{"id":1038995,"date":"2024-12-31T12:28:49","date_gmt":"2024-12-31T04:28:49","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1038995.html"},"modified":"2024-12-31T12:28:52","modified_gmt":"2024-12-31T04:28:52","slug":"python-%e5%a6%82%e4%bd%95%e6%b7%bb%e5%8a%a0%e4%b8%80%e8%a1%8c%e6%95%b0%e6%8d%ae","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1038995.html","title":{"rendered":"python \u5982\u4f55\u6dfb\u52a0\u4e00\u884c\u6570\u636e"},"content":{"rendered":"

\"python<\/p>\n

Python\u4e2d\u6dfb\u52a0\u4e00\u884c\u6570\u636e\u7684\u65b9\u5f0f\u6709\u591a\u79cd\uff0c\u5305\u62ec\u4f7f\u7528\u5217\u8868\u3001\u5b57\u5178\u3001Pandas\u5e93\u7b49\u3002\u6700\u5e38\u7528\u7684\u65b9\u6cd5\u662f\u4f7f\u7528Pandas\u5e93\uff0c\u5b83\u53ef\u4ee5\u65b9\u4fbf\u5730\u64cd\u4f5c\u548c\u7ba1\u7406\u6570\u636e\u3002\u8be6\u7ec6\u63cf\u8ff0\uff1aPandas\u5e93\u652f\u6301DataFrame\u7ed3\u6784\uff0c\u63d0\u4f9b\u4e86append<\/code>\u548cloc<\/code>\u65b9\u6cd5\u6765\u6dfb\u52a0\u6570\u636e\u3002<\/strong><\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528\u5217\u8868\u6dfb\u52a0\u4e00\u884c\u6570\u636e<\/h2>\n<\/p>\n

\u5728Python\u4e2d\uff0c\u5217\u8868\u662f\u4e00\u79cd\u975e\u5e38\u5e38\u7528\u7684\u6570\u636e\u7ed3\u6784\u3002\u53ef\u4ee5\u4f7f\u7528append<\/code>\u65b9\u6cd5\u5411\u5217\u8868\u4e2d\u6dfb\u52a0\u4e00\u884c\u6570\u636e\u3002<\/p>\n<\/p>\n

data = []<\/p>\n
data.append(['John', 28, 'Engineer'])<\/p>\n
print(data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u521b\u5efa\u4e86\u4e00\u4e2a\u7a7a\u5217\u8868data<\/code>\uff0c\u7136\u540e\u4f7f\u7528append<\/code>\u65b9\u6cd5\u5411\u5176\u4e2d\u6dfb\u52a0\u4e86\u4e00\u884c\u6570\u636e\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528\u5b57\u5178\u6dfb\u52a0\u4e00\u884c\u6570\u636e<\/h2>\n<\/p>\n
\u5b57\u5178\u5728Python\u4e2d\u4e5f\u662f\u975e\u5e38\u5e38\u7528\u7684\u6570\u636e\u7ed3\u6784\u3002\u53ef\u4ee5\u4f7f\u7528\u952e\u503c\u5bf9\u7684\u5f62\u5f0f\u5411\u5b57\u5178\u4e2d\u6dfb\u52a0\u6570\u636e\u3002<\/p>\n<\/p>\n
data = {}<\/p>\n
data['John'] = {'Age': 28, 'Job': 'Engineer'}<\/p>\n
print(data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u521b\u5efa\u4e86\u4e00\u4e2a\u7a7a\u5b57\u5178data<\/code>\uff0c\u7136\u540e\u4f7f\u7528\u952e\u503c\u5bf9\u7684\u5f62\u5f0f\u5411\u5176\u4e2d\u6dfb\u52a0\u4e86\u4e00\u884c\u6570\u636e\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528Pandas\u5e93\u6dfb\u52a0\u4e00\u884c\u6570\u636e<\/h2>\n<\/p>\n
Pandas\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u5e93\uff0c\u7279\u522b\u9002\u5408\u5904\u7406\u8868\u683c\u6570\u636e\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Pandas\u7684DataFrame<\/code>\u7ed3\u6784\u6765\u6dfb\u52a0\u6570\u636e\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u521b\u5efa\u4e00\u4e2aDataFrame<\/strong><\/h2>\n
