{"id":1155666,"date":"2025-01-13T18:06:27","date_gmt":"2025-01-13T10:06:27","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1155666.html"},"modified":"2025-01-13T18:06:29","modified_gmt":"2025-01-13T10:06:29","slug":"python%e4%b8%ad%e5%a6%82%e4%bd%95%e6%8f%90%e5%8f%96awk","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1155666.html","title":{"rendered":"Python\u4e2d\u5982\u4f55\u63d0\u53d6awk"},"content":{"rendered":"

\"Python\u4e2d\u5982\u4f55\u63d0\u53d6awk\"<\/p>\n

\u5728Python\u4e2d\u63d0\u53d6awk\u547d\u4ee4\u7684\u66ff\u4ee3\u65b9\u6cd5\u3001\u4f7f\u7528subprocess\u6a21\u5757\u3001\u4f7f\u7528pandas\u5e93<\/strong><\/p>\n<\/p>\n

\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u6765\u63d0\u53d6\u548c\u66ff\u4ee3awk\u547d\u4ee4\uff0c\u5176\u4e2d\u5e38\u89c1\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528subprocess\u6a21\u5757\u3001\u4f7f\u7528pandas\u5e93\u5904\u7406\u6570\u636e<\/strong>\u3002subprocess\u6a21\u5757\u53ef\u4ee5\u6267\u884cShell\u547d\u4ee4\uff0c\u800cpandas\u5e93\u5219\u53ef\u4ee5\u9ad8\u6548\u5730\u5904\u7406\u6570\u636e\u3002\u5176\u4e2d\uff0c\u4f7f\u7528pandas\u5e93\u5904\u7406\u6570\u636e\u662f\u4e00\u79cd\u66f4\u52a0Pythonic\u4e14\u5f3a\u5927\u7075\u6d3b\u7684\u65b9\u6cd5<\/strong>\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528pandas\u5e93\u6765\u66ff\u4ee3awk\u547d\u4ee4\u7684\u529f\u80fd\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528subprocess\u6a21\u5757<\/h3>\n<\/p>\n

1.1 subprocess\u6a21\u5757\u7b80\u4ecb<\/h4>\n<\/p>\n

subprocess\u6a21\u5757\u5141\u8bb8\u4f60\u751f\u6210\u65b0\u7684\u8fdb\u7a0b\u3001\u8fde\u63a5\u5b83\u4eec\u7684\u8f93\u5165\/\u8f93\u51fa\/\u9519\u8bef\u7ba1\u9053\uff0c\u5e76\u83b7\u5f97\u5b83\u4eec\u7684\u8fd4\u56de\u7801\u3002\u5b83\u63d0\u4f9b\u4e86\u4e00\u4e2a\u66f4\u5f3a\u5927\u7684\u63a5\u53e3\u6765\u521b\u5efa\u548c\u7ba1\u7406\u5b50\u8fdb\u7a0b\u3002<\/p>\n<\/p>\n

1.2 \u4f7f\u7528subprocess\u6267\u884cawk\u547d\u4ee4<\/h4>\n<\/p>\n

\u4f7f\u7528subprocess\u6a21\u5757\u6765\u6267\u884cawk\u547d\u4ee4\u7684\u57fa\u672c\u6b65\u9aa4\u5305\u62ec\uff1a<\/p>\n<\/p>\n

    \n
  • \u521b\u5efa\u5b50\u8fdb\u7a0b<\/li>\n
  • \u6267\u884cawk\u547d\u4ee4<\/li>\n
  • \u6355\u83b7\u8f93\u51fa<\/li>\n<\/ul>\n

