{"id":1078436,"date":"2025-01-08T12:11:33","date_gmt":"2025-01-08T04:11:33","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1078436.html"},"modified":"2025-01-08T12:11:36","modified_gmt":"2025-01-08T04:11:36","slug":"%e7%94%a8python%e5%a6%82%e4%bd%95%e8%bf%9b%e8%a1%8c%e7%bc%a9%e5%b0%be%e5%a4%84%e7%90%86-3","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1078436.html","title":{"rendered":"\u7528python\u5982\u4f55\u8fdb\u884c\u7f29\u5c3e\u5904\u7406"},"content":{"rendered":"

\"\u7528python\u5982\u4f55\u8fdb\u884c\u7f29\u5c3e\u5904\u7406\"<\/p>\n

\u5f00\u5934\u6bb5\u843d:<\/p>\n

\u7528Python\u8fdb\u884c\u7f29\u5c3e\u5904\u7406\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528Pandas\u3001Numpy\u5e93\u3001\u901a\u8fc7\u8bbe\u5b9a\u9608\u503c\u5bf9\u6570\u636e\u8fdb\u884c\u88c1\u526a\u3002<\/strong> \u7f29\u5c3e\u5904\u7406\u662f\u4e00\u79cd\u5e38\u7528\u7684\u6570\u636e\u6e05\u7406\u6280\u672f\uff0c\u5b83\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u53bb\u9664\u6570\u636e\u96c6\u4e2d\u7684\u6781\u7aef\u503c\uff0c\u4ece\u800c\u4f7f\u6570\u636e\u66f4\u52a0\u5e73\u6ed1\u3001\u7a33\u5b9a\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Python\u8fdb\u884c\u7f29\u5c3e\u5904\u7406\uff0c\u5e76\u5bf9Pandas\u5e93\u7684\u5e94\u7528\u8fdb\u884c\u8be6\u7ec6\u63cf\u8ff0\u3002<\/p>\n<\/p>\n

\u4e00\u3001PANDAS\u5e93\u7684\u5e94\u7528<\/p>\n<\/p>\n

Pandas\u662fPython\u4e2d\u4e00\u4e2a\u975e\u5e38\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u51fd\u6570\u63a5\u53e3\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u6e05\u7406\u3001\u8f6c\u6362\u548c\u5206\u6790\u3002\u4f7f\u7528Pandas\u8fdb\u884c\u7f29\u5c3e\u5904\u7406\u4e3b\u8981\u6d89\u53ca\u5230\u4ee5\u4e0b\u51e0\u4e2a\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n

    \n
  1. \u5bfc\u5165\u6570\u636e<\/strong>\uff1a\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165\u6570\u636e\uff0cPandas\u63d0\u4f9b\u4e86\u591a\u79cd\u6570\u636e\u5bfc\u5165\u65b9\u5f0f\uff0c\u53ef\u4ee5\u4eceCSV\u3001Excel\u3001SQL\u7b49\u591a\u79cd\u683c\u5f0f\u4e2d\u8bfb\u53d6\u6570\u636e\u3002<\/li>\n<\/ol>\n

    import pandas as pd<\/p>\n
    \u4eceCSV\u6587\u4ef6\u4e2d\u8bfb\u53d6\u6570\u636e<\/strong><\/h2>\n
    data = pd.read_csv('data.csv')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u8ba1\u7b97\u9608\u503c<\/strong>\uff1a\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u9700\u8981\u8ba1\u7b97\u51fa\u7f29\u5c3e\u5904\u7406\u7684\u9608\u503c\uff0c\u901a\u5e38\u6211\u4eec\u4f1a\u9009\u62e9\u6570\u636e\u7684\u67d0\u4e2a\u767e\u5206\u4f4d\u6570\u4f5c\u4e3a\u9608\u503c\u3002Pandas\u63d0\u4f9b\u4e86quantile<\/code>\u65b9\u6cd5\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8ba1\u7b97\u51fa\u6570\u636e\u7684\u767e\u5206\u4f4d\u6570\u3002<\/li>\n<\/ol>\n
