{"id":996851,"date":"2024-12-27T09:18:29","date_gmt":"2024-12-27T01:18:29","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/996851.html"},"modified":"2024-12-27T09:18:33","modified_gmt":"2024-12-27T01:18:33","slug":"python%e5%a6%82%e4%bd%95%e5%a4%84%e7%90%86xls","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/996851.html","title":{"rendered":"python\u5982\u4f55\u5904\u7406xls"},"content":{"rendered":"

\"python\u5982\u4f55\u5904\u7406xls\"<\/p>\n

Python\u5904\u7406xls\u6587\u4ef6\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528\u591a\u4e2a\u5e93\u6765\u5b9e\u73b0\uff0c\u5e38\u89c1\u7684\u5305\u62ec\uff1axlrd<\/code>\u3001openpyxl<\/code>\u3001pandas<\/code>\u3002\u5176\u4e2d\uff0cxlrd<\/code>\u9002\u7528\u4e8e\u8bfb\u53d6\u65e7\u7248xls\u6587\u4ef6\u3001openpyxl<\/code>\u9002\u7528\u4e8e\u5904\u7406xlsx\u6587\u4ef6\u3001pandas<\/code>\u529f\u80fd\u5f3a\u5927\u4e14\u6613\u4e8e\u6570\u636e\u5206\u6790\u3002<\/strong> <\/p>\n<\/p>\n

pandas<\/code>\u662f\u5904\u7406xls\u6587\u4ef6\u7684\u6700\u5e38\u7528\u5e93\uff0c\u56e0\u4e3a\u5b83\u4e0d\u4ec5\u80fd\u8bfb\u53d6\u548c\u5199\u5165Excel\u6587\u4ef6\uff0c\u8fd8\u80fd\u8fdb\u884c\u6570\u636e\u5206\u6790\u548c\u5904\u7406\u3002\u901a\u8fc7pandas<\/code>\u7684read_excel()<\/code>\u51fd\u6570\uff0c\u53ef\u4ee5\u8f7b\u677e\u8bfb\u53d6xls\u6587\u4ef6\uff0c\u5e76\u5c06\u5176\u8f6c\u6362\u4e3aDataFrame\u683c\u5f0f\uff0c\u65b9\u4fbf\u540e\u7eed\u7684\u6570\u636e\u64cd\u4f5c\u548c\u5206\u6790\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u5229\u7528\u8fd9\u4e9b\u5e93\u6765\u5904\u7406xls\u6587\u4ef6\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5229\u7528XLRD\u8bfb\u53d6XLS\u6587\u4ef6<\/p>\n<\/p>\n

xlrd<\/code>\u662f\u4e13\u95e8\u7528\u4e8e\u8bfb\u53d6Excel\u6587\u4ef6\u7684Python\u5e93\uff0c\u7279\u522b\u662f\u65e7\u7248\u7684xls\u683c\u5f0f\u3002\u867d\u7136\u5b83\u4e0d\u652f\u6301xlsx\u683c\u5f0f\u6587\u4ef6\uff0c\u4f46\u5728\u5904\u7406\u65e7\u7248\u6587\u4ef6\u65f6\u4ecd\u7136\u975e\u5e38\u6709\u7528\u3002\u8981\u4f7f\u7528xlrd<\/code>\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5\u8fd9\u4e2a\u5e93\uff1a<\/p>\n<\/p>\n

pip install xlrd<\/p>\n
<\/code><\/pre>\n<\/p>\n
    \n
  1. \u8bfb\u53d6XLS\u6587\u4ef6<\/strong><\/li>\n<\/ol>\n
    \u4f7f\u7528xlrd<\/code>\u8bfb\u53d6xls\u6587\u4ef6\u7684\u57fa\u672c\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
    import xlrd<\/p>\n
    \u6253\u5f00xls\u6587\u4ef6<\/strong><\/h2>\n
    workbook = xlrd.open_workbook('example.xls')<\/p>\n
    \u9009\u62e9\u5de5\u4f5c\u8868<\/strong><\/h2>\n
    sheet = workbook.sheet_by_index(0)<\/p>\n
    \u8bfb\u53d6\u5355\u5143\u683c\u6570\u636e<\/strong><\/h2>\n
