{"id":973806,"date":"2024-12-27T05:59:52","date_gmt":"2024-12-26T21:59:52","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/973806.html"},"modified":"2024-12-27T05:59:54","modified_gmt":"2024-12-26T21:59:54","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python-%e6%95%b0%e6%8d%ae%e6%a1%86","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/973806.html","title":{"rendered":"\u5982\u4f55\u7528python \u6570\u636e\u6846"},"content":{"rendered":"

\"\u5982\u4f55\u7528python<\/p>\n

\u4f7f\u7528Python\u6570\u636e\u6846\u7684\u6838\u5fc3\u6b65\u9aa4\u5305\u62ec\uff1a\u5bfc\u5165Pandas\u5e93\u3001\u521b\u5efa\u6570\u636e\u6846\u3001\u8bfb\u53d6\u548c\u5199\u5165\u6570\u636e\u3001\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u64cd\u4f5c\u4e0e\u5206\u6790\u3002<\/strong>\u5176\u4e2d\uff0c\u5bfc\u5165Pandas\u5e93\u662f\u57fa\u7840\uff0c\u521b\u5efa\u6570\u636e\u6846\u662f\u6570\u636e\u64cd\u4f5c\u7684\u8d77\u59cb\u6b65\u9aa4\uff0c\u6570\u636e\u6e05\u6d17\u662f\u786e\u4fdd\u6570\u636e\u8d28\u91cf\u7684\u91cd\u8981\u73af\u8282\uff0c\u6570\u636e\u64cd\u4f5c\u4e0e\u5206\u6790\u5219\u662f\u5b9e\u73b0\u6570\u636e\u4ef7\u503c\u7684\u5173\u952e\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528\u6570\u636e\u6846\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5bfc\u5165Pandas\u5e93<\/p>\n<\/p>\n

Pandas\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u6570\u636e\u5904\u7406\u5e93\u4e4b\u4e00\uff0c\u5b83\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u636e\u7ed3\u6784\u548c\u6570\u636e\u5206\u6790\u5de5\u5177\u3002\u4f7f\u7528\u6570\u636e\u6846\u7684\u7b2c\u4e00\u6b65\u662f\u5bfc\u5165Pandas\u5e93\u3002\u4f60\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u4ee3\u7801\u5b9e\u73b0\uff1a<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5bfc\u5165Pandas\u5e93\u540e\uff0c\u4f60\u4fbf\u53ef\u4ee5\u4f7f\u7528\u5b83\u6240\u63d0\u4f9b\u7684\u5404\u79cd\u529f\u80fd\u6765\u5904\u7406\u6570\u636e\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u521b\u5efa\u6570\u636e\u6846<\/p>\n<\/p>\n
\u521b\u5efa\u6570\u636e\u6846\u662f\u6570\u636e\u64cd\u4f5c\u7684\u8d77\u59cb\u6b65\u9aa4\u3002\u6570\u636e\u6846\u662f\u4e00\u4e2a\u4e8c\u7ef4\u7684\u8868\u683c\u6570\u636e\u7ed3\u6784\uff0c\u5b83\u7c7b\u4f3c\u4e8eExcel\u8868\u683c\u6216SQL\u6570\u636e\u5e93\u4e2d\u7684\u8868\u3002\u4f60\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u521b\u5efa\u6570\u636e\u6846\uff0c\u4f8b\u5982\uff0c\u4ece\u5b57\u5178\u3001\u5217\u8868\u3001NumPy\u6570\u7ec4\u7b49\u521b\u5efa\u3002\u4ee5\u4e0b\u662f\u4ece\u5b57\u5178\u521b\u5efa\u6570\u636e\u6846\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
data = {<\/p>\n
    'Name': ['Alice', 'Bob', 'Charlie'],<\/p>\n
    'Age': [25, 30, 35],<\/p>\n
    'City': ['New York', 'Los Angeles', 'Chicago']<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u521b\u5efa\u4e86\u4e00\u4e2a\u5305\u542b\u4e09\u5217\uff08Name\u3001Age\u3001City\uff09\u7684\u6570\u636e\u6846\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u8bfb\u53d6\u548c\u5199\u5165\u6570\u636e<\/p>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u4f60\u901a\u5e38\u9700\u8981\u4ece\u5916\u90e8\u6587\u4ef6\u8bfb\u53d6\u6570\u636e\uff0c\u6216\u5c06\u5904\u7406\u540e\u7684\u6570\u636e\u5199\u5165\u6587\u4ef6\u3002Pandas\u652f\u6301\u591a\u79cd\u683c\u5f0f\u7684\u6570\u636e\u8bfb\u53d6\u548c\u5199\u5165\uff0c\u4f8b\u5982CSV\u3001Excel\u3001SQL\u7b49\u3002<\/p>\n<\/p>\n
