{"id":969391,"date":"2024-12-27T05:16:28","date_gmt":"2024-12-26T21:16:28","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/969391.html"},"modified":"2024-12-27T05:16:30","modified_gmt":"2024-12-26T21:16:30","slug":"python%e5%a6%82%e4%bd%95%e8%8e%b7%e5%8f%96%e6%95%b0%e6%8d%ae%e6%8e%92%e5%90%8d","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/969391.html","title":{"rendered":"python\u5982\u4f55\u83b7\u53d6\u6570\u636e\u6392\u540d"},"content":{"rendered":"

\"python\u5982\u4f55\u83b7\u53d6\u6570\u636e\u6392\u540d\"<\/p>\n

\u8981\u5728Python\u4e2d\u83b7\u53d6\u6570\u636e\u6392\u540d\uff0c\u53ef\u4ee5\u4f7f\u7528pandas\u5e93\u3001sorted\u51fd\u6570\u3001heapq\u5e93\u3001scipy\u5e93\u7b49\u65b9\u6cd5\u3002<\/strong>\u5176\u4e2d\uff0cpandas\u5e93\u63d0\u4f9b\u4e86DataFrame\u7684rank\u65b9\u6cd5\uff0c\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0\u6570\u636e\u6392\u540d\uff1bsorted\u51fd\u6570\u53ef\u4ee5\u5bf9\u5217\u8868\u8fdb\u884c\u6392\u5e8f\uff0c\u5e76\u901a\u8fc7\u7d22\u5f15\u83b7\u53d6\u6392\u540d\uff1bheapq\u5e93\u53ef\u4ee5\u7528\u6765\u5904\u7406\u5927\u6570\u636e\u96c6\u7684\u90e8\u5206\u6392\u540d\uff1bscipy\u5e93\u5219\u9002\u7528\u4e8e\u79d1\u5b66\u8ba1\u7b97\u4e2d\u7684\u6392\u540d\u95ee\u9898\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528PANDAS\u5e93<\/p>\n<\/p>\n

pandas\u662fPython\u4e2d\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u5de5\u5177\uff0c\u5e7f\u6cdb\u7528\u4e8e\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u3002pandas\u63d0\u4f9b\u4e86DataFrame\u5bf9\u8c61\uff0c\u5b83\u5177\u6709\u4e30\u5bcc\u7684\u529f\u80fd\uff0c\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0\u6570\u636e\u6392\u540d\u3002<\/p>\n<\/p>\n

    \n
  1. DataFrame\u7684rank\u65b9\u6cd5<\/strong><\/li>\n<\/ol>\n

    DataFrame\u7684rank\u65b9\u6cd5\u53ef\u4ee5\u5bf9\u6570\u636e\u8fdb\u884c\u6392\u540d\u3002\u5b83\u53ef\u4ee5\u6839\u636e\u6307\u5b9a\u7684\u8f74\uff08\u884c\u6216\u5217\uff09\u5bf9\u6570\u636e\u8fdb\u884c\u6392\u5e8f\uff0c\u5e76\u8fd4\u56de\u6392\u540d\u7ed3\u679c\u3002\u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff0crank\u65b9\u6cd5\u5bf9\u6570\u636e\u8fdb\u884c\u5347\u5e8f\u6392\u540d\u3002<\/p>\n<\/p>\n

    import pandas as pd<\/p>\n
    \u521b\u5efa\u4e00\u4e2aDataFrame<\/strong><\/h2>\n
    data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],<\/p>\n
            'Score': [88, 92, 85, 90]}<\/p>\n
    df = pd.DataFrame(data)<\/p>\n
    \u4f7f\u7528rank\u65b9\u6cd5\u8fdb\u884c\u6392\u540d<\/strong><\/h2>\n
    df['Rank'] = df['Score'].rank(ascending=False)<\/p>\n
    print(df)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u521b\u5efa\u4e86\u4e00\u4e2aDataFrame\uff0c\u5176\u4e2d\u5305\u542b\u59d3\u540d\u548c\u6210\u7ee9\u3002\u6211\u4eec\u4f7f\u7528rank\u65b9\u6cd5\u5bf9\u6210\u7ee9\u8fdb\u884c\u964d\u5e8f\u6392\u540d\uff0c\u5e76\u5c06\u7ed3\u679c\u5b58\u50a8\u5728\u4e00\u4e2a\u65b0\u7684\u5217\u4e2d\u3002<\/p>\n<\/p>\n
      \n
    1. \u6307\u5b9a\u6392\u540d\u65b9\u6cd5<\/strong><\/li>\n<\/ol>\n