    value = sheet.cell_value(0, 0)  # \u8bfb\u53d6\u7b2c\u4e00\u884c\u7b2c\u4e00\u5217\u7684\u503c<\/p>\n
    print(value)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u901a\u8fc7xlrd.open_workbook()<\/code>\u51fd\u6570\u6253\u5f00\u4e00\u4e2axls\u6587\u4ef6\uff0c\u7136\u540e\u901a\u8fc7sheet_by_index()<\/code>\u65b9\u6cd5\u9009\u62e9\u8981\u64cd\u4f5c\u7684\u5de5\u4f5c\u8868\u3002\u63a5\u7740\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528cell_value()<\/code>\u65b9\u6cd5\u8bfb\u53d6\u7279\u5b9a\u5355\u5143\u683c\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n
      \n
    1. \u904d\u5386\u6574\u4e2a\u5de5\u4f5c\u8868<\/strong><\/li>\n<\/ol>\n
      \u5982\u679c\u9700\u8981\u904d\u5386\u6574\u4e2a\u5de5\u4f5c\u8868\uff0c\u53ef\u4ee5\u4f7f\u7528\u5faa\u73af\uff1a<\/p>\n<\/p>\n
      for row in range(sheet.nrows):<\/p>\n
          for col in range(sheet.ncols):<\/p>\n
              print(sheet.cell_value(row, col))<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      sheet.nrows<\/code>\u548csheet.ncols<\/code>\u5206\u522b\u8fd4\u56de\u8868\u683c\u7684\u884c\u6570\u548c\u5217\u6570\uff0c\u6211\u4eec\u53ef\u4ee5\u5229\u7528\u8fd9\u4e9b\u5c5e\u6027\u6765\u904d\u5386\u6574\u4e2a\u8868\u683c\u5e76\u8bfb\u53d6\u6bcf\u4e2a\u5355\u5143\u683c\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n
      \u4e8c\u3001\u5229\u7528OPENPYXL\u5904\u7406XLSX\u6587\u4ef6<\/p>\n<\/p>\n
      openpyxl<\/code>\u662f\u4e00\u4e2a\u7528\u4e8e\u8bfb\u53d6\u548c\u5199\u5165Excel 2010 xlsx\/xlsm\/xltx\/xltm\u6587\u4ef6\u7684Python\u5e93\u3002\u5b83\u4e0d\u652f\u6301xls\u683c\u5f0f\uff0c\u4f46\u5728\u5904\u7406xlsx\u6587\u4ef6\u65f6\u975e\u5e38\u5f3a\u5927\u3002\u9996\u5148\uff0c\u9700\u8981\u5b89\u88c5openpyxl<\/code>\uff1a<\/p>\n<\/p>\n
      pip install openpyxl<\/p>\n
      <\/code><\/pre>\n<\/p>\n
        \n
      1. \u8bfb\u53d6XLSX\u6587\u4ef6<\/strong><\/li>\n<\/ol>\n
        \u4f7f\u7528openpyxl<\/code>\u8bfb\u53d6xlsx\u6587\u4ef6\u7684\u57fa\u672c\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
        from openpyxl import load_workbook<\/p>\n
        \u52a0\u8f7dxlsx\u6587\u4ef6<\/strong><\/h2>\n
        workbook = load_workbook('example.xlsx')<\/p>\n
        \u9009\u62e9\u5de5\u4f5c\u8868<\/strong><\/h2>\n
        sheet = workbook.active<\/p>\n
        \u8bfb\u53d6\u5355\u5143\u683c\u6570\u636e<\/strong><\/h2>\n
        value = sheet['A1'].value  # \u8bfb\u53d6A1\u5355\u5143\u683c\u7684\u503c<\/p>\n
        print(value)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528load_workbook()<\/code>\u51fd\u6570\u52a0\u8f7d\u4e00\u4e2axlsx\u6587\u4ef6\uff0c\u5e76\u4f7f\u7528active<\/code>\u5c5e\u6027\u9009\u62e9\u9ed8\u8ba4\u7684\u6d3b\u52a8\u5de5\u4f5c\u8868\u3002\u63a5\u7740\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u5355\u5143\u683c\u7684\u5730\u5740\uff08\u4f8b\u5982A1<\/code>\uff09\u6765\u8bfb\u53d6\u7279\u5b9a\u5355\u5143\u683c\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n
