{"id":995041,"date":"2024-12-27T09:01:08","date_gmt":"2024-12-27T01:01:08","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/995041.html"},"modified":"2024-12-27T09:01:13","modified_gmt":"2024-12-27T01:01:13","slug":"python%e5%a6%82%e4%bd%95%e5%a4%9a%e6%9d%a1%e4%bb%b6%e5%af%bc%e5%87%ba","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/995041.html","title":{"rendered":"python\u5982\u4f55\u591a\u6761\u4ef6\u5bfc\u51fa"},"content":{"rendered":"

\"python\u5982\u4f55\u591a\u6761\u4ef6\u5bfc\u51fa\"<\/p>\n

\u5728Python\u4e2d\u8fdb\u884c\u591a\u6761\u4ef6\u5bfc\u51fa\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u6cd5\u5b9e\u73b0\uff0c\u6bd4\u5982\u4f7f\u7528\u6761\u4ef6\u8bed\u53e5\u3001\u5217\u8868\u63a8\u5bfc\u5f0f\u3001Pandas\u5e93\u7b49\u3002\u901a\u8fc7\u6784\u5efa\u903b\u8f91\u8868\u8fbe\u5f0f\u3001\u8fc7\u6ee4\u6570\u636e\u96c6\u3001\u7ed3\u5408Pandas\u7684query()\u65b9\u6cd5\u7b49\u65b9\u5f0f\uff0c\u53ef\u4ee5\u9ad8\u6548\u5730\u5b9e\u73b0\u6570\u636e\u5bfc\u51fa\u3002<\/strong>\u5176\u4e2d\uff0c\u4f7f\u7528Pandas\u5e93\u662f\u5904\u7406\u548c\u5bfc\u51fa\u5927\u89c4\u6a21\u6570\u636e\u7684\u5e38\u7528\u65b9\u5f0f\u3002\u63a5\u4e0b\u6765\uff0c\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528\u8fd9\u4e9b\u65b9\u6cd5\u5b9e\u73b0\u591a\u6761\u4ef6\u5bfc\u51fa\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528\u6761\u4ef6\u8bed\u53e5<\/p>\n<\/p>\n

\u5728Python\u4e2d\uff0c\u6761\u4ef6\u8bed\u53e5\u662f\u63a7\u5236\u7a0b\u5e8f\u6d41\u7684\u57fa\u7840\u5de5\u5177\u3002\u901a\u8fc7\u4f7f\u7528if-else\u8bed\u53e5\uff0c\u53ef\u4ee5\u6839\u636e\u4e0d\u540c\u6761\u4ef6\u5bfc\u51fa\u6240\u9700\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n

    \n
  1. \u4f7f\u7528if-else\u8bed\u53e5\u8fdb\u884c\u591a\u6761\u4ef6\u5224\u65ad<\/li>\n<\/ol>\n

    \u5728\u5904\u7406\u7b80\u5355\u6570\u636e\u96c6\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528if-else\u8bed\u53e5\u5b9e\u73b0\u591a\u6761\u4ef6\u5bfc\u51fa\u3002\u901a\u8fc7\u6784\u5efa\u5d4c\u5957\u6761\u4ef6\uff0c\u80fd\u591f\u5bf9\u6570\u636e\u8fdb\u884c\u7cbe\u786e\u7684\u7b5b\u9009\u3002<\/p>\n<\/p>\n

    data = [15, 25, 35, 45, 55]<\/p>\n
    filtered_data = []<\/p>\n
    for value in data:<\/p>\n
        if value > 20 and value < 50:<\/p>\n
            filtered_data.append(value)<\/p>\n
    print(filtered_data)  # \u8f93\u51fa\uff1a[25, 35, 45]<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u7ed3\u5408\u51fd\u6570\u8fdb\u884c\u6570\u636e\u7b5b\u9009<\/li>\n<\/ol>\n
      \u901a\u8fc7\u5b9a\u4e49\u51fd\u6570\uff0c\u53ef\u4ee5\u63d0\u9ad8\u4ee3\u7801\u7684\u53ef\u91cd\u7528\u6027\u548c\u53ef\u8bfb\u6027\u3002\u5728\u51fd\u6570\u4e2d\u5b9e\u73b0\u591a\u6761\u4ef6\u5224\u65ad\uff0c\u80fd\u591f\u66f4\u7075\u6d3b\u5730\u5904\u7406\u6570\u636e\u3002<\/p>\n<\/p>\n