    \u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\u4ee3\u7801\uff0c\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528subprocess\u6a21\u5757\u6267\u884cawk\u547d\u4ee4\uff1a<\/p>\n<\/p>\n

    import subprocess<\/p>\n
    command = "awk '{print $1}' input_file.txt"<\/p>\n
    process = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)<\/p>\n
    stdout, stderr = process.communicate()<\/p>\n
    if process.returncode == 0:<\/p>\n
        print(stdout.decode())<\/p>\n
    else:<\/p>\n
        print(f"Error: {stderr.decode()}")<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528subprocess.Popen\u6765\u521b\u5efa\u5b50\u8fdb\u7a0b\u5e76\u6267\u884cawk\u547d\u4ee4\u3002\u7136\u540e\uff0c\u901a\u8fc7communicate\u65b9\u6cd5\u6355\u83b7\u547d\u4ee4\u7684\u8f93\u51fa\u548c\u9519\u8bef\u3002<\/p>\n<\/p>\n
    1.3 \u6ce8\u610f\u4e8b\u9879<\/h4>\n<\/p>\n
    \u4f7f\u7528subprocess\u6a21\u5757\u65f6\u9700\u8981\u6ce8\u610f\u4ee5\u4e0b\u51e0\u70b9\uff1a<\/p>\n<\/p>\n
      \n
    • \u5b89\u5168\u6027\uff1a\u5982\u679c\u4f60\u7684\u8f93\u5165\u662f\u7528\u6237\u63d0\u4f9b\u7684\uff0c\u5219\u9700\u8981\u7279\u522b\u5c0f\u5fc3\u6ce8\u5165\u653b\u51fb\u3002<\/li>\n
    • \u6027\u80fd\uff1a\u8c03\u7528\u5916\u90e8\u547d\u4ee4\u4f1a\u5f15\u5165\u989d\u5916\u7684\u5f00\u9500\uff0c\u5c24\u5176\u662f\u5728\u5904\u7406\u5927\u91cf\u6570\u636e\u65f6\u3002<\/li>\n<\/ul>\n
      \u4e8c\u3001\u4f7f\u7528pandas\u5e93<\/h3>\n<\/p>\n
      2.1 pandas\u5e93\u7b80\u4ecb<\/h4>\n<\/p>\n
      pandas\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u548c\u64cd\u4f5c\u5e93\uff0c\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u636e\u7ed3\u6784\u548c\u6570\u636e\u5206\u6790\u5de5\u5177\u3002\u5b83\u80fd\u591f\u5904\u7406\u7ed3\u6784\u5316\u6570\u636e\uff08\u5982CSV\u6587\u4ef6\u3001Excel\u6587\u4ef6\u3001SQL\u6570\u636e\u5e93\u7b49\uff09\uff0c\u5e76\u63d0\u4f9b\u4e30\u5bcc\u7684\u6570\u636e\u64cd\u4f5c\u548c\u5206\u6790\u529f\u80fd\u3002<\/p>\n<\/p>\n
      2.2 \u5b89\u88c5pandas<\/h4>\n<\/p>\n
      \u5728\u4f7f\u7528pandas\u4e4b\u524d\uff0c\u9700\u8981\u5148\u5b89\u88c5\u5b83\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
      pip install pandas<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      2.3 \u4f7f\u7528pandas\u66ff\u4ee3awk\u547d\u4ee4<\/h4>\n<\/p>\n
      pandas\u5e93\u63d0\u4f9b\u4e86\u591a\u79cd\u6570\u636e\u64cd\u4f5c\u65b9\u6cd5\uff0c\u53ef\u4ee5\u8f7b\u677e\u66ff\u4ee3awk\u547d\u4ee4\u7684\u529f\u80fd\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684awk\u529f\u80fd\u53ca\u5176\u5728pandas\u4e2d\u7684\u66ff\u4ee3\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
      2.3.1 \u63d0\u53d6\u5217<\/h5>\n<\/p>\n
      awk\u547d\u4ee4\uff1aawk '{print $1}' input_file.txt<\/code><\/p>\n<\/p>\n
