{"id":932974,"date":"2024-12-26T18:07:56","date_gmt":"2024-12-26T10:07:56","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/932974.html"},"modified":"2024-12-26T18:07:58","modified_gmt":"2024-12-26T10:07:58","slug":"python%e5%a6%82%e4%bd%95%e6%8b%bc%e6%8e%a5dataframe","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/932974.html","title":{"rendered":"python\u5982\u4f55\u62fc\u63a5dataframe"},"content":{"rendered":"

\"python\u5982\u4f55\u62fc\u63a5dataframe\"<\/p>\n

\u5728Python\u4e2d\u62fc\u63a5DataFrame\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528pandas\u5e93\u7684concat\u3001merge\u3001join\u7b49\u65b9\u6cd5\u6765\u5b9e\u73b0\u3002concat\u65b9\u6cd5\u7528\u4e8e\u6cbf\u7740\u4e00\u4e2a\u8f74\uff08\u884c\u6216\u5217\uff09\u62fc\u63a5DataFrame\u3001merge\u65b9\u6cd5\u7528\u4e8e\u6839\u636e\u4e00\u4e2a\u6216\u591a\u4e2a\u952e\u5408\u5e76\u6570\u636e\uff0cjoin\u65b9\u6cd5\u7528\u4e8e\u5728\u7d22\u5f15\u4e0a\u5408\u5e76DataFrame\u3002\u4f7f\u7528\u8fd9\u4e9b\u65b9\u6cd5\u53ef\u4ee5\u65b9\u4fbf\u5730\u5c06\u591a\u4e2aDataFrame\u6574\u5408\u6210\u4e00\u4e2a\u3002<\/strong>\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u51e0\u79cd\u65b9\u6cd5\u53ca\u5176\u4f7f\u7528\u573a\u666f\u3002<\/p>\n<\/p>\n

\u4e00\u3001PANDAS\u5e93\u7b80\u4ecb<\/p>\n<\/p>\n

Pandas\u662fPython\u4e2d\u4e00\u4e2a\u5f3a\u5927\u4e14\u5e38\u7528\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u5feb\u901f\u3001\u7075\u6d3b\u548c\u5177\u8868\u73b0\u529b\u7684\u6570\u636e\u7ed3\u6784\uff0c\u65e8\u5728\u7b80\u5316\u548c\u63d0\u5347\u6570\u636e\u5206\u6790\u7684\u6548\u7387\u3002DataFrame\u662fPandas\u5e93\u4e2d\u6700\u6838\u5fc3\u7684\u6570\u636e\u7ed3\u6784\u4e4b\u4e00\uff0c\u7c7b\u4f3c\u4e8e\u7535\u5b50\u8868\u683c\u6216SQL\u8868\uff0c\u662f\u4e00\u4e2a\u4e8c\u7ef4\u3001\u5927\u5c0f\u53ef\u53d8\u3001\u5177\u6709\u6807\u8bb0\u8f74\u7684\u6570\u636e\u7ed3\u6784\u3002Pandas\u5e93\u7684\u529f\u80fd\u4e0d\u4ec5\u4ec5\u9650\u4e8e\u6570\u636e\u7684\u5b58\u50a8\u548c\u5c55\u793a\uff0c\u8fd8\u5305\u62ec\u6570\u636e\u7684\u6e05\u6d17\u3001\u5904\u7406\u3001\u5206\u6790\u7b49\u4e00\u7cfb\u5217\u64cd\u4f5c\u3002<\/p>\n<\/p>\n

\u4e8c\u3001\u4f7f\u7528CONCAT\u65b9\u6cd5\u62fc\u63a5DataFrame<\/p>\n<\/p>\n

    \n
  1. \n

    \u62fc\u63a5\u884c\u6570\u636e<\/strong><\/p>\n<\/p>\n

    \u4f7f\u7528pandas\u7684concat\u65b9\u6cd5\u53ef\u4ee5\u6cbf\u7740\u884c\u65b9\u5411\u62fc\u63a5\u591a\u4e2aDataFrame\u3002\u8fd9\u79cd\u64cd\u4f5c\u901a\u5e38\u7528\u4e8e\u5c06\u76f8\u540c\u7ed3\u6784\u7684\u6570\u636e\u96c6\u7ec4\u5408\u5728\u4e00\u8d77\u3002<\/p>\n<\/p>\n

