{"id":1004483,"date":"2024-12-27T10:26:47","date_gmt":"2024-12-27T02:26:47","guid":{"rendered":""},"modified":"2024-12-27T10:26:51","modified_gmt":"2024-12-27T02:26:51","slug":"python%e5%a6%82%e4%bd%95%e5%88%9b%e5%bb%ba%e6%95%b0%e6%8d%ae%e9%9b%86","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1004483.html","title":{"rendered":"python\u5982\u4f55\u521b\u5efa\u6570\u636e\u96c6"},"content":{"rendered":"

\"python\u5982\u4f55\u521b\u5efa\u6570\u636e\u96c6\"<\/p>\n

Python\u521b\u5efa\u6570\u636e\u96c6\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528pandas\u521b\u5efaDataFrame\u3001\u4f7f\u7528numpy\u521b\u5efa\u6570\u7ec4\u3001\u4eceCSV\u6587\u4ef6\u5bfc\u5165\u6570\u636e\u3001\u4f7f\u7528scikit-learn\u751f\u6210\u6a21\u62df\u6570\u636e\u96c6\u3002<\/strong>\u63a5\u4e0b\u6765\uff0c\u6211\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528pandas\u521b\u5efaDataFrame\u8fd9\u4e00\u65b9\u6cd5\u3002Pandas\u5e93\u662fPython\u4e2d\u7528\u4e8e\u6570\u636e\u64cd\u4f5c\u548c\u5206\u6790\u7684\u5f3a\u5927\u5de5\u5177\u3002\u521b\u5efaDataFrame\u662f\u6570\u636e\u5206\u6790\u7684\u57fa\u7840\u6b65\u9aa4\u4e4b\u4e00\u3002\u8981\u521b\u5efa\u4e00\u4e2aDataFrame\uff0c\u53ef\u4ee5\u4f7f\u7528\u5b57\u5178\u3001\u5217\u8868\u7b49\u6570\u636e\u7ed3\u6784\u3002\u901a\u8fc7\u6307\u5b9a\u5217\u540d\u548c\u6570\u636e\uff0c\u53ef\u4ee5\u8f7b\u677e\u751f\u6210\u4e00\u4e2aDataFrame\u3002\u4f8b\u5982\uff0c\u4f7f\u7528\u5b57\u5178\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u6570\u636e\u96c6\uff0c\u5176\u4e2d\u952e\u4e3a\u5217\u540d\uff0c\u503c\u4e3a\u6570\u636e\u5217\u8868\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u5982\u4f55\u5728Python\u4e2d\u521b\u5efa\u6570\u636e\u96c6\u7684\u5176\u4ed6\u65b9\u6cd5\u548c\u76f8\u5173\u6280\u672f\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528PANDAS\u521b\u5efa\u6570\u636e\u96c6<\/p>\n<\/p>\n

Pandas\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u5e93\u4e4b\u4e00\uff0c\u5b83\u63d0\u4f9b\u4e86\u9ad8\u6548\u3001\u4fbf\u6377\u7684\u6570\u636e\u64cd\u4f5c\u529f\u80fd\u3002\u901a\u8fc7Pandas\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u521b\u5efa\u548c\u7ba1\u7406\u6570\u636e\u96c6\u3002<\/p>\n<\/p>\n

    \n
  1. \u4f7f\u7528\u5b57\u5178\u521b\u5efaDataFrame<\/li>\n<\/ol>\n

    \u4f7f\u7528\u5b57\u5178\u662f\u521b\u5efaDataFrame\u6700\u76f4\u63a5\u7684\u65b9\u6cd5\u4e4b\u4e00\u3002\u5b57\u5178\u7684\u952e\u4f5c\u4e3aDataFrame\u7684\u5217\u540d\uff0c\u952e\u5bf9\u5e94\u7684\u503c\u4f5c\u4e3a\u5217\u6570\u636e\u3002<\/p>\n<\/p>\n

    import pandas as pd<\/p>\n
    \u521b\u5efa\u5b57\u5178<\/strong><\/h2>\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
    \u5c06\u5b57\u5178\u8f6c\u6362\u4e3aDataFrame<\/strong><\/h2>\n
    df = pd.DataFrame(data)<\/p>\n
    print(df)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u4f7f\u7528\u5217\u8868\u521b\u5efaDataFrame<\/li>\n<\/ol>\n
