{"id":1126775,"date":"2025-01-08T20:04:13","date_gmt":"2025-01-08T12:04:13","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1126775.html"},"modified":"2025-01-08T20:04:15","modified_gmt":"2025-01-08T12:04:15","slug":"python%e5%a6%82%e4%bd%95%e6%8a%8atxt%e7%9a%84%e6%af%8f%e4%b8%80%e5%88%97%e6%95%b0%e6%8d%ae","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1126775.html","title":{"rendered":"python\u5982\u4f55\u628atxt\u7684\u6bcf\u4e00\u5217\u6570\u636e"},"content":{"rendered":"

\"python\u5982\u4f55\u628atxt\u7684\u6bcf\u4e00\u5217\u6570\u636e\"<\/p>\n

\u8981\u5c06txt\u6587\u4ef6\u4e2d\u7684\u6bcf\u4e00\u5217\u6570\u636e\u63d0\u53d6\u51fa\u6765\u5e76\u8fdb\u884c\u5904\u7406\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u7684\u591a\u79cd\u5de5\u5177\u548c\u65b9\u6cd5\uff0c\u5982\u8bfb\u53d6\u6587\u4ef6\u3001\u5206\u5272\u5b57\u7b26\u4e32\u3001\u5904\u7406\u6570\u636e\u7b49\u3002\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528\u5185\u5efa\u7684\u6587\u4ef6\u64cd\u4f5c\u51fd\u6570\u3001Pandas\u5e93\u3001Numpy\u5e93\u7b49\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u5b9e\u73b0\u8fd9\u4e9b\u64cd\u4f5c\u3002<\/strong><\/p>\n<\/p>\n

\u4e00\u3001\u8bfb\u53d6txt\u6587\u4ef6<\/p>\n<\/p>\n

    \n
  1. \u4f7f\u7528\u5185\u5efa\u7684\u6587\u4ef6\u64cd\u4f5c\u51fd\u6570\uff1a<\/p>\n

    Python\u63d0\u4f9b\u4e86\u57fa\u672c\u7684\u6587\u4ef6\u64cd\u4f5c\u51fd\u6570\uff0c\u5982open()\u3001read()\u3001write()\u7b49\uff0c\u53ef\u4ee5\u7528\u6765\u8bfb\u53d6\u548c\u5199\u5165txt\u6587\u4ef6\u3002\u4f7f\u7528\u8fd9\u4e9b\u51fd\u6570\u53ef\u4ee5\u5b9e\u73b0\u57fa\u672c\u7684\u6587\u4ef6\u64cd\u4f5c\u3002<\/li>\n<\/p>\n<\/ol>\n

    # \u8bfb\u53d6\u6587\u4ef6\u5e76\u5c06\u6bcf\u4e00\u884c\u5b58\u5165\u5217\u8868<\/p>\n
    with open('data.txt', 'r') as file:<\/p>\n
        lines = file.readlines()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u4f7f\u7528Pandas\u5e93\uff1a<\/p>\n
      Pandas\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u63d0\u4f9b\u4e86\u8bfb\u53d6txt\u6587\u4ef6\u7684\u65b9\u4fbf\u65b9\u6cd5\uff0c\u5982read_csv()\u7b49\uff0c\u53ef\u4ee5\u5c06txt\u6587\u4ef6\u76f4\u63a5\u8bfb\u53d6\u4e3aDataFrame\u683c\u5f0f\uff0c\u65b9\u4fbf\u540e\u7eed\u6570\u636e\u5904\u7406\u3002<\/li>\n<\/p>\n<\/ol>\n
      import pandas as pd<\/p>\n
      \u8bfb\u53d6txt\u6587\u4ef6\u5e76\u5b58\u5165DataFrame<\/strong><\/h2>\n
      df = pd.read_csv('data.txt', delimiter='\\t')  # \u5047\u8bbe\u4ee5\u5236\u8868\u7b26\u5206\u9694<\/p>\n
      <\/code><\/pre>\n<\/p>\n
        \n
      1. \u4f7f\u7528Numpy\u5e93\uff1a<\/p>\n
        Numpy\u662f\u4e00\u4e2a\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u63d0\u4f9b\u4e86\u8bfb\u53d6txt\u6587\u4ef6\u7684\u51fd\u6570\uff0c\u5982loadtxt()\u3001genfromtxt()\u7b49\uff0c\u53ef\u4ee5\u5c06txt\u6587\u4ef6\u8bfb\u53d6\u4e3a\u6570\u7ec4\u683c\u5f0f\u3002<\/li>\n<\/p>\n<\/ol>\n
        import numpy as np<\/p>\n
        \u8bfb\u53d6txt\u6587\u4ef6\u5e76\u5b58\u5165\u6570\u7ec4<\/strong><\/h2>\n
