{"id":986371,"date":"2024-12-27T07:44:21","date_gmt":"2024-12-26T23:44:21","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/986371.html"},"modified":"2024-12-27T07:44:23","modified_gmt":"2024-12-26T23:44:23","slug":"%e5%a6%82%e4%bd%95%e5%88%a9%e7%94%a8python%e6%89%93%e5%bc%80pandas","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/986371.html","title":{"rendered":"\u5982\u4f55\u5229\u7528python\u6253\u5f00pandas"},"content":{"rendered":"

\"\u5982\u4f55\u5229\u7528python\u6253\u5f00pandas\"<\/p>\n

\u5229\u7528Python\u6253\u5f00Pandas\uff0c\u53ef\u4ee5\u901a\u8fc7\u5b89\u88c5Pandas\u5e93\u3001\u5bfc\u5165\u5e93\u3001\u521b\u5efa\u6570\u636e\u7ed3\u6784\u3001\u8bfb\u53d6\u6570\u636e\u6587\u4ef6\u7b49\u6b65\u9aa4\u5b9e\u73b0\u3002<\/strong> \u5176\u4e2d\uff0c\u5b89\u88c5Pandas\u5e93\u662f\u6700\u57fa\u7840\u7684\u6b65\u9aa4\uff0c\u53ef\u4ee5\u901a\u8fc7pip\u547d\u4ee4\u6765\u5b8c\u6210\u3002\u5bfc\u5165\u5e93\u4e4b\u540e\uff0c\u53ef\u4ee5\u521b\u5efaDataFrame\u6216Series\u6570\u636e\u7ed3\u6784\uff0c\u4ece\u800c\u8fdb\u884c\u6570\u636e\u5904\u7406\u3002\u5bf9\u4e8e\u8bfb\u53d6\u6570\u636e\u6587\u4ef6\uff0cPandas\u652f\u6301\u591a\u79cd\u683c\u5f0f\uff0c\u5982CSV\u3001Excel\u3001SQL\u7b49\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u6bcf\u4e2a\u6b65\u9aa4\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5b89\u88c5Pandas\u5e93<\/p>\n<\/p>\n

\u8981\u5728Python\u4e2d\u4f7f\u7528Pandas\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5\u8fd9\u4e2a\u5e93\u3002Pandas\u662f\u4e00\u4e2a\u5f00\u6e90\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u53ef\u4ee5\u901a\u8fc7Python\u7684\u5305\u7ba1\u7406\u5de5\u5177pip\u8fdb\u884c\u5b89\u88c5\u3002<\/p>\n<\/p>\n

    \n
  1. \n

    \u5b89\u88c5Pandas<\/strong><\/p>\n<\/p>\n

    \u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u5728\u547d\u4ee4\u884c\u6216\u7ec8\u7aef\u4e2d\u5b89\u88c5Pandas\uff1a<\/p>\n<\/p>\n