      \u5982\u679c\u6570\u636e\u662f\u7ed3\u6784\u5316\u7684\uff0c\u4e5f\u53ef\u4ee5\u4f7f\u7528\u5217\u8868\u521b\u5efaDataFrame\u3002\u5217\u8868\u4e2d\u7684\u6bcf\u4e2a\u5b50\u5217\u8868\u4ee3\u8868\u4e00\u4e2a\u6570\u636e\u8bb0\u5f55\u3002<\/p>\n<\/p>\n
      import pandas as pd<\/p>\n
      \u521b\u5efa\u5217\u8868<\/strong><\/h2>\n
      data = [<\/p>\n
          ['Alice', 25, 'New York'],<\/p>\n
          ['Bob', 30, 'Los Angeles'],<\/p>\n
          ['Charlie', 35, 'Chicago']<\/p>\n
      ]<\/p>\n
      \u6307\u5b9a\u5217\u540d\u5e76\u521b\u5efaDataFrame<\/strong><\/h2>\n
      df = pd.DataFrame(data, columns=['Name', 'Age', 'City'])<\/p>\n
      print(df)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u4e8c\u3001\u4f7f\u7528NUMPY\u521b\u5efa\u6570\u636e\u96c6<\/p>\n<\/p>\n
      Numpy\u662fPython\u4e2d\u53e6\u4e00\u4e2a\u7528\u4e8e\u6570\u636e\u5904\u7406\u7684\u5f3a\u5927\u5e93\uff0c\u7279\u522b\u9002\u5408\u4e8e\u6570\u503c\u8ba1\u7b97\u548c\u5927\u89c4\u6a21\u6570\u636e\u96c6\u7684\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
        \n
      1. \u521b\u5efa\u6570\u7ec4<\/li>\n<\/ol>\n
        Numpy\u4e2d\u7684\u6570\u7ec4\u662f\u521b\u5efa\u6570\u636e\u96c6\u7684\u57fa\u7840\u3002\u53ef\u4ee5\u4f7f\u7528numpy.array()<\/code>\u51fd\u6570\u5c06\u5217\u8868\u6216\u5143\u7ec4\u8f6c\u6362\u4e3aNumpy\u6570\u7ec4\u3002<\/p>\n<\/p>\n
        import numpy as np<\/p>\n
        \u521b\u5efaNumpy\u6570\u7ec4<\/strong><\/h2>\n
        data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])<\/p>\n
        print(data)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        1. \u4f7f\u7528\u968f\u673a\u6570\u751f\u6210\u6570\u636e<\/li>\n<\/ol>\n
          Numpy\u8fd8\u63d0\u4f9b\u4e86\u751f\u6210\u968f\u673a\u6570\u7684\u529f\u80fd\uff0c\u53ef\u4ee5\u7528\u6765\u521b\u5efa\u6a21\u62df\u6570\u636e\u96c6\u3002<\/p>\n<\/p>\n
          import numpy as np<\/p>\n
          \u751f\u6210\u968f\u673a\u6570\u636e<\/strong><\/h2>\n
          random_data = np.random.rand(5, 3)<\/p>\n
          print(random_data)<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u4e09\u3001\u4eceCSV\u6587\u4ef6\u5bfc\u5165\u6570\u636e<\/p>\n<\/p>\n
          CSV\uff08Comma-Separated Values\uff09\u662f\u4e00\u79cd\u5e38\u89c1\u7684\u6570\u636e\u5b58\u50a8\u683c\u5f0f\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u5f88\u591a\u6570\u636e\u96c6\u90fd\u4ee5CSV\u683c\u5f0f\u5b58\u50a8\u3002<\/p>\n<\/p>\n
            \n
          1. \u4f7f\u7528Pandas\u8bfb\u53d6CSV\u6587\u4ef6<\/li>\n<\/ol>\n
            Pandas\u63d0\u4f9b\u4e86\u8bfb\u53d6CSV\u6587\u4ef6\u7684\u51fd\u6570read_csv()<\/code>\uff0c\u53ef\u4ee5\u8f7b\u677e\u5c06CSV\u6587\u4ef6\u5bfc\u5165\u4e3aDataFrame\u3002<\/p>\n<\/p>\n
            import pandas as pd<\/p>\n
            \u4eceCSV\u6587\u4ef6\u8bfb\u53d6\u6570\u636e<\/strong><\/h2>\n