    \n
  1. \u8bfb\u53d6\u6570\u636e<\/li>\n<\/ol>\n
    \u8981\u4eceCSV\u6587\u4ef6\u8bfb\u53d6\u6570\u636e\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528pd.read_csv()<\/code>\u51fd\u6570\uff1a<\/p>\n<\/p>\n
    df = pd.read_csv('data.csv')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5982\u679c\u9700\u8981\u8bfb\u53d6Excel\u6587\u4ef6\uff0c\u53ef\u4ee5\u4f7f\u7528pd.read_excel()<\/code>\uff1a<\/p>\n<\/p>\n
    df = pd.read_excel('data.xlsx', sheet_name='Sheet1')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u5199\u5165\u6570\u636e<\/li>\n<\/ol>\n
      \u8981\u5c06\u6570\u636e\u6846\u5199\u5165CSV\u6587\u4ef6\uff0c\u53ef\u4ee5\u4f7f\u7528to_csv()<\/code>\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
      df.to_csv('output.csv', index=False)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u5982\u679c\u9700\u8981\u5199\u5165Excel\u6587\u4ef6\uff0c\u53ef\u4ee5\u4f7f\u7528to_excel()<\/code>\uff1a<\/p>\n<\/p>\n
      df.to_excel('output.xlsx', index=False)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u56db\u3001\u6570\u636e\u6e05\u6d17<\/p>\n<\/p>\n
      \u6570\u636e\u6e05\u6d17\u662f\u786e\u4fdd\u6570\u636e\u8d28\u91cf\u7684\u91cd\u8981\u73af\u8282\u3002\u5728\u6570\u636e\u5206\u6790\u4e2d\uff0c\u6e05\u6d17\u6b65\u9aa4\u5305\u62ec\u5904\u7406\u7f3a\u5931\u503c\u3001\u53bb\u9664\u91cd\u590d\u6570\u636e\u3001\u4fee\u6b63\u6570\u636e\u7c7b\u578b\u7b49\u3002<\/p>\n<\/p>\n
        \n
      1. \u5904\u7406\u7f3a\u5931\u503c<\/li>\n<\/ol>\n
        Pandas\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u5904\u7406\u7f3a\u5931\u503c\u3002\u4f8b\u5982\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528dropna()<\/code>\u65b9\u6cd5\u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c\uff1a<\/p>\n<\/p>\n
        df.dropna(inplace=True)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u6216\u8005\u4f7f\u7528fillna()<\/code>\u65b9\u6cd5\u7528\u6307\u5b9a\u503c\u586b\u5145\u7f3a\u5931\u503c\uff1a<\/p>\n<\/p>\n
        df.fillna(value=0, inplace=True)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        1. \u53bb\u9664\u91cd\u590d\u6570\u636e<\/li>\n<\/ol>\n
          \u53ef\u4ee5\u4f7f\u7528drop_duplicates()<\/code>\u65b9\u6cd5\u53bb\u9664\u91cd\u590d\u884c\uff1a<\/p>\n<\/p>\n
          df.drop_duplicates(inplace=True)<\/p>\n
          <\/code><\/pre>\n<\/p>\n
            \n
          1. \u4fee\u6b63\u6570\u636e\u7c7b\u578b<\/li>\n<\/ol>\n
            \u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u4f60\u53ef\u80fd\u9700\u8981\u5c06\u6570\u636e\u8f6c\u6362\u4e3a\u9002\u5f53\u7684\u6570\u636e\u7c7b\u578b\u3002\u4f8b\u5982\uff0c\u5c06\u5b57\u7b26\u4e32\u8868\u793a\u7684\u6570\u5b57\u8f6c\u6362\u4e3a\u6574\u6570\uff1a<\/p>\n<\/p>\n
