{"id":1095717,"date":"2025-01-08T14:53:58","date_gmt":"2025-01-08T06:53:58","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1095717.html"},"modified":"2025-01-08T14:54:00","modified_gmt":"2025-01-08T06:54:00","slug":"python%e5%a6%82%e4%bd%95%e5%ba%94%e7%94%a8%e5%9c%a8-%e4%bf%9d%e9%99%a9%e5%85%b3%e8%81%94-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1095717.html","title":{"rendered":"python\u5982\u4f55\u5e94\u7528\u5728 \u4fdd\u9669\u5173\u8054"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/24211236\/9dcdeb33-8c79-41cf-ae8d-ae6604169fc6.webp\" alt=\"python\u5982\u4f55\u5e94\u7528\u5728 \u4fdd\u9669\u5173\u8054\" \/><\/p>\n<p><p> <strong>Python\u5728\u4fdd\u9669\u5173\u8054\u4e2d\u7684\u5e94\u7528\u4e3b\u8981\u5305\u62ec\u6570\u636e\u6e05\u7406\u4e0e\u5904\u7406\u3001\u9884\u6d4b\u5206\u6790\u3001\u81ea\u52a8\u5316\u4efb\u52a1\u4ee5\u53ca\u5ba2\u6237\u670d\u52a1\u4f18\u5316<\/strong>\u3002\u5176\u4e2d\uff0c\u6570\u636e\u6e05\u7406\u4e0e\u5904\u7406\u662f\u57fa\u7840\u5de5\u4f5c\uff0c\u901a\u8fc7\u4f7f\u7528Python\u7684\u5f3a\u5927\u6570\u636e\u5904\u7406\u5e93\uff0c\u5982Pandas\u548cNumPy\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u9ad8\u6548\u5730\u6e05\u7406\u548c\u7ec4\u7ec7\u5927\u91cf\u7684\u5ba2\u6237\u6570\u636e\uff0c\u4ece\u800c\u4e3a\u540e\u7eed\u7684\u5206\u6790\u548c\u5efa\u6a21\u63d0\u4f9b\u9ad8\u8d28\u91cf\u7684\u6570\u636e\u57fa\u7840\u3002<\/p>\n<\/p>\n<p><h3>\u4e00\u3001\u6570\u636e\u6e05\u7406\u4e0e\u5904\u7406<\/h3>\n<\/p>\n<p><p><strong>\u6570\u636e\u6e05\u7406\u4e0e\u5904\u7406\u662f\u4fdd\u9669\u5173\u8054\u7684\u57fa\u7840\u5de5\u4f5c<\/strong>\u3002\u4fdd\u9669\u516c\u53f8\u901a\u5e38\u9700\u8981\u5904\u7406\u5927\u91cf\u7684\u5ba2\u6237\u6570\u636e\uff0c\u8fd9\u4e9b\u6570\u636e\u53ef\u80fd\u5305\u542b\u8bb8\u591a\u7f3a\u5931\u503c\u3001\u4e0d\u4e00\u81f4\u7684\u683c\u5f0f\u548c\u566a\u58f0\u6570\u636e\u3002Python\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5e93\uff0c\u5982Pandas\u548cNumPy\uff0c\u53ef\u4ee5\u5e2e\u52a9\u4fdd\u9669\u516c\u53f8\u9ad8\u6548\u5730\u6e05\u7406\u548c\u7ec4\u7ec7\u6570\u636e\u3002<\/p>\n<\/p>\n<p><h4>1\u3001\u6570\u636e\u6e05\u7406<\/h4>\n<\/p>\n<p><p>\u6570\u636e\u6e05\u7406\u662f\u6307\u8bc6\u522b\u5e76\u7ea0\u6b63\u6216\u5220\u9664\u6570\u636e\u96c6\u4e2d\u7684\u9519\u8bef\u6216\u4e0d\u5b8c\u6574\u7684\u6570\u636e\u3002\u4f7f\u7528Pandas\u5e93\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u8f7b\u677e\u5730\u5904\u7406\u7f3a\u5931\u503c\u3001\u91cd\u590d\u6570\u636e\u548c\u683c\u5f0f\u4e0d\u4e00\u81f4\u7684\u95ee\u9898\u3002\u4f8b\u5982\uff0cPandas\u7684<code>dropna()<\/code>\u65b9\u6cd5\u53ef\u4ee5\u7528\u6765\u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c\u6216\u5217\uff0c\u800c<code>fillna()<\/code>\u65b9\u6cd5\u53ef\u4ee5\u7528\u6765\u586b\u5145\u7f3a\u5931\u503c\u3002<\/p>\n<\/p>\n<p><h4>2\u3001\u6570\u636e\u8f6c\u6362<\/h4>\n<\/p>\n<p><p>\u6570\u636e\u8f6c\u6362\u662f\u6307\u5c06\u6570\u636e\u4ece\u4e00\u79cd\u683c\u5f0f\u8f6c\u6362\u4e3a\u53e6\u4e00\u79cd\u683c\u5f0f\uff0c\u4ee5\u4fbf\u66f4\u597d\u5730\u8fdb\u884c\u5206\u6790\u3002Python\u7684Pandas\u5e93\u63d0\u4f9b\u4e86\u8bb8\u591a\u7528\u4