      pandas\u66ff\u4ee3\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
      import pandas as pd<\/p>\n
      df = pd.read_csv('input_file.txt', delim_whitespace=True, header=None)<\/p>\n
      print(df[0])<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528pandas\u7684read_csv\u51fd\u6570\u8bfb\u53d6\u6570\u636e\uff0c\u5e76\u901a\u8fc7\u6307\u5b9adelim_whitespace=True\u6765\u5904\u7406\u4ee5\u7a7a\u767d\u5206\u9694\u7684\u6587\u4ef6\u3002\u7136\u540e\uff0c\u901a\u8fc7\u5217\u7d22\u5f15\u6765\u63d0\u53d6\u7b2c\u4e00\u5217\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n
      2.3.2 \u7b5b\u9009\u884c<\/h5>\n<\/p>\n
      awk\u547d\u4ee4\uff1aawk '$1 > 10' input_file.txt<\/code><\/p>\n<\/p>\n
      pandas\u66ff\u4ee3\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
      import pandas as pd<\/p>\n
      df = pd.read_csv('input_file.txt', delim_whitespace=True, header=None)<\/p>\n
      filtered_df = df[df[0] > 10]<\/p>\n
      print(filtered_df)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528pandas\u7684\u5e03\u5c14\u7d22\u5f15\u6765\u7b5b\u9009\u7b2c\u4e00\u5217\u5927\u4e8e10\u7684\u884c\u3002<\/p>\n<\/p>\n
      2.3.3 \u5206\u7ec4\u548c\u805a\u5408<\/h5>\n<\/p>\n
      awk\u547d\u4ee4\uff1aawk '{arr[$1]+=$2} END {for (i in arr) print i, arr[i]}' input_file.txt<\/code><\/p>\n<\/p>\n
      pandas\u66ff\u4ee3\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
      import pandas as pd<\/p>\n
      df = pd.read_csv('input_file.txt', delim_whitespace=True, header=None)<\/p>\n
      grouped_df = df.groupby(0)[1].sum()<\/p>\n
      print(grouped_df)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528pandas\u7684groupby\u65b9\u6cd5\u5bf9\u7b2c\u4e00\u5217\u8fdb\u884c\u5206\u7ec4\uff0c\u5e76\u5bf9\u7b2c\u4e8c\u5217\u8fdb\u884c\u6c42\u548c\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
      2.4 pandas\u7684\u4f18\u52bf<\/h4>\n<\/p>\n
      \u4e0eawk\u76f8\u6bd4\uff0cpandas\u5177\u6709\u4ee5\u4e0b\u4f18\u52bf\uff1a<\/p>\n<\/p>\n
        \n
      • \u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u80fd\u529b<\/strong>\uff1apandas\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u64cd\u4f5c\u548c\u5206\u6790\u529f\u80fd\uff0c\u53ef\u4ee5\u5904\u7406\u590d\u6742\u7684\u6570\u636e\u5904\u7406\u9700\u6c42\u3002<\/li>\n
      • \u826f\u597d\u7684\u96c6\u6210\u6027<\/strong>\uff1apandas\u4e0e\u5176\u4ed6Python\u5e93\uff08\u5982NumPy\u3001Matplotlib\u7b49\uff09\u65e0\u7f1d\u96c6\u6210\uff0c\u65b9\u4fbf\u8fdb\u884c\u8fdb\u4e00\u6b65\u7684\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\u3002<\/li>\n
      • \u6613\u4e8e\u7ef4\u62a4\u548c\u6269\u5c55<\/strong>\uff1a\u4f7f\u7528pandas\u7f16\u5199\u7684\u4ee3\u7801\u66f4\u52a0\u6613\u8bfb\u3001\u6613\u4e8e\u7ef4\u62a4\u548c\u6269\u5c55\u3002<\/li>\n<\/ul>\n
        \u4e09\u3001pandas\u8be6\u7ec6\u5e94\u7528\u793a\u4f8b<\/h3>\n<\/p>\n