    import pandas as pd<\/p>\n
    df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2'],<\/p>\n
                        'B': ['B0', 'B1', 'B2']})<\/p>\n
    df2 = pd.DataFrame({'A': ['A3', 'A4', 'A5'],<\/p>\n
                        'B': ['B3', 'B4', 'B5']})<\/p>\n
    result = pd.concat([df1, df2])<\/p>\n
    print(result)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u89e3\u91ca<\/strong>\uff1a\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0cdf1\u548cdf2\u662f\u5177\u6709\u76f8\u540c\u7ed3\u6784\u7684DataFrame\uff0c\u901a\u8fc7pd.concat([df1, df2])\uff0c\u6211\u4eec\u5728\u884c\u65b9\u5411\u4e0a\u62fc\u63a5\u4e86\u8fd9\u4e24\u4e2aDataFrame\uff0c\u751f\u6210\u4e86\u4e00\u4e2a\u65b0\u7684DataFrame result\u3002<\/p>\n<\/p>\n<\/li>\n
  2. \n
    \u62fc\u63a5\u5217\u6570\u636e<\/strong><\/p>\n<\/p>\n
    pandas\u7684concat\u65b9\u6cd5\u4e5f\u53ef\u4ee5\u7528\u4e8e\u6cbf\u7740\u5217\u65b9\u5411\u62fc\u63a5DataFrame\u3002\u9700\u8981\u6ce8\u610f\u7684\u662f\uff0c\u5728\u62fc\u63a5\u5217\u65f6\uff0c\u884c\u7d22\u5f15\u5fc5\u987b\u5bf9\u9f50\u3002<\/p>\n<\/p>\n
    df3 = pd.DataFrame({'C': ['C0', 'C1', 'C2']})<\/p>\n
    result = pd.concat([df1, df3], axis=1)<\/p>\n
    print(result)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u89e3\u91ca<\/strong>\uff1a\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u901a\u8fc7pd.concat([df1, df3], axis=1)\u5728\u5217\u65b9\u5411\u4e0a\u62fc\u63a5\u4e86df1\u548cdf3\uff0c\u751f\u6210\u4e86\u4e00\u4e2a\u65b0\u7684DataFrame result\u3002\u9700\u8981\u6ce8\u610f\u7684\u662f\uff0cdf1\u548cdf3\u7684\u884c\u7d22\u5f15\u5fc5\u987b\u5bf9\u9f50\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
    \u4e09\u3001\u4f7f\u7528MERGE\u65b9\u6cd5\u5408\u5e76DataFrame<\/p>\n<\/p>\n
      \n
    1. \n
      \u57fa\u4e8e\u952e\u5408\u5e76\u6570\u636e<\/strong><\/p>\n<\/p>\n
      \u4f7f\u7528merge\u65b9\u6cd5\u53ef\u4ee5\u6839\u636e\u4e00\u4e2a\u6216\u591a\u4e2a\u952e\u5408\u5e76DataFrame\uff0c\u7c7b\u4f3c\u4e8eSQL\u4e2d\u7684JOIN\u64cd\u4f5c\u3002merge\u65b9\u6cd5\u901a\u8fc7\u6307\u5b9aon\u53c2\u6570\u6765\u786e\u5b9a\u5408\u5e76\u7684\u952e\u3002<\/p>\n<\/p>\n
      df4 = pd.DataFrame({'key': ['K0', 'K1', 'K2'],<\/p>\n
                          'C': ['C0', 'C1', 'C2']})<\/p>\n
      result = pd.merge(df1, df4, left_on='A', right_on='key')<\/p>\n
      print(result)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u89e3\u91ca<\/strong>\uff1a\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0cdf1\u548cdf4\u901a\u8fc7A\u5217\u548ckey\u5217\u8fdb\u884c\u5408\u5e76\u3002\u901a\u8fc7pd.merge(df1, df4, left_on='A', right_on='key')\uff0c\u6211\u4eec\u751f\u6210\u4e86\u4e00\u4e2a\u65b0\u7684DataFrame result\u3002<\/p>\n<\/p>\n<\/li>\n
    2. \n