      # \u8ba1\u7b97\u4e0a\u4e0b\u9608\u503c<\/p>\n
      lower_threshold = data['column_name'].quantile(0.05)<\/p>\n
      upper_threshold = data['column_name'].quantile(0.95)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
        \n
      1. \u5e94\u7528\u9608\u503c\u8fdb\u884c\u7f29\u5c3e\u5904\u7406<\/strong>\uff1a\u4e00\u65e6\u786e\u5b9a\u4e86\u9608\u503c\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u4f7f\u7528Pandas\u7684clip<\/code>\u65b9\u6cd5\u5bf9\u6570\u636e\u8fdb\u884c\u88c1\u526a\uff0c\u5c06\u8d85\u8fc7\u9608\u503c\u7684\u6570\u636e\u66ff\u6362\u4e3a\u9608\u503c\u3002<\/li>\n<\/ol>\n
        # \u5bf9\u6570\u636e\u8fdb\u884c\u7f29\u5c3e\u5904\u7406<\/p>\n
        data['column_name'] = data['column_name'].clip(lower=lower_threshold, upper=upper_threshold)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        1. \u4fdd\u5b58\u5904\u7406\u540e\u7684\u6570\u636e<\/strong>\uff1a\u6700\u540e\uff0c\u5c06\u5904\u7406\u540e\u7684\u6570\u636e\u4fdd\u5b58\u5230\u6587\u4ef6\u4e2d\uff0c\u4ee5\u4fbf\u540e\u7eed\u5206\u6790\u4f7f\u7528\u3002<\/li>\n<\/ol>\n
          # \u4fdd\u5b58\u5904\u7406\u540e\u7684\u6570\u636e<\/p>\n
          data.to_csv('processed_data.csv', index=False)<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u5b8c\u6210\u6570\u636e\u7684\u7f29\u5c3e\u5904\u7406\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Numpy\u5e93\u8fdb\u884c\u7f29\u5c3e\u5904\u7406\u3002<\/p>\n<\/p>\n
          \u4e8c\u3001NUMPY\u5e93\u7684\u5e94\u7528<\/p>\n<\/p>\n
          Numpy\u662fPython\u4e2d\u53e6\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u591a\u79cd\u6570\u7ec4\u64cd\u4f5c\u51fd\u6570\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u8fd0\u7b97\u3002\u4f7f\u7528Numpy\u8fdb\u884c\u7f29\u5c3e\u5904\u7406\u4e3b\u8981\u6d89\u53ca\u5230\u4ee5\u4e0b\u51e0\u4e2a\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n
            \n
          1. \u5bfc\u5165\u6570\u636e<\/strong>\uff1a\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165\u6570\u636e\uff0cNumpy\u63d0\u4f9b\u4e86\u591a\u79cd\u6570\u636e\u5bfc\u5165\u65b9\u5f0f\uff0c\u53ef\u4ee5\u4eceCSV\u3001Excel\u7b49\u591a\u79cd\u683c\u5f0f\u4e2d\u8bfb\u53d6\u6570\u636e\u3002<\/li>\n<\/ol>\n
            import numpy as np<\/p>\n
            \u4eceCSV\u6587\u4ef6\u4e2d\u8bfb\u53d6\u6570\u636e<\/strong><\/h2>\n