      def filter_data(value):<\/p>\n
          return value > 20 and value < 50<\/p>\n
      data = [15, 25, 35, 45, 55]<\/p>\n
      filtered_data = list(filter(filter_data, data))<\/p>\n
      print(filtered_data)  # \u8f93\u51fa\uff1a[25, 35, 45]<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u4e8c\u3001\u4f7f\u7528\u5217\u8868\u63a8\u5bfc\u5f0f<\/p>\n<\/p>\n
      \u5217\u8868\u63a8\u5bfc\u5f0f\u662fPython\u7684\u7279\u8272\u529f\u80fd\uff0c\u53ef\u4ee5\u7528\u7b80\u6d01\u7684\u8bed\u6cd5\u5b9e\u73b0\u590d\u6742\u7684\u6570\u636e\u64cd\u4f5c\u3002\u5728\u8fdb\u884c\u591a\u6761\u4ef6\u5bfc\u51fa\u65f6\uff0c\u5217\u8868\u63a8\u5bfc\u5f0f\u662f\u975e\u5e38\u9ad8\u6548\u7684\u9009\u62e9\u3002<\/p>\n<\/p>\n
        \n
      1. \u57fa\u672c\u5217\u8868\u63a8\u5bfc\u5f0f<\/li>\n<\/ol>\n
        \u5217\u8868\u63a8\u5bfc\u5f0f\u5141\u8bb8\u5728\u4e00\u884c\u4e2d\u5b9e\u73b0\u6570\u636e\u7b5b\u9009\u548c\u8f6c\u6362\u3002\u901a\u8fc7\u5728\u63a8\u5bfc\u5f0f\u4e2d\u6784\u5efa\u6761\u4ef6\u8868\u8fbe\u5f0f\uff0c\u53ef\u4ee5\u5feb\u901f\u5b9e\u73b0\u591a\u6761\u4ef6\u6570\u636e\u5bfc\u51fa\u3002<\/p>\n<\/p>\n
        data = [15, 25, 35, 45, 55]<\/p>\n
        filtered_data = [value for value in data if value > 20 and value < 50]<\/p>\n
        print(filtered_data)  # \u8f93\u51fa\uff1a[25, 35, 45]<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        1. \u5d4c\u5957\u5217\u8868\u63a8\u5bfc\u5f0f<\/li>\n<\/ol>\n
          \u5bf9\u4e8e\u590d\u6742\u7684\u6570\u636e\u7ed3\u6784\uff0c\u53ef\u4ee5\u4f7f\u7528\u5d4c\u5957\u5217\u8868\u63a8\u5bfc\u5f0f\u5b9e\u73b0\u591a\u6761\u4ef6\u7b5b\u9009\u3002\u4f8b\u5982\uff0c\u5728\u5904\u7406\u4e8c\u7ef4\u6570\u636e\u65f6\uff0c\u53ef\u4ee5\u5728\u63a8\u5bfc\u5f0f\u4e2d\u5d4c\u5957\u591a\u5c42\u5faa\u73af\u3002<\/p>\n<\/p>\n
          matrix = [[10, 20, 30], [40, 50, 60], [70, 80, 90]]<\/p>\n
          filtered_data = [value for row in matrix for value in row if value > 20 and value < 80]<\/p>\n
          print(filtered_data)  # \u8f93\u51fa\uff1a[30, 40, 50, 60, 70]<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u4e09\u3001\u4f7f\u7528Pandas\u5e93<\/p>\n<\/p>\n
          Pandas\u662fPython\u4e2d\u7528\u4e8e\u6570\u636e\u5206\u6790\u548c\u5904\u7406\u7684\u5f3a\u5927\u5e93\uff0c\u7279\u522b\u9002\u7528\u4e8e\u5904\u7406\u5927\u578b\u6570\u636e\u96c6\u3002\u901a\u8fc7Pandas\u53ef\u4ee5\u65b9\u4fbf\u5730\u5b9e\u73b0\u591a\u6761\u4ef6\u5bfc\u51fa\u3002<\/p>\n<\/p>\n
            \n
          1. \u4f7f\u7528DataFrame\u8fdb\u884c\u6570\u636e\u7b5b\u9009<\/li>\n<\/ol>\n
            Pandas\u7684DataFrame\u5bf9\u8c61\u63d0\u4f9b\u4e86\u7075\u6d3b\u7684\u6570\u636e\u7ed3\u6784\uff0c\u53ef\u4ee5\u901a\u8fc7\u5e03\u5c14\u7d22\u5f15\u8fdb\u884c\u591a\u6761\u4ef6\u7b5b\u9009\u3002<\/p>\n<\/p>\n