      \u4e0d\u540c\u7c7b\u578b\u7684\u5408\u5e76<\/strong><\/p>\n<\/p>\n
      merge\u65b9\u6cd5\u652f\u6301\u4e0d\u540c\u7c7b\u578b\u7684\u5408\u5e76\u64cd\u4f5c\uff0c\u5305\u62ecinner\u3001outer\u3001left\u3001right\u7b49\u3002<\/p>\n<\/p>\n
      result_inner = pd.merge(df1, df4, left_on='A', right_on='key', how='inner')<\/p>\n
      result_outer = pd.merge(df1, df4, left_on='A', right_on='key', how='outer')<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u89e3\u91ca<\/strong>\uff1a\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0cresult_inner\u662fdf1\u548cdf4\u901a\u8fc7inner join\u5408\u5e76\u7684\u7ed3\u679c\uff0c\u800cresult_outer\u662f\u901a\u8fc7outer join\u5408\u5e76\u7684\u7ed3\u679c\u3002\u8fd9\u4e24\u79cd\u5408\u5e76\u65b9\u5f0f\u5728\u5904\u7406\u6570\u636e\u65f6\u6709\u4e0d\u540c\u7684\u5e94\u7528\u573a\u666f\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
      \u56db\u3001\u4f7f\u7528JOIN\u65b9\u6cd5\u5408\u5e76DataFrame<\/p>\n<\/p>\n
        \n
      1. \n
        \u57fa\u4e8e\u7d22\u5f15\u5408\u5e76<\/strong><\/p>\n<\/p>\n
        join\u65b9\u6cd5\u7528\u4e8e\u5728\u7d22\u5f15\u4e0a\u5408\u5e76DataFrame\uff0c\u9ed8\u8ba4\u60c5\u51b5\u4e0b\u662f\u5de6\u8fde\u63a5\u3002<\/p>\n<\/p>\n
        df5 = pd.DataFrame({'D': ['D0', 'D1', 'D2']}, index=[0, 1, 2])<\/p>\n
        result = df1.join(df5)<\/p>\n
        print(result)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u89e3\u91ca<\/strong>\uff1a\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0cdf1\u548cdf5\u901a\u8fc7\u7d22\u5f15\u8fdb\u884c\u5408\u5e76\u3002\u901a\u8fc7df1.join(df5)\uff0c\u6211\u4eec\u751f\u6210\u4e86\u4e00\u4e2a\u65b0\u7684DataFrame result\u3002<\/p>\n<\/p>\n<\/li>\n
      2. \n
        \u4e0d\u540c\u7684\u8fde\u63a5\u7c7b\u578b<\/strong><\/p>\n<\/p>\n
        join\u65b9\u6cd5\u4e5f\u652f\u6301\u4e0d\u540c\u7c7b\u578b\u7684\u8fde\u63a5\uff0c\u5305\u62ecinner\u3001outer\u3001left\u3001right\u7b49\u3002<\/p>\n<\/p>\n
        df6 = pd.DataFrame({'E': ['E0', 'E1', 'E2']}, index=[1, 2, 3])<\/p>\n
        result_outer = df1.join(df6, how='outer')<\/p>\n
        print(result_outer)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u89e3\u91ca<\/strong>\uff1a\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0cresult_outer\u662fdf1\u548cdf6\u901a\u8fc7outer join\u5408\u5e76\u7684\u7ed3\u679c\u3002\u901a\u8fc7\u6307\u5b9ahow='outer'\uff0c\u6211\u4eec\u53ef\u4ee5\u63a7\u5236\u5408\u5e76\u7684\u7c7b\u578b\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
        \u4e94\u3001DataFrame\u62fc\u63a5\u7684\u6ce8\u610f\u4e8b\u9879<\/p>\n<\/p>\n
          \n
        1. \n
          \u6570\u636e\u5bf9\u9f50<\/strong><\/p>\n<\/p>\n