            df = pd.read_csv('data.csv')<\/p>\n
            print(df)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. \u6307\u5b9a\u5206\u9694\u7b26\u548c\u7f16\u7801<\/li>\n<\/ol>\n
              \u5728\u8bfb\u53d6CSV\u6587\u4ef6\u65f6\uff0c\u53ef\u4ee5\u6307\u5b9a\u5206\u9694\u7b26\u548c\u7f16\u7801\uff0c\u4ee5\u9002\u5e94\u4e0d\u540c\u683c\u5f0f\u7684\u6570\u636e\u6587\u4ef6\u3002<\/p>\n<\/p>\n
              import pandas as pd<\/p>\n
              \u8bfb\u53d6\u4f7f\u7528\u5206\u53f7\u5206\u9694\u7684CSV\u6587\u4ef6<\/strong><\/h2>\n
              df = pd.read_csv('data.csv', sep=';', encoding='utf-8')<\/p>\n
              print(df)<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u56db\u3001\u4f7f\u7528SCIKIT-LEARN\u751f\u6210\u6a21\u62df\u6570\u636e\u96c6<\/p>\n<\/p>\n
              Scikit-learn\u662fPython\u4e2d\u7528\u4e8e\u673a\u5668\u5b66\u4e60<\/a>\u7684\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u751f\u6210\u6a21\u62df\u6570\u636e\u96c6\u7684\u529f\u80fd\uff0c\u9002\u7528\u4e8e\u7b97\u6cd5\u6d4b\u8bd5\u548c\u9a8c\u8bc1\u3002<\/p>\n<\/p>\n
                \n
              1. \u751f\u6210\u7ebf\u6027\u6570\u636e\u96c6<\/li>\n<\/ol>\n
                make_regression()<\/code>\u51fd\u6570\u53ef\u4ee5\u751f\u6210\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u7684\u6570\u636e\u96c6\u3002<\/p>\n<\/p>\n
                from sklearn.datasets import make_regression<\/p>\n
                \u751f\u6210\u7ebf\u6027\u6570\u636e\u96c6<\/strong><\/h2>\n
                X, y = make_regression(n_samples=100, n_features=1, noise=0.1)<\/p>\n
                print(X, y)<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                  \n
                1. \u751f\u6210\u5206\u7c7b\u6570\u636e\u96c6<\/li>\n<\/ol>\n
                  make_classification()<\/code>\u51fd\u6570\u53ef\u4ee5\u751f\u6210\u7528\u4e8e\u5206\u7c7b\u7b97\u6cd5\u7684\u6570\u636e\u96c6\u3002<\/p>\n<\/p>\n
                  from sklearn.datasets import make_classification<\/p>\n
                  \u751f\u6210\u5206\u7c7b\u6570\u636e\u96c6<\/strong><\/h2>\n
                  X, y = make_classification(n_samples=100, n_features=5, n_classes=3)<\/p>\n
                  print(X, y)<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u4e94\u3001\u4f7f\u7528SQLALCHEMY\u4ece\u6570\u636e\u5e93\u5bfc\u5165\u6570\u636e<\/p>\n<\/p>\n
                  SQLAlchemy\u662fPython\u4e2d\u7528\u4e8e\u6570\u636e\u5e93\u64cd\u4f5c\u7684ORM\uff08\u5bf9\u8c61\u5173\u7cfb\u6620\u5c04\uff09\u5e93\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u4ece\u6570\u636e\u5e93\u4e2d\u5bfc\u5165\u6570\u636e\u3002<\/p>\n<\/p>\n
                    \n
                  1. \u8fde\u63a5\u6570\u636e\u5e93<\/li>\n<\/ol>\n
                    \u9996\u5148\uff0c\u9700\u8981\u4f7f\u7528SQLAlchemy\u8fde\u63a5\u5230\u6570\u636e\u5e93\u3002<\/p>\n<\/p>\n
                    from sqlalchemy import create_engine<\/p>\n