            data = np.genfromtxt('data.csv', delimiter=',', skip_header=1)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. \u8ba1\u7b97\u9608\u503c<\/strong>\uff1a\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u9700\u8981\u8ba1\u7b97\u51fa\u7f29\u5c3e\u5904\u7406\u7684\u9608\u503c\uff0c\u901a\u5e38\u6211\u4eec\u4f1a\u9009\u62e9\u6570\u636e\u7684\u67d0\u4e2a\u767e\u5206\u4f4d\u6570\u4f5c\u4e3a\u9608\u503c\u3002Numpy\u63d0\u4f9b\u4e86percentile<\/code>\u65b9\u6cd5\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8ba1\u7b97\u51fa\u6570\u636e\u7684\u767e\u5206\u4f4d\u6570\u3002<\/li>\n<\/ol>\n
              # \u8ba1\u7b97\u4e0a\u4e0b\u9608\u503c<\/p>\n
              lower_threshold = np.percentile(data[:, column_index], 5)<\/p>\n
              upper_threshold = np.percentile(data[:, column_index], 95)<\/p>\n
              <\/code><\/pre>\n<\/p>\n
                \n
              1. \u5e94\u7528\u9608\u503c\u8fdb\u884c\u7f29\u5c3e\u5904\u7406<\/strong>\uff1a\u4e00\u65e6\u786e\u5b9a\u4e86\u9608\u503c\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u4f7f\u7528Numpy\u7684clip<\/code>\u65b9\u6cd5\u5bf9\u6570\u636e\u8fdb\u884c\u88c1\u526a\uff0c\u5c06\u8d85\u8fc7\u9608\u503c\u7684\u6570\u636e\u66ff\u6362\u4e3a\u9608\u503c\u3002<\/li>\n<\/ol>\n
                # \u5bf9\u6570\u636e\u8fdb\u884c\u7f29\u5c3e\u5904\u7406<\/p>\n
                data[:, column_index] = np.clip(data[:, column_index], lower_threshold, upper_threshold)<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                  \n
                1. \u4fdd\u5b58\u5904\u7406\u540e\u7684\u6570\u636e<\/strong>\uff1a\u6700\u540e\uff0c\u5c06\u5904\u7406\u540e\u7684\u6570\u636e\u4fdd\u5b58\u5230\u6587\u4ef6\u4e2d\uff0c\u4ee5\u4fbf\u540e\u7eed\u5206\u6790\u4f7f\u7528\u3002<\/li>\n<\/ol>\n
                  # \u4fdd\u5b58\u5904\u7406\u540e\u7684\u6570\u636e<\/p>\n
                  np.savetxt('processed_data.csv', data, delimiter=',')<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u4f7f\u7528Numpy\u5e93\u5b8c\u6210\u6570\u636e\u7684\u7f29\u5c3e\u5904\u7406\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8ba8\u8bba\u7f29\u5c3e\u5904\u7406\u7684\u5e94\u7528\u573a\u666f\u548c\u6ce8\u610f\u4e8b\u9879\u3002<\/p>\n<\/p>\n
                  \u4e09\u3001\u7f29\u5c3e\u5904\u7406\u7684\u5e94\u7528\u573a\u666f<\/p>\n<\/p>\n
                  \u7f29\u5c3e\u5904\u7406\u5728\u6570\u636e\u6e05\u7406\u548c\u9884\u5904\u7406\u4e2d\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\uff0c\u7279\u522b\u662f\u5728\u4ee5\u4e0b\u51e0\u4e2a\u573a\u666f\u4e2d\uff1a<\/p>\n<\/p>\n
                    \n
                  1. \n
                    \u5904\u7406\u6781\u7aef\u503c<\/strong>\uff1a\u6781\u7aef\u503c\u53ef\u80fd\u4f1a\u5bf9\u6570\u636e\u5206\u6790\u7ed3\u679c\u4ea7\u751f\u663e\u8457\u5f71\u54cd\uff0c\u7f29\u5c3e\u5904\u7406\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u53bb\u9664\u8fd9\u4e9b\u6781\u7aef\u503c\uff0c\u4f7f\u6570\u636e\u66f4\u52a0\u5e73\u6ed1\u3001\u7a33\u5b9a\u3002<\/p>\n<\/p>\n<\/li>\n
