{"id":1127458,"date":"2025-01-08T20:10:32","date_gmt":"2025-01-08T12:10:32","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1127458.html"},"modified":"2025-01-08T20:10:35","modified_gmt":"2025-01-08T12:10:35","slug":"%e5%a6%82%e4%bd%95%e8%ae%a1%e7%ae%97%e7%99%be%e5%88%86%e4%bd%8d%e6%95%b0-python%e5%ae%9e%e7%8e%b0","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1127458.html","title":{"rendered":"\u5982\u4f55\u8ba1\u7b97\u767e\u5206\u4f4d\u6570-python\u5b9e\u73b0"},"content":{"rendered":"

\"\u5982\u4f55\u8ba1\u7b97\u767e\u5206\u4f4d\u6570-python\u5b9e\u73b0\"<\/p>\n

\u767e\u5206\u4f4d\u6570\uff08percentile\uff09<\/strong>\u662f\u7edf\u8ba1\u5b66\u4e2d\u4e00\u4e2a\u91cd\u8981\u7684\u6982\u5ff5\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u6570\u636e\u5206\u6790\u548c\u673a\u5668\u5b66\u4e60<\/a>\u9886\u57df\u3002\u8ba1\u7b97\u767e\u5206\u4f4d\u6570\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5728Python\u4e2d\uff0c\u4e3b\u8981\u4f7f\u7528NumPy\u548cPandas\u5e93\u6765\u5b9e\u73b0\u3002\u672c\u6587\u5c06\u8be6\u7ec6\u89e3\u91ca\u5982\u4f55\u5728Python\u4e2d\u8ba1\u7b97\u767e\u5206\u4f4d\u6570\uff0c\u5e76\u63d0\u4f9b\u591a\u4e2a\u793a\u4f8b\u4ee3\u7801\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u767e\u5206\u4f4d\u6570\u7684\u5b9a\u4e49\u548c\u8ba1\u7b97\u65b9\u6cd5<\/h2>\n<\/p>\n

\u767e\u5206\u4f4d\u6570<\/strong>\u662f\u5c06\u6570\u636e\u96c6\u6309\u767e\u5206\u6bd4\u5212\u5206\u7684\u4e00\u79cd\u65b9\u5f0f\u3002\u4f8b\u5982\uff0c\u7b2c25\u767e\u5206\u4f4d\u6570\u8868\u793a\u6570\u636e\u96c6\u4e2d\u670925%\u7684\u6570\u636e\u70b9\u5c0f\u4e8e\u6216\u7b49\u4e8e\u8fd9\u4e2a\u503c\u3002\u767e\u5206\u4f4d\u6570\u6709\u52a9\u4e8e\u7406\u89e3\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u3002<\/p>\n<\/p>\n

1. \u767e\u5206\u4f4d\u6570\u7684\u5b9a\u4e49<\/h3>\n<\/p>\n

\u767e\u5206\u4f4d\u6570\u901a\u5e38\u7528\u4ee5\u4e0b\u516c\u5f0f\u8ba1\u7b97\uff1a<\/p>\n

[ P_k = (N + 1) \\times \\frac{k}{100} ]<\/p>\n

\u5176\u4e2d\uff0c( P_k ) \u662f\u7b2ck\u767e\u5206\u4f4d\u6570\uff0cN\u662f\u6570\u636e\u96c6\u4e2d\u5143\u7d20\u7684\u603b\u6570\uff0ck\u662f\u6240\u6c42\u7684\u767e\u5206\u4f4d\u6570\u3002<\/p>\n<\/p>\n

2. \u8ba1\u7b97\u767e\u5206\u4f4d\u6570\u7684\u6b65\u9aa4<\/h3>\n<\/p>\n
    \n
  1. \u6392\u5e8f\u6570\u636e\u96c6\uff1a<\/strong> \u5c06\u6570\u636e\u4ece\u5c0f\u5230\u5927\u6392\u5e8f\u3002<\/li>\n
  2. \u786e\u5b9a\u4f4d\u7f6e\uff1a<\/strong> \u4f7f\u7528\u767e\u5206\u4f4d\u6570\u516c\u5f0f\u786e\u5b9a\u6570\u636e\u96c6\u4e2d\u767e\u5206\u4f4d\u6570\u7684\u4f4d\u7f6e\u3002<\/li>\n
  3. \u63d2\u503c\uff1a<\/strong> \u5982\u679c\u4f4d\u7f6e\u4e0d\u662f\u6574\u6570\uff0c\u4f7f\u7528\u63d2\u503c\u6cd5\u8ba1\u7b97\u3002<\/li>\n<\/ol>\n