        \u4e3a\u4e86\u66f4\u597d\u5730\u5c55\u793apandas\u7684\u5f3a\u5927\u529f\u80fd\uff0c\u6211\u4eec\u5c06\u901a\u8fc7\u4e00\u4e2a\u8be6\u7ec6\u7684\u5e94\u7528\u793a\u4f8b\u6765\u5c55\u793a\u5982\u4f55\u4f7f\u7528pandas\u8fdb\u884c\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u3002<\/p>\n<\/p>\n
        3.1 \u793a\u4f8b\u6570\u636e\u96c6<\/h4>\n<\/p>\n
        \u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2aCSV\u6587\u4ef6data.csv<\/code>\uff0c\u5185\u5bb9\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
        Date,Product,Sales,Revenue<\/p>\n
        2023-01-01,ProductA,100,2000<\/p>\n
        2023-01-01,ProductB,150,3000<\/p>\n
        2023-01-02,ProductA,200,4000<\/p>\n
        2023-01-02,ProductB,250,5000<\/p>\n
        2023-01-03,ProductA,300,6000<\/p>\n
        2023-01-03,ProductB,350,7000<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        3.2 \u6570\u636e\u8bfb\u53d6\u548c\u9884\u5904\u7406<\/h4>\n<\/p>\n
        \u9996\u5148\uff0c\u6211\u4eec\u4f7f\u7528pandas\u8bfb\u53d6\u6570\u636e\u5e76\u8fdb\u884c\u9884\u5904\u7406\uff1a<\/p>\n<\/p>\n
        import pandas as pd<\/p>\n
        \u8bfb\u53d6\u6570\u636e<\/strong><\/h2>\n
        df = pd.read_csv('data.csv')<\/p>\n
        \u67e5\u770b\u6570\u636e\u57fa\u672c\u4fe1\u606f<\/strong><\/h2>\n
        print(df.info())<\/p>\n
        print(df.head())<\/p>\n
        \u8f6c\u6362\u65e5\u671f\u5217\u4e3adatetime\u7c7b\u578b<\/strong><\/h2>\n
        df['Date'] = pd.to_datetime(df['Date'])<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        3.3 \u6570\u636e\u7b5b\u9009\u548c\u8fc7\u6ee4<\/h4>\n<\/p>\n
        \u6211\u4eec\u53ef\u4ee5\u6839\u636e\u7279\u5b9a\u6761\u4ef6\u7b5b\u9009\u6570\u636e\uff0c\u4f8b\u5982\uff0c\u7b5b\u9009\u51fa\u9500\u552e\u91cf\u5927\u4e8e200\u7684\u8bb0\u5f55\uff1a<\/p>\n<\/p>\n
        filtered_df = df[df['Sales'] > 200]<\/p>\n
        print(filtered_df)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        3.4 \u6570\u636e\u5206\u7ec4\u548c\u805a\u5408<\/h4>\n<\/p>\n
        \u6211\u4eec\u53ef\u4ee5\u6839\u636e\u4ea7\u54c1\u540d\u79f0\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\uff0c\u5e76\u8ba1\u7b97\u6bcf\u4e2a\u4ea7\u54c1\u7684\u603b\u9500\u552e\u91cf\u548c\u603b\u6536\u5165\uff1a<\/p>\n<\/p>\n
        grouped_df = df.groupby('Product').agg({'Sales': 'sum', 'Revenue': 'sum'})<\/p>\n
        print(grouped_df)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        3.5 \u6570\u636e\u900f\u89c6\u8868<\/h4>\n<\/p>\n
        \u6211\u4eec\u53ef\u4ee5\u521b\u5efa\u6570\u636e\u900f\u89c6\u8868\uff0c\u5c55\u793a\u6bcf\u4e2a\u4ea7\u54c1\u5728\u6bcf\u4e00\u5929\u7684\u9500\u552e\u60c5\u51b5\uff1a<\/p>\n<\/p>\n
        pivot_df = df.pivot_table(index='Date', columns='Product', values='Sales', aggfunc='sum')<\/p>\n