                    \u521b\u5efa\u6570\u636e\u5e93\u5f15\u64ce<\/strong><\/h2>\n
                    engine = create_engine('sqlite:\/\/\/example.db')<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                      \n
                    1. \u67e5\u8be2\u6570\u636e\u5e76\u5bfc\u5165\u4e3aDataFrame<\/li>\n<\/ol>\n
                      \u4f7f\u7528SQLAlchemy\u67e5\u8be2\u6570\u636e\u5e93\uff0c\u5e76\u5c06\u7ed3\u679c\u8f6c\u6362\u4e3aPandas DataFrame\u3002<\/p>\n<\/p>\n
                      import pandas as pd<\/p>\n
                      \u67e5\u8be2\u6570\u636e<\/strong><\/h2>\n
                      query = "SELECT * FROM users"<\/p>\n
                      df = pd.read_sql(query, engine)<\/p>\n
                      print(df)<\/p>\n
                      <\/code><\/pre>\n<\/p>\n
                      \u516d\u3001\u4f7f\u7528JSON\u6587\u4ef6\u5bfc\u5165\u6570\u636e<\/p>\n<\/p>\n
                      JSON\uff08JavaScript Object Notation\uff09\u662f\u4e00\u79cd\u8f7b\u91cf\u7ea7\u7684\u6570\u636e\u4ea4\u6362\u683c\u5f0f\uff0c\u5e7f\u6cdb\u7528\u4e8eWeb\u5e94\u7528\u4e2d\u3002<\/p>\n<\/p>\n
                        \n
                      1. \u8bfb\u53d6JSON\u6587\u4ef6<\/li>\n<\/ol>\n
                        \u4f7f\u7528Pandas\u53ef\u4ee5\u8f7b\u677e\u8bfb\u53d6JSON\u6587\u4ef6\u5e76\u8f6c\u6362\u4e3aDataFrame\u3002<\/p>\n<\/p>\n
                        import pandas as pd<\/p>\n
                        \u4eceJSON\u6587\u4ef6\u8bfb\u53d6\u6570\u636e<\/strong><\/h2>\n
                        df = pd.read_json('data.json')<\/p>\n
                        print(df)<\/p>\n
                        <\/code><\/pre>\n<\/p>\n
                          \n
                        1. \u5904\u7406\u5d4c\u5957JSON\u6570\u636e<\/li>\n<\/ol>\n
                          \u5bf9\u4e8e\u5d4c\u5957\u7684JSON\u6570\u636e\uff0c\u53ef\u4ee5\u4f7f\u7528json_normalize()<\/code>\u51fd\u6570\u5c06\u5176\u5c55\u5e73\u6210\u8868\u683c\u7ed3\u6784\u3002<\/p>\n<\/p>\n
                          import pandas as pd<\/p>\n
                          import json<\/p>\n
                          \u8bfb\u53d6\u5d4c\u5957JSON\u6570\u636e<\/strong><\/h2>\n
                          with open('nested_data.json') as f:<\/p>\n
                              data = json.load(f)<\/p>\n
                          \u5c55\u5e73\u5d4c\u5957\u6570\u636e<\/strong><\/h2>\n
                          df = pd.json_normalize(data)<\/p>\n
                          print(df)<\/p>\n
                          <\/code><\/pre>\n<\/p>\n
                          \u4e03\u3001\u4eceEXCEL\u6587\u4ef6\u5bfc\u5165\u6570\u636e<\/p>\n<\/p>\n
                          Excel\u662f\u53e6\u4e00\u79cd\u5e38\u89c1\u7684\u6570\u636e\u5b58\u50a8\u683c\u5f0f\u3002\u5728\u6570\u636e\u5206\u6790\u4e2d\uff0c\u5e38\u9700\u8981\u4eceExcel\u6587\u4ef6\u5bfc\u5165\u6570\u636e\u3002<\/p>\n<\/p>\n
