{"id":975833,"date":"2024-12-27T06:16:35","date_gmt":"2024-12-26T22:16:35","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/975833.html"},"modified":"2024-12-27T06:16:37","modified_gmt":"2024-12-26T22:16:37","slug":"python%e4%b8%ad%e5%a6%82%e4%bd%95%e8%ae%a1%e7%ae%97%e5%9d%87%e4%bb%b7","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/975833.html","title":{"rendered":"python\u4e2d\u5982\u4f55\u8ba1\u7b97\u5747\u4ef7"},"content":{"rendered":"

\"python\u4e2d\u5982\u4f55\u8ba1\u7b97\u5747\u4ef7\"<\/p>\n

\u5728Python\u4e2d\u8ba1\u7b97\u5747\u4ef7\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u4e3b\u8981\u5305\u62ec\uff1a\u4f7f\u7528\u5185\u7f6e\u7684sum()\u548clen()\u51fd\u6570\u3001\u4f7f\u7528NumPy\u5e93\u3001\u4ee5\u53ca\u4f7f\u7528Pandas\u5e93\u7b49\u3002<\/strong>\u4e0b\u9762\u6211\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u65b9\u6cd5\u6765\u8ba1\u7b97\u5747\u4ef7\uff0c\u5e76\u63d0\u4f9b\u4e00\u4e9b\u793a\u4f8b\u4ee3\u7801\u4ee5\u5e2e\u52a9\u8bfb\u8005\u66f4\u597d\u5730\u7406\u89e3\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528\u5185\u7f6e\u51fd\u6570\u8ba1\u7b97\u5747\u4ef7<\/p>\n<\/p>\n

\u5728Python\u4e2d\uff0c\u8ba1\u7b97\u5747\u4ef7\u6700\u76f4\u63a5\u7684\u65b9\u6cd5\u662f\u4f7f\u7528\u5185\u7f6e\u7684sum()\u548clen()\u51fd\u6570\u3002\u8fd9\u79cd\u65b9\u6cd5\u9002\u7528\u4e8e\u5904\u7406\u7b80\u5355\u7684\u5217\u8868\u6570\u636e\u3002<\/p>\n<\/p>\n

    \n
  1. \n

    \u4f7f\u7528sum()\u548clen()\u8ba1\u7b97\u5747\u4ef7<\/p>\n<\/p>\n

    sum()\u51fd\u6570\u7528\u4e8e\u8ba1\u7b97\u5217\u8868\u4e2d\u6240\u6709\u5143\u7d20\u7684\u548c\uff0clen()\u51fd\u6570\u7528\u4e8e\u83b7\u53d6\u5217\u8868\u7684\u957f\u5ea6\u3002\u901a\u8fc7\u5c06\u603b\u548c\u9664\u4ee5\u5217\u8868\u957f\u5ea6\u5373\u53ef\u5f97\u5230\u5747\u4ef7\u3002<\/p>\n<\/p>\n

    def calculate_average(values):<\/p>\n
        return sum(values) \/ len(values) if values else 0<\/p>\n
    data = [10, 20, 30, 40, 50]<\/p>\n
    average = calculate_average(data)<\/p>\n
    print(f"The average is: {average}")<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u5b9a\u4e49\u4e86\u4e00\u4e2a\u51fd\u6570calculate_average<\/code>\uff0c\u5b83\u63a5\u6536\u4e00\u4e2a\u5217\u8868\u5e76\u8fd4\u56de\u5176\u5747\u503c\u3002\u6211\u4eec\u901a\u8fc7\u7b80\u5355\u7684\u6570\u5b66\u8fd0\u7b97\u5373\u53ef\u8ba1\u7b97\u51fa\u5747\u503c\uff0c\u8fd9\u79cd\u65b9\u6cd5\u7b80\u5355\u6613\u884c\uff0c\u9002\u5408\u4e8e\u5c0f\u89c4\u6a21\u6570\u636e\u96c6\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
    \u4e8c\u3001\u4f7f\u7528NumPy\u5e93\u8ba1\u7b97\u5747\u4ef7<\/p>\n<\/p>\n