                  2. \n
                    \u6570\u636e\u6807\u51c6\u5316<\/strong>\uff1a\u7f29\u5c3e\u5904\u7406\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u5c06\u6570\u636e\u9650\u5236\u5728\u4e00\u5b9a\u8303\u56f4\u5185\uff0c\u6709\u52a9\u4e8e\u540e\u7eed\u7684\u6807\u51c6\u5316\u5904\u7406\uff0c\u4f7f\u4e0d\u540c\u6570\u636e\u96c6\u5177\u6709\u53ef\u6bd4\u6027\u3002<\/p>\n<\/p>\n<\/li>\n
                  3. \n
                    \u63d0\u9ad8\u6a21\u578b\u6027\u80fd<\/strong>\uff1a\u5728\u673a\u5668\u5b66\u4e60<\/a>\u6a21\u578b\u7684\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\uff0c\u6781\u7aef\u503c\u53ef\u80fd\u4f1a\u5bfc\u81f4\u6a21\u578b\u8fc7\u62df\u5408\u6216\u6b20\u62df\u5408\uff0c\u7f29\u5c3e\u5904\u7406\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u63d0\u9ad8\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\uff0c\u4ece\u800c\u63d0\u9ad8\u6a21\u578b\u6027\u80fd\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
                    \u56db\u3001\u7f29\u5c3e\u5904\u7406\u7684\u6ce8\u610f\u4e8b\u9879<\/p>\n<\/p>\n
                    \u5728\u8fdb\u884c\u7f29\u5c3e\u5904\u7406\u65f6\uff0c\u6211\u4eec\u9700\u8981\u6ce8\u610f\u4ee5\u4e0b\u51e0\u70b9\uff1a<\/p>\n<\/p>\n
                      \n
                    1. \n
                      \u9009\u62e9\u5408\u9002\u7684\u9608\u503c<\/strong>\uff1a\u9608\u503c\u7684\u9009\u62e9\u5bf9\u7f29\u5c3e\u5904\u7406\u7684\u6548\u679c\u5f71\u54cd\u5f88\u5927\uff0c\u901a\u5e38\u6211\u4eec\u4f1a\u9009\u62e9\u6570\u636e\u7684\u67d0\u4e2a\u767e\u5206\u4f4d\u6570\u4f5c\u4e3a\u9608\u503c\uff0c\u4f46\u5177\u4f53\u9009\u62e9\u54ea\u4e2a\u767e\u5206\u4f4d\u6570\u9700\u8981\u6839\u636e\u6570\u636e\u7684\u5b9e\u9645\u60c5\u51b5\u6765\u51b3\u5b9a\u3002<\/p>\n<\/p>\n<\/li>\n
                    2. \n
                      \u4fdd\u7559\u539f\u59cb\u6570\u636e<\/strong>\uff1a\u5728\u8fdb\u884c\u7f29\u5c3e\u5904\u7406\u65f6\uff0c\u6211\u4eec\u6700\u597d\u4fdd\u7559\u4e00\u4efd\u539f\u59cb\u6570\u636e\uff0c\u4ee5\u4fbf\u5728\u9700\u8981\u65f6\u53ef\u4ee5\u8fdb\u884c\u56de\u6eaf\u548c\u6bd4\u8f83\u3002<\/p>\n<\/p>\n<\/li>\n
                    3. \n
                      \u7ed3\u5408\u5176\u4ed6\u6570\u636e\u6e05\u7406\u65b9\u6cd5<\/strong>\uff1a\u7f29\u5c3e\u5904\u7406\u662f\u4e00\u79cd\u5e38\u7528\u7684\u6570\u636e\u6e05\u7406\u65b9\u6cd5\uff0c\u4f46\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6211\u4eec\u53ef\u80fd\u9700\u8981\u7ed3\u5408\u5176\u4ed6\u6570\u636e\u6e05\u7406\u65b9\u6cd5\uff08\u5982\u53bb\u566a\u3001\u63d2\u503c\u7b49\uff09\u6765\u8fbe\u5230\u66f4\u597d\u7684\u6548\u679c\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