            df['Age'] = df['Age'].astype(int)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u4e94\u3001\u6570\u636e\u64cd\u4f5c\u4e0e\u5206\u6790<\/p>\n<\/p>\n
            \u6570\u636e\u64cd\u4f5c\u4e0e\u5206\u6790\u662f\u5b9e\u73b0\u6570\u636e\u4ef7\u503c\u7684\u5173\u952e\u3002Pandas\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5de5\u5177\u6765\u6267\u884c\u6570\u636e\u64cd\u4f5c\u548c\u5206\u6790\uff0c\u4f8b\u5982\uff0c\u9009\u62e9\u6570\u636e\u3001\u8fc7\u6ee4\u6570\u636e\u3001\u5206\u7ec4\u805a\u5408\u7b49\u3002<\/p>\n<\/p>\n
              \n
            1. \u9009\u62e9\u6570\u636e<\/li>\n<\/ol>\n
              \u4f60\u53ef\u4ee5\u901a\u8fc7\u5217\u540d\u6216\u4f4d\u7f6e\u9009\u62e9\u6570\u636e\u3002\u4f8b\u5982\uff0c\u9009\u62e9\u4e00\u5217\u6570\u636e\uff1a<\/p>\n<\/p>\n
              ages = df['Age']<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u6216\u8005\u9009\u62e9\u591a\u5217\u6570\u636e\uff1a<\/p>\n<\/p>\n
              subset = df[['Name', 'City']]<\/p>\n
              <\/code><\/pre>\n<\/p>\n
                \n
              1. \u8fc7\u6ee4\u6570\u636e<\/li>\n<\/ol>\n
                \u53ef\u4ee5\u4f7f\u7528\u5e03\u5c14\u7d22\u5f15\u6765\u8fc7\u6ee4\u6570\u636e\u3002\u4f8b\u5982\uff0c\u9009\u62e9\u5e74\u9f84\u5927\u4e8e30\u7684\u884c\uff1a<\/p>\n<\/p>\n
                filtered_df = df[df['Age'] > 30]<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                  \n
                1. \u5206\u7ec4\u805a\u5408<\/li>\n<\/ol>\n
                  \u4f7f\u7528groupby()<\/code>\u65b9\u6cd5\u53ef\u4ee5\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\uff0c\u7136\u540e\u4f7f\u7528\u805a\u5408\u51fd\u6570\u8fdb\u884c\u8ba1\u7b97\u3002\u4f8b\u5982\uff0c\u8ba1\u7b97\u6bcf\u4e2a\u57ce\u5e02\u7684\u5e73\u5747\u5e74\u9f84\uff1a<\/p>\n<\/p>\n
                  grouped = df.groupby('City')['Age'].mean()<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u516d\u3001\u53ef\u89c6\u5316\u6570\u636e<\/p>\n<\/p>\n
                  \u53ef\u89c6\u5316\u662f\u6570\u636e\u5206\u6790\u7684\u91cd\u8981\u7ec4\u6210\u90e8\u5206\u3002Pandas\u4e0eMatplotlib\u7b49\u53ef\u89c6\u5316\u5e93\u9ad8\u5ea6\u517c\u5bb9\uff0c\u53ef\u4ee5\u8f7b\u677e\u521b\u5efa\u56fe\u8868\u3002\u4f8b\u5982\uff0c\u7ed8\u5236\u6761\u5f62\u56fe\uff1a<\/p>\n<\/p>\n
                  import matplotlib.pyplot as plt<\/p>\n
                  df['Age'].plot(kind='bar')<\/p>\n
                  plt.show()<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u4e03\u3001\u5904\u7406\u5927\u6570\u636e\u96c6<\/p>\n<\/p>\n
                  \u5728\u5904\u7406\u5927\u6570\u636e\u96c6\u65f6\uff0c\u53ef\u80fd\u4f1a\u9047\u5230\u6027\u80fd\u95ee\u9898\u3002Pandas\u63d0\u4f9b\u4e86\u4e00\u4e9b\u4f18\u5316\u6280\u5de7\uff0c\u4f8b\u5982\u4f7f\u7528\u5206\u5757\u8bfb\u53d6\u5927\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
                  for chunk in pd.read_csv('large_data.csv', chunksize=10000):<\/p>\n
                      process(chunk)<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u8fd9\u79cd\u65b9\u6cd5\u53ef\u4ee5\u6709\u6548\u51cf\u5c11\u5185\u5b58\u4f7f\u7528\uff0c\u63d0\u9ad8\u5904\u7406\u901f\u5ea6\u3002<\/p>\n<\/p>\n