      pandas\u7684rank\u65b9\u6cd5\u63d0\u4f9b\u4e86\u591a\u79cd\u6392\u540d\u65b9\u6cd5\uff0c\u4f8b\u5982\u5e73\u5747\u6392\u540d\u3001\u6700\u5c0f\u6392\u540d\u3001\u6700\u5927\u6392\u540d\u548c\u7b2c\u4e00\u540d\u6392\u540d\u3002\u53ef\u4ee5\u901a\u8fc7method\u53c2\u6570\u6307\u5b9a\u6392\u540d\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
      # \u4f7f\u7528\u4e0d\u540c\u7684\u6392\u540d\u65b9\u6cd5<\/p>\n
      df['Average Rank'] = df['Score'].rank(method='average', ascending=False)<\/p>\n
      df['Min Rank'] = df['Score'].rank(method='min', ascending=False)<\/p>\n
      df['Max Rank'] = df['Score'].rank(method='max', ascending=False)<\/p>\n
      df['First Rank'] = df['Score'].rank(method='first', ascending=False)<\/p>\n
      print(df)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528\u4e0d\u540c\u7684\u6392\u540d\u65b9\u6cd5\u3002\u5e73\u5747\u6392\u540d\u4e3a\u76f8\u540c\u503c\u5206\u914d\u5e73\u5747\u6392\u540d\uff0c\u6700\u5c0f\u6392\u540d\u4e3a\u76f8\u540c\u503c\u5206\u914d\u6700\u5c0f\u6392\u540d\uff0c\u6700\u5927\u6392\u540d\u4e3a\u76f8\u540c\u503c\u5206\u914d\u6700\u5927\u6392\u540d\uff0c\u7b2c\u4e00\u540d\u6392\u540d\u6309\u51fa\u73b0\u987a\u5e8f\u6392\u540d\u3002<\/p>\n<\/p>\n
      \u4e8c\u3001\u4f7f\u7528SORTED\u51fd\u6570<\/p>\n<\/p>\n
      Python\u5185\u7f6e\u7684sorted\u51fd\u6570\u53ef\u4ee5\u5bf9\u5217\u8868\u8fdb\u884c\u6392\u5e8f\u3002\u901a\u8fc7\u7ed3\u5408enumerate\u51fd\u6570\uff0c\u53ef\u4ee5\u5b9e\u73b0\u6570\u636e\u6392\u540d\u3002<\/p>\n<\/p>\n
        \n
      1. \u5bf9\u5217\u8868\u8fdb\u884c\u6392\u5e8f<\/strong><\/li>\n<\/ol>\n
        scores = [88, 92, 85, 90]<\/p>\n
        \u5bf9\u5217\u8868\u8fdb\u884c\u6392\u5e8f<\/strong><\/h2>\n
        sorted_scores = sorted(scores, reverse=True)<\/p>\n
        print(sorted_scores)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u5bf9\u6210\u7ee9\u5217\u8868\u8fdb\u884c\u964d\u5e8f\u6392\u5e8f\u3002<\/p>\n<\/p>\n
          \n
        1. \u83b7\u53d6\u6392\u540d<\/strong><\/li>\n<\/ol>\n
          # \u83b7\u53d6\u6392\u540d<\/p>\n
          rank = [sorted_scores.index(x) + 1 for x in scores]<\/p>\n
          print(rank)<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u901a\u8fc7\u7d22\u5f15\u83b7\u53d6\u6bcf\u4e2a\u6210\u7ee9\u7684\u6392\u540d\u3002\u9700\u8981\u6ce8\u610f\u7684\u662f\uff0csorted\u51fd\u6570\u8fd4\u56de\u7684\u662f\u6392\u5e8f\u540e\u7684\u5217\u8868\uff0c\u6211\u4eec\u9700\u8981\u901a\u8fc7\u7d22\u5f15\u83b7\u53d6\u539f\u59cb\u5217\u8868\u4e2d\u6bcf\u4e2a\u5143\u7d20\u7684\u6392\u540d\u3002<\/p>\n<\/p>\n
          \u4e09\u3001\u4f7f\u7528HEAPQ\u5e93<\/p>\n<\/p>\n
          heapq\u662fPython\u7684\u4e00\u4e2a\u5806\u961f\u5217\u7b97\u6cd5\u5e93\uff0c\u53ef\u4ee5\u7528\u6765\u5904\u7406\u5927\u6570\u636e\u96c6\u7684\u90e8\u5206\u6392\u540d\u3002heapq\u63d0\u4f9b\u4e86nlargest\u548cnsmallest\u65b9\u6cd5\uff0c\u53ef\u4ee5\u83b7\u53d6\u6570\u636e\u96c6\u4e2d\u6700\u5927\u7684n\u4e2a\u5143\u7d20\u6216\u6700\u5c0f\u7684n\u4e2a\u5143\u7d20\u3002<\/p>\n<\/p>\n
            \n
          1. \u83b7\u53d6\u6700\u5927n\u4e2a\u5143\u7d20<\/strong><\/li>\n<\/ol>\n
