{"id":1091723,"date":"2025-01-08T14:14:26","date_gmt":"2025-01-08T06:14:26","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1091723.html"},"modified":"2025-01-08T14:14:29","modified_gmt":"2025-01-08T06:14:29","slug":"python%e5%a6%82%e4%bd%95%e7%bb%9f%e8%ae%a1%e6%8e%92%e5%90%8d%e5%89%8d%e5%87%a0%e4%b8%aa%e7%9a%84%e6%95%b0%e6%8d%ae-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1091723.html","title":{"rendered":"python\u5982\u4f55\u7edf\u8ba1\u6392\u540d\u524d\u51e0\u4e2a\u7684\u6570\u636e"},"content":{"rendered":"

\"python\u5982\u4f55\u7edf\u8ba1\u6392\u540d\u524d\u51e0\u4e2a\u7684\u6570\u636e\"<\/p>\n

\u4e00\u3001Python\u7edf\u8ba1\u6392\u540d\u524d\u51e0\u4e2a\u6570\u636e\u7684\u65b9\u6cd5\u6709\uff1a\u4f7f\u7528Counter\u7c7b\u3001\u4f7f\u7528pandas\u5e93\u3001\u4f7f\u7528heapq\u5e93\u3001\u624b\u52a8\u5b9e\u73b0\u6392\u5e8f\u65b9\u6cd5\u7b49\u3002\u5176\u4e2d\uff0c\u4f7f\u7528pandas\u5e93<\/strong>\u662f\u6bd4\u8f83\u65b9\u4fbf\u548c\u5e38\u7528\u7684\u65b9\u5f0f\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u5904\u7406\u529f\u80fd\uff0c\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0\u6570\u636e\u7684\u6392\u540d\u7edf\u8ba1\u3002<\/p>\n<\/p>\n

\u4f7f\u7528pandas\u5e93\u7edf\u8ba1\u6392\u540d\u524d\u51e0\u4e2a\u6570\u636e\u7684\u57fa\u672c\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<\/p>\n

    \n
  1. \u5bfc\u5165pandas\u5e93\u5e76\u8bfb\u53d6\u6570\u636e\uff1b<\/li>\n
  2. \u4f7f\u7528DataFrame\u5bf9\u8c61\u7684sort_values\u65b9\u6cd5\u5bf9\u6570\u636e\u8fdb\u884c\u6392\u5e8f\uff1b<\/li>\n
  3. \u4f7f\u7528head\u65b9\u6cd5\u83b7\u53d6\u6392\u540d\u524d\u51e0\u7684\u6570\u636e\u3002<\/li>\n<\/ol>\n

    \u4ee5\u4e0b\u662f\u8be6\u7ec6\u7684\u4ecb\u7ecd\uff1a<\/p>\n<\/p>\n

    \n

    \u4e00\u3001\u4f7f\u7528Counter\u7c7b<\/h2>\n<\/p>\n

    1. \u5bfc\u5165Counter\u7c7b<\/h3>\n<\/p>\n

    \u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u4ececollections\u6a21\u5757\u4e2d\u5bfc\u5165Counter\u7c7b\u3002Counter\u7c7b\u662f\u4e00\u4e2a\u7528\u6765\u7edf\u8ba1\u5143\u7d20\u51fa\u73b0\u6b21\u6570\u7684\u5bb9\u5668\uff0c\u7279\u522b\u9002\u7528\u4e8e\u7edf\u8ba1\u9891\u7387\u6570\u636e\u3002<\/p>\n<\/p>\n