    NumPy\u662fPython\u4e2d\u4e00\u4e2a\u5f3a\u5927\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u5b66\u51fd\u6570\u3002\u5bf9\u4e8e\u5927\u89c4\u6a21\u6570\u636e\u96c6\u6216\u9700\u8981\u8fdb\u884c\u590d\u6742\u6570\u636e\u5904\u7406\u7684\u573a\u5408\uff0cNumPy\u662f\u4e00\u4e2a\u975e\u5e38\u597d\u7684\u9009\u62e9\u3002<\/p>\n<\/p>\n
      \n
    1. \n
      \u4f7f\u7528NumPy\u7684mean()\u51fd\u6570<\/p>\n<\/p>\n
      NumPy\u5e93\u63d0\u4f9b\u4e86\u4e00\u4e2a\u4e13\u95e8\u7684\u51fd\u6570mean()\u6765\u8ba1\u7b97\u5747\u503c\uff0c\u4f7f\u7528\u8d77\u6765\u975e\u5e38\u65b9\u4fbf\u3002<\/p>\n<\/p>\n
      import numpy as np<\/p>\n
      data = np.array([10, 20, 30, 40, 50])<\/p>\n
      average = np.mean(data)<\/p>\n
      print(f"The average using NumPy is: {average}")<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      NumPy\u7684mean()\u51fd\u6570\u4e0d\u4ec5\u8ba1\u7b97\u901f\u5ea6\u5feb\uff0c\u800c\u4e14\u53ef\u4ee5\u5904\u7406\u591a\u7ef4\u6570\u7ec4\u7684\u6570\u636e\uff0c\u975e\u5e38\u9002\u5408\u4e8e\u79d1\u5b66\u8ba1\u7b97\u548c\u6570\u636e\u5206\u6790\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
      \u4e09\u3001\u4f7f\u7528Pandas\u5e93\u8ba1\u7b97\u5747\u4ef7<\/p>\n<\/p>\n
      Pandas\u662fPython\u4e2d\u7528\u4e8e\u6570\u636e\u5206\u6790\u7684\u53e6\u4e00\u4e2a\u91cd\u8981\u5e93\uff0c\u5c24\u5176\u662f\u5728\u5904\u7406\u8868\u683c\u6570\u636e\u65f6\u975e\u5e38\u6709\u7528\u3002Pandas\u63d0\u4f9b\u4e86\u7c7b\u4f3c\u4e8eSQL\u7684\u529f\u80fd\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
        \n
      1. \n
        \u4f7f\u7528Pandas\u7684mean()\u65b9\u6cd5<\/p>\n<\/p>\n
        Pandas\u7684DataFrame\u548cSeries\u5bf9\u8c61\u90fd\u652f\u6301mean()\u65b9\u6cd5\uff0c\u7528\u4e8e\u8ba1\u7b97\u5747\u503c\u3002<\/p>\n<\/p>\n
        import pandas as pd<\/p>\n
        data = pd.Series([10, 20, 30, 40, 50])<\/p>\n
        average = data.mean()<\/p>\n
        print(f"The average using Pandas is: {average}")<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e86Pandas\u7684Series\u5bf9\u8c61\u6765\u5b58\u50a8\u6570\u636e\uff0c\u5e76\u8c03\u7528\u5176mean()\u65b9\u6cd5\u6765\u8ba1\u7b97\u5747\u503c\u3002Pandas\u7684\u5f3a\u5927\u4e4b\u5904\u5728\u4e8e\u5176\u6570\u636e\u5904\u7406\u80fd\u529b\uff0c\u5c24\u5176\u5728\u5904\u7406\u7f3a\u5931\u6570\u636e\u548c\u8fdb\u884c\u6570\u636e\u6e05\u6d17\u65f6\u975e\u5e38\u65b9\u4fbf\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
        \u56db\u3001\u5904\u7406\u7a7a\u503c\u548c\u5f02\u5e38\u503c<\/p>\n<\/p>\n
        \u5728\u5b9e\u9645\u6570\u636e\u5904\u7406\u4e2d\uff0c\u7ecf\u5e38\u4f1a\u9047\u5230\u7a7a\u503c\u6216\u5f02\u5e38\u503c\uff0c\u8fd9\u4f1a\u5f71\u54cd\u5747\u4ef7\u8ba1\u7b97\u7684\u51c6\u786e\u6027\u3002\u4e0b\u9762\u4ecb\u7ecd\u5982\u4f55\u5728\u8ba1\u7b97\u5747\u503c\u65f6\u5904\u7406\u8fd9\u4e9b\u95ee\u9898\u3002<\/p>\n<\/p>\n