            import pandas as pd<\/p>\n
            data = {<\/p>\n
                'A': [10, 20, 30, 40, 50],<\/p>\n
                'B': [60, 70, 80, 90, 100]<\/p>\n
            }<\/p>\n
            df = pd.DataFrame(data)<\/p>\n
            filtered_df = df[(df['A'] > 20) & (df['B'] < 90)]<\/p>\n
            print(filtered_df)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. \u4f7f\u7528query()\u65b9\u6cd5\u8fdb\u884c\u7b5b\u9009<\/li>\n<\/ol>\n
              query()\u65b9\u6cd5\u662fPandas\u4e2d\u4e00\u4e2a\u975e\u5e38\u5f3a\u5927\u7684\u529f\u80fd\uff0c\u5141\u8bb8\u901a\u8fc7\u5b57\u7b26\u4e32\u8868\u8fbe\u5f0f\u8fdb\u884c\u6570\u636e\u7b5b\u9009\uff0c\u7279\u522b\u9002\u5408\u8fdb\u884c\u591a\u6761\u4ef6\u5bfc\u51fa\u3002<\/p>\n<\/p>\n
              filtered_df = df.query('A > 20 & B < 90')<\/p>\n
              print(filtered_df)<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u56db\u3001\u7ed3\u5408NumPy\u5e93<\/p>\n<\/p>\n
              NumPy\u662fPython\u4e2d\u7528\u4e8e\u8fdb\u884c\u79d1\u5b66\u8ba1\u7b97\u7684\u5e93\uff0c\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u7ec4\u64cd\u4f5c\u529f\u80fd\u3002\u5728\u8fdb\u884c\u591a\u6761\u4ef6\u5bfc\u51fa\u65f6\uff0c\u53ef\u4ee5\u7ed3\u5408NumPy\u5b9e\u73b0\u5feb\u901f\u7684\u6570\u636e\u5904\u7406\u3002<\/p>\n<\/p>\n
                \n
              1. \u4f7f\u7528\u5e03\u5c14\u6570\u7ec4\u8fdb\u884c\u7b5b\u9009<\/li>\n<\/ol>\n
                NumPy\u7684\u5e03\u5c14\u6570\u7ec4\u53ef\u4ee5\u7528\u4e8e\u5feb\u901f\u7b5b\u9009\u6570\u636e\uff0c\u901a\u8fc7\u6784\u5efa\u6761\u4ef6\u8868\u8fbe\u5f0f\uff0c\u53ef\u4ee5\u9ad8\u6548\u5b9e\u73b0\u591a\u6761\u4ef6\u5bfc\u51fa\u3002<\/p>\n<\/p>\n
                import numpy as np<\/p>\n
                data = np.array([10, 20, 30, 40, 50])<\/p>\n
                mask = (data > 20) & (data < 50)<\/p>\n
                filtered_data = data[mask]<\/p>\n
                print(filtered_data)  # \u8f93\u51fa\uff1a[30, 40]<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                  \n
                1. \u4f7f\u7528NumPy\u7684where()\u51fd\u6570<\/li>\n<\/ol>\n
                  NumPy\u7684where()\u51fd\u6570\u53ef\u4ee5\u6839\u636e\u6761\u4ef6\u751f\u6210\u65b0\u6570\u7ec4\uff0c\u9002\u5408\u5728\u5927\u89c4\u6a21\u6570\u636e\u5904\u7406\u4e2d\u4f7f\u7528\u3002<\/p>\n<\/p>\n
                  result = np.where((data > 20) & (data < 50), data, -1)<\/p>\n
                  print(result)  # \u8f93\u51fa\uff1a[-1 -1 30 40 -1]<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u4e94\u3001\u7ed3\u5408SQLAlchemy\u8fdb\u884c\u6570\u636e\u5e93\u64cd\u4f5c<\/p>\n<\/p>\n
