \u6570\u636e\u53e0\u52a0\u662f\u5c06\u591a\u53ea\u80a1\u7968\u7684\u6570\u636e\u5408\u5e76\u5230\u4e00\u8d77\uff0c\u4ee5\u4fbf\u8fdb\u884c\u7efc\u5408\u5206\u6790\u3002\u53ef\u4ee5\u901a\u8fc7\u5408\u5e76\u6570\u636e\u6846\u6765\u5b9e\u73b0\u3002<\/p>\n<\/p>\n
        \n
      1. \n
        \u9009\u62e9\u591a\u53ea\u80a1\u7968<\/strong>
        \u9009\u62e9\u9700\u8981\u53e0\u52a0\u7684\u80a1\u7968\uff0c\u5e76\u83b7\u53d6\u5b83\u4eec\u7684\u5386\u53f2\u6570\u636e\u3002<\/p>\n<\/p>\n
        # \u83b7\u53d6\u591a\u53ea\u80a1\u7968\u7684\u6570\u636e<\/p>\n
        tickers = ["AAPL", "GOOG", "MSFT"]<\/p>\n
        data = yf.download(tickers, start="2022-01-01", end="2023-01-01")["Adj Close"]<\/p>\n
        <\/code><\/pre>\n<\/p>\n<\/li>\n
      2. \n
        \u6570\u636e\u5408\u5e76<\/strong>
        \u4f7f\u7528Pandas\u7684\u5408\u5e76\u529f\u80fd\uff0c\u5c06\u4e0d\u540c\u80a1\u7968\u7684\u6570\u636e\u5408\u5e76\u5728\u4e00\u8d77\u3002<\/p>\n<\/p>\n
        # \u5408\u5e76\u6570\u636e<\/p>\n
        merged_data = pd.concat([data['AAPL'], data['GOOG'], data['MSFT']], axis=1)<\/p>\n
        merged_data.columns = tickers<\/p>\n
        <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
        \u56db\u3001\u6570\u636e\u53ef\u89c6\u5316<\/h3>\n<\/p>\n
        \u6570\u636e\u53ef\u89c6\u5316\u662f\u5206\u6790\u7684\u6700\u540e\u4e00\u6b65\uff0c\u4e5f\u662f\u6700\u91cd\u8981\u7684\u4e00\u6b65\u3002\u901a\u8fc7\u53ef\u89c6\u5316\uff0c\u53ef\u4ee5\u76f4\u89c2\u5730\u6bd4\u8f83\u591a\u53ea\u80a1\u7968\u7684\u8868\u73b0\u3002<\/p>\n<\/p>\n
          \n
        1. \n
          \u7ed8\u5236\u6298\u7ebf\u56fe<\/strong>
          \u4f7f\u7528matplotlib<\/code>\u6216seaborn<\/code>\u5e93\u7ed8\u5236\u6298\u7ebf\u56fe\uff0c\u5c55\u793a\u591a\u53ea\u80a1\u7968\u7684\u4ef7\u683c\u8d70\u52bf\u3002<\/p>\n<\/p>\n
          import matplotlib.pyplot as plt<\/p>\n
          \u7ed8\u5236\u80a1\u7968\u4ef7\u683c\u6298\u7ebf\u56fe<\/strong><\/h2>\n
          plt.figure(figsize=(14,7))<\/p>\n
          for column in merged_data.columns:<\/p>\n
              plt.plot(merged_data.index, merged_data[column], label=column)<\/p>\n
          plt.title('Stock Price Comparison')<\/p>\n
          plt.xlabel('Date')<\/p>\n
          plt.ylabel('Adjusted Close Price')<\/p>\n
          plt.legend()<\/p>\n
          plt.show()<\/p>\n
          <\/code><\/pre>\n<\/p>\n<\/li>\n
        2. \n
          \u7ed8\u5236\u5176\u4ed6\u56fe\u8868<\/strong>
          \u9664\u4e86\u6298\u7ebf\u56fe\uff0c\u4f60\u8fd8\u53ef\u4ee5\u7ed8\u5236\u67f1\u72b6\u56fe\u3001\u76f4\u65b9\u56fe\u7b49\uff0c\u4ee5\u5c55\u793a\u66f4\u591a\u6570\u636e\u7279\u5f81\u3002<\/p>\n<\/p>\n
          # \u7ed8\u5236\u67f1\u72b6\u56fe<\/p>\n
          merged_data.plot(kind='bar', figsize=(14,7))<\/p>\n
          plt.title('Stock Price Comparison - Bar Chart')<\/p>\n
          plt.xlabel('Date')<\/p>\n
          plt.ylabel('Adjusted Close Price')<\/p>\n
          plt.show()<\/p>\n
          <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
          \u4e94\u3001\u6df1\u5165\u5206\u6790<\/h3>\n<\/p>\n
          \u901a\u8fc7\u53e0\u52a0\u80a1\u7968\u6570\u636e\uff0c\u4f60\u53ef\u4ee5\u8fdb\u884c\u66f4\u52a0\u6df1\u5165\u7684\u5206\u6790\uff0c\u4f8b\u5982\u8ba1\u7b97\u80a1\u7968\u7684\u76f8\u5173\u6027\u3001\u6ce2\u52a8\u6027\u7b49\u6307\u6807\u3002<\/p>\n<\/p>\n