        data = np.loadtxt('data.txt', delimiter='\\t')  # \u5047\u8bbe\u4ee5\u5236\u8868\u7b26\u5206\u9694<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u4e8c\u3001\u5904\u7406\u6bcf\u4e00\u5217\u6570\u636e<\/strong><\/p>\n<\/p>\n
        \u8bfb\u53d6\u6587\u4ef6\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7\u4e0d\u540c\u7684\u65b9\u6cd5\u63d0\u53d6\u548c\u5904\u7406\u6bcf\u4e00\u5217\u6570\u636e\u3002\u4ee5\u4e0b\u5206\u522b\u4ecb\u7ecd\u4f7f\u7528\u5185\u5efa\u51fd\u6570\u3001Pandas\u548cNumpy\u7684\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
          \n
        1. \u4f7f\u7528\u5185\u5efa\u51fd\u6570\u63d0\u53d6\u6bcf\u4e00\u5217\uff1a<\/li>\n<\/ol>\n
          # \u5047\u8bbe\u6587\u4ef6\u4e2d\u7684\u6bcf\u4e00\u884c\u6570\u636e\u4ee5\u7a7a\u683c\u5206\u9694<\/p>\n
          columns = list(zip(*[line.split() for line in lines]))<\/p>\n
          \u5904\u7406\u7b2c\u4e00\u5217\u6570\u636e<\/strong><\/h2>\n
          column1 = [int(value) for value in columns[0]]<\/p>\n
          <\/code><\/pre>\n<\/p>\n
            \n
          1. \u4f7f\u7528Pandas\u63d0\u53d6\u6bcf\u4e00\u5217\uff1a<\/li>\n<\/ol>\n
            # \u5047\u8bbe\u7b2c\u4e00\u5217\u662f\u6574\u6570<\/p>\n
            column1 = df.iloc[:, 0].astype(int).tolist()<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. \u4f7f\u7528Numpy\u63d0\u53d6\u6bcf\u4e00\u5217\uff1a<\/li>\n<\/ol>\n
              # \u5047\u8bbe\u7b2c\u4e00\u5217\u662f\u6574\u6570<\/p>\n
              column1 = data[:, 0].astype(int)<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u4e09\u3001\u5177\u4f53\u7684\u5904\u7406\u65b9\u6cd5<\/strong><\/p>\n<\/p>\n
              \u5904\u7406\u6bcf\u4e00\u5217\u7684\u6570\u636e\u53ef\u4ee5\u5305\u62ec\u591a\u79cd\u64cd\u4f5c\uff0c\u5982\u8ba1\u7b97\u5e73\u5747\u503c\u3001\u65b9\u5dee\u3001\u6700\u5927\u503c\u3001\u6700\u5c0f\u503c\u7b49\u3002\u4ee5\u4e0b\u4ee5\u8ba1\u7b97\u5e73\u5747\u503c\u4e3a\u4f8b\uff0c\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u5b9e\u73b0\u8fd9\u4e9b\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
                \n
              1. \u4f7f\u7528\u5185\u5efa\u51fd\u6570\u8ba1\u7b97\u5e73\u5747\u503c\uff1a<\/li>\n<\/ol>\n
                # \u8ba1\u7b97\u7b2c\u4e00\u5217\u6570\u636e\u7684\u5e73\u5747\u503c<\/p>\n
                average = sum(column1) \/ len(column1)<\/p>\n
                print(f'\u7b2c\u4e00\u5217\u6570\u636e\u7684\u5e73\u5747\u503c\u662f\uff1a{average}')<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                  \n
                1. \u4f7f\u7528Pandas\u8ba1\u7b97\u5e73\u5747\u503c\uff1a<\/li>\n<\/ol>\n
                  # \u8ba1\u7b97\u7b2c\u4e00\u5217\u6570\u636e\u7684\u5e73\u5747\u503c<\/p>\n
                  average = df.iloc[:, 0].mean()<\/p>\n
                  print(f'\u7b2c\u4e00\u5217\u6570\u636e\u7684\u5e73\u5747\u503c\u662f\uff1a{average}')<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                    \n