          \n
        1. \u5199\u5165XLSX\u6587\u4ef6<\/strong><\/li>\n<\/ol>\n
          openpyxl<\/code>\u4e0d\u4ec5\u80fd\u8bfb\u53d6\uff0c\u8fd8\u80fd\u5199\u5165xlsx\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
          from openpyxl import Workbook<\/p>\n
          \u521b\u5efa\u4e00\u4e2a\u65b0\u7684\u5de5\u4f5c\u7c3f<\/strong><\/h2>\n
          workbook = Workbook()<\/p>\n
          \u83b7\u53d6\u6d3b\u52a8\u5de5\u4f5c\u8868<\/strong><\/h2>\n
          sheet = workbook.active<\/p>\n
          \u5199\u5165\u6570\u636e<\/strong><\/h2>\n
          sheet['A1'] = 'Hello'<\/p>\n
          sheet['B1'] = 'World'<\/p>\n
          \u4fdd\u5b58\u6587\u4ef6<\/strong><\/h2>\n
          workbook.save('output.xlsx')<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u521b\u5efa\u4e00\u4e2a\u65b0\u7684\u5de5\u4f5c\u7c3f\uff0c\u5e76\u901a\u8fc7active<\/code>\u5c5e\u6027\u83b7\u53d6\u6d3b\u52a8\u5de5\u4f5c\u8868\u3002\u7136\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u50cf\u64cd\u4f5c\u5b57\u5178\u4e00\u6837\u76f4\u63a5\u5bf9\u5355\u5143\u683c\u8d4b\u503c\uff0c\u6700\u540e\u901a\u8fc7save()<\/code>\u65b9\u6cd5\u4fdd\u5b58\u6587\u4ef6\u3002<\/p>\n<\/p>\n
          \u4e09\u3001\u5229\u7528PANDAS\u8fdb\u884c\u6570\u636e\u5904\u7406<\/p>\n<\/p>\n
          pandas<\/code>\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u5b83\u80fd\u591f\u8f7b\u677e\u8bfb\u53d6\u548c\u5199\u5165Excel\u6587\u4ef6\uff0c\u5e76\u5bf9\u6570\u636e\u8fdb\u884c\u5404\u79cd\u5206\u6790\u64cd\u4f5c\u3002\u9996\u5148\uff0c\u786e\u4fdd\u5b89\u88c5\u4e86pandas<\/code>\uff1a<\/p>\n<\/p>\n
          pip install pandas<\/p>\n
          <\/code><\/pre>\n<\/p>\n
            \n
          1. \u8bfb\u53d6XLS\/XLSX\u6587\u4ef6<\/strong><\/li>\n<\/ol>\n
            \u4f7f\u7528pandas<\/code>\u8bfb\u53d6Excel\u6587\u4ef6\u7684\u57fa\u672c\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
            import pandas as pd<\/p>\n
            \u8bfb\u53d6Excel\u6587\u4ef6<\/strong><\/h2>\n
            df = pd.read_excel('example.xlsx')<\/p>\n
            \u663e\u793a\u6570\u636e<\/strong><\/h2>\n
            print(df.head())<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            read_excel()<\/code>\u51fd\u6570\u53ef\u4ee5\u8bfb\u53d6xls\u548cxlsx\u683c\u5f0f\u7684\u6587\u4ef6\uff0c\u5e76\u5c06\u5176\u8f6c\u6362\u4e3aDataFrame\u683c\u5f0f\uff0c\u65b9\u4fbf\u8fdb\u884c\u6570\u636e\u5206\u6790\u3002<\/p>\n<\/p>\n
              \n
            1. \u6570\u636e\u5206\u6790\u4e0e\u5904\u7406<\/strong><\/li>\n<\/ol>\n