                  \u5bf9\u4e8e\u5b58\u50a8\u5728\u6570\u636e\u5e93\u4e2d\u7684\u6570\u636e\uff0cSQLAlchemy\u63d0\u4f9b\u4e86ORM\u6620\u5c04\u529f\u80fd\uff0c\u53ef\u4ee5\u901a\u8fc7Python\u4ee3\u7801\u5b9e\u73b0\u591a\u6761\u4ef6\u5bfc\u51fa\u3002<\/p>\n<\/p>\n
                    \n
                  1. \u5b9a\u4e49\u6570\u636e\u6a21\u578b<\/li>\n<\/ol>\n
                    \u4f7f\u7528SQLAlchemy\u5b9a\u4e49\u6570\u636e\u6a21\u578b\uff0c\u53ef\u4ee5\u5c06\u6570\u636e\u5e93\u8868\u6620\u5c04\u4e3aPython\u7c7b\uff0c\u65b9\u4fbf\u8fdb\u884c\u6570\u636e\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
                    from sqlalchemy import create_engine, Column, Integer, String<\/p>\n
                    from sqlalchemy.ext.declarative import declarative_base<\/p>\n
                    from sqlalchemy.orm import sessionmaker<\/p>\n
                    Base = declarative_base()<\/p>\n
                    class User(Base):<\/p>\n
                        __tablename__ = 'users'<\/p>\n
                        id = Column(Integer, primary_key=True)<\/p>\n
                        name = Column(String)<\/p>\n
                        age = Column(Integer)<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                      \n
                    1. \u4f7f\u7528SQLAlchemy\u8fdb\u884c\u67e5\u8be2<\/li>\n<\/ol>\n
                      \u901a\u8fc7SQLAlchemy\u7684\u67e5\u8be2\u63a5\u53e3\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u8868\u8fbe\u5f0f\u5b9e\u73b0\u591a\u6761\u4ef6\u5bfc\u51fa\u3002<\/p>\n<\/p>\n
                      engine = create_engine('sqlite:\/\/\/:memory:')<\/p>\n
                      Base.metadata.create_all(engine)<\/p>\n
                      Session = sessionmaker(bind=engine)<\/p>\n
                      session = Session()<\/p>\n
                      \u6dfb\u52a0\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
                      session.add_all([<\/p>\n
                          User(name='Alice', age=25),<\/p>\n
                          User(name='Bob', age=30),<\/p>\n
                          User(name='Charlie', age=35)<\/p>\n
                      ])<\/p>\n
                      session.commit()<\/p>\n
                      \u67e5\u8be2\u591a\u6761\u4ef6\u6570\u636e<\/strong><\/h2>\n
                      filtered_users = session.query(User).filter(User.age > 20, User.age < 35).all()<\/p>\n
                      for user in filtered_users:<\/p>\n
                          print(user.name)<\/p>\n
                      <\/code><\/pre>\n<\/p>\n
                      \u516d\u3001\u7ed3\u5408\u5176\u4ed6Python\u5e93<\/p>\n<\/p>\n