                  1. \u4f7f\u7528Numpy\u8ba1\u7b97\u5e73\u5747\u503c\uff1a<\/li>\n<\/ol>\n
                    # \u8ba1\u7b97\u7b2c\u4e00\u5217\u6570\u636e\u7684\u5e73\u5747\u503c<\/p>\n
                    average = np.mean(column1)<\/p>\n
                    print(f'\u7b2c\u4e00\u5217\u6570\u636e\u7684\u5e73\u5747\u503c\u662f\uff1a{average}')<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                    \u56db\u3001\u5904\u7406\u591a\u5217\u6570\u636e<\/strong><\/p>\n<\/p>\n
                    \u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u53ef\u80fd\u9700\u8981\u5904\u7406\u591a\u4e2a\u5217\u7684\u6570\u636e\u3002\u53ef\u4ee5\u901a\u8fc7\u5faa\u73af\u6216\u5411\u91cf\u5316\u64cd\u4f5c\u6765\u5b9e\u73b0\u3002\u4ee5\u4e0b\u5206\u522b\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u5185\u5efa\u51fd\u6570\u3001Pandas\u548cNumpy\u5904\u7406\u591a\u5217\u6570\u636e\u3002<\/p>\n<\/p>\n
                      \n
                    1. \u4f7f\u7528\u5185\u5efa\u51fd\u6570\u5904\u7406\u591a\u5217\u6570\u636e\uff1a<\/li>\n<\/ol>\n
                      # \u8ba1\u7b97\u6bcf\u4e00\u5217\u6570\u636e\u7684\u5e73\u5747\u503c<\/p>\n
                      averages = [sum(column) \/ len(column) for column in columns]<\/p>\n
                      for i, avg in enumerate(averages):<\/p>\n
                          print(f'\u7b2c{i+1}\u5217\u6570\u636e\u7684\u5e73\u5747\u503c\u662f\uff1a{avg}')<\/p>\n
                      <\/code><\/pre>\n<\/p>\n
                        \n
                      1. \u4f7f\u7528Pandas\u5904\u7406\u591a\u5217\u6570\u636e\uff1a<\/li>\n<\/ol>\n
                        # \u8ba1\u7b97\u6bcf\u4e00\u5217\u6570\u636e\u7684\u5e73\u5747\u503c<\/p>\n
                        averages = df.mean()<\/p>\n
                        for i, avg in enumerate(averages):<\/p>\n
                            print(f'\u7b2c{i+1}\u5217\u6570\u636e\u7684\u5e73\u5747\u503c\u662f\uff1a{avg}')<\/p>\n
                        <\/code><\/pre>\n<\/p>\n
                          \n
                        1. \u4f7f\u7528Numpy\u5904\u7406\u591a\u5217\u6570\u636e\uff1a<\/li>\n<\/ol>\n
                          # \u8ba1\u7b97\u6bcf\u4e00\u5217\u6570\u636e\u7684\u5e73\u5747\u503c<\/p>\n
                          averages = np.mean(data, axis=0)<\/p>\n
                          for i, avg in enumerate(averages):<\/p>\n
                              print(f'\u7b2c{i+1}\u5217\u6570\u636e\u7684\u5e73\u5747\u503c\u662f\uff1a{avg}')<\/p>\n
                          <\/code><\/pre>\n<\/p>\n
                          \u4e94\u3001\u4fdd\u5b58\u5904\u7406\u540e\u7684\u6570\u636e<\/strong><\/p>\n<\/p>\n
                          \u5904\u7406\u5b8c\u6570\u636e\u540e\uff0c\u53ef\u80fd\u9700\u8981\u5c06\u7ed3\u679c\u4fdd\u5b58\u5230\u65b0\u7684txt\u6587\u4ef6\u4e2d\u3002\u4ee5\u4e0b\u5206\u522b\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u5185\u5efa\u51fd\u6570\u3001Pandas\u548cNumpy\u4fdd\u5b58\u6570\u636e\u3002<\/p>\n<\/p>\n