                            \n
                          1. \u4f7f\u7528Pandas\u8bfb\u53d6Excel\u6587\u4ef6<\/li>\n<\/ol>\n
                            Pandas\u63d0\u4f9b\u4e86read_excel()<\/code>\u51fd\u6570\uff0c\u53ef\u4ee5\u76f4\u63a5\u8bfb\u53d6Excel\u6587\u4ef6\u3002<\/p>\n<\/p>\n
                            import pandas as pd<\/p>\n
                            \u4eceExcel\u6587\u4ef6\u8bfb\u53d6\u6570\u636e<\/strong><\/h2>\n
                            df = pd.read_excel('data.xlsx')<\/p>\n
                            print(df)<\/p>\n
                            <\/code><\/pre>\n<\/p>\n
                              \n
                            1. \u8bfb\u53d6\u7279\u5b9a\u5de5\u4f5c\u8868<\/li>\n<\/ol>\n
                              \u5982\u679cExcel\u6587\u4ef6\u5305\u542b\u591a\u4e2a\u5de5\u4f5c\u8868\uff0c\u53ef\u4ee5\u6307\u5b9a\u9700\u8981\u8bfb\u53d6\u7684\u5de5\u4f5c\u8868\u3002<\/p>\n<\/p>\n
                              import pandas as pd<\/p>\n
                              \u8bfb\u53d6\u7279\u5b9a\u5de5\u4f5c\u8868<\/strong><\/h2>\n
                              df = pd.read_excel('data.xlsx', sheet_name='Sheet2')<\/p>\n
                              print(df)<\/p>\n
                              <\/code><\/pre>\n<\/p>\n
                              \u516b\u3001\u4f7f\u7528API\u5bfc\u5165\u6570\u636e<\/p>\n<\/p>\n
                              \u5728\u6570\u636e\u9a71\u52a8\u7684\u5e94\u7528\u7a0b\u5e8f\u4e2d\uff0cAPI\uff08\u5e94\u7528\u7a0b\u5e8f\u7f16\u7a0b\u63a5\u53e3\uff09\u901a\u5e38\u7528\u4e8e\u4ece\u7f51\u7edc\u670d\u52a1\u83b7\u53d6\u6570\u636e\u3002<\/p>\n<\/p>\n
                                \n
                              1. \u4f7f\u7528\u8bf7\u6c42\u5e93\u83b7\u53d6API\u6570\u636e<\/li>\n<\/ol>\n
                                Python\u7684requests\u5e93\u53ef\u4ee5\u7528\u4e8e\u53d1\u9001HTTP\u8bf7\u6c42\uff0c\u83b7\u53d6API\u63d0\u4f9b\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n
                                import requests<\/p>\n
                                \u53d1\u9001GET\u8bf7\u6c42<\/strong><\/h2>\n
                                response = requests.get('https:\/\/api.example.com\/data')<\/p>\n
                                data = response.json()<\/p>\n
                                print(data)<\/p>\n
                                <\/code><\/pre>\n<\/p>\n
                                  \n
                                1. \u5c06API\u6570\u636e\u8f6c\u6362\u4e3aDataFrame<\/li>\n<\/ol>\n
                                  \u83b7\u53d6\u7684\u6570\u636e\u901a\u5e38\u4e3aJSON\u683c\u5f0f\uff0c\u53ef\u4ee5\u8f6c\u6362\u4e3aPandas DataFrame\u4ee5\u4fbf\u8fdb\u4e00\u6b65\u5206\u6790\u3002<\/p>\n<\/p>\n
                                  import pandas as pd<\/p>\n
                                  \u5c06JSON\u6570\u636e\u8f6c\u6362\u4e3aDataFrame<\/strong><\/h2>\n