df = pd.DataFrame(columns=['Name', 'Age', 'Job'])<\/p>\n
\u6dfb\u52a0\u4e00\u884c\u6570\u636e<\/strong><\/h2>\n
df = df.append({'Name': 'John', 'Age': 28, 'Job': 'Engineer'}, ignore_index=True)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u5bfc\u5165\u4e86Pandas\u5e93\uff0c\u7136\u540e\u521b\u5efa\u4e86\u4e00\u4e2a\u7a7a\u7684DataFrame<\/code>\uff0c\u63a5\u7740\u4f7f\u7528append<\/code>\u65b9\u6cd5\u6dfb\u52a0\u4e86\u4e00\u884c\u6570\u636e\u3002ignore_index=True<\/code>\u53c2\u6570\u7684\u4f5c\u7528\u662f\u8ba9Pandas\u81ea\u52a8\u4e3a\u65b0\u884c\u5206\u914d\u7d22\u5f15\u3002<\/p>\n<\/p>\n
\u8be6\u7ec6\u63cf\u8ff0\uff1a\u4f7f\u7528Pandas\u5e93\u6dfb\u52a0\u4e00\u884c\u6570\u636e<\/h2>\n<\/p>\n
Pandas\u5e93\u662f\u6570\u636e\u79d1\u5b66\u5bb6\u548c\u5206\u6790\u5e08\u975e\u5e38\u559c\u7231\u7684\u5de5\u5177\uff0c\u56e0\u4e3a\u5b83\u63d0\u4f9b\u4e86\u975e\u5e38\u7075\u6d3b\u548c\u9ad8\u6548\u7684\u6570\u636e\u64cd\u4f5c\u65b9\u6cd5\u3002\u5728Pandas\u4e2d\uff0c\u6570\u636e\u901a\u5e38\u5b58\u50a8\u5728DataFrame<\/code>\u7ed3\u6784\u4e2d\u3002DataFrame<\/code>\u53ef\u4ee5\u770b\u4f5c\u662f\u4e00\u4e2a\u8868\u683c\uff0c\u7c7b\u4f3c\u4e8eExcel\u8868\u683c\u6216\u6570\u636e\u5e93\u4e2d\u7684\u6570\u636e\u8868\u3002<\/p>\n<\/p>\n
\u5728Pandas\u4e2d\u6dfb\u52a0\u4e00\u884c\u6570\u636e\u6709\u591a\u79cd\u65b9\u6cd5\uff0c\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u51e0\u79cd\u5e38\u7528\u7684\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
    \n
  1. \u4f7f\u7528append<\/code>\u65b9\u6cd5<\/li>\n<\/ol>\n
    \n
    append<\/code>\u65b9\u6cd5\u662f\u6700\u7b80\u5355\u548c\u76f4\u63a5\u7684\u65b9\u6cd5\u4e4b\u4e00\u3002\u5b83\u53ef\u4ee5\u5c06\u4e00\u4e2a\u5b57\u5178\u6216\u53e6\u4e00\u4e2aDataFrame<\/code>\u7684\u5185\u5bb9\u6dfb\u52a0\u5230\u73b0\u6709\u7684DataFrame<\/code>\u4e2d\u3002<\/p>\n<\/p>\n
    import pandas as pd<\/p>\n
    \u521b\u5efa\u4e00\u4e2aDataFrame<\/strong><\/h2>\n
    df = pd.DataFrame(columns=['Name', 'Age', 'Job'])<\/p>\n
    \u6dfb\u52a0\u4e00\u884c\u6570\u636e<\/strong><\/h2>\n
    df = df.append({'Name': 'John', 'Age': 28, 'Job': 'Engineer'}, ignore_index=True)<\/p>\n
    print(df)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u521b\u5efa\u4e86\u4e00\u4e2a\u7a7a\u7684DataFrame<\/code>\uff0c\u7136\u540e\u4f7f\u7528append<\/code>\u65b9\u6cd5\u5c06\u4e00\u4e2a\u5b57\u5178\u5f62\u5f0f\u7684\u6570\u636e\u6dfb\u52a0\u5230DataFrame<\/code>\u4e2d\u3002ignore_index=True<\/code>\u53c2\u6570\u7684\u4f5c\u7528\u662f\u8ba9Pandas\u81ea\u52a8\u4e3a\u65b0\u884c\u5206\u914d\u7d22\u5f15\u3002<\/p>\n<\/p>\n
    \u9700\u8981\u6ce8\u610f\u7684\u662f\uff0cappend<\/code>\u65b9\u6cd5\u6bcf\u6b21\u90fd\u4f1a\u521b\u5efa\u4e00\u4e2a\u65b0\u7684DataFrame<\/code>\uff0c\u56e0\u6b64\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\uff0c\u9891\u7e41\u4f7f\u7528append<\/code>\u65b9\u6cd5\u53ef\u80fd\u4f1a\u5bfc\u81f4\u6027\u80fd\u95ee\u9898\u3002<\/p>\n<\/p>\n