                            \n
                          1. \u4f7f\u7528\u5185\u5efa\u51fd\u6570\u4fdd\u5b58\u6570\u636e\uff1a<\/li>\n<\/ol>\n
                            # \u5c06\u5e73\u5747\u503c\u4fdd\u5b58\u5230\u65b0\u7684txt\u6587\u4ef6\u4e2d<\/p>\n
                            with open('averages.txt', 'w') as file:<\/p>\n
                                for i, avg in enumerate(averages):<\/p>\n
                                    file.write(f'\u7b2c{i+1}\u5217\u6570\u636e\u7684\u5e73\u5747\u503c\u662f\uff1a{avg}\\n')<\/p>\n
                            <\/code><\/pre>\n<\/p>\n
                              \n
                            1. \u4f7f\u7528Pandas\u4fdd\u5b58\u6570\u636e\uff1a<\/li>\n<\/ol>\n
                              # \u5c06\u5e73\u5747\u503c\u4fdd\u5b58\u5230\u65b0\u7684txt\u6587\u4ef6\u4e2d<\/p>\n
                              averages.to_csv('averages.txt', sep='\\t', header=False)<\/p>\n
                              <\/code><\/pre>\n<\/p>\n
                                \n
                              1. \u4f7f\u7528Numpy\u4fdd\u5b58\u6570\u636e\uff1a<\/li>\n<\/ol>\n
                                # \u5c06\u5e73\u5747\u503c\u4fdd\u5b58\u5230\u65b0\u7684txt\u6587\u4ef6\u4e2d<\/p>\n
                                np.savetxt('averages.txt', averages, delimiter='\\t', header='Averages', comments='')<\/p>\n
                                <\/code><\/pre>\n<\/p>\n
                                \u516d\u3001\u603b\u7ed3<\/strong><\/p>\n<\/p>\n
                                \u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u8bfb\u53d6txt\u6587\u4ef6\uff0c\u5e76\u5bf9\u6bcf\u4e00\u5217\u6570\u636e\u8fdb\u884c\u5904\u7406\u3002Python\u63d0\u4f9b\u4e86\u591a\u79cd\u5de5\u5177\u548c\u65b9\u6cd5\uff0c\u5982\u5185\u5efa\u51fd\u6570\u3001Pandas\u548cNumpy\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u5b9e\u73b0\u8fd9\u4e9b\u64cd\u4f5c\u3002\u5177\u4f53\u9009\u62e9\u54ea\u79cd\u65b9\u6cd5\uff0c\u53ef\u4ee5\u6839\u636e\u5b9e\u9645\u9700\u6c42\u548c\u6570\u636e\u7279\u70b9\u6765\u51b3\u5b9a\u3002\u65e0\u8bba\u662f\u7b80\u5355\u7684\u6587\u4ef6\u64cd\u4f5c\uff0c\u8fd8\u662f\u590d\u6742\u7684\u6570\u636e\u5206\u6790\uff0cPython\u90fd\u80fd\u63d0\u4f9b\u9ad8\u6548\u3001\u7075\u6d3b\u7684\u89e3\u51b3\u65b9\u6848\u3002<\/p>\n<\/p>\n
                                \u5e0c\u671b\u4ee5\u4e0a\u5185\u5bb9\u5bf9\u4f60\u6709\u6240\u5e2e\u52a9\u3002\u5982\u6709\u4efb\u4f55\u7591\u95ee\uff0c\u8bf7\u968f\u65f6\u8be2\u95ee\u3002<\/p>\n<\/p>\n
                                \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                                 \u5982\u4f55\u4f7f\u7528Python\u8bfb\u53d6txt\u6587\u4ef6\u4e2d\u7684\u6bcf\u4e00\u5217\u6570\u636e\uff1f<\/strong>