              pandas<\/code>\u7684\u5f3a\u5927\u4e4b\u5904\u5728\u4e8e\u5176\u6570\u636e\u5904\u7406\u80fd\u529b\u3002\u4f8b\u5982\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u8fc7\u6ee4\u6570\u636e\u3001\u8ba1\u7b97\u7edf\u8ba1\u4fe1\u606f\u7b49\uff1a<\/p>\n<\/p>\n
              # \u8fc7\u6ee4\u6570\u636e<\/p>\n
              filtered_data = df[df['Column1'] > 10]<\/p>\n
              \u8ba1\u7b97\u7edf\u8ba1\u4fe1\u606f<\/strong><\/h2>\n
              mean_value = df['Column2'].mean()<\/p>\n
              print('Mean:', mean_value)<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u8fc7\u6ee4\u51faColumn1<\/code>\u5217\u4e2d\u503c\u5927\u4e8e10\u7684\u884c\uff0c\u7136\u540e\u8ba1\u7b97Column2<\/code>\u5217\u7684\u5e73\u5747\u503c\u3002<\/p>\n<\/p>\n
                \n
              1. \u5199\u5165XLSX\u6587\u4ef6<\/strong><\/li>\n<\/ol>\n
                \u4f7f\u7528pandas<\/code>\u5c06\u6570\u636e\u5199\u5165Excel\u6587\u4ef6\u4e5f\u5f88\u7b80\u5355\uff1a<\/p>\n<\/p>\n
                # \u5199\u5165Excel\u6587\u4ef6<\/p>\n
                df.to_excel('output.xlsx', index=False)<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                \u901a\u8fc7to_excel()<\/code>\u51fd\u6570\uff0c\u53ef\u4ee5\u5c06DataFrame\u4e2d\u7684\u6570\u636e\u5199\u5165xlsx\u6587\u4ef6\u4e2d\uff0c\u53c2\u6570index=False<\/code>\u8868\u793a\u4e0d\u5199\u5165\u884c\u7d22\u5f15\u3002<\/p>\n<\/p>\n
                \u56db\u3001\u5904\u7406\u5927\u6587\u4ef6\u4e0e\u6027\u80fd\u4f18\u5316<\/p>\n<\/p>\n
                \u5904\u7406\u5927\u578bExcel\u6587\u4ef6\u65f6\uff0c\u5185\u5b58\u548c\u6027\u80fd\u53ef\u80fd\u4f1a\u6210\u4e3a\u95ee\u9898\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u4f18\u5316\u5efa\u8bae\uff1a<\/p>\n<\/p>\n
                  \n
                1. \u5206\u5757\u8bfb\u53d6<\/strong><\/li>\n<\/ol>\n
                  \u5bf9\u4e8e\u975e\u5e38\u5927\u7684\u6587\u4ef6\uff0c\u53ef\u4ee5\u4f7f\u7528pandas<\/code>\u7684read_excel()<\/code>\u7684chunksize<\/code>\u53c2\u6570\u5206\u5757\u8bfb\u53d6\uff1a<\/p>\n<\/p>\n
                  for chunk in pd.read_excel('large_file.xlsx', chunksize=1000):<\/p>\n
                      # \u5bf9\u6bcf\u4e2a\u5757\u8fdb\u884c\u5904\u7406<\/p>\n
                      process(chunk)<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u8fd9\u6837\u53ef\u4ee5\u907f\u514d\u4e00\u6b21\u6027\u52a0\u8f7d\u6574\u4e2a\u6587\u4ef6\u5bfc\u81f4\u7684\u5185\u5b58\u95ee\u9898\u3002<\/p>\n<\/p>\n
                    \n
                  1. \u53ea\u8bfb\u53d6\u5fc5\u8981\u7684\u5217<\/strong><\/li>\n<\/ol>\n
                    \u5982\u679c\u53ea\u9700\u8981\u7279\u5b9a\u7684\u5217\uff0c\u53ef\u4ee5\u4f7f\u7528usecols<\/code>\u53c2\u6570\uff1a<\/p>\n<\/p>\n