    \u4e8c\u3001\u4f7f\u7528Python\u8ba1\u7b97\u767e\u5206\u4f4d\u6570<\/h2>\n<\/p>\n

    1. \u4f7f\u7528NumPy\u8ba1\u7b97\u767e\u5206\u4f4d\u6570<\/h3>\n<\/p>\n

    NumPy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u503c\u8ba1\u7b97\u5e93\uff0c\u63d0\u4f9b\u4e86\u7b80\u5355\u7684\u51fd\u6570\u6765\u8ba1\u7b97\u767e\u5206\u4f4d\u6570\u3002<\/p>\n<\/p>\n

    import numpy as np<\/p>\n
    data = [10, 20, 30, 40, 50]<\/p>\n
    percentile_25 = np.percentile(data, 25)<\/p>\n
    percentile_50 = np.percentile(data, 50)<\/p>\n
    percentile_75 = np.percentile(data, 75)<\/p>\n
    print("25th Percentile:", percentile_25)<\/p>\n
    print("50th Percentile:", percentile_50)<\/p>\n
    print("75th Percentile:", percentile_75)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    2. \u4f7f\u7528Pandas\u8ba1\u7b97\u767e\u5206\u4f4d\u6570<\/h3>\n<\/p>\n
    Pandas\u662f\u4e00\u4e2a\u7528\u4e8e\u6570\u636e\u5206\u6790\u7684\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u4e3a\u7075\u6d3b\u7684\u65b9\u6cd5\u6765\u5904\u7406\u6570\u636e\u3002<\/p>\n<\/p>\n
    import pandas as pd<\/p>\n
    data = pd.Series([10, 20, 30, 40, 50])<\/p>\n
    percentile_25 = data.quantile(0.25)<\/p>\n
    percentile_50 = data.quantile(0.50)<\/p>\n
    percentile_75 = data.quantile(0.75)<\/p>\n
    print("25th Percentile:", percentile_25)<\/p>\n
    print("50th Percentile:", percentile_50)<\/p>\n
    print("75th Percentile:", percentile_75)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e09\u3001\u8be6\u7ec6\u8bb2\u89e3\u6bcf\u4e2a\u6b65\u9aa4<\/h2>\n<\/p>\n
    1. \u6392\u5e8f\u6570\u636e\u96c6<\/h3>\n<\/p>\n
    \u5728\u8ba1\u7b97\u767e\u5206\u4f4d\u6570\u4e4b\u524d\uff0c\u5fc5\u987b\u5148\u5bf9\u6570\u636e\u96c6\u8fdb\u884c\u6392\u5e8f\u3002NumPy\u548cPandas\u90fd\u4f1a\u81ea\u52a8\u8fdb\u884c\u6392\u5e8f\uff0c\u4f46\u4e86\u89e3\u6392\u5e8f\u8fc7\u7a0b\u6709\u52a9\u4e8e\u6df1\u5165\u7406\u89e3\u767e\u5206\u4f4d\u6570\u7684\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
    data = [50, 20, 10, 40, 30]<\/p>\n
    sorted_data = sorted(data)<\/p>\n
    print("Sorted Data:", sorted_data)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    2. \u786e\u5b9a\u4f4d\u7f6e<\/h3>\n<\/p>\n
    \u6839\u636e\u767e\u5206\u4f4d\u6570\u516c\u5f0f\uff0c\u53ef\u4ee5\u786e\u5b9a\u767e\u5206\u4f4d\u6570\u5728\u6570\u636e\u96c6\u4e2d\u7684\u4f4d\u7f6e\u3002<\/p>\n<\/p>\n
    N = len(data)<\/p>\n
    k = 25<\/p>\n
    position = (N + 1) * k \/ 100<\/p>\n
    print("Position for 25th Percentile:", position)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    3. \u63d2\u503c\u8ba1\u7b97<\/h3>\n<\/p>\n
    \u5982\u679c\u4f4d\u7f6e\u4e0d\u662f\u6574\u6570\uff0c\u4f7f\u7528\u63d2\u503c\u6cd5\u8ba1\u7b97\u767e\u5206\u4f4d\u6570\u503c\u3002<\/p>\n<\/p>\n
    import numpy as np<\/p>\n
    def percentile(data, k):<\/p>\n
        data = sorted(data)<\/p>\n
        N = len(data)<\/p>\n
        pos = (N + 1) * k \/ 100<\/p>\n
        if pos.is_integer():<\/p>\n
            return data[int(pos) - 1]<\/p>\n