    from collections import Counter<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    2. \u7edf\u8ba1\u6570\u636e\u5e76\u83b7\u53d6\u6392\u540d\u524d\u51e0\u4e2a\u7684\u6570\u636e<\/h3>\n<\/p>\n
    \u5047\u8bbe\u6211\u4eec\u6709\u4e00\u7ec4\u6570\u636e\uff0c\u8fd9\u4e9b\u6570\u636e\u53ef\u4ee5\u662f\u4efb\u610f\u7c7b\u578b\u7684\u5143\u7d20\uff0c\u4f8b\u5982\u5b57\u7b26\u4e32\u3001\u6570\u5b57\u7b49\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Counter\u7c7b\u6765\u7edf\u8ba1\u6bcf\u4e2a\u5143\u7d20\u51fa\u73b0\u7684\u6b21\u6570\uff0c\u5e76\u83b7\u53d6\u51fa\u73b0\u6b21\u6570\u6392\u540d\u524d\u51e0\u4e2a\u7684\u5143\u7d20\u3002<\/p>\n<\/p>\n
    data = ['apple', 'banana', 'apple', 'orange', 'banana', 'apple']<\/p>\n
    counter = Counter(data)<\/p>\n
    top_n = counter.most_common(3)  # \u83b7\u53d6\u51fa\u73b0\u6b21\u6570\u6392\u540d\u524d3\u7684\u5143\u7d20<\/p>\n
    print(top_n)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u6b64\u4ee3\u7801\u4f1a\u8f93\u51fa\uff1a<\/p>\n<\/p>\n
    [('apple', 3), ('banana', 2), ('orange', 1)]<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e8c\u3001\u4f7f\u7528pandas\u5e93<\/h2>\n<\/p>\n
    1. \u5bfc\u5165pandas\u5e93<\/h3>\n<\/p>\n
    \u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165pandas\u5e93\uff0c\u5e76\u8bfb\u53d6\u6570\u636e\u3002pandas\u5e93\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5de5\u5177\uff0c\u7279\u522b\u9002\u7528\u4e8e\u5904\u7406\u8868\u683c\u6570\u636e\u3002<\/p>\n<\/p>\n
    import pandas as pd<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    2. \u8bfb\u53d6\u6570\u636e\u5e76\u6392\u5e8f<\/h3>\n<\/p>\n
    \u6211\u4eec\u53ef\u4ee5\u4f7f\u7528pandas\u5e93\u7684DataFrame\u5bf9\u8c61\u8bfb\u53d6\u6570\u636e\uff0c\u5e76\u4f7f\u7528sort_values\u65b9\u6cd5\u5bf9\u6570\u636e\u8fdb\u884c\u6392\u5e8f\u3002<\/p>\n<\/p>\n
    data = {<\/p>\n
        'fruit': ['apple', 'banana', 'orange', 'banana', 'apple', 'apple'],<\/p>\n
        'count': [3, 2, 1, 2, 3, 3]<\/p>\n
    }<\/p>\n
    df = pd.DataFrame(data)<\/p>\n
    df_sorted = df.sort_values(by='count', ascending=False)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    3. \u83b7\u53d6\u6392\u540d\u524d\u51e0\u4e2a\u7684\u6570\u636e<\/h3>\n<\/p>\n
    \u6211\u4eec\u53ef\u4ee5\u4f7f\u7528head\u65b9\u6cd5\u83b7\u53d6\u6392\u540d\u524d\u51e0\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n
    top_n = df_sorted.head(3)<\/p>\n
    print(top_n)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u6b64\u4ee3\u7801\u4f1a\u8f93\u51fa\uff1a<\/p>\n<\/p>\n
        fruit  count<\/p>\n
    0   apple      3<\/p>\n
    4   apple      3<\/p>\n
    5   apple      3<\/p>\n
    1  banana      2<\/p>\n
    3  banana      2<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e09\u3001\u4f7f\u7528heapq\u5e93<\/h2>\n<\/p>\n
    1. \u5bfc\u5165heapq\u5e93<\/h3>\n<\/p>\n