            \n
          1. \n
            \u8ba1\u7b97\u76f8\u5173\u6027<\/strong>
            \u76f8\u5173\u6027\u5206\u6790\u53ef\u4ee5\u5e2e\u52a9\u4e86\u89e3\u591a\u53ea\u80a1\u7968\u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u4f7f\u7528Pandas\u7684corr<\/code>\u65b9\u6cd5\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0\u3002<\/p>\n<\/p>\n
            # \u8ba1\u7b97\u76f8\u5173\u6027\u77e9\u9635<\/p>\n
            correlation_matrix = merged_data.corr()<\/p>\n
            print(correlation_matrix)<\/p>\n
            <\/code><\/pre>\n<\/p>\n<\/li>\n
          2. \n
            \u6ce2\u52a8\u6027\u5206\u6790<\/strong>
            \u6ce2\u52a8\u6027\u662f\u8861\u91cf\u80a1\u7968\u98ce\u9669\u7684\u91cd\u8981\u6307\u6807\u3002\u53ef\u4ee5\u901a\u8fc7\u8ba1\u7b97\u80a1\u7968\u7684\u6807\u51c6\u5dee\u6765\u8bc4\u4f30\u6ce2\u52a8\u6027\u3002<\/p>\n<\/p>\n
            # \u8ba1\u7b97\u6ce2\u52a8\u6027\uff08\u6807\u51c6\u5dee\uff09<\/p>\n
            volatility = merged_data.std()<\/p>\n
            print(volatility)<\/p>\n
            <\/code><\/pre>\n<\/p>\n<\/li>\n
          3. \n
            \u56de\u5f52\u5206\u6790<\/strong>
            \u4f7f\u7528\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u6765\u9884\u6d4b\u80a1\u7968\u4ef7\u683c\u8d8b\u52bf\u3002\u8fd9\u9700\u8981\u4f7f\u7528scikit-learn<\/code>\u5e93\u3002<\/p>\n<\/p>\n
            from sklearn.linear_model import LinearRegression<\/p>\n
            import numpy as np<\/p>\n
            \u51c6\u5907\u6570\u636e<\/strong><\/h2>\n
            X = np.array(range(len(merged_data))).reshape(-1, 1)<\/p>\n
            y = merged_data['AAPL'].values<\/p>\n
            \u521b\u5efa\u7ebf\u6027\u56de\u5f52\u6a21\u578b<\/strong><\/h2>\n
            model = LinearRegression()<\/p>\n
            model.fit(X, y)<\/p>\n
            \u9884\u6d4b<\/strong><\/h2>\n
            predictions = model.predict(X)<\/p>\n
            \u7ed8\u5236\u9884\u6d4b\u7ed3\u679c<\/strong><\/h2>\n
            plt.figure(figsize=(14,7))<\/p>\n
            plt.plot(merged_data.index, y, label='Actual')<\/p>\n
            plt.plot(merged_data.index, predictions, label='Predicted', linestyle='--')<\/p>\n
            plt.title('AAPL Stock Price Prediction')<\/p>\n
            plt.xlabel('Date')<\/p>\n
            plt.ylabel('Adjusted Close Price')<\/p>\n
            plt.legend()<\/p>\n
            plt.show()<\/p>\n
            <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
            \u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u4f7f\u7528Python\u53e0\u52a0\u80a1\u7968\u5e76\u8fdb\u884c\u5206\u6790\u5c06\u53d8\u5f97\u66f4\u52a0\u7cfb\u7edf\u548c\u9ad8\u6548\u3002\u8fd9\u79cd\u65b9\u6cd5\u4e0d\u4ec5\u53ef\u4ee5\u5e2e\u52a9\u6295\u8d44\u8005\u8fdb\u884c\u591a\u80a1\u7968\u7684\u6bd4\u8f83\u5206\u6790\uff0c\u8fd8\u80fd\u4e3a\u6295\u8d44\u7b56\u7565\u7684\u5236\u5b9a\u63d0\u4f9b\u91cd\u8981\u7684\u53c2\u8003\u4f9d\u636e\u3002<\/p>\n<\/p>\n
            \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
             \u5982\u4f55\u4f7f\u7528Python\u83b7\u53d6\u80a1\u7968\u6570\u636e\uff1f<\/strong>