                                  df = pd.DataFrame(data)<\/p>\n
                                  print(df)<\/p>\n
                                  <\/code><\/pre>\n<\/p>\n
                                  \u4ee5\u4e0a\u662fPython\u4e2d\u521b\u5efa\u6570\u636e\u96c6\u7684\u591a\u79cd\u65b9\u6cd5\uff0c\u6db5\u76d6\u4e86\u4ece\u57fa\u672c\u7684\u6570\u636e\u7ed3\u6784\u5230\u9ad8\u7ea7\u7684\u6570\u636e\u6e90\u96c6\u6210\u3002\u4e0d\u540c\u7684\u65b9\u6cd5\u9002\u7528\u4e8e\u4e0d\u540c\u7684\u573a\u666f\uff0c\u9009\u62e9\u5408\u9002\u7684\u5de5\u5177\u548c\u65b9\u6cd5\u80fd\u591f\u6781\u5927\u5730\u63d0\u9ad8\u6570\u636e\u5904\u7406\u7684\u6548\u7387\u548c\u51c6\u786e\u6027\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6839\u636e\u5177\u4f53\u7684\u9700\u6c42\u548c\u6570\u636e\u683c\u5f0f\uff0c\u53ef\u4ee5\u7075\u6d3b\u8fd0\u7528\u8fd9\u4e9b\u65b9\u6cd5\u6765\u521b\u5efa\u548c\u7ba1\u7406\u6570\u636e\u96c6\u3002<\/p>\n<\/p>\n
                                  \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                                   \u5982\u4f55\u4f7f\u7528Python\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u6570\u636e\u96c6\uff1f<\/strong>
                                  \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u6765\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u6570\u636e\u96c6\u3002\u9996\u5148\uff0c\u60a8\u9700\u8981\u5b89\u88c5Pandas\u5e93\uff08\u5982\u679c\u5c1a\u672a\u5b89\u88c5\uff09\u3002\u53ef\u4ee5\u901a\u8fc7\u547d\u4ee4pip install pandas<\/code>\u6765\u5b89\u88c5\u3002\u63a5\u4e0b\u6765\uff0c\u4f7f\u7528DataFrame\u51fd\u6570\u6765\u6784\u5efa\u6570\u636e\u96c6\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a\u5305\u542b\u59d3\u540d\u3001\u5e74\u9f84\u548c\u57ce\u5e02\u7684\u7b80\u5355\u6570\u636e\u96c6\uff1a<\/p>\n
                                  import pandas as pd\n\ndata = {\n    '\u59d3\u540d': ['Alice', 'Bob', 'Charlie'],\n    '\u5e74\u9f84': [25, 30, 35],\n    '\u57ce\u5e02': ['\u5317\u4eac', '\u4e0a\u6d77', '\u5e7f\u5dde']\n}\n\ndf = pd.DataFrame(data)\nprint(df)\n<\/code><\/pre>\n
                                  \u53ef\u4ee5\u4f7f\u7528\u54ea\u4e9b\u6570\u636e\u6e90\u6765\u521b\u5efa\u6570\u636e\u96c6\uff1f<\/strong>
                                  \u521b\u5efa\u6570\u636e\u96c6\u65f6\uff0c\u53ef\u4ee5\u9009\u62e9\u591a\u79cd\u6570\u636e\u6e90\u3002\u5e38\u89c1\u7684\u6570\u636e\u6e90\u5305\u62ecCSV\u6587\u4ef6\u3001Excel\u6587\u4ef6\u3001\u6570\u636e\u5e93\u3001API\u63a5\u53e3\u7b49\u3002\u4f7f\u7528Pandas\u5e93\u53ef\u4ee5\u8f7b\u677e\u8bfb\u53d6\u8fd9\u4e9b\u6570\u636e\u6e90\u3002\u4f8b\u5982\uff0c\u4f7f\u7528pd.read_csv('file.csv')<\/code>\u53ef\u4ee5\u4eceCSV\u6587\u4ef6\u4e2d\u52a0\u8f7d\u6570\u636e\uff0c\u800cpd.read_excel('file.xlsx')<\/code>\u53ef\u4ee5\u4eceExcel\u6587\u4ef6\u4e2d\u8bfb\u53d6\u6570\u636e\u3002<\/p>\n