                      \u4e94\u3001\u7f29\u5c3e\u5904\u7406\u7684\u4f18\u7f3a\u70b9<\/p>\n<\/p>\n
                      \u7f29\u5c3e\u5904\u7406\u4f5c\u4e3a\u4e00\u79cd\u5e38\u7528\u7684\u6570\u636e\u6e05\u7406\u65b9\u6cd5\uff0c\u5177\u6709\u4ee5\u4e0b\u4f18\u70b9\uff1a<\/p>\n<\/p>\n
                        \n
                      1. \n
                        \u7b80\u5355\u6613\u7528<\/strong>\uff1a\u7f29\u5c3e\u5904\u7406\u7684\u65b9\u6cd5\u7b80\u5355\u6613\u7528\uff0c\u80fd\u591f\u5feb\u901f\u53bb\u9664\u6570\u636e\u4e2d\u7684\u6781\u7aef\u503c\u3002<\/p>\n<\/p>\n<\/li>\n
                      2. \n
                        \u63d0\u9ad8\u6570\u636e\u8d28\u91cf<\/strong>\uff1a\u901a\u8fc7\u7f29\u5c3e\u5904\u7406\uff0c\u53ef\u4ee5\u63d0\u9ad8\u6570\u636e\u8d28\u91cf\uff0c\u4f7f\u6570\u636e\u66f4\u52a0\u5e73\u6ed1\u3001\u7a33\u5b9a\u3002<\/p>\n<\/p>\n<\/li>\n
                      3. \n
                        \u6709\u52a9\u4e8e\u6a21\u578b\u8bad\u7ec3<\/strong>\uff1a\u7f29\u5c3e\u5904\u7406\u53ef\u4ee5\u63d0\u9ad8\u673a\u5668\u5b66\u4e60\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\uff0c\u4ece\u800c\u63d0\u9ad8\u6a21\u578b\u6027\u80fd\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
                        \u4f46\u7f29\u5c3e\u5904\u7406\u4e5f\u6709\u4e00\u4e9b\u7f3a\u70b9\uff1a<\/p>\n<\/p>\n
                          \n
                        1. \n
                          \u4fe1\u606f\u4e22\u5931<\/strong>\uff1a\u5728\u8fdb\u884c\u7f29\u5c3e\u5904\u7406\u65f6\uff0c\u53ef\u80fd\u4f1a\u4e22\u5931\u4e00\u4e9b\u6709\u7528\u7684\u6781\u7aef\u503c\u4fe1\u606f\u3002<\/p>\n<\/p>\n<\/li>\n
                        2. \n
                          \u9608\u503c\u9009\u62e9\u96be<\/strong>\uff1a\u9608\u503c\u7684\u9009\u62e9\u5bf9\u7f29\u5c3e\u5904\u7406\u7684\u6548\u679c\u5f71\u54cd\u5f88\u5927\uff0c\u4f46\u9009\u62e9\u5408\u9002\u7684\u9608\u503c\u5e76\u4e0d\u5bb9\u6613\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
                          \u516d\u3001\u603b\u7ed3<\/p>\n<\/p>\n
                          \u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u6211\u4eec\u5b66\u4e60\u4e86\u5982\u4f55\u4f7f\u7528Python\u8fdb\u884c\u7f29\u5c3e\u5904\u7406\uff0c\u5305\u62ec\u4f7f\u7528Pandas\u548cNumpy\u5e93\u7684\u65b9\u6cd5\u3001\u7f29\u5c3e\u5904\u7406\u7684\u5e94\u7528\u573a\u666f\u548c\u6ce8\u610f\u4e8b\u9879\u3002\u7f29\u5c3e\u5904\u7406\u662f\u4e00\u79cd\u5e38\u7528\u7684\u6570\u636e\u6e05\u7406\u6280\u672f\uff0c\u5b83\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u53bb\u9664\u6570\u636e\u96c6\u4e2d\u7684\u6781\u7aef\u503c\uff0c\u4ece\u800c\u4f7f\u6570\u636e\u66f4\u52a0\u5e73\u6ed1\u3001\u7a33\u5b9a\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6211\u4eec\u9700\u8981\u6839\u636e\u6570\u636e\u7684\u5177\u4f53\u60c5\u51b5\u9009\u62e9\u5408\u9002\u7684\u9608\u503c\uff0c\u5e76\u7ed3\u5408\u5176\u4ed6\u6570\u636e\u6e05\u7406\u65b9\u6cd5\u6765\u8fbe\u5230\u66f4\u597d\u7684\u6548\u679c\u3002<\/p>\n<\/p>\n