                    df = pd.read_excel('example.xlsx', usecols=['Column1', 'Column2'])<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                    \u8fd9\u6837\u53ef\u4ee5\u51cf\u5c11\u5185\u5b58\u5360\u7528\u548c\u63d0\u9ad8\u8bfb\u53d6\u901f\u5ea6\u3002<\/p>\n<\/p>\n
                      \n
                    1. \u4f7f\u7528NUMPY\u52a0\u901f\u8ba1\u7b97<\/strong><\/li>\n<\/ol>\n
                      \u5bf9\u4e8e\u6570\u503c\u8ba1\u7b97-intensive\u7684\u4efb\u52a1\uff0c\u53ef\u4ee5\u5c06\u6570\u636e\u8f6c\u6362\u4e3anumpy<\/code>\u6570\u7ec4\u4ee5\u63d0\u9ad8\u8ba1\u7b97\u6548\u7387\uff1a<\/p>\n<\/p>\n
                      import numpy as np<\/p>\n
                      data_array = df['Column1'].to_numpy()<\/p>\n
                      result = np.mean(data_array)<\/p>\n
                      <\/code><\/pre>\n<\/p>\n
                      \u4e94\u3001\u603b\u7ed3<\/p>\n<\/p>\n
                      \u901a\u8fc7\u672c\u6587\uff0c\u6211\u4eec\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u5982\u4f55\u4f7f\u7528Python\u5904\u7406xls\u548cxlsx\u6587\u4ef6\u3002\u6839\u636e\u4e0d\u540c\u7684\u9700\u6c42\u548c\u6587\u4ef6\u683c\u5f0f\uff0c\u53ef\u4ee5\u9009\u62e9\u9002\u5408\u7684\u5e93\uff0c\u5982xlrd<\/code>\u3001openpyxl<\/code>\u3001pandas<\/code>\u7b49\u3002\u65e0\u8bba\u662f\u7b80\u5355\u7684\u8bfb\u53d6\u548c\u5199\u5165\u64cd\u4f5c\uff0c\u8fd8\u662f\u590d\u6742\u7684\u6570\u636e\u5206\u6790\u548c\u5904\u7406\uff0cPython\u90fd\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u5de5\u5177\u6765\u6ee1\u8db3\u9700\u6c42\u3002\u5728\u5b9e\u9645\u9879\u76ee\u4e2d\uff0c\u6839\u636e\u6570\u636e\u91cf\u548c\u6027\u80fd\u8981\u6c42\uff0c\u5408\u7406\u9009\u62e9\u548c\u4f18\u5316\u4ee3\u7801\uff0c\u53ef\u4ee5\u663e\u8457\u63d0\u9ad8\u6548\u7387\u548c\u7528\u6237\u4f53\u9a8c\u3002<\/p>\n<\/p>\n
                      \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                       \u5982\u4f55\u4f7f\u7528Python\u8bfb\u53d6XLS\u6587\u4ef6\uff1f<\/strong>
                      \u8981\u4f7f\u7528Python\u8bfb\u53d6XLS\u6587\u4ef6\uff0c\u53ef\u4ee5\u4f7f\u7528pandas<\/code>\u5e93\u548cxlrd<\/code>\u5e93\u3002\u9996\u5148\uff0c\u786e\u4fdd\u5b89\u88c5\u4e86\u8fd9\u4e24\u4e2a\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7\u547d\u4ee4pip install pandas xlrd<\/code>\u8fdb\u884c\u5b89\u88c5\u3002\u7136\u540e\uff0c\u4f7f\u7528pandas<\/code>\u4e2d\u7684read_excel<\/code>\u51fd\u6570\u6765\u8bfb\u53d6\u6587\u4ef6\u3002\u4f8b\u5982\uff0cdf = pd.read_excel('file.xls')<\/code>\u5c06\u8bfb\u53d6\u540d\u4e3afile.xls<\/code>\u7684Excel\u6587\u4ef6\uff0c\u5e76\u5c06\u5176\u5b58\u50a8\u4e3aDataFrame\u5bf9\u8c61\uff0c\u65b9\u4fbf\u540e\u7eed\u7684\u6570\u636e\u5904\u7406\u3002<\/p>\n