                                  \u5982\u4f55\u4e3a\u6570\u636e\u96c6\u6dfb\u52a0\u65b0\u5217\u6216\u4fee\u6539\u73b0\u6709\u5217\uff1f<\/strong>
                                  \u5728Pandas\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u7b80\u5355\u7684\u8d4b\u503c\u64cd\u4f5c\u6765\u6dfb\u52a0\u65b0\u5217\u6216\u4fee\u6539\u73b0\u6709\u5217\u3002\u5982\u679c\u9700\u8981\u6dfb\u52a0\u4e00\u5217\uff0c\u4f8b\u5982\u201c\u5de5\u8d44\u201d\uff0c\u53ef\u4ee5\u8fd9\u6837\u505a\uff1a<\/p>\n
                                  df['\u5de5\u8d44'] = [5000, 6000, 7000]\n<\/code><\/pre>\n
                                  \u8981\u4fee\u6539\u73b0\u6709\u5217\uff0c\u6bd4\u5982\u5c06\u201c\u5e74\u9f84\u201d\u5217\u7684\u503c\u589e\u52a01\u5e74\uff0c\u53ef\u4ee5\u4f7f\u7528\u5982\u4e0b\u4ee3\u7801\uff1a<\/p>\n
                                  df['\u5e74\u9f84'] += 1\n<\/code><\/pre>\n
                                  \u5728Python\u4e2d\uff0c\u5982\u4f55\u5904\u7406\u7f3a\u5931\u6570\u636e\u4ee5\u521b\u5efa\u5b8c\u6574\u7684\u6570\u636e\u96c6\uff1f<\/strong>
                                  \u5728\u521b\u5efa\u6570\u636e\u96c6\u65f6\uff0c\u7f3a\u5931\u6570\u636e\u662f\u5e38\u89c1\u7684\u95ee\u9898\u3002Pandas\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u5904\u7406\u7f3a\u5931\u6570\u636e\uff0c\u4f8b\u5982\u4f7f\u7528dropna()<\/code>\u51fd\u6570\u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c\uff0c\u6216\u4f7f\u7528fillna()<\/code>\u65b9\u6cd5\u7528\u7279\u5b9a\u503c\u66ff\u6362\u7f3a\u5931\u503c\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a<\/p>\n
                                  df.fillna(0, inplace=True)  # \u75280\u66ff\u6362\u6240\u6709\u7f3a\u5931\u503c\n<\/code><\/pre>\n
                                  \u901a\u8fc7\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u60a8\u53ef\u4ee5\u786e\u4fdd\u6570\u636e\u96c6\u7684\u5b8c\u6574\u6027\uff0c\u4ee5\u4fbf\u8fdb\u884c\u540e\u7eed\u5206\u6790\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u521b\u5efa\u6570\u636e\u96c6\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528pandas\u521b\u5efaDataFrame\u3001\u4f7f\u7528numpy\u521b\u5efa\u6570\u7ec4\u3001\u4eceCSV\u6587\u4ef6\u5bfc […]","protected":false},"author":3,"featured_media":1004498,"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\/1004483"}],"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=1004483"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1004483\/revisions"}],"predecessor-version":[{"id":1004500,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1004483\/revisions\/1004500"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1004498"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1004483"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1004483"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1004483"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}