                          \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                           \u5982\u4f55\u4f7f\u7528Python\u8fdb\u884c\u7f29\u5c3e\u5904\u7406\uff1f<\/strong>
                          \u7f29\u5c3e\u5904\u7406\u662f\u4e00\u79cd\u7edf\u8ba1\u6280\u672f\uff0c\u7528\u4e8e\u5904\u7406\u6570\u636e\u4e2d\u7684\u5f02\u5e38\u503c\u6216\u6781\u7aef\u503c\u3002Python\u63d0\u4f9b\u4e86\u591a\u79cd\u5e93\u548c\u65b9\u6cd5\u6765\u5b9e\u73b0\u8fd9\u4e00\u8fc7\u7a0b\u3002\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528NumPy\u548cPandas\u5e93\u6765\u8bc6\u522b\u548c\u53bb\u9664\u6781\u7aef\u503c\u3002\u60a8\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u9608\u503c\uff0c\u6bd4\u5982\u6807\u51c6\u5dee\u6216\u56db\u5206\u4f4d\u6570\u8303\u56f4\uff0c\u6765\u5b9a\u4e49\u54ea\u4e9b\u6570\u636e\u70b9\u9700\u8981\u88ab\u7f29\u5c3e\u5904\u7406\u3002\u5177\u4f53\u5b9e\u73b0\u53ef\u4ee5\u53c2\u8003\u4ee5\u4e0b\u4ee3\u7801\u793a\u4f8b\uff1a<\/p>\n
                          import pandas as pd\n\n# \u521b\u5efa\u793a\u4f8b\u6570\u636e\ndata = {'value': [10, 12, 15, 20, 100, 200]}\ndf = pd.DataFrame(data)\n\n# \u8ba1\u7b97\u56db\u5206\u4f4d\u6570\nQ1 = df['value'].quantile(0.25)\nQ3 = df['value'].quantile(0.75)\nIQR = Q3 - Q1\n\n# \u786e\u5b9a\u7f29\u5c3e\u8303\u56f4\nlower_bound = Q1 - 1.5 * IQR\nupper_bound = Q3 + 1.5 * IQR\n\n# \u8fc7\u6ee4\u6570\u636e\nfiltered_data = df[(df['value'] >= lower_bound) & (df['value'] <= upper_bound)]\nprint(filtered_data)\n<\/code><\/pre>\n
                          \u7f29\u5c3e\u5904\u7406\u5bf9\u6570\u636e\u5206\u6790\u6709\u4ec0\u4e48\u5f71\u54cd\uff1f<\/strong>
                          \u7f29\u5c3e\u5904\u7406\u53ef\u4ee5\u663e\u8457\u63d0\u9ad8\u6570\u636e\u5206\u6790\u7684\u51c6\u786e\u6027\u3002\u901a\u8fc7\u53bb\u9664\u6781\u7aef\u503c\uff0c\u60a8\u80fd\u591f\u66f4\u597d\u5730\u6355\u6349\u6570\u636e\u7684\u771f\u5b9e\u5206\u5e03\uff0c\u907f\u514d\u6a21\u578b\u53d7\u5230\u6781\u7aef\u503c\u7684\u5e72\u6270\u3002\u8fd9\u5728\u6784\u5efa\u56de\u5f52\u6a21\u578b\u6216\u8fdb\u884c\u805a\u7c7b\u5206\u6790\u65f6\u5c24\u4e3a\u91cd\u8981\uff0c\u786e\u4fdd\u6a21\u578b\u4e0d\u4f1a\u56e0\u4e3a\u5c11\u6570\u5f02\u5e38\u503c\u800c\u4ea7\u751f\u504f\u5dee\u3002<\/p>\n
                          \u5728\u7f29\u5c3e\u5904\u7406\u540e\uff0c\u5982\u4f55\u8bc4\u4f30\u6570\u636e\u8d28\u91cf\uff1f<\/strong>