          \n
        1. \n
          \u5ffd\u7565\u7a7a\u503c<\/p>\n<\/p>\n
          \u5728\u8ba1\u7b97\u5747\u503c\u65f6\uff0c\u901a\u5e38\u9700\u8981\u5ffd\u7565\u7a7a\u503c\u3002\u65e0\u8bba\u662fNumPy\u8fd8\u662fPandas\uff0c\u90fd\u53ef\u4ee5\u8f7b\u677e\u505a\u5230\u8fd9\u4e00\u70b9\u3002<\/p>\n<\/p>\n
          import pandas as pd<\/p>\n
          import numpy as np<\/p>\n
          data = pd.Series([10, 20, np.nan, 40, 50])<\/p>\n
          average = data.mean()<\/p>\n
          print(f"The average ignoring NaN is: {average}")<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          Pandas\u7684mean()\u65b9\u6cd5\u9ed8\u8ba4\u4f1a\u5ffd\u7565NaN\u503c\uff0c\u56e0\u6b64\u53ef\u4ee5\u76f4\u63a5\u8ba1\u7b97\u51fa\u6709\u6548\u6570\u636e\u7684\u5747\u503c\u3002<\/p>\n<\/p>\n<\/li>\n
        2. \n
          \u5904\u7406\u5f02\u5e38\u503c<\/p>\n<\/p>\n
          \u5f02\u5e38\u503c\u901a\u5e38\u6307\u7684\u662f\u6570\u636e\u96c6\u4e2d\u4e0e\u5176\u4ed6\u6570\u636e\u70b9\u5dee\u5f02\u8f83\u5927\u7684\u503c\uff0c\u8fd9\u4e9b\u503c\u53ef\u80fd\u4f1a\u5bf9\u5747\u503c\u4ea7\u751f\u8f83\u5927\u7684\u5f71\u54cd\u3002\u5728\u8ba1\u7b97\u5747\u503c\u4e4b\u524d\uff0c\u53ef\u4ee5\u4f7f\u7528\u7edf\u8ba1\u65b9\u6cd5\u6216\u4e1a\u52a1\u89c4\u5219\u8bc6\u522b\u5e76\u5904\u7406\u5f02\u5e38\u503c\u3002<\/p>\n<\/p>\n
          def remove_outliers(data, z_threshold=3):<\/p>\n
              mean = np.mean(data)<\/p>\n
              std_dev = np.std(data)<\/p>\n
              filtered_data = [x for x in data if (abs(x - mean) \/ std_dev) < z_threshold]<\/p>\n
              return filtered_data<\/p>\n
          data = [10, 20, 30, 400, 50]<\/p>\n
          filtered_data = remove_outliers(data)<\/p>\n
          average = np.mean(filtered_data)<\/p>\n
          print(f"The average after removing outliers is: {average}")<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u901a\u8fc7\u8ba1\u7b97\u6570\u636e\u7684\u6807\u51c6\u5dee\u5e76\u8bbe\u5b9a\u9608\u503c\uff0c\u53ef\u4ee5\u8bc6\u522b\u5e76\u53bb\u9664\u5f02\u5e38\u503c\uff0c\u4ece\u800c\u5f97\u5230\u66f4\u51c6\u786e\u7684\u5747\u503c\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
          \u4e94\u3001\u5e94\u7528\u573a\u666f\u548c\u6ce8\u610f\u4e8b\u9879<\/p>\n<\/p>\n
            \n
          1. \n
            \u5e94\u7528\u573a\u666f<\/p>\n<\/p>\n