    pip install pandas<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u8fd9\u4e2a\u547d\u4ee4\u4f1a\u4e0b\u8f7d\u5e76\u5b89\u88c5Pandas\u53ca\u5176\u4f9d\u8d56\u9879\u3002\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u4f60\u5c31\u53ef\u4ee5\u5728Python\u9879\u76ee\u4e2d\u4f7f\u7528Pandas\u4e86\u3002<\/p>\n<\/p>\n<\/li>\n
  2. \n
    \u68c0\u67e5\u5b89\u88c5<\/strong><\/p>\n<\/p>\n
    \u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u5728Python\u89e3\u91ca\u5668\u4e2d\u8f93\u5165\u4ee5\u4e0b\u4ee3\u7801\u6765\u68c0\u67e5Pandas\u662f\u5426\u5b89\u88c5\u6210\u529f\uff1a<\/p>\n<\/p>\n
    import pandas as pd<\/p>\n
    print(pd.__version__)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5982\u679c\u6ca1\u6709\u9519\u8bef\u5e76\u4e14\u8f93\u51fa\u7248\u672c\u53f7\uff0c\u8bf4\u660ePandas\u5b89\u88c5\u6210\u529f\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
    \u4e8c\u3001\u5bfc\u5165Pandas\u5e93<\/p>\n<\/p>\n
    \u5b89\u88c5\u6210\u529f\u540e\uff0c\u5728\u4f60\u7684Python\u811a\u672c\u6216\u4ea4\u4e92\u5f0f\u73af\u5883\u4e2d\u5bfc\u5165Pandas\u5e93\uff0c\u4ee5\u4fbf\u4f7f\u7528Pandas\u63d0\u4f9b\u7684\u5404\u79cd\u529f\u80fd\u3002<\/p>\n<\/p>\n
      \n
    1. \n
      \u5bfc\u5165\u5e93<\/strong><\/p>\n<\/p>\n
      \u5728Python\u811a\u672c\u7684\u5f00\u5934\u6dfb\u52a0\u4ee5\u4e0b\u4ee3\u7801\u4ee5\u5bfc\u5165Pandas\uff1a<\/p>\n<\/p>\n
      import pandas as pd<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u8fd9\u91ccpd<\/code>\u662fPandas\u7684\u60ef\u7528\u522b\u540d\uff0c\u65b9\u4fbf\u5728\u4ee3\u7801\u4e2d\u4f7f\u7528\u3002<\/p>\n<\/p>\n<\/li>\n
    2. \n
      \u5bfc\u5165\u6210\u529f\u68c0\u67e5<\/strong><\/p>\n<\/p>\n
      \u53ef\u4ee5\u901a\u8fc7\u5c1d\u8bd5\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684DataFrame\u6765\u786e\u8ba4Pandas\u5bfc\u5165\u6210\u529f\uff1a<\/p>\n<\/p>\n
      df = pd.DataFrame({'Column1': [1, 2], 'Column2': [3, 4]})<\/p>\n
      print(df)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u5982\u679c\u8f93\u51fa\u5982\u4e0b\u683c\u5f0f\u7684\u8868\u683c\uff0c\u5219\u8bf4\u660e\u5bfc\u5165\u6210\u529f\uff1a<\/p>\n<\/p>\n
         Column1  Column2<\/p>\n
      0        1        3<\/p>\n
      1        2        4<\/p>\n
      <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
      \u4e09\u3001\u521b\u5efa\u6570\u636e\u7ed3\u6784<\/p>\n<\/p>\n
      Pandas\u4e3b\u8981\u63d0\u4f9b\u4e24\u79cd\u6570\u636e\u7ed3\u6784\uff1aSeries\u548cDataFrame\u3002Series\u662f\u4e00\u4e2a\u4e00\u7ef4\u7684\u6570\u636e\u7ed3\u6784\uff0c\u7c7b\u4f3c\u4e8ePython\u7684\u5217\u8868\uff0c\u800cDataFrame\u662f\u4e00\u4e2a\u4e8c\u7ef4\u7684\u6570\u636e\u7ed3\u6784\uff0c\u7c7b\u4f3c\u4e8e\u7535\u5b50\u8868\u683c\u3002<\/p>\n<\/p>\n
        \n
      1. \n
        \u521b\u5efaSeries<\/strong><\/p>\n<\/p>\n
        Series\u662fPandas\u4e2d\u7684\u4e00\u7ef4\u6570\u7ec4\uff0c\u53ef\u4ee5\u7528\u6765\u5b58\u50a8\u4efb\u4f55\u6570\u636e\u7c7b\u578b\u7684\u6570\u636e\u3002\u4f60\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u4ee3\u7801\u521b\u5efa\u4e00\u4e2aSeries\uff1a<\/p>\n<\/p>\n
        s = pd.Series([1, 3, 5, 7, 9])<\/p>\n
        print(s)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u8fd9\u6bb5\u4ee3\u7801\u5c06\u8f93\u51fa\u4e00\u4e2a\u4e00\u7ef4\u6570\u7ec4\uff0c\u5176\u4e2d\u5305\u542b\u6570\u5b571\u30013\u30015\u30017\u548c9\u3002<\/p>\n<\/p>\n<\/li>\n
      2. \n
        \u521b\u5efaDataFrame<\/strong><\/p>\n<\/p>\n
        DataFrame\u662fPandas\u4e2d\u6700\u5e38\u7528\u7684\u6570\u636e\u7ed3\u6784\uff0c\u7c7b\u4f3c\u4e8e\u7535\u5b50\u8868\u683c\u3002\u5b83\u7531\u884c\u548c\u5217\u7ec4\u6210\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u4ee3\u7801\u521b\u5efa\u4e00\u4e2aDataFrame\uff1a<\/p>\n<\/p>\n
        data = {'Name': ['Tom', 'Jerry', 'Mickey'], 'Age': [20, 22, 21]}<\/p>\n