            import heapq<\/p>\n
            scores = [88, 92, 85, 90]<\/p>\n
            \u83b7\u53d6\u6700\u59273\u4e2a\u5143\u7d20<\/strong><\/h2>\n
            largest_scores = heapq.nlargest(3, scores)<\/p>\n
            print(largest_scores)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528nlargest\u65b9\u6cd5\u83b7\u53d6\u6210\u7ee9\u5217\u8868\u4e2d\u6700\u5927\u76843\u4e2a\u5143\u7d20\u3002<\/p>\n<\/p>\n
              \n
            1. \u83b7\u53d6\u6700\u5c0fn\u4e2a\u5143\u7d20<\/strong><\/li>\n<\/ol>\n
              # \u83b7\u53d6\u6700\u5c0f3\u4e2a\u5143\u7d20<\/p>\n
              smallest_scores = heapq.nsmallest(3, scores)<\/p>\n
              print(smallest_scores)<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528nsmallest\u65b9\u6cd5\u83b7\u53d6\u6210\u7ee9\u5217\u8868\u4e2d\u6700\u5c0f\u76843\u4e2a\u5143\u7d20\u3002<\/p>\n<\/p>\n
              \u56db\u3001\u4f7f\u7528SCIPY\u5e93<\/p>\n<\/p>\n
              scipy\u662fPython\u7684\u4e00\u4e2a\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u5b66\u3001\u79d1\u5b66\u548c\u5de5\u7a0b\u8ba1\u7b97\u529f\u80fd\u3002scipy.stats\u6a21\u5757\u63d0\u4f9b\u4e86rankdata\u65b9\u6cd5\uff0c\u53ef\u4ee5\u5bf9\u6570\u636e\u8fdb\u884c\u6392\u540d\u3002<\/p>\n<\/p>\n
                \n
              1. \u4f7f\u7528rankdata\u65b9\u6cd5<\/strong><\/li>\n<\/ol>\n
                from scipy.stats import rankdata<\/p>\n
                scores = [88, 92, 85, 90]<\/p>\n
                \u4f7f\u7528rankdata\u65b9\u6cd5\u8fdb\u884c\u6392\u540d<\/strong><\/h2>\n
                ranks = rankdata(scores, method='dense')<\/p>\n
                print(ranks)<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528rankdata\u65b9\u6cd5\u5bf9\u6210\u7ee9\u8fdb\u884c\u6392\u540d\u3002method\u53c2\u6570\u53ef\u4ee5\u6307\u5b9a\u6392\u540d\u65b9\u6cd5\uff0c\u4f8b\u5982'dense'\u3001'ordinal'\u7b49\u3002<\/p>\n<\/p>\n
                  \n
                1. \u4e0d\u540c\u7684\u6392\u540d\u65b9\u6cd5<\/strong><\/li>\n<\/ol>\n
                  # \u4f7f\u7528\u4e0d\u540c\u7684\u6392\u540d\u65b9\u6cd5<\/p>\n
                  dense_ranks = rankdata(scores, method='dense')<\/p>\n
                  ordinal_ranks = rankdata(scores, method='ordinal')<\/p>\n
                  average_ranks = rankdata(scores, method='average')<\/p>\n
                  print(dense_ranks, ordinal_ranks, average_ranks)<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528\u4e0d\u540c\u7684\u6392\u540d\u65b9\u6cd5\u3002\u7a20\u5bc6\u6392\u540d\u4e3a\u76f8\u540c\u503c\u5206\u914d\u76f8\u540c\u6392\u540d\uff0c\u987a\u5e8f\u6392\u540d\u4e3a\u76f8\u540c\u503c\u5206\u914d\u4e0d\u540c\u6392\u540d\uff0c\u5e73\u5747\u6392\u540d\u4e3a\u76f8\u540c\u503c\u5206\u914d\u5e73\u5747\u6392\u540d\u3002<\/p>\n<\/p>\n
                  \u4e94\u3001\u603b\u7ed3\u4e0e\u5e94\u7528<\/p>\n<\/p>\n