                          \u5728\u8fdb\u884c\u7f29\u5c3e\u5904\u7406\u540e\uff0c\u8bc4\u4f30\u6570\u636e\u8d28\u91cf\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u8fdb\u884c\u3002\u9996\u5148\uff0c\u68c0\u67e5\u6570\u636e\u7684\u5206\u5e03\u53d8\u5316\uff0c\u4f7f\u7528\u53ef\u89c6\u5316\u5de5\u5177\u5982\u7bb1\u7ebf\u56fe\u6216\u76f4\u65b9\u56fe\u3002\u5176\u6b21\uff0c\u901a\u8fc7\u8ba1\u7b97\u57fa\u672c\u7edf\u8ba1\u91cf\uff08\u5982\u5747\u503c\u3001\u4e2d\u4f4d\u6570\u3001\u6807\u51c6\u5dee\u7b49\uff09\u6765\u89c2\u5bdf\u6570\u636e\u7684\u53d8\u5316\u3002\u6b64\u5916\uff0c\u8fdb\u884c\u6570\u636e\u5b8c\u6574\u6027\u68c0\u67e5\uff0c\u786e\u4fdd\u5904\u7406\u540e\u7684\u6570\u636e\u96c6\u6ca1\u6709\u4e22\u5931\u91cd\u8981\u4fe1\u606f\u6216\u4ea7\u751f\u65b0\u7684\u5f02\u5e38\u503c\u3002<\/p>\n
                          \u4ec0\u4e48\u60c5\u51b5\u4e0b\u4e0d\u5e94\u8be5\u8fdb\u884c\u7f29\u5c3e\u5904\u7406\uff1f<\/strong>
                          \u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u7f29\u5c3e\u5904\u7406\u53ef\u80fd\u5e76\u4e0d\u9002\u5408\u3002\u4f8b\u5982\uff0c\u5f53\u6781\u7aef\u503c\u5b9e\u9645\u4e0a\u4ee3\u8868\u4e86\u91cd\u8981\u7684\u4e1a\u52a1\u4fe1\u606f\u6216\u8d8b\u52bf\u65f6\uff0c\u53bb\u9664\u8fd9\u4e9b\u503c\u4f1a\u5bfc\u81f4\u4fe1\u606f\u4e22\u5931\u3002\u6b64\u5916\uff0c\u5728\u67d0\u4e9b\u9886\u57df\uff0c\u5982\u91d1\u878d\u5e02\u573a\uff0c\u6781\u7aef\u503c\u53ef\u80fd\u662f\u4ea4\u6613\u7b56\u7565\u7684\u91cd\u8981\u7ec4\u6210\u90e8\u5206\u3002\u5728\u51b3\u5b9a\u662f\u5426\u8fdb\u884c\u7f29\u5c3e\u5904\u7406\u65f6\uff0c\u52a1\u5fc5\u8003\u8651\u6570\u636e\u7684\u80cc\u666f\u548c\u5206\u6790\u76ee\u6807\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5f00\u5934\u6bb5\u843d: \u7528Python\u8fdb\u884c\u7f29\u5c3e\u5904\u7406\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528Pandas\u3001Numpy\u5e93\u3001\u901a\u8fc7\u8bbe\u5b9a\u9608\u503c\u5bf9\u6570\u636e\u8fdb\u884c\u88c1\u526a\u3002 \u7f29 […]","protected":false},"author":3,"featured_media":1078439,"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\/1078436"}],"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=1078436"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1078436\/revisions"}],"predecessor-version":[{"id":1078441,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1078436\/revisions\/1078441"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1078439"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1078436"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1078436"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1078436"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}