        df = pd.DataFrame(data)<\/p>\n
        print(df)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u8fd9\u6bb5\u4ee3\u7801\u5c06\u8f93\u51fa\u4e00\u4e2a\u5305\u542b\u540d\u79f0\u548c\u5e74\u9f84\u7684\u8868\u683c\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
        \u56db\u3001\u8bfb\u53d6\u6570\u636e\u6587\u4ef6<\/p>\n<\/p>\n
        Pandas\u652f\u6301\u8bfb\u53d6\u591a\u79cd\u683c\u5f0f\u7684\u6570\u636e\u6587\u4ef6\uff0c\u5982CSV\u3001Excel\u3001SQL\u7b49\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528Pandas\u63d0\u4f9b\u7684\u5404\u79cd\u51fd\u6570\u6765\u8bfb\u53d6\u4e0d\u540c\u683c\u5f0f\u7684\u6570\u636e\u6587\u4ef6\u3002<\/p>\n<\/p>\n
          \n
        1. \n
          \u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/p>\n<\/p>\n
          CSV\u6587\u4ef6\u662f\u6700\u5e38\u89c1\u7684\u6570\u636e\u6587\u4ef6\u683c\u5f0f\u4e4b\u4e00\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528read_csv<\/code>\u51fd\u6570\u6765\u8bfb\u53d6CSV\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
          df = pd.read_csv('data.csv')<\/p>\n
          print(df.head())<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u8fd9\u6bb5\u4ee3\u7801\u5c06\u8bfb\u53d6\u540d\u4e3adata.csv<\/code>\u7684CSV\u6587\u4ef6\uff0c\u5e76\u8f93\u51fa\u524d5\u884c\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n<\/li>\n
        2. \n
          \u8bfb\u53d6Excel\u6587\u4ef6<\/strong><\/p>\n<\/p>\n
          Excel\u6587\u4ef6\u4e5f\u662f\u5e38\u7528\u7684\u6570\u636e\u6587\u4ef6\u683c\u5f0f\u4e4b\u4e00\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528read_excel<\/code>\u51fd\u6570\u6765\u8bfb\u53d6Excel\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
          df = pd.read_excel('data.xlsx', sheet_name='Sheet1')<\/p>\n
          print(df.head())<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u8fd9\u6bb5\u4ee3\u7801\u5c06\u8bfb\u53d6\u540d\u4e3adata.xlsx<\/code>\u7684Excel\u6587\u4ef6\u4e2d\u7684Sheet1<\/code>\u8868\uff0c\u5e76\u8f93\u51fa\u524d5\u884c\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n<\/li>\n
        3. \n
          \u8bfb\u53d6SQL\u6570\u636e\u5e93<\/strong><\/p>\n<\/p>\n
          \u5982\u679c\u4f60\u7684\u6570\u636e\u5b58\u50a8\u5728SQL\u6570\u636e\u5e93\u4e2d\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528Pandas\u4e0e\u6570\u636e\u5e93\u8fdb\u884c\u4ea4\u4e92\u3002\u9996\u5148\u9700\u8981\u5b89\u88c5SQLAlchemy\u6216\u5176\u4ed6\u6570\u636e\u5e93\u8fde\u63a5\u5668\uff0c\u7136\u540e\u4f7f\u7528read_sql<\/code>\u51fd\u6570\u8bfb\u53d6\u6570\u636e\uff1a<\/p>\n<\/p>\n
          from sqlalchemy import create_engine<\/p>\n
          engine = create_engine('sqlite:\/\/\/my_database.db')<\/p>\n
          df = pd.read_sql('SELECT * FROM my_table', engine)<\/p>\n
          print(df.head())<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u8fd9\u6bb5\u4ee3\u7801\u5c06\u4eceSQLite\u6570\u636e\u5e93\u4e2d\u7684my_table<\/code>\u8868\u8bfb\u53d6\u6570\u636e\u5e76\u8f93\u51fa\u524d5\u884c\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
          \u4e94\u3001\u5904\u7406\u6570\u636e<\/p>\n<\/p>\n
          Pandas\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u5904\u7406\u529f\u80fd\uff0c\u53ef\u4ee5\u5bf9\u6570\u636e\u8fdb\u884c\u6e05\u6d17\u3001\u8f6c\u6362\u3001\u5206\u6790\u7b49\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
            \n
          1. \n
            \u6570\u636e\u6e05\u6d17<\/strong><\/p>\n<\/p>\n
            \u5728\u6570\u636e\u5206\u6790\u4e2d\uff0c\u6570\u636e\u6e05\u6d17\u662f\u975e\u5e38\u91cd\u8981\u7684\u4e00\u6b65\u3002Pandas\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u6e05\u6d17\u6570\u636e\uff0c\u4f8b\u5982\u53bb\u9664\u7f3a\u5931\u503c\u3001\u53bb\u9664\u91cd\u590d\u503c\u7b49\u3002<\/p>\n<\/p>\n
            # \u53bb\u9664\u7f3a\u5931\u503c<\/p>\n