        else:<\/p>\n
            lower = data[int(pos) - 1]<\/p>\n
            upper = data[int(pos)]<\/p>\n
            return lower + (upper - lower) * (pos - int(pos))<\/p>\n
    data = [10, 20, 30, 40, 50]<\/p>\n
    percentile_25 = percentile(data, 25)<\/p>\n
    print("25th Percentile (Custom Function):", percentile_25)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u56db\u3001\u5b9e\u9645\u5e94\u7528\u4e2d\u7684\u767e\u5206\u4f4d\u6570<\/h2>\n<\/p>\n
    1. \u6570\u636e\u5206\u6790<\/h3>\n<\/p>\n
    \u767e\u5206\u4f4d\u6570\u5728\u6570\u636e\u5206\u6790\u4e2d\u6709\u5e7f\u6cdb\u5e94\u7528\u3002\u4f8b\u5982\uff0c\u5728\u91d1\u878d\u9886\u57df\uff0c\u6295\u8d44\u8005\u4f7f\u7528\u767e\u5206\u4f4d\u6570\u6765\u8bc4\u4f30\u6295\u8d44\u7ec4\u5408\u7684\u98ce\u9669\u548c\u56de\u62a5\u3002<\/p>\n<\/p>\n
    import numpy as np<\/p>\n
    import pandas as pd<\/p>\n
    \u793a\u4f8b\u6570\u636e\uff1a\u80a1\u7968\u4ef7\u683c\u53d8\u5316\u767e\u5206\u6bd4<\/strong><\/h2>\n
    stock_returns = np.random.normal(loc=0.01, scale=0.02, size=1000)<\/p>\n
    df = pd.DataFrame(stock_returns, columns=['Returns'])<\/p>\n
    \u8ba1\u7b9710th, 50th, 90th\u767e\u5206\u4f4d\u6570<\/strong><\/h2>\n
    percentiles = [10, 50, 90]<\/p>\n
    result = df['Returns'].quantile([p \/ 100 for p in percentiles])<\/p>\n
    print("Percentiles:\\n", result)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    2. \u673a\u5668\u5b66\u4e60<\/h3>\n<\/p>\n
    \u5728\u673a\u5668\u5b66\u4e60\u4e2d\uff0c\u767e\u5206\u4f4d\u6570\u7528\u4e8e\u7279\u5f81\u9009\u62e9\u548c\u6570\u636e\u9884\u5904\u7406\u3002\u4f8b\u5982\uff0c\u4f7f\u7528\u767e\u5206\u4f4d\u6570\u5c06\u5f02\u5e38\u503c\uff08outliers\uff09\u4ece\u6570\u636e\u96c6\u4e2d\u79fb\u9664\u3002<\/p>\n<\/p>\n
    import numpy as np<\/p>\n
    data = np.random.normal(loc=0, scale=1, size=1000)<\/p>\n
    lower_bound = np.percentile(data, 1)<\/p>\n
    upper_bound = np.percentile(data, 99)<\/p>\n
    filtered_data = data[(data >= lower_bound) & (data <= upper_bound)]<\/p>\n
    print("Filtered Data Size:", len(filtered_data))<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    3. \u533b\u5b66\u7edf\u8ba1<\/h3>\n<\/p>\n
    \u5728\u533b\u5b66\u7edf\u8ba1\u4e2d\uff0c\u767e\u5206\u4f4d\u6570\u7528\u4e8e\u5206\u6790\u60a3\u8005\u7684\u5065\u5eb7\u6570\u636e\u3002\u4f8b\u5982\uff0c\u513f\u7ae5\u7684\u8eab\u9ad8\u548c\u4f53\u91cd\u5e38\u7528\u767e\u5206\u4f4d\u6570\u6765\u8bc4\u4f30\u751f\u957f\u53d1\u80b2\u60c5\u51b5\u3002<\/p>\n<\/p>\n
    import pandas as pd<\/p>\n
    \u793a\u4f8b\u6570\u636e\uff1a\u513f\u7ae5\u8eab\u9ad8<\/strong><\/h2>\n
    data = pd.Series([100, 110, 120, 130, 140, 150, 160, 170, 180, 190])<\/p>\n
    percentiles = [5, 25, 50, 75, 95]<\/p>\n
    result = data.quantile([p \/ 100 for p in percentiles])<\/p>\n
    print("Children Height Percentiles:\\n", result)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e94\u3001\u767e\u5206\u4f4d\u6570\u8ba1\u7b97\u7684\u6ce8\u610f\u4e8b\u9879<\/h2>\n<\/p>\n