      \n
    1. \u4f7f\u7528loc<\/code>\u65b9\u6cd5<\/li>\n<\/ol>\n
      \n
      loc<\/code>\u65b9\u6cd5\u662fPandas\u4e2d\u975e\u5e38\u5f3a\u5927\u7684\u4e00\u4e2a\u65b9\u6cd5\uff0c\u53ef\u4ee5\u901a\u8fc7\u6807\u7b7e\u8bbf\u95ee\u6570\u636e\u3002\u6211\u4eec\u4e5f\u53ef\u4ee5\u4f7f\u7528loc<\/code>\u65b9\u6cd5\u5411DataFrame<\/code>\u4e2d\u6dfb\u52a0\u6570\u636e\u3002<\/p>\n<\/p>\n
      import pandas as pd<\/p>\n
      \u521b\u5efa\u4e00\u4e2aDataFrame<\/strong><\/h2>\n
      df = pd.DataFrame(columns=['Name', 'Age', 'Job'])<\/p>\n
      \u6dfb\u52a0\u4e00\u884c\u6570\u636e<\/strong><\/h2>\n
      df.loc[0] = ['John', 28, 'Engineer']<\/p>\n
      print(df)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528loc<\/code>\u65b9\u6cd5\u76f4\u63a5\u5411DataFrame<\/code>\u4e2d\u6dfb\u52a0\u4e86\u4e00\u884c\u6570\u636e\u3002\u4e0eappend<\/code>\u65b9\u6cd5\u4e0d\u540c\uff0cloc<\/code>\u65b9\u6cd5\u4e0d\u4f1a\u521b\u5efa\u65b0\u7684DataFrame<\/code>\uff0c\u56e0\u6b64\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\u6027\u80fd\u66f4\u597d\u3002<\/p>\n<\/p>\n
        \n
      1. \u4f7f\u7528concat<\/code>\u65b9\u6cd5<\/li>\n<\/ol>\n
        \n
        concat<\/code>\u65b9\u6cd5\u53ef\u4ee5\u5c06\u591a\u4e2aDataFrame<\/code>\u62fc\u63a5\u5728\u4e00\u8d77\u3002\u6211\u4eec\u53ef\u4ee5\u5148\u521b\u5efa\u4e00\u4e2a\u65b0\u7684DataFrame<\/code>\uff0c\u7136\u540e\u4f7f\u7528concat<\/code>\u65b9\u6cd5\u5c06\u5176\u6dfb\u52a0\u5230\u73b0\u6709\u7684DataFrame<\/code>\u4e2d\u3002<\/p>\n<\/p>\n
        import pandas as pd<\/p>\n
        \u521b\u5efa\u4e00\u4e2aDataFrame<\/strong><\/h2>\n
        df = pd.DataFrame(columns=['Name', 'Age', 'Job'])<\/p>\n
        \u521b\u5efa\u4e00\u4e2a\u65b0\u7684DataFrame<\/strong><\/h2>\n
        new_data = pd.DataFrame([['John', 28, 'Engineer']], columns=['Name', 'Age', 'Job'])<\/p>\n
        \u4f7f\u7528concat\u65b9\u6cd5\u6dfb\u52a0\u6570\u636e<\/strong><\/h2>\n
        df = pd.concat([df, new_data], ignore_index=True)<\/p>\n
        print(df)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u521b\u5efa\u4e86\u4e00\u4e2a\u7a7a\u7684DataFrame<\/code>\uff0c\u7136\u540e\u521b\u5efa\u4e86\u4e00\u4e2a\u5305\u542b\u65b0\u6570\u636e\u7684DataFrame<\/code>\uff0c\u63a5\u7740\u4f7f\u7528concat<\/code>\u65b9\u6cd5\u5c06\u65b0\u6570\u636e\u6dfb\u52a0\u5230\u73b0\u6709\u7684DataFrame<\/code>\u4e2d\u3002ignore_index=True<\/code>\u53c2\u6570\u7684\u4f5c\u7528\u662f\u8ba9Pandas\u81ea\u52a8\u4e3a\u65b0\u884c\u5206\u914d\u7d22\u5f15\u3002<\/p>\n<\/p>\n
        \u56db\u3001\u6bd4\u8f83\u4e0d\u540c\u65b9\u6cd5\u7684\u6027\u80fd<\/h2>\n<\/p>\n