          \u5728\u8fdb\u884cDataFrame\u7684\u62fc\u63a5\u65f6\uff0c\u6570\u636e\u7684\u5bf9\u9f50\u662f\u4e00\u4e2a\u975e\u5e38\u91cd\u8981\u7684\u56e0\u7d20\u3002\u65e0\u8bba\u662f\u884c\u65b9\u5411\u8fd8\u662f\u5217\u65b9\u5411\u7684\u62fc\u63a5\uff0c\u7d22\u5f15\u90fd\u5fc5\u987b\u5bf9\u9f50\uff0c\u5426\u5219\u53ef\u80fd\u4f1a\u5bfc\u81f4\u6570\u636e\u7684\u9519\u4f4d\u3002<\/p>\n<\/p>\n<\/li>\n
        2. \n
          \u6570\u636e\u7f3a\u5931<\/strong><\/p>\n<\/p>\n
          \u5728\u5408\u5e76\u64cd\u4f5c\u4e2d\uff0c\u53ef\u80fd\u4f1a\u9047\u5230\u6570\u636e\u7f3a\u5931\u7684\u95ee\u9898\u3002\u6bd4\u5982\u5728outer join\u4e2d\uff0c\u67d0\u4e9b\u5217\u7684\u6570\u636e\u53ef\u80fd\u4f1a\u7f3a\u5931\uff0c\u8fd9\u65f6\u53ef\u4ee5\u901a\u8fc7fillna\u65b9\u6cd5\u6765\u586b\u5145\u7f3a\u5931\u503c\u3002<\/p>\n<\/p>\n<\/li>\n
        3. \n
          \u6027\u80fd\u95ee\u9898<\/strong><\/p>\n<\/p>\n
          \u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\uff0c\u62fc\u63a5\u64cd\u4f5c\u53ef\u80fd\u4f1a\u5bfc\u81f4\u6027\u80fd\u95ee\u9898\u3002\u4e3a\u4e86\u63d0\u9ad8\u6027\u80fd\uff0c\u53ef\u4ee5\u8003\u8651\u4f7f\u7528\u591a\u7ebf\u7a0b\u6216\u5206\u5e03\u5f0f\u8ba1\u7b97\u6765\u52a0\u901f\u6570\u636e\u7684\u62fc\u63a5\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
          \u516d\u3001\u603b\u7ed3<\/p>\n<\/p>\n
          \u5728Python\u4e2d\uff0c\u4f7f\u7528pandas\u5e93\u7684concat\u3001merge\u3001join\u7b49\u65b9\u6cd5\u53ef\u4ee5\u65b9\u4fbf\u5730\u62fc\u63a5DataFrame\u3002concat\u65b9\u6cd5\u4e3b\u8981\u7528\u4e8e\u6cbf\u7740\u4e00\u4e2a\u8f74\uff08\u884c\u6216\u5217\uff09\u62fc\u63a5DataFrame\uff0cmerge\u65b9\u6cd5\u7528\u4e8e\u6839\u636e\u4e00\u4e2a\u6216\u591a\u4e2a\u952e\u5408\u5e76\u6570\u636e\uff0cjoin\u65b9\u6cd5\u7528\u4e8e\u5728\u7d22\u5f15\u4e0a\u5408\u5e76DataFrame\u3002\u901a\u8fc7\u5408\u7406\u5730\u4f7f\u7528\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u53ef\u4ee5\u6709\u6548\u5730\u6574\u5408\u591a\u4e2a\u6570\u636e\u96c6\uff0c\u4e3a\u540e\u7eed\u7684\u6570\u636e\u5206\u6790\u6253\u4e0b\u57fa\u7840\u3002\u5728\u5b9e\u9645\u64cd\u4f5c\u4e2d\uff0c\u8fd8\u9700\u8981\u6ce8\u610f\u6570\u636e\u7684\u5bf9\u9f50\u3001\u7f3a\u5931\u503c\u7684\u5904\u7406\u4ee5\u53ca\u6027\u80fd\u95ee\u9898\uff0c\u4ee5\u786e\u4fdd\u6570\u636e\u7684\u51c6\u786e\u6027\u548c\u5904\u7406\u6548\u7387\u3002<\/p>\n<\/p>\n
          \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
           \u5982\u4f55\u5728Python\u4e2d\u6709\u6548\u62fc\u63a5\u591a\u4e2aDataFrame\uff1f<\/strong>