    1. \u6570\u636e\u96c6\u5927\u5c0f<\/h3>\n<\/p>\n
    \u5bf9\u4e8e\u5c0f\u6570\u636e\u96c6\uff0c\u767e\u5206\u4f4d\u6570\u7684\u8ba1\u7b97\u53ef\u80fd\u4e0d\u591f\u51c6\u786e\u3002\u5927\u6570\u636e\u96c6\u63d0\u4f9b\u66f4\u4e3a\u7a33\u5b9a\u548c\u51c6\u786e\u7684\u7ed3\u679c\u3002<\/p>\n<\/p>\n
    2. \u6570\u636e\u5206\u5e03<\/h3>\n<\/p>\n
    \u6570\u636e\u7684\u5206\u5e03\u5f62\u72b6\u5f71\u54cd\u767e\u5206\u4f4d\u6570\u7684\u8ba1\u7b97\u7ed3\u679c\u3002\u5bf9\u4e8e\u975e\u5bf9\u79f0\u5206\u5e03\uff08\u5982\u504f\u6001\u5206\u5e03\uff09\uff0c\u767e\u5206\u4f4d\u6570\u66f4\u80fd\u53cd\u6620\u6570\u636e\u7684\u5206\u5e03\u7279\u5f81\u3002<\/p>\n<\/p>\n
    3. \u63d2\u503c\u65b9\u6cd5<\/h3>\n<\/p>\n
    \u4e0d\u540c\u7684\u63d2\u503c\u65b9\u6cd5\u53ef\u80fd\u4f1a\u5bfc\u81f4\u4e0d\u540c\u7684\u767e\u5206\u4f4d\u6570\u7ed3\u679c\u3002NumPy\u548cPandas\u63d0\u4f9b\u4e86\u591a\u4e2a\u63d2\u503c\u9009\u9879\uff0c\u5982\u7ebf\u6027\u63d2\u503c\u3001\u6700\u8fd1\u90bb\u63d2\u503c\u7b49\u3002<\/p>\n<\/p>\n
    import numpy as np<\/p>\n
    data = [10, 20, 30, 40, 50]<\/p>\n
    percentile_25_linear = np.percentile(data, 25, interpolation='linear')<\/p>\n
    percentile_25_nearest = np.percentile(data, 25, interpolation='nearest')<\/p>\n
    print("25th Percentile (Linear Interpolation):", percentile_25_linear)<\/p>\n
    print("25th Percentile (Nearest Interpolation):", percentile_25_nearest)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u516d\u3001\u603b\u7ed3<\/h2>\n<\/p>\n
    \u8ba1\u7b97\u767e\u5206\u4f4d\u6570\u662f\u6570\u636e\u5206\u6790\u548c\u673a\u5668\u5b66\u4e60\u4e2d\u7684\u57fa\u672c\u6280\u80fd\u3002\u901a\u8fc7\u672c\u6587\uff0c\u6211\u4eec\u4e86\u89e3\u4e86\u767e\u5206\u4f4d\u6570\u7684\u5b9a\u4e49\u3001\u8ba1\u7b97\u65b9\u6cd5\u3001\u4ee5\u53ca\u5728Python\u4e2d\u4f7f\u7528NumPy\u548cPandas\u5e93\u5b9e\u73b0\u767e\u5206\u4f4d\u6570\u8ba1\u7b97<\/strong>\u3002\u6211\u4eec\u8fd8\u63a2\u8ba8\u4e86\u767e\u5206\u4f4d\u6570\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u7684\u91cd\u8981\u6027\uff0c\u5e76\u63d0\u4f9b\u4e86\u591a\u4e2a\u793a\u4f8b\u4ee3\u7801\u3002\u638c\u63e1\u8fd9\u4e9b\u77e5\u8bc6\u548c\u6280\u80fd\u5c06\u6709\u52a9\u4e8e\u66f4\u597d\u5730\u7406\u89e3\u548c\u5206\u6790\u6570\u636e\u3002<\/p>\n<\/p>\n
    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
     \u5982\u4f55\u5728Python\u4e2d\u8ba1\u7b97\u6570\u636e\u96c6\u7684\u767e\u5206\u4f4d\u6570\uff1f<\/strong>
    \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528numpy<\/code>\u5e93\u4e2d\u7684percentile<\/code>\u51fd\u6570\u6765\u8ba1\u7b97\u6570\u636e\u96c6\u7684\u767e\u5206\u4f4d\u6570\u3002\u9996\u5148\uff0c\u9700\u8981\u5c06\u6570\u636e\u96c6\u52a0\u8f7d\u4e3a\u4e00\u4e2a\u6570\u7ec4\uff0c\u7136\u540e\u8c03\u7528numpy.percentile(data, percentile_value)<\/code>\uff0c\u5176\u4e2ddata<\/code>\u662f\u4f60\u7684\u6570\u636e\u96c6\uff0cpercentile_value<\/code>\u662f\u4f60\u60f3\u8981\u8ba1\u7b97\u7684\u767e\u5206\u4f4d\u6570\uff08\u598225\u300150\u300175\u7b49\uff09\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n