        \u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\uff0c\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u975e\u5e38\u91cd\u8981\u3002\u4e0b\u9762\u6211\u4eec\u5c06\u6bd4\u8f83append<\/code>\u3001loc<\/code>\u548cconcat<\/code>\u65b9\u6cd5\u7684\u6027\u80fd\u3002<\/p>\n<\/p>\n
        import pandas as pd<\/p>\n
        import time<\/p>\n
        \u521b\u5efa\u4e00\u4e2a\u7a7a\u7684DataFrame<\/strong><\/h2>\n
        df = pd.DataFrame(columns=['Name', 'Age', 'Job'])<\/p>\n
        \u4f7f\u7528append\u65b9\u6cd5\u6dfb\u52a010000\u884c\u6570\u636e<\/strong><\/h2>\n
        start_time = time.time()<\/p>\n
        for i in range(10000):<\/p>\n
            df = df.append({'Name': 'John', 'Age': 28, 'Job': 'Engineer'}, ignore_index=True)<\/p>\n
        print("Append method took %s seconds" % (time.time() - start_time))<\/p>\n
        \u4f7f\u7528loc\u65b9\u6cd5\u6dfb\u52a010000\u884c\u6570\u636e<\/strong><\/h2>\n
        df = pd.DataFrame(columns=['Name', 'Age', 'Job'])<\/p>\n
        start_time = time.time()<\/p>\n
        for i in range(10000):<\/p>\n
            df.loc[i] = ['John', 28, 'Engineer']<\/p>\n
        print("Loc method took %s seconds" % (time.time() - start_time))<\/p>\n
        \u4f7f\u7528concat\u65b9\u6cd5\u6dfb\u52a010000\u884c\u6570\u636e<\/strong><\/h2>\n
        df = pd.DataFrame(columns=['Name', 'Age', 'Job'])<\/p>\n
        start_time = time.time()<\/p>\n
        new_data = pd.DataFrame([['John', 28, 'Engineer']] * 10000, columns=['Name', 'Age', 'Job'])<\/p>\n
        df = pd.concat([df, new_data], ignore_index=True)<\/p>\n
        print("Concat method took %s seconds" % (time.time() - start_time))<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u5206\u522b\u4f7f\u7528append<\/code>\u3001loc<\/code>\u548cconcat<\/code>\u65b9\u6cd5\u5411DataFrame<\/code>\u4e2d\u6dfb\u52a0\u4e8610000\u884c\u6570\u636e\uff0c\u5e76\u8bb0\u5f55\u4e86\u6bcf\u79cd\u65b9\u6cd5\u6240\u8017\u8d39\u7684\u65f6\u95f4\u3002\u5b9e\u9a8c\u7ed3\u679c\u8868\u660e\uff0cloc<\/code>\u65b9\u6cd5\u548cconcat<\/code>\u65b9\u6cd5\u7684\u6027\u80fd\u660e\u663e\u4f18\u4e8eappend<\/code>\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
        \u4e94\u3001\u603b\u7ed3<\/h2>\n<\/p>\n
        \u5728Python\u4e2d\uff0c\u6dfb\u52a0\u4e00\u884c\u6570\u636e\u6709\u591a\u79cd\u65b9\u6cd5\uff0c\u5305\u62ec\u4f7f\u7528\u5217\u8868\u3001\u5b57\u5178\u548cPandas\u5e93\u3002\u5bf9\u4e8e\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\uff0c\u63a8\u8350\u4f7f\u7528Pandas\u5e93\uff0c\u5e76\u9009\u62e9\u6027\u80fd\u8f83\u4f18\u7684\u65b9\u6cd5\uff0c\u5982loc<\/code>\u548cconcat<\/code>\u65b9\u6cd5\u3002\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u53ef\u4ee5\u5927\u5927\u63d0\u9ad8\u6570\u636e\u5904\u7406\u7684\u6548\u7387\u3002<\/p>\n<\/p>\n
        \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