                  \u516b\u3001\u7ed3\u5408\u5176\u4ed6\u5de5\u5177<\/p>\n<\/p>\n
                  Pandas\u53ef\u4ee5\u4e0e\u5176\u4ed6\u6570\u636e\u79d1\u5b66\u5de5\u5177\u7ed3\u5408\u4f7f\u7528\uff0c\u4f8b\u5982NumPy\u3001SciPy\u3001Scikit-learn\u7b49\uff0c\u4ee5\u5b9e\u73b0\u66f4\u590d\u6742\u7684\u5206\u6790\u548c\u5efa\u6a21\u4efb\u52a1\u3002\u4f8b\u5982\uff0c\u7ed3\u5408Scikit-learn\u8fdb\u884c\u673a\u5668\u5b66\u4e60\u6a21\u578b\u8bad\u7ec3\uff1a<\/p>\n<\/p>\n
                  from sklearn.model_selection import trAI<\/a>n_test_split<\/p>\n
                  from sklearn.linear_model import LinearRegression<\/p>\n
                  X = df[['Age']]<\/p>\n
                  y = df['Salary']<\/p>\n
                  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)<\/p>\n
                  model = LinearRegression()<\/p>\n
                  model.fit(X_train, y_train)<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u4e5d\u3001\u603b\u7ed3<\/p>\n<\/p>\n
                  \u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u4f60\u53ef\u4ee5\u638c\u63e1\u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528\u6570\u636e\u6846\u8fdb\u884c\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u3002Pandas\u4f5c\u4e3a\u4e00\u79cd\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u5de5\u5177\uff0c\u5177\u6709\u7075\u6d3b\u6027\u548c\u9ad8\u6548\u6027\uff0c\u53ef\u4ee5\u6ee1\u8db3\u5927\u591a\u6570\u6570\u636e\u79d1\u5b66\u4efb\u52a1\u7684\u9700\u6c42\u3002\u65e0\u8bba\u662f\u5904\u7406\u7b80\u5355\u7684\u8868\u683c\u6570\u636e\uff0c\u8fd8\u662f\u8fdb\u884c\u590d\u6742\u7684\u6570\u636e\u5206\u6790\uff0cPandas\u90fd\u80fd\u591f\u63d0\u4f9b\u5f3a\u6709\u529b\u7684\u652f\u6301\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u7ed3\u5408\u5176\u4ed6\u5de5\u5177\u548c\u4f18\u5316\u6280\u5de7\uff0c\u4f60\u53ef\u4ee5\u8fdb\u4e00\u6b65\u63d0\u5347\u6570\u636e\u5904\u7406\u7684\u6548\u7387\u548c\u6548\u679c\u3002<\/p>\n<\/p>\n
                  \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                   \u5982\u4f55\u4f7f\u7528Python\u521b\u5efa\u6570\u636e\u6846\uff1f<\/strong>
                  \u5728Python\u4e2d\uff0c\u4f7f\u7528Pandas\u5e93\u53ef\u4ee5\u8f7b\u677e\u521b\u5efa\u6570\u636e\u6846\u3002\u9996\u5148\uff0c\u9700\u8981\u5b89\u88c5Pandas\u5e93\uff0c\u4f7f\u7528\u547d\u4ee4pip install pandas<\/code>\u3002\u521b\u5efa\u6570\u636e\u6846\u65f6\uff0c\u53ef\u4ee5\u5c06\u6570\u636e\u4ee5\u5b57\u5178\u3001\u5217\u8868\u6216\u5176\u4ed6\u6570\u636e\u7ed3\u6784\u7684\u5f62\u5f0f\u4f20\u5165\u3002\u4f8b\u5982\uff0c\u4f7f\u7528\u5b57\u5178\u521b\u5efa\u6570\u636e\u6846\u65f6\uff0c\u53ef\u4ee5\u5c06\u5217\u540d\u4f5c\u4e3a\u5b57\u5178\u7684\u952e\uff0c\u5c06\u6570\u636e\u4f5c\u4e3a\u503c\u4f20\u5165\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a  <\/p>\n
                  import pandas as pd\n\ndata = {\n    '\u59d3\u540d': ['\u5c0f\u660e', '\u5c0f\u7ea2', '\u5c0f\u521a'],\n    '\u5e74\u9f84': [25, 30, 22],\n    '\u57ce\u5e02': ['\u5317\u4eac', '\u4e0a\u6d77', '\u5e7f\u5dde']\n}\ndf = pd.DataFrame(data)\nprint(df)\n<\/code><\/pre>\n