        print(pivot_df)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        3.6 \u6570\u636e\u53ef\u89c6\u5316<\/h4>\n<\/p>\n
        \u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u5bf9\u6570\u636e\u8fdb\u884c\u53ef\u89c6\u5316\u5c55\u793a\uff1a<\/p>\n<\/p>\n
        import matplotlib.pyplot as plt<\/p>\n
        \u7ed8\u5236\u6bcf\u4e2a\u4ea7\u54c1\u7684\u9500\u552e\u8d8b\u52bf<\/strong><\/h2>\n
        for product in df['Product'].unique():<\/p>\n
            product_df = df[df['Product'] == product]<\/p>\n
            plt.plot(product_df['Date'], product_df['Sales'], label=product)<\/p>\n
        plt.xlabel('Date')<\/p>\n
        plt.ylabel('Sales')<\/p>\n
        plt.title('Sales Trend by Product')<\/p>\n
        plt.legend()<\/p>\n
        plt.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u56db\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
        \u901a\u8fc7\u4ee5\u4e0a\u793a\u4f8b\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\uff0cpandas\u5e93\u5728\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u65b9\u9762\u5177\u6709\u5f3a\u5927\u7684\u529f\u80fd\u3002\u4e0eawk\u76f8\u6bd4\uff0cpandas\u4e0d\u4ec5\u63d0\u4f9b\u4e86\u66f4\u5f3a\u5927\u7684\u6570\u636e\u64cd\u4f5c\u80fd\u529b\uff0c\u8fd8\u80fd\u591f\u4e0e\u5176\u4ed6Python\u5e93\u65e0\u7f1d\u96c6\u6210\uff0c\u65b9\u4fbf\u8fdb\u884c\u8fdb\u4e00\u6b65\u7684\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
        \u5728Python\u4e2d\uff0c\u4f7f\u7528pandas\u5e93\u5904\u7406\u6570\u636e\u662f\u4e00\u79cd\u66f4\u52a0Pythonic\u4e14\u5f3a\u5927\u7075\u6d3b\u7684\u65b9\u6cd5<\/strong>\u3002\u901a\u8fc7\u638c\u63e1pandas\u7684\u57fa\u672c\u64cd\u4f5c\u548c\u9ad8\u7ea7\u529f\u80fd\uff0c\u6211\u4eec\u53ef\u4ee5\u9ad8\u6548\u5730\u8fdb\u884c\u6570\u636e\u5904\u7406\u548c\u5206\u6790\uff0c\u66ff\u4ee3awk\u547d\u4ee4\uff0c\u5b9e\u73b0\u66f4\u590d\u6742\u7684\u6570\u636e\u64cd\u4f5c\u9700\u6c42\u3002<\/p>\n<\/p>\n
        \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
         \u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528awk\u7684\u529f\u80fd\uff1f<\/strong>
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        \u9664\u4e86\u4f7f\u7528awk<\/code>\u672c\u8eab\uff0cPython\u7684open()<\/code>\u51fd\u6570\u914d\u5408\u6587\u4ef6\u8fed\u4ee3\u5668\u53ef\u4ee5\u9010\u884c\u8bfb\u53d6\u6587\u4ef6\uff0c\u7ed3\u5408\u5217\u8868\u63a8\u5bfc\u5f0f\u6216\u751f\u6210\u5668\u8868\u8fbe\u5f0f\u8fdb\u884c\u6570\u636e\u5904\u7406\u3002\u6b64\u5916\uff0cpandas<\/code>\u5e93\u63d0\u4f9b\u7684read_csv()<\/code>\u51fd\u6570\u4e5f\u80fd\u591f\u76f4\u63a5\u8bfb\u53d6\u6587\u4ef6\u5e76\u5904\u7406\u6570\u636e\uff0c\u9002\u5408\u5927\u89c4\u6a21\u6570\u636e\u5206\u6790\u3002<\/p>\n
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