    import numpy as np\n\ndata = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\npercentile_50 = np.percentile(data, 50)\nprint(percentile_50)  # \u8f93\u51fa 5.5\n<\/code><\/pre>\n
    \u5728Python\u4e2d\u8ba1\u7b97\u767e\u5206\u4f4d\u6570\u65f6\u9700\u8981\u8003\u8651\u54ea\u4e9b\u6570\u636e\u7c7b\u578b\uff1f<\/strong>
    \u5728\u8ba1\u7b97\u767e\u5206\u4f4d\u6570\u65f6\uff0c\u786e\u4fdd\u6570\u636e\u96c6\u662f\u6570\u503c\u7c7b\u578b\u3002\u5982\u679c\u6570\u636e\u96c6\u5305\u542b\u5b57\u7b26\u4e32\u6216\u975e\u6570\u503c\u6570\u636e\uff0c\u8ba1\u7b97\u53ef\u80fd\u4f1a\u5931\u8d25\u6216\u8fd4\u56de\u9519\u8bef\u7ed3\u679c\u3002\u53ef\u4ee5\u4f7f\u7528pandas<\/code>\u5e93\u5c06\u6570\u636e\u8f6c\u6362\u4e3a\u6570\u503c\u7c7b\u578b\uff0c\u65b9\u6cd5\u662f\u4f7f\u7528pd.to_numeric()<\/code>\u51fd\u6570\u3002\u8fd9\u6837\u53ef\u4ee5\u6709\u6548\u5904\u7406\u542b\u6709\u7f3a\u5931\u503c\u6216\u975e\u6570\u503c\u7684\u6570\u636e\u96c6\u3002<\/p>\n
    \u5982\u4f55\u5904\u7406\u5305\u542b\u7f3a\u5931\u503c\u7684\u6570\u636e\u96c6\u4ee5\u8ba1\u7b97\u767e\u5206\u4f4d\u6570\uff1f<\/strong>
    \u5728\u8ba1\u7b97\u767e\u5206\u4f4d\u6570\u65f6\uff0c\u5982\u679c\u6570\u636e\u96c6\u4e2d\u5b58\u5728\u7f3a\u5931\u503c\uff0c\u53ef\u4ee5\u4f7f\u7528numpy<\/code>\u6216pandas<\/code>\u5e93\u7684\u76f8\u5173\u65b9\u6cd5\u6765\u5904\u7406\u3002\u4f7f\u7528numpy.nanpercentile()<\/code>\u53ef\u4ee5\u5ffd\u7565NaN<\/code>\u503c\u8fdb\u884c\u8ba1\u7b97\uff0c\u800c\u5728\u4f7f\u7528pandas<\/code>\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528dropna()<\/code>\u65b9\u6cd5\u5220\u9664\u7f3a\u5931\u503c\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n
    import numpy as np\nimport pandas as pd\n\ndata_with_nan = [1, 2, np.nan, 4, 5]\ncleaned_data = pd.Series(data_with_nan).dropna()\npercentile_50 = np.percentile(cleaned_data, 50)\nprint(percentile_50)  # \u8f93\u51fa 3.0\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"\u767e\u5206\u4f4d\u6570\uff08percentile\uff09\u662f\u7edf\u8ba1\u5b66\u4e2d\u4e00\u4e2a\u91cd\u8981\u7684\u6982\u5ff5\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u6570\u636e\u5206\u6790\u548c\u673a\u5668\u5b66\u4e60\u9886\u57df\u3002\u8ba1\u7b97\u767e\u5206\u4f4d\u6570\u7684\u65b9\u6cd5 […]","protected":false},"author":3,"featured_media":1127468,"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\/1127458"}],"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=1127458"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1127458\/revisions"}],"predecessor-version":[{"id":1127471,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1127458\/revisions\/1127471"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1127468"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1127458"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1127458"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1127458"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}