                  \u901a\u8fc7\u4e0a\u8ff0\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u5728Python\u4e2d\u8f7b\u677e\u5b9e\u73b0\u6570\u636e\u6392\u540d\u3002\u6839\u636e\u4e0d\u540c\u7684\u9700\u6c42\uff0c\u53ef\u4ee5\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u8fdb\u884c\u6570\u636e\u6392\u540d\u3002pandas\u5e93\u9002\u7528\u4e8e\u6570\u636e\u5206\u6790\u548c\u5904\u7406\uff0csorted\u51fd\u6570\u9002\u7528\u4e8e\u7b80\u5355\u7684\u5217\u8868\u6392\u5e8f\uff0cheapq\u5e93\u9002\u7528\u4e8e\u5927\u6570\u636e\u96c6\u7684\u90e8\u5206\u6392\u540d\uff0cscipy\u5e93\u9002\u7528\u4e8e\u79d1\u5b66\u8ba1\u7b97\u4e2d\u7684\u6392\u540d\u95ee\u9898\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u53ef\u4ee5\u7ed3\u5408\u4f7f\u7528\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u4ee5\u6ee1\u8db3\u4e0d\u540c\u7684\u9700\u6c42\u3002<\/p>\n<\/p>\n
                  \u5728\u6570\u636e\u5206\u6790\u4e2d\uff0c\u6570\u636e\u6392\u540d\u662f\u4e00\u4e2a\u5e38\u89c1\u7684\u4efb\u52a1\u3002\u901a\u8fc7\u5bf9\u6570\u636e\u8fdb\u884c\u6392\u540d\uff0c\u53ef\u4ee5\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u7684\u5206\u5e03\u548c\u7279\u5f81\u3002\u4f8b\u5982\uff0c\u5728\u5b66\u751f\u6210\u7ee9\u5206\u6790\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u6392\u540d\u4e86\u89e3\u5b66\u751f\u7684\u5b66\u4e60\u60c5\u51b5\uff1b\u5728\u5e02\u573a\u5206\u6790\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u6392\u540d\u4e86\u89e3\u4ea7\u54c1\u7684\u7ade\u4e89\u529b\u3002\u603b\u4e4b\uff0c\u6570\u636e\u6392\u540d\u662f\u6570\u636e\u5206\u6790\u4e2d\u7684\u91cd\u8981\u5de5\u5177\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u7406\u89e3\u548c\u5229\u7528\u6570\u636e\u3002<\/p>\n<\/p>\n
                  \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                   \u5982\u4f55\u4f7f\u7528Python\u5bf9\u6570\u636e\u8fdb\u884c\u6392\u540d\uff1f<\/strong>
                  \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528pandas\u5e93\u6765\u5bf9\u6570\u636e\u8fdb\u884c\u6392\u540d\u3002\u9996\u5148\uff0c\u60a8\u9700\u8981\u5c06\u6570\u636e\u5bfc\u5165\u4e3aDataFrame\u683c\u5f0f\u3002\u4f7f\u7528DataFrame\u7684rank()<\/code>\u65b9\u6cd5\u53ef\u4ee5\u8f7b\u677e\u83b7\u53d6\u6392\u540d\u3002\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7df['column_name'].rank()<\/code>\u6765\u83b7\u53d6\u7279\u5b9a\u5217\u7684\u6392\u540d\uff0c\u5e76\u9009\u62e9\u4e0d\u540c\u7684\u6392\u540d\u65b9\u6cd5\uff0c\u5982\u5e73\u5747\u6392\u540d\u6216\u964d\u5e8f\u6392\u5217\u3002<\/p>\n
                  \u662f\u5426\u53ef\u4ee5\u5bf9\u591a\u4e2a\u5217\u540c\u65f6\u8fdb\u884c\u6392\u540d\uff1f<\/strong>
                  \u662f\u7684\uff0c\u60a8\u53ef\u4ee5\u5bf9\u591a\u4e2a\u5217\u540c\u65f6\u8fdb\u884c\u6392\u540d\u3002\u5728\u4f7f\u7528rank()<\/code>\u65b9\u6cd5\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6eaxis=1<\/code>\u6765\u5b9e\u73b0\u5217\u95f4\u7684\u6bd4\u8f83\u3002\u4f8b\u5982\uff0cdf[['col1', 'col2']].rank(axis=1)<\/code>\u5c06\u4e3a\u6bcf\u4e00\u884c\u4e2d\u7684\u591a\u4e2a\u5217\u751f\u6210\u6392\u540d\u3002<\/p>\n
                  \u5982\u4f55\u5904\u7406\u7f3a\u5931\u503c\u5728\u6392\u540d\u4e2d\u7684\u5f71\u54cd\uff1f<\/strong>
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