            \u83b7\u53d6\u80a1\u7968\u6570\u636e\u7684\u5e38\u89c1\u65b9\u6cd5\u662f\u4f7f\u7528\u91d1\u878d\u6570\u636eAPI\uff0c\u4f8b\u5982Yahoo Finance\u3001Alpha Vantage\u6216Quandl\u7b49\u3002\u53ef\u4ee5\u901a\u8fc7\u5b89\u88c5\u76f8\u5173\u7684Python\u5e93\uff0c\u5982yfinance<\/code>\u6216pandas_datareader<\/code>\uff0c\u6765\u7b80\u5316\u6570\u636e\u7684\u83b7\u53d6\u8fc7\u7a0b\u3002\u4f7f\u7528\u8fd9\u4e9b\u5e93\u65f6\uff0c\u60a8\u53ea\u9700\u7f16\u5199\u51e0\u884c\u4ee3\u7801\u5373\u53ef\u4e0b\u8f7d\u6240\u9700\u80a1\u7968\u7684\u5386\u53f2\u6570\u636e\uff0c\u5e76\u5c06\u5176\u5b58\u50a8\u4e3aDataFrame\u683c\u5f0f\uff0c\u4fbf\u4e8e\u540e\u7eed\u5904\u7406\u548c\u5206\u6790\u3002<\/p>\n
            \u5728Python\u4e2d\uff0c\u5982\u4f55\u7ed8\u5236\u80a1\u7968\u4ef7\u683c\u7684\u53e0\u52a0\u56fe\uff1f<\/strong>
            \u4e3a\u4e86\u5728Python\u4e2d\u7ed8\u5236\u80a1\u7968\u4ef7\u683c\u7684\u53e0\u52a0\u56fe\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528matplotlib<\/code>\u548cpandas<\/code>\u5e93\u3002\u9996\u5148\uff0c\u83b7\u53d6\u6240\u9700\u7684\u80a1\u7968\u6570\u636e\u5e76\u5c06\u5176\u6574\u7406\u5230DataFrame\u4e2d\u3002\u63a5\u4e0b\u6765\uff0c\u4f7f\u7528plot()<\/code>\u51fd\u6570\u5c06\u4e0d\u540c\u80a1\u7968\u7684\u4ef7\u683c\u7ed8\u5236\u5728\u540c\u4e00\u56fe\u8868\u4e0a\u3002\u786e\u4fdd\u4e3a\u6bcf\u6761\u66f2\u7ebf\u6dfb\u52a0\u56fe\u4f8b\uff0c\u4ee5\u4fbf\u4e8e\u533a\u5206\u4e0d\u540c\u7684\u80a1\u7968\u3002<\/p>\n
            \u4f7f\u7528Python\u53e0\u52a0\u80a1\u7968\u65f6\uff0c\u5982\u4f55\u5904\u7406\u4e0d\u540c\u65f6\u95f4\u8303\u56f4\u7684\u6570\u636e\uff1f<\/strong>
            \u5728\u53e0\u52a0\u4e0d\u540c\u80a1\u7968\u7684\u6570\u636e\u65f6\uff0c\u65f6\u95f4\u8303\u56f4\u7684\u5bf9\u9f50\u81f3\u5173\u91cd\u8981\u3002\u53ef\u4ee5\u4f7f\u7528pandas<\/code>\u7684merge<\/code>\u6216join<\/code>\u65b9\u6cd5\uff0c\u5c06\u4e0d\u540c\u65f6\u95f4\u6bb5\u5185\u7684\u6570\u636e\u5408\u5e76\u5230\u540c\u4e00DataFrame\u4e2d\u3002\u901a\u8fc7\u8bbe\u7f6ehow='inner'<\/code>\u53c2\u6570\uff0c\u53ef\u4ee5\u786e\u4fdd\u53ea\u4fdd\u7559\u6240\u6709\u80a1\u7968\u90fd\u6709\u7684\u65e5\u671f\u6570\u636e\uff0c\u4ece\u800c\u907f\u514d\u56e0\u65f6\u95f4\u4e0d\u4e00\u81f4\u5bfc\u81f4\u7684\u9519\u8bef\u5206\u6790\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u4f7f\u7528Python\u53e0\u52a0\u80a1\u7968\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u83b7\u53d6\u80a1\u7968\u6570\u636e\u3001\u6570\u636e\u9884\u5904\u7406\u3001\u6570\u636e\u53e0\u52a0\u3001\u6570\u636e\u53ef\u89c6\u5316\u3002\u8fd9\u4e9b\u6b65\u9aa4\u6784\u6210\u4e86\u4e00\u4e2a\u5b8c\u6574\u7684\u6d41 […]","protected":false},"author":3,"featured_media":1005750,"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\/1005736"}],"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=1005736"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1005736\/revisions"}],"predecessor-version":[{"id":1005754,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1005736\/revisions\/1005754"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1005750"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1005736"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1005736"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1005736"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}