                  \u5982\u4f55\u4eceCSV\u6587\u4ef6\u8bfb\u53d6\u6570\u636e\u5e76\u521b\u5efa\u6570\u636e\u6846\uff1f<\/strong>
                  Pandas\u5e93\u63d0\u4f9b\u4e86read_csv<\/code>\u51fd\u6570\uff0c\u5141\u8bb8\u4eceCSV\u6587\u4ef6\u4e2d\u8bfb\u53d6\u6570\u636e\u5e76\u8f6c\u6362\u4e3a\u6570\u636e\u6846\u3002\u53ea\u9700\u63d0\u4f9b\u6587\u4ef6\u8def\u5f84\uff0cPandas\u4f1a\u81ea\u52a8\u89e3\u6790\u6570\u636e\u3002\u4f8b\u5982\uff1a  <\/p>\n
                  import pandas as pd\n\ndf = pd.read_csv('data.csv')\nprint(df.head())  # \u663e\u793a\u524d\u4e94\u884c\u6570\u636e\n<\/code><\/pre>\n
                  \u8fd9\u6837\uff0c\u60a8\u5c31\u80fd\u8f7b\u677e\u5730\u5c06CSV\u6587\u4ef6\u4e2d\u7684\u6570\u636e\u52a0\u8f7d\u5230\u6570\u636e\u6846\u4e2d\uff0c\u4fbf\u4e8e\u540e\u7eed\u7684\u6570\u636e\u5206\u6790\u548c\u5904\u7406\u3002<\/p>\n
                  \u5982\u4f55\u5bf9\u6570\u636e\u6846\u8fdb\u884c\u57fa\u672c\u7684\u6570\u636e\u64cd\u4f5c\u548c\u5206\u6790\uff1f<\/strong>
                  \u4f7f\u7528Pandas\u6570\u636e\u6846\u65f6\uff0c\u53ef\u4ee5\u8fdb\u884c\u591a\u79cd\u64cd\u4f5c\uff0c\u4f8b\u5982\u7b5b\u9009\u3001\u6392\u5e8f\u3001\u5206\u7ec4\u548c\u7edf\u8ba1\u5206\u6790\u3002\u901a\u8fc7\u5e03\u5c14\u7d22\u5f15\u53ef\u4ee5\u7b5b\u9009\u7b26\u5408\u7279\u5b9a\u6761\u4ef6\u7684\u6570\u636e\uff0c\u4f8b\u5982\uff1a  <\/p>\n
                  young_people = df[df['\u5e74\u9f84'] < 30]\nprint(young_people)\n<\/code><\/pre>\n
                  \u6b64\u5916\uff0c\u5229\u7528groupby<\/code>\u51fd\u6570\u53ef\u4ee5\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\uff0c\u5e76\u8fdb\u884c\u805a\u5408\u8ba1\u7b97\uff0c\u5982\u6c42\u5747\u503c\u3001\u603b\u548c\u7b49\u3002\u793a\u4f8b\uff1a  <\/p>\n
                  grouped_data = df.groupby('\u57ce\u5e02')['\u5e74\u9f84'].mean()\nprint(grouped_data)\n<\/code><\/pre>\n
                  \u8fd9\u6837\u53ef\u4ee5\u5feb\u901f\u83b7\u5f97\u5404\u57ce\u5e02\u7684\u5e73\u5747\u5e74\u9f84\u4fe1\u606f\uff0c\u6709\u52a9\u4e8e\u6df1\u5165\u5206\u6790\u6570\u636e\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u4f7f\u7528Python\u6570\u636e\u6846\u7684\u6838\u5fc3\u6b65\u9aa4\u5305\u62ec\uff1a\u5bfc\u5165Pandas\u5e93\u3001\u521b\u5efa\u6570\u636e\u6846\u3001\u8bfb\u53d6\u548c\u5199\u5165\u6570\u636e\u3001\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u64cd\u4f5c\u4e0e\u5206\u6790 […]","protected":false},"author":3,"featured_media":973811,"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\/973806"}],"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=973806"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/973806\/revisions"}],"predecessor-version":[{"id":973813,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/973806\/revisions\/973813"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/973811"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=973806"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=973806"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=973806"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}