            \u5747\u503c\u8ba1\u7b97\u5728\u6570\u636e\u5206\u6790\u3001\u8d22\u52a1\u62a5\u8868\u3001\u79d1\u5b66\u7814\u7a76\u7b49\u591a\u4e2a\u9886\u57df\u90fd\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\u3002\u4f8b\u5982\uff0c\u5728\u80a1\u7968\u5e02\u573a\u5206\u6790\u4e2d\uff0c\u5747\u4ef7\u53ef\u4ee5\u7528\u4e8e\u5224\u65ad\u80a1\u7968\u7684\u957f\u671f\u8d8b\u52bf\uff1b\u5728\u5b9e\u9a8c\u7814\u7a76\u4e2d\uff0c\u5747\u503c\u53ef\u4ee5\u7528\u4e8e\u6bd4\u8f83\u4e0d\u540c\u5b9e\u9a8c\u7ec4\u7684\u8868\u73b0\u3002<\/p>\n<\/p>\n<\/li>\n
          2. \n
            \u6ce8\u610f\u4e8b\u9879<\/p>\n<\/p>\n
              \n
            • \u6570\u636e\u7c7b\u578b<\/strong>\uff1a\u786e\u4fdd\u6570\u636e\u7c7b\u578b\u7684\u4e00\u81f4\u6027\uff0c\u5c24\u5176\u5728\u4f7f\u7528NumPy\u548cPandas\u65f6\uff0c\u6570\u636e\u7c7b\u578b\u4e0d\u4e00\u81f4\u53ef\u80fd\u5bfc\u81f4\u8ba1\u7b97\u9519\u8bef\u3002<\/li>\n
            • \u6570\u636e\u89c4\u6a21<\/strong>\uff1a\u5bf9\u4e8e\u5927\u89c4\u6a21\u6570\u636e\u96c6\uff0c\u9009\u62e9\u5408\u9002\u7684\u5de5\u5177\u548c\u65b9\u6cd5\uff0cNumPy\u548cPandas\u5728\u5904\u7406\u5927\u6570\u636e\u65f6\u5177\u6709\u663e\u8457\u7684\u6027\u80fd\u4f18\u52bf\u3002<\/li>\n
            • \u5f02\u5e38\u503c\u5904\u7406<\/strong>\uff1a\u6839\u636e\u5177\u4f53\u573a\u666f\uff0c\u5408\u7406\u8bc6\u522b\u548c\u5904\u7406\u5f02\u5e38\u503c\uff0c\u4ee5\u514d\u5f71\u54cd\u5747\u503c\u7684\u51c6\u786e\u6027\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n
              \u603b\u7ed3\u800c\u8a00\uff0c\u8ba1\u7b97\u5747\u4ef7\u662f\u6570\u636e\u5206\u6790\u4e2d\u7684\u57fa\u7840\u64cd\u4f5c\uff0cPython\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u5b9e\u73b0\u8fd9\u4e00\u529f\u80fd\u3002\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u548c\u5de5\u5177\u4e0d\u4ec5\u80fd\u63d0\u9ad8\u8ba1\u7b97\u6548\u7387\uff0c\u8fd8\u80fd\u4fdd\u8bc1\u7ed3\u679c\u7684\u51c6\u786e\u6027\u3002\u901a\u8fc7\u5bf9\u4e0d\u540c\u65b9\u6cd5\u7684\u5b66\u4e60\u548c\u5b9e\u8df5\uff0c\u8bfb\u8005\u53ef\u4ee5\u6839\u636e\u81ea\u8eab\u9700\u6c42\u7075\u6d3b\u5e94\u7528\u8fd9\u4e9b\u6280\u672f\u3002<\/p>\n<\/p>\n
              \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
               \u5982\u4f55\u4f7f\u7528Python\u8ba1\u7b97\u4e00\u7ec4\u6570\u5b57\u7684\u5747\u4ef7\uff1f<\/strong>
              \u8981\u8ba1\u7b97\u4e00\u7ec4\u6570\u5b57\u7684\u5747\u4ef7\uff0c\u53ef\u4ee5\u4f7f\u7528\u5185\u7f6e\u7684sum()<\/code>\u548clen()<\/code>\u51fd\u6570\u3002\u9996\u5148\uff0c\u5c06\u6240\u6709\u6570\u5b57\u5b58\u50a8\u5728\u4e00\u4e2a\u5217\u8868\u4e2d\uff0c\u7136\u540e\u4f7f\u7528sum()<\/code>\u51fd\u6570\u8ba1\u7b97\u603b\u548c\uff0c\u6700\u540e\u7528\u603b\u548c\u9664\u4ee5\u5217\u8868\u7684\u957f\u5ea6\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a  <\/p>\n