                      \u9664\u4e86\u4e0a\u8ff0\u63d0\u5230\u7684\u65b9\u6cd5\uff0cPython\u4e2d\u8fd8\u6709\u8bb8\u591a\u5176\u4ed6\u5e93\u53ef\u4ee5\u7528\u4e8e\u6570\u636e\u5bfc\u51fa\uff0c\u5982Dask\u3001Vaex\u7b49\u3002\u5b83\u4eec\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\u8868\u73b0\u51fa\u8272\uff0c\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u4f7f\u7528\u3002<\/p>\n<\/p>\n
                        \n
                      1. \u4f7f\u7528Dask\u8fdb\u884c\u5927\u89c4\u6a21\u6570\u636e\u5904\u7406<\/li>\n<\/ol>\n
                        Dask\u662f\u4e00\u4e2a\u7528\u4e8e\u5e76\u884c\u8ba1\u7b97\u7684\u5e93\uff0c\u9002\u5408\u5904\u7406\u8d85\u8fc7\u5185\u5b58\u5bb9\u91cf\u7684\u5927\u578b\u6570\u636e\u96c6\u3002<\/p>\n<\/p>\n
                        import dask.dataframe as dd<\/p>\n
                        \u5047\u8bbe\u6709\u4e00\u4e2a\u5927\u7684CSV\u6587\u4ef6<\/strong><\/h2>\n
                        df = dd.read_csv('large_data.csv')<\/p>\n
                        \u591a\u6761\u4ef6\u7b5b\u9009<\/strong><\/h2>\n
                        filtered_df = df[(df['column1'] > 20) & (df['column2'] < 50)]<\/p>\n
                        <\/code><\/pre>\n<\/p>\n
                          \n
                        1. \u4f7f\u7528Vaex\u8fdb\u884c\u5feb\u901f\u6570\u636e\u5904\u7406<\/li>\n<\/ol>\n
                          Vaex\u662f\u4e00\u4e2a\u7528\u4e8e\u5927\u6570\u636e\u96c6\u7684\u5feb\u901f\u6570\u636e\u5e27\u5e93\uff0c\u9002\u5408\u7528\u4e8e\u4ea4\u4e92\u5f0f\u7684\u6570\u636e\u5206\u6790\u3002<\/p>\n<\/p>\n
                          import vaex<\/p>\n
                          \u52a0\u8f7d\u5927\u6570\u636e\u96c6<\/strong><\/h2>\n
                          df = vaex.open('large_data.hdf5')<\/p>\n
                          \u591a\u6761\u4ef6\u7b5b\u9009<\/strong><\/h2>\n
                          filtered_df = df[(df.column1 > 20) & (df.column2 < 50)]<\/p>\n
                          <\/code><\/pre>\n<\/p>\n
                          \u901a\u8fc7\u7ed3\u5408\u4e0d\u540c\u7684\u65b9\u6cd5\u548c\u5e93\uff0cPython\u53ef\u4ee5\u7075\u6d3b\u5730\u8fdb\u884c\u591a\u6761\u4ef6\u6570\u636e\u5bfc\u51fa\u3002\u9009\u62e9\u5408\u9002\u7684\u5de5\u5177\u548c\u65b9\u6cd5\uff0c\u4e0d\u4ec5\u53ef\u4ee5\u63d0\u9ad8\u6570\u636e\u5904\u7406\u6548\u7387\uff0c\u8fd8\u80fd\u7b80\u5316\u4ee3\u7801\u590d\u6742\u5ea6\u3002\u6839\u636e\u6570\u636e\u89c4\u6a21\u548c\u5e94\u7528\u573a\u666f\uff0c\u5408\u7406\u4f7f\u7528\u8fd9\u4e9b\u6280\u672f\uff0c\u5c06\u4f7f\u6570\u636e\u5bfc\u51fa\u8fc7\u7a0b\u66f4\u52a0\u9ad8\u6548\u548c\u4fbf\u6377\u3002<\/p>\n<\/p>\n
                          \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                           \u5982\u4f55\u5728Python\u4e2d\u6839\u636e\u591a\u4e2a\u6761\u4ef6\u7b5b\u9009\u6570\u636e\u5e76\u5bfc\u51fa\uff1f<\/strong>