          \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528pandas<\/code>\u5e93\u4e2d\u7684concat()<\/code>\u51fd\u6570\u6765\u62fc\u63a5\u591a\u4e2aDataFrame\u3002\u8be5\u51fd\u6570\u5141\u8bb8\u4f60\u6cbf\u7740\u6307\u5b9a\u7684\u8f74\uff08\u884c\u6216\u5217\uff09\u5c06\u591a\u4e2aDataFrame\u5408\u5e76\u3002\u4e3e\u4e2a\u4f8b\u5b50\uff0c\u5982\u679c\u4f60\u6709\u4e24\u4e2aDataFrame\uff0c\u5206\u522b\u4e3adf1<\/code>\u548cdf2<\/code>\uff0c\u4f60\u53ef\u4ee5\u901a\u8fc7pd.concat([df1, df2])<\/code>\u5c06\u5b83\u4eec\u6309\u884c\u62fc\u63a5\uff0c\u6216\u8005\u901a\u8fc7pd.concat([df1, df2], axis=1)<\/code>\u6309\u5217\u62fc\u63a5\u3002<\/p>\n
          \u5728\u62fc\u63a5DataFrame\u65f6\u5982\u4f55\u5904\u7406\u91cd\u590d\u7d22\u5f15\uff1f<\/strong>
          \u5728\u62fc\u63a5DataFrame\u65f6\uff0c\u5982\u679c\u5b58\u5728\u91cd\u590d\u7684\u7d22\u5f15\uff0cconcat()<\/code>\u51fd\u6570\u4f1a\u9ed8\u8ba4\u4fdd\u7559\u8fd9\u4e9b\u7d22\u5f15\u3002\u4e3a\u4e86\u907f\u514d\u7d22\u5f15\u51b2\u7a81\uff0c\u53ef\u4ee5\u4f7f\u7528ignore_index=True<\/code>\u53c2\u6570\uff0c\u8be5\u53c2\u6570\u4f1a\u5728\u62fc\u63a5\u65f6\u91cd\u65b0\u751f\u6210\u7d22\u5f15\u3002\u4f8b\u5982\uff0cpd.concat([df1, df2], ignore_index=True)<\/code>\u4f1a\u521b\u5efa\u4e00\u4e2a\u65b0\u7684\u8fde\u7eed\u7d22\u5f15\uff0c\u800c\u4e0d\u662f\u4fdd\u7559\u539f\u6709\u7d22\u5f15\u3002<\/p>\n
          \u5728DataFrame\u62fc\u63a5\u65f6\u5982\u4f55\u786e\u4fdd\u6570\u636e\u7684\u5b8c\u6574\u6027\uff1f<\/strong>
          \u786e\u4fdd\u6570\u636e\u7684\u5b8c\u6574\u6027\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528join<\/code>\u53c2\u6570\u6765\u5b9e\u73b0\u3002concat()<\/code>\u51fd\u6570\u63d0\u4f9b\u4e86join='inner'<\/code>\u548cjoin='outer'<\/code>\u9009\u9879\u3002\u9009\u62e9outer<\/code>\u4f1a\u4fdd\u7559\u6240\u6709DataFrame\u4e2d\u7684\u6240\u6709\u5217\uff0c\u800c\u9009\u62e9inner<\/code>\u5219\u53ea\u4f1a\u4fdd\u7559\u6240\u6709DataFrame\u4e2d\u7684\u516c\u5171\u5217\u3002\u8fd9\u6837\u53ef\u4ee5\u7075\u6d3b\u63a7\u5236\u62fc\u63a5\u540e\u7684\u7ed3\u679c\uff0c\u786e\u4fdd\u6570\u636e\u7684\u5b8c\u6574\u6027\u4e0e\u4e00\u81f4\u6027\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u62fc\u63a5DataFrame\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528pandas\u5e93\u7684concat\u3001merge\u3001join\u7b49\u65b9\u6cd5\u6765\u5b9e […]","protected":false},"author":3,"featured_media":932975,"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\/932974"}],"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=932974"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/932974\/revisions"}],"predecessor-version":[{"id":932976,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/932974\/revisions\/932976"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/932975"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=932974"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=932974"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=932974"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}