                                \u8981\u8bfb\u53d6txt\u6587\u4ef6\u4e2d\u7684\u6bcf\u4e00\u5217\u6570\u636e\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u5185\u7f6e\u7684open()<\/code>\u51fd\u6570\u6216pandas<\/code>\u5e93\u3002\u901a\u8fc7\u4f7f\u7528open()<\/code>\u51fd\u6570\uff0c\u53ef\u4ee5\u9010\u884c\u8bfb\u53d6\u6587\u4ef6\u5e76\u4f7f\u7528split()<\/code>\u65b9\u6cd5\u5c06\u6bcf\u884c\u62c6\u5206\u4e3a\u5217\u3002\u5982\u679c\u9009\u62e9\u4f7f\u7528pandas<\/code>\u5e93\uff0c\u4f7f\u7528pd.read_csv()<\/code>\u51fd\u6570\u53ef\u4ee5\u8f7b\u677e\u5904\u7406\u5217\u6570\u636e\uff0c\u751a\u81f3\u53ef\u4ee5\u6307\u5b9a\u5206\u9694\u7b26\u3002<\/p>\n
                                \u5728Python\u4e2d\u5982\u4f55\u5c06txt\u6587\u4ef6\u4e2d\u7684\u5217\u6570\u636e\u8f6c\u6362\u4e3a\u5217\u8868\uff1f<\/strong>
                                \u53ef\u4ee5\u4f7f\u7528\u5217\u8868\u63a8\u5bfc\u5f0f\u6765\u5c06\u6bcf\u5217\u6570\u636e\u8f6c\u6362\u4e3a\u5217\u8868\u3002\u9996\u5148\uff0c\u8bfb\u53d6\u6587\u4ef6\u7684\u6bcf\u4e00\u884c\uff0c\u5c06\u5176\u5206\u5272\u6210\u591a\u4e2a\u5143\u7d20\uff0c\u7136\u540e\u63d0\u53d6\u6240\u9700\u7684\u5217\u5e76\u5c06\u5176\u5b58\u50a8\u5728\u4e00\u4e2a\u5217\u8868\u4e2d\u3002\u8fd9\u79cd\u65b9\u6cd5\u5141\u8bb8\u7075\u6d3b\u9009\u62e9\u6240\u9700\u7684\u5217\u3002<\/p>\n
                                \u4f7f\u7528Python\u5904\u7406txt\u6587\u4ef6\u65f6\uff0c\u5982\u4f55\u5904\u7406\u5217\u4e2d\u7f3a\u5931\u7684\u6570\u636e\uff1f<\/strong>
                                \u5728\u5904\u7406txt\u6587\u4ef6\u4e2d\u7684\u5217\u6570\u636e\u65f6\uff0c\u7f3a\u5931\u6570\u636e\u662f\u4e00\u4e2a\u5e38\u89c1\u95ee\u9898\u3002\u53ef\u4ee5\u4f7f\u7528pandas<\/code>\u7684dropna()<\/code>\u65b9\u6cd5\u6765\u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c\uff0c\u6216\u8005\u4f7f\u7528fillna()<\/code>\u65b9\u6cd5\u586b\u8865\u7f3a\u5931\u503c\u3002\u5728\u4f7f\u7528open()<\/code>\u65b9\u6cd5\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u6761\u4ef6\u5224\u65ad\u6765\u5904\u7406\u7f3a\u5931\u503c\uff0c\u786e\u4fdd\u6570\u636e\u7684\u5b8c\u6574\u6027\u548c\u51c6\u786e\u6027\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u8981\u5c06txt\u6587\u4ef6\u4e2d\u7684\u6bcf\u4e00\u5217\u6570\u636e\u63d0\u53d6\u51fa\u6765\u5e76\u8fdb\u884c\u5904\u7406\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u7684\u591a\u79cd\u5de5\u5177\u548c\u65b9\u6cd5\uff0c\u5982\u8bfb\u53d6\u6587\u4ef6\u3001\u5206\u5272\u5b57\u7b26\u4e32\u3001 […]","protected":false},"author":3,"featured_media":1126778,"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\/1126775"}],"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=1126775"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1126775\/revisions"}],"predecessor-version":[{"id":1126782,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1126775\/revisions\/1126782"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1126778"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1126775"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1126775"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1126775"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}