    heapq\u5e93\u63d0\u4f9b\u4e86\u5806\u961f\u5217\u7b97\u6cd5\u7684\u5b9e\u73b0\uff0c\u7279\u522b\u9002\u7528\u4e8e\u83b7\u53d6\u6700\u5927\u6216\u6700\u5c0f\u7684\u82e5\u5e72\u4e2a\u5143\u7d20\u3002<\/p>\n<\/p>\n
    import heapq<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    2. \u83b7\u53d6\u6392\u540d\u524d\u51e0\u4e2a\u7684\u6570\u636e<\/h3>\n<\/p>\n
    \u6211\u4eec\u53ef\u4ee5\u4f7f\u7528heapq.nlargest\u51fd\u6570\u83b7\u53d6\u6392\u540d\u524d\u51e0\u4e2a\u7684\u5143\u7d20\u3002<\/p>\n<\/p>\n
    data = [3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5]<\/p>\n
    top_n = heapq.nlargest(3, data)<\/p>\n
    print(top_n)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u6b64\u4ee3\u7801\u4f1a\u8f93\u51fa\uff1a<\/p>\n<\/p>\n
    [9, 6, 5]<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u56db\u3001\u624b\u52a8\u5b9e\u73b0\u6392\u5e8f\u65b9\u6cd5<\/h2>\n<\/p>\n
    1. \u4f7f\u7528sorted\u51fd\u6570<\/h3>\n<\/p>\n
    \u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Python\u5185\u7f6e\u7684sorted\u51fd\u6570\u5bf9\u6570\u636e\u8fdb\u884c\u6392\u5e8f\uff0c\u5e76\u83b7\u53d6\u6392\u540d\u524d\u51e0\u4e2a\u7684\u5143\u7d20\u3002<\/p>\n<\/p>\n
    data = [3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5]<\/p>\n
    sorted_data = sorted(data, reverse=True)<\/p>\n
    top_n = sorted_data[:3]<\/p>\n
    print(top_n)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u6b64\u4ee3\u7801\u4f1a\u8f93\u51fa\uff1a<\/p>\n<\/p>\n
    [9, 6, 5]<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    2. \u81ea\u5b9a\u4e49\u6392\u5e8f\u51fd\u6570<\/h3>\n<\/p>\n
    \u6211\u4eec\u8fd8\u53ef\u4ee5\u81ea\u5b9a\u4e49\u6392\u5e8f\u51fd\u6570\uff0c\u5b9e\u73b0\u5bf9\u6570\u636e\u7684\u6392\u5e8f\uff0c\u5e76\u83b7\u53d6\u6392\u540d\u524d\u51e0\u4e2a\u7684\u5143\u7d20\u3002<\/p>\n<\/p>\n
    data = [('apple', 3), ('banana', 2), ('orange', 1), ('banana', 2), ('apple', 3), ('apple', 3)]<\/p>\n
    sorted_data = sorted(data, key=lambda x: x[1], reverse=True)<\/p>\n
    top_n = sorted_data[:3]<\/p>\n
    print(top_n)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u6b64\u4ee3\u7801\u4f1a\u8f93\u51fa\uff1a<\/p>\n<\/p>\n
    [('apple', 3), ('apple', 3), ('apple', 3)]<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \n
    \u4ee5\u4e0a\u65b9\u6cd5\u5404\u6709\u4f18\u7f3a\u70b9\uff0c\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u3002\u4f7f\u7528pandas\u5e93\u662f\u6bd4\u8f83\u65b9\u4fbf\u548c\u5e38\u7528\u7684\u65b9\u5f0f\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u5904\u7406\u529f\u80fd\uff0c\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0\u6570\u636e\u7684\u6392\u540d\u7edf\u8ba1\u3002\u800cCounter\u7c7b\u3001heapq\u5e93\u548c\u624b\u52a8\u5b9e\u73b0\u6392\u5e8f\u65b9\u6cd5\u5219\u9002\u7528\u4e8e\u4e0d\u540c\u7c7b\u578b\u7684\u6570\u636e\u5904\u7406\u573a\u666f\u3002<\/p>\n<\/p>\n
    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
     \u5982\u4f55\u4f7f\u7528Python\u5bf9\u6570\u636e\u8fdb\u884c\u6392\u540d\u7edf\u8ba1\uff1f<\/strong>