              numbers = [10, 20, 30, 40, 50]\naverage = sum(numbers) \/ len(numbers)\nprint(average)  # \u8f93\u51fa\u5747\u4ef7\n<\/code><\/pre>\n
              \u5728Python\u4e2d\uff0c\u5982\u4f55\u5904\u7406\u7a7a\u5217\u8868\u4ee5\u907f\u514d\u8ba1\u7b97\u5747\u4ef7\u65f6\u51fa\u73b0\u9519\u8bef\uff1f<\/strong>
              \u5728\u8ba1\u7b97\u5747\u4ef7\u4e4b\u524d\uff0c\u68c0\u67e5\u5217\u8868\u662f\u5426\u4e3a\u7a7a\u662f\u975e\u5e38\u91cd\u8981\u7684\u3002\u5982\u679c\u5217\u8868\u4e3a\u7a7a\uff0c\u76f4\u63a5\u8ba1\u7b97\u5747\u4ef7\u4f1a\u5bfc\u81f4\u9664\u4ee5\u96f6\u7684\u9519\u8bef\u3002\u53ef\u4ee5\u4f7f\u7528\u6761\u4ef6\u8bed\u53e5\u8fdb\u884c\u5224\u65ad\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a  <\/p>\n
              numbers = []\nif numbers:\n    average = sum(numbers) \/ len(numbers)\nelse:\n    average = 0  # \u6216\u8005\u5176\u4ed6\u9002\u5f53\u7684\u9ed8\u8ba4\u503c\nprint(average)\n<\/code><\/pre>\n
              \u5728Python\u4e2d\uff0c\u5982\u4f55\u8ba1\u7b97\u5e26\u6743\u91cd\u7684\u5747\u4ef7\uff1f<\/strong>
              \u8ba1\u7b97\u5e26\u6743\u91cd\u7684\u5747\u4ef7\u901a\u5e38\u9700\u8981\u5c06\u6bcf\u4e2a\u6570\u5b57\u4e0e\u5176\u5bf9\u5e94\u7684\u6743\u91cd\u76f8\u4e58\uff0c\u7136\u540e\u5c06\u6240\u6709\u4e58\u79ef\u7684\u603b\u548c\u9664\u4ee5\u6743\u91cd\u7684\u603b\u548c\u3002\u53ef\u4ee5\u5229\u7528zip()<\/code>\u51fd\u6570\u5c06\u6570\u5b57\u548c\u6743\u91cd\u914d\u5bf9\uff0c\u8fdb\u884c\u8ba1\u7b97\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a  <\/p>\n
              numbers = [10, 20, 30]\nweights = [1, 2, 3]\nweighted_average = sum(n * w for n, w in zip(numbers, weights)) \/ sum(weights)\nprint(weighted_average)  # \u8f93\u51fa\u5e26\u6743\u91cd\u7684\u5747\u4ef7\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u8ba1\u7b97\u5747\u4ef7\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u4e3b\u8981\u5305\u62ec\uff1a\u4f7f\u7528\u5185\u7f6e\u7684sum()\u548clen()\u51fd\u6570\u3001\u4f7f\u7528NumPy\u5e93\u3001\u4ee5\u53ca\u4f7f […]","protected":false},"author":3,"featured_media":975841,"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\/975833"}],"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=975833"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/975833\/revisions"}],"predecessor-version":[{"id":975843,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/975833\/revisions\/975843"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/975841"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=975833"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=975833"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=975833"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}