            df.dropna(inplace=True)<\/p>\n
            \u53bb\u9664\u91cd\u590d\u503c<\/strong><\/h2>\n
            df.drop_duplicates(inplace=True)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u4ee5\u4e0a\u4ee3\u7801\u5c06\u5220\u9664DataFrame\u4e2d\u7684\u7f3a\u5931\u503c\u548c\u91cd\u590d\u503c\u3002<\/p>\n<\/p>\n<\/li>\n
          2. \n
            \u6570\u636e\u8f6c\u6362<\/strong><\/p>\n<\/p>\n
            \u6570\u636e\u8f6c\u6362\u662f\u6307\u5c06\u6570\u636e\u4ece\u4e00\u79cd\u683c\u5f0f\u8f6c\u6362\u4e3a\u53e6\u4e00\u79cd\u683c\u5f0f\u3002Pandas\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u8f6c\u6362\u6570\u636e\uff0c\u4f8b\u5982\u66f4\u6539\u6570\u636e\u7c7b\u578b\u3001\u91cd\u547d\u540d\u5217\u7b49\u3002<\/p>\n<\/p>\n
            # \u66f4\u6539\u6570\u636e\u7c7b\u578b<\/p>\n
            df['Age'] = df['Age'].astype(float)<\/p>\n
            \u91cd\u547d\u540d\u5217<\/strong><\/h2>\n
            df.rename(columns={'Name': 'Full Name'}, inplace=True)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u4ee5\u4e0a\u4ee3\u7801\u5c06Age<\/code>\u5217\u7684\u6570\u636e\u7c7b\u578b\u66f4\u6539\u4e3a\u6d6e\u70b9\u6570\uff0c\u5e76\u5c06Name<\/code>\u5217\u91cd\u547d\u540d\u4e3aFull Name<\/code>\u3002<\/p>\n<\/p>\n<\/li>\n
          3. \n
            \u6570\u636e\u5206\u6790<\/strong><\/p>\n<\/p>\n
            Pandas\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u5206\u6790\u529f\u80fd\uff0c\u53ef\u4ee5\u8fdb\u884c\u6570\u636e\u6c47\u603b\u3001\u5206\u7ec4\u3001\u7edf\u8ba1\u5206\u6790\u7b49\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
            # \u6570\u636e\u6c47\u603b<\/p>\n
            summary = df.describe()<\/p>\n
            \u6570\u636e\u5206\u7ec4<\/strong><\/h2>\n
            grouped = df.groupby('Category').sum()<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u4ee5\u4e0a\u4ee3\u7801\u5c06\u5bf9DataFrame\u8fdb\u884c\u6c47\u603b\u5206\u6790\uff0c\u5e76\u6839\u636eCategory<\/code>\u5217\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\u6c47\u603b\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
            \u516d\u3001\u53ef\u89c6\u5316\u6570\u636e<\/p>\n<\/p>\n
            Pandas\u4e0eMatplotlib\u548cSeaborn\u7b49\u53ef\u89c6\u5316\u5e93\u7ed3\u5408\u4f7f\u7528\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u5bf9\u6570\u636e\u8fdb\u884c\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
              \n
            1. \n
              \u4f7f\u7528Matplotlib<\/strong><\/p>\n<\/p>\n
              Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u53ef\u89c6\u5316\u5e93\u4e4b\u4e00\uff0c\u53ef\u4ee5\u7528\u6765\u7ed8\u5236\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\u3002<\/p>\n<\/p>\n
              import matplotlib.pyplot as plt<\/p>\n
              \u7ed8\u5236\u6298\u7ebf\u56fe<\/strong><\/h2>\n
              df['Age'].plot(kind='line')<\/p>\n
              plt.show()<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u4ee5\u4e0a\u4ee3\u7801\u5c06\u7ed8\u5236Age<\/code>\u5217\u7684\u6298\u7ebf\u56fe\u3002<\/p>\n<\/p>\n<\/li>\n
            2. \n
              \u4f7f\u7528Seaborn<\/strong><\/p>\n<\/p>\n
              Seaborn\u662f\u57fa\u4e8eMatplotlib\u6784\u5efa\u7684\u9ad8\u7ea7\u53ef\u89c6\u5316\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u7f8e\u89c2\u7684\u56fe\u8868\u6837\u5f0f\u3002<\/p>\n<\/p>\n
              import seaborn as sns<\/p>\n
              \u7ed8\u5236\u6761\u5f62\u56fe<\/strong><\/h2>\n
              sns.barplot(x='Name', y='Age', data=df)<\/p>\n
              plt.show()<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u4ee5\u4e0a\u4ee3\u7801\u5c06\u7ed8\u5236Name<\/code>\u548cAge<\/code>\u5217\u7684\u6761\u5f62\u56fe\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
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