    \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u65b9\u6cd5\u5bf9\u6570\u636e\u8fdb\u884c\u6392\u540d\u7edf\u8ba1\u3002\u5e38\u89c1\u7684\u65b9\u5f0f\u5305\u62ec\u4f7f\u7528Pandas\u5e93\u4e2d\u7684rank()<\/code>\u51fd\u6570\u3001sort_values()<\/code>\u65b9\u6cd5\uff0c\u6216\u8005\u501f\u52a9NumPy\u5e93\u7684argsort()<\/code>\u51fd\u6570\u3002\u901a\u8fc7\u8fd9\u4e9b\u5de5\u5177\uff0c\u60a8\u53ef\u4ee5\u8f7b\u677e\u5730\u5bf9\u6570\u636e\u8fdb\u884c\u6392\u5e8f\uff0c\u5e76\u63d0\u53d6\u6392\u540d\u524d\u51e0\u7684\u9879\u3002<\/p>\n
    \u5728\u7edf\u8ba1\u6392\u540d\u65f6\uff0c\u5982\u4f55\u5904\u7406\u91cd\u590d\u503c\uff1f<\/strong>
    \u5904\u7406\u91cd\u590d\u503c\u65f6\uff0c\u60a8\u53ef\u4ee5\u9009\u62e9\u591a\u79cd\u7b56\u7565\u3002\u4f8b\u5982\uff0c\u4f7f\u7528Pandas\u7684rank()<\/code>\u51fd\u6570\u65f6\uff0c\u53ef\u4ee5\u6307\u5b9a\u53c2\u6570\u6765\u51b3\u5b9a\u5982\u4f55\u5bf9\u91cd\u590d\u503c\u8fdb\u884c\u6392\u540d\u3002\u5e38\u7528\u7684\u65b9\u5f0f\u5305\u62ec\u201cmin\u201d\u3001\u201cmax\u201d\u6216\u201caverage\u201d\u3002\u8fd9\u4e9b\u9009\u9879\u5141\u8bb8\u60a8\u6839\u636e\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u6392\u540d\u65b9\u5f0f\uff0c\u4ee5\u786e\u4fdd\u6570\u636e\u7684\u51c6\u786e\u6027\u3002<\/p>\n
    \u5982\u4f55\u5c06\u6392\u540d\u7ed3\u679c\u53ef\u89c6\u5316\uff0c\u4ee5\u4fbf\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\uff1f<\/strong>
    \u53ef\u89c6\u5316\u6392\u540d\u7ed3\u679c\u662f\u7406\u89e3\u6570\u636e\u7684\u91cd\u8981\u4e00\u6b65\u3002\u60a8\u53ef\u4ee5\u4f7f\u7528Matplotlib\u6216Seaborn\u5e93\u6765\u521b\u5efa\u6761\u5f62\u56fe\u6216\u6298\u7ebf\u56fe\uff0c\u5c55\u793a\u6392\u540d\u524d\u51e0\u7684\u6570\u636e\u9879\u3002\u901a\u8fc7\u8fd9\u4e9b\u56fe\u8868\uff0c\u60a8\u53ef\u4ee5\u66f4\u76f4\u89c2\u5730\u8bc6\u522b\u8d8b\u52bf\u548c\u6a21\u5f0f\uff0c\u4ece\u800c\u4e3a\u51b3\u7b56\u63d0\u4f9b\u66f4\u6709\u529b\u7684\u652f\u6301\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u4e00\u3001Python\u7edf\u8ba1\u6392\u540d\u524d\u51e0\u4e2a\u6570\u636e\u7684\u65b9\u6cd5\u6709\uff1a\u4f7f\u7528Counter\u7c7b\u3001\u4f7f\u7528pandas\u5e93\u3001\u4f7f\u7528heapq\u5e93\u3001\u624b\u52a8\u5b9e […]","protected":false},"author":3,"featured_media":1091730,"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\/1091723"}],"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=1091723"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1091723\/revisions"}],"predecessor-version":[{"id":1091733,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1091723\/revisions\/1091733"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1091730"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1091723"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1091723"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1091723"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}