                      Python\u4e2d\u6709\u54ea\u4e9b\u5e93\u53ef\u4ee5\u5904\u7406XLS\u6587\u4ef6\uff1f<\/strong>
                      \u5728Python\u4e2d\uff0c\u6709\u51e0\u4e2a\u6d41\u884c\u7684\u5e93\u53ef\u4ee5\u5904\u7406XLS\u6587\u4ef6\uff0c\u5305\u62ecpandas<\/code>\u3001xlrd<\/code>\u3001openpyxl<\/code>\u548cpyxlsb<\/code>\u3002pandas<\/code>\u9002\u5408\u8fdb\u884c\u6570\u636e\u5206\u6790\u548c\u5904\u7406\uff0cxlrd<\/code>\u4e13\u6ce8\u4e8e\u8bfb\u53d6Excel\u6587\u4ef6\uff0copenpyxl<\/code>\u53ef\u4ee5\u5904\u7406XLSX\u683c\u5f0f\u7684\u6587\u4ef6\u5e76\u652f\u6301\u5199\u5165\uff0c\u800cpyxlsb<\/code>\u5219\u7528\u4e8e\u8bfb\u53d6XLSB\u683c\u5f0f\u7684\u6587\u4ef6\u3002\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5e93\uff0c\u53ef\u4ee5\u66f4\u9ad8\u6548\u5730\u5b8c\u6210\u4efb\u52a1\u3002<\/p>\n
                      \u5904\u7406XLS\u6587\u4ef6\u65f6\u5982\u4f55\u5904\u7406\u6570\u636e\u7c7b\u578b\u95ee\u9898\uff1f<\/strong>
                      \u5728\u8bfb\u53d6XLS\u6587\u4ef6\u65f6\uff0c\u6570\u636e\u7c7b\u578b\u53ef\u80fd\u4f1a\u51fa\u73b0\u95ee\u9898\u3002\u4f8b\u5982\uff0c\u67d0\u4e9b\u5217\u53ef\u80fd\u88ab\u9519\u8bef\u5730\u8bc6\u522b\u4e3a\u5b57\u7b26\u4e32\u6216\u6570\u5b57\u3002\u53ef\u4ee5\u4f7f\u7528pandas<\/code>\u7684dtype<\/code>\u53c2\u6570\u6765\u6307\u5b9a\u6bcf\u5217\u7684\u6570\u636e\u7c7b\u578b\uff0c\u786e\u4fdd\u6570\u636e\u7684\u51c6\u786e\u6027\u3002\u4f8b\u5982\uff0cdf = pd.read_excel('file.xls', dtype={'column_name': str})<\/code>\u53ef\u4ee5\u5f3a\u5236\u5c06\u7279\u5b9a\u5217\u7684\u6570\u636e\u7c7b\u578b\u8bbe\u7f6e\u4e3a\u5b57\u7b26\u4e32\u3002\u8fd9\u6837\u53ef\u4ee5\u907f\u514d\u5728\u6570\u636e\u5206\u6790\u65f6\u51fa\u73b0\u9519\u8bef\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u5904\u7406xls\u6587\u4ef6\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528\u591a\u4e2a\u5e93\u6765\u5b9e\u73b0\uff0c\u5e38\u89c1\u7684\u5305\u62ec\uff1axlrd\u3001openpyxl\u3001pandas\u3002\u5176\u4e2d […]","protected":false},"author":3,"featured_media":996865,"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\/996851"}],"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=996851"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/996851\/revisions"}],"predecessor-version":[{"id":996868,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/996851\/revisions\/996868"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/996865"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=996851"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=996851"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=996851"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}