                          \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u6765\u5904\u7406\u6570\u636e\u3002\u9996\u5148\uff0c\u60a8\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2aDataFrame\u5e76\u5e94\u7528\u591a\u6761\u4ef6\u7b5b\u9009\u3002\u4f7f\u7528\u5e03\u5c14\u7d22\u5f15\u6765\u8fc7\u6ee4\u7b26\u5408\u6761\u4ef6\u7684\u6570\u636e\uff0c\u6700\u540e\u4f7f\u7528to_csv()<\/code>\u6216to_excel()<\/code>\u65b9\u6cd5\u5c06\u7ed3\u679c\u5bfc\u51fa\u5230\u6240\u9700\u7684\u6587\u4ef6\u683c\u5f0f\u3002<\/p>\n
                          Pandas\u5e93\u5728\u591a\u6761\u4ef6\u5bfc\u51fa\u4e2d\u6709\u54ea\u4e9b\u5e38\u89c1\u7528\u6cd5\uff1f<\/strong>
                          Pandas\u5e93\u63d0\u4f9b\u4e86\u7075\u6d3b\u7684\u529f\u80fd\uff0c\u53ef\u4ee5\u4f7f\u7528\u903b\u8f91\u8fd0\u7b97\u7b26\uff08\u5982&<\/code>\u548c|<\/code>\uff09\u7ed3\u5408\u591a\u4e2a\u6761\u4ef6\u8fdb\u884c\u7b5b\u9009\u3002\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7df[(df['column1'] > value1) & (df['column2'] < value2)]<\/code>\u6765\u7b5b\u9009\u7b26\u5408\u7279\u5b9a\u6761\u4ef6\u7684\u6570\u636e\uff0c\u4e4b\u540e\u518d\u5c06\u5176\u5bfc\u51fa\u3002<\/p>\n
                          \u5bfc\u51fa\u65f6\u53ef\u4ee5\u9009\u62e9\u54ea\u4e9b\u6587\u4ef6\u683c\u5f0f\uff1f<\/strong>
                          \u5728Python\u4e2d\uff0c\u4f7f\u7528Pandas\u5e93\u65f6\uff0c\u60a8\u53ef\u4ee5\u9009\u62e9\u591a\u79cd\u6587\u4ef6\u683c\u5f0f\u8fdb\u884c\u5bfc\u51fa\uff0c\u5305\u62ecCSV\u3001Excel\u3001JSON\u7b49\u3002\u901a\u8fc7\u4f7f\u7528to_csv('filename.csv')<\/code>\u5bfc\u51fa\u4e3aCSV\u683c\u5f0f\uff0c\u4f7f\u7528to_excel('filename.xlsx')<\/code>\u5bfc\u51fa\u4e3aExcel\u683c\u5f0f\uff0c\u5177\u4f53\u9009\u62e9\u53d6\u51b3\u4e8e\u60a8\u7684\u9700\u6c42\u548c\u4f7f\u7528\u573a\u666f\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u8fdb\u884c\u591a\u6761\u4ef6\u5bfc\u51fa\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u6cd5\u5b9e\u73b0\uff0c\u6bd4\u5982\u4f7f\u7528\u6761\u4ef6\u8bed\u53e5\u3001\u5217\u8868\u63a8\u5bfc\u5f0f\u3001Pandas\u5e93\u7b49\u3002\u901a\u8fc7\u6784\u5efa\u903b […]","protected":false},"author":3,"featured_media":995054,"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\/995041"}],"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=995041"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/995041\/revisions"}],"predecessor-version":[{"id":995057,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/995041\/revisions\/995057"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/995054"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=995041"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=995041"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=995041"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}