         \u5982\u4f55\u5728Python\u4e2d\u5c06\u6570\u636e\u6dfb\u52a0\u5230\u5217\u8868\u4e2d\uff1f<\/strong>
        \u5728Python\u4e2d\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528append()<\/code>\u65b9\u6cd5\u5c06\u65b0\u6570\u636e\u6dfb\u52a0\u5230\u5217\u8868\u7684\u672b\u5c3e\u3002\u4f8b\u5982\uff0c\u5982\u679c\u60a8\u6709\u4e00\u4e2a\u5217\u8868data<\/code>\uff0c\u53ef\u4ee5\u901a\u8fc7data.append(new_item)<\/code>\u5c06new_item<\/code>\u6dfb\u52a0\u5230\u5217\u8868\u4e2d\u3002\u8fd9\u6837\uff0c\u60a8\u53ef\u4ee5\u52a8\u6001\u5730\u6269\u5c55\u5217\u8868\u5185\u5bb9\u3002<\/p>\n
        Python\u4e2d\u5982\u4f55\u5411CSV\u6587\u4ef6\u6dfb\u52a0\u4e00\u884c\u6570\u636e\uff1f<\/strong>
        \u8981\u5411CSV\u6587\u4ef6\u6dfb\u52a0\u4e00\u884c\u6570\u636e\uff0c\u53ef\u4ee5\u4f7f\u7528csv<\/code>\u6a21\u5757\u3002\u60a8\u53ef\u4ee5\u6253\u5f00\u6587\u4ef6\u5e76\u4f7f\u7528csv.writer<\/code>\u5bf9\u8c61\u7684writerow()<\/code>\u65b9\u6cd5\u3002\u4f8b\u5982\uff0c\u6253\u5f00\u6587\u4ef6\u65f6\u4f7f\u7528with open('file.csv', mode='a', newline='') as file:<\/code>\uff0c\u7136\u540e\u8c03\u7528writer.writerow(['column1', 'column2', 'column3'])<\/code>\u5c06\u65b0\u884c\u5199\u5165\u6587\u4ef6\u3002<\/p>\n
        \u5728Python\u5b57\u5178\u4e2d\u5982\u4f55\u6dfb\u52a0\u65b0\u7684\u952e\u503c\u5bf9\uff1f<\/strong>
        \u5728\u5b57\u5178\u4e2d\u6dfb\u52a0\u65b0\u7684\u952e\u503c\u5bf9\u975e\u5e38\u7b80\u5355\u3002\u60a8\u53ea\u9700\u4f7f\u7528dictionary[key] = value<\/code>\u7684\u8bed\u6cd5\u3002\u4f8b\u5982\uff0c\u5982\u679c\u60a8\u6709\u4e00\u4e2a\u5b57\u5178my_dict<\/code>\uff0c\u60f3\u8981\u6dfb\u52a0\u4e00\u4e2a\u65b0\u7684\u952e'new_key'<\/code>\u548c\u5bf9\u5e94\u7684\u503c'new_value'<\/code>\uff0c\u53ea\u9700\u6267\u884cmy_dict['new_key'] = 'new_value'<\/code>\u5373\u53ef\u3002\u8fd9\u5c06\u81ea\u52a8\u66f4\u65b0\u5b57\u5178\u5185\u5bb9\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u4e2d\u6dfb\u52a0\u4e00\u884c\u6570\u636e\u7684\u65b9\u5f0f\u6709\u591a\u79cd\uff0c\u5305\u62ec\u4f7f\u7528\u5217\u8868\u3001\u5b57\u5178\u3001Pandas\u5e93\u7b49\u3002\u6700\u5e38\u7528\u7684\u65b9\u6cd5\u662f\u4f7f\u7528Pandas\u5e93 […]","protected":false},"author":3,"featured_media":1039007,"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\/1038995"}],"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=1038995"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1038995\/revisions"}],"predecessor-version":[{"id":1039008,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1038995\/revisions\/1039008"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1039007"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1038995"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1038995"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1038995"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}