e8e\u6570\u636e\u8f6c\u6362\u7684\u51fd\u6570\uff0c\u4f8b\u5982<code>to_datetime()<\/code>\u51fd\u6570\u53ef\u4ee5\u5c06\u5b57\u7b26\u4e32\u683c\u5f0f\u7684\u65e5\u671f\u8f6c\u6362\u4e3aPandas\u7684Datetime\u5bf9\u8c61\uff0c\u4ece\u800c\u4fbf\u4e8e\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u3002<\/p>\n<\/p>\n<p><h3>\u4e8c\u3001\u9884\u6d4b\u5206\u6790<\/h3>\n<\/p>\n<p><p><strong>\u9884\u6d4b\u5206\u6790\u662f\u4fdd\u9669\u5173\u8054\u4e2d\u7684\u91cd\u8981\u5e94\u7528<\/strong>\u3002\u901a\u8fc7\u4f7f\u7528Python\u7684<a href=\"https:\/\/docs.pingcode.com\/ask\/59192.html\" target=\"_blank\">\u673a\u5668\u5b66\u4e60<\/a>\u5e93\uff0c\u5982Scikit-learn\u548cTensorFlow\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u6784\u5efa\u9884\u6d4b\u6a21\u578b\uff0c\u4ee5\u9884\u6d4b\u5ba2\u6237\u7684\u884c\u4e3a\u3001\u8bc4\u4f30\u98ce\u9669\u548c\u5236\u5b9a\u4fdd\u9669\u5b9a\u4ef7\u7b56\u7565\u3002<\/p>\n<\/p>\n<p><h4>1\u3001\u6784\u5efa\u9884\u6d4b\u6a21\u578b<\/h4>\n<\/p>\n<p><p>\u6784\u5efa\u9884\u6d4b\u6a21\u578b\u7684\u7b2c\u4e00\u6b65\u662f\u9009\u62e9\u5408\u9002\u7684\u7b97\u6cd5\u3002\u5e38\u89c1\u7684\u7b97\u6cd5\u5305\u62ec\u7ebf\u6027\u56de\u5f52\u3001\u51b3\u7b56\u6811\u548c\u968f\u673a\u68ee\u6797\u7b49\u3002\u4f7f\u7528Scikit-learn\u5e93\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u8f7b\u677e\u5730\u9009\u62e9\u548c\u5e94\u7528\u8fd9\u4e9b\u7b97\u6cd5\u3002\u4f8b\u5982\uff0c\u4f7f\u7528<code>LinearRegression<\/code>\u7c7b\u53ef\u4ee5\u6784\u5efa\u7ebf\u6027\u56de\u5f52\u6a21\u578b\uff0c\u800c\u4f7f\u7528<code>RandomForestClassifier<\/code>\u7c7b\u53ef\u4ee5\u6784\u5efa\u968f\u673a\u68ee\u6797\u5206\u7c7b\u5668\u3002<\/p>\n<\/p>\n<p><h4>2\u3001\u6a21\u578b\u8bc4\u4f30<\/h4>\n<\/p>\n<p><p>\u6a21\u578b\u8bc4\u4f30\u662f\u6307\u8bc4\u4f30\u9884\u6d4b\u6a21\u578b\u7684\u6027\u80fd\uff0c\u4ee5\u786e\u4fdd\u5176\u51c6\u786e\u6027\u548c\u53ef\u9760\u6027\u3002Scikit-learn\u5e93\u63d0\u4f9b\u4e86\u591a\u79cd\u8bc4\u4f30\u6307\u6807\uff0c\u5982\u51c6\u786e\u7387\u3001\u7cbe\u786e\u7387\u3001\u53ec\u56de\u7387\u548cF1\u5f97\u5206\u7b49\uff0c\u53ef\u4ee5\u5e2e\u52a9\u4fdd\u9669\u516c\u53f8\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\u3002\u4f8b\u5982\uff0c\u4f7f\u7528<code>accuracy_score<\/code>\u51fd\u6570\u53ef\u4ee5\u8ba1\u7b97\u6a21\u578b\u7684\u51c6\u786e\u7387\uff0c\u800c\u4f7f\u7528<code>confusion_matrix<\/code>\u51fd\u6570\u53ef\u4ee5\u751f\u6210\u6df7\u6dc6\u77e9\u9635\u3002<\/p>\n<\/p>\n<p><h3>\u4e09\u3001\u81ea\u52a8\u5316\u4efb\u52a1<\/h3>\n<\/p>\n<p><p><strong>\u81ea\u52a8\u5316\u4efb\u52a1\u662f\u4fdd\u9669\u5173\u8054\u4e2d\u7684\u53e6\u4e00\u4e2a\u91cd\u8981\u5e94\u7528<\/strong>\u3002\u901a\u8fc7\u4f7f\u7528Python\u7684\u81ea\u52a8\u5316\u5e93\uff0c\u5982Selenium\u548cBeautifulSoup\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u81ea\u52a8\u5316\u8bb8\u591a\u91cd\u590d\u6027\u4efb\u52a1\uff0c\u4ece\u800c\u63d0\u9ad8\u5de5\u4f5c\u6548\u7387\u548c\u51cf\u5c11\u4eba\u4e3a\u9519\u8bef\u3002<\/p>\n<\/p>\n<p><h4>1\u3001\u7f51\u9875\u6570\u636e\u6293\u53d6<\/h4>\n<\/p>\n<p><p>\u7f51\u9875\u6570\u636e\u6293\u53d6\u662f\u6307\u4ece\u7f51\u9875\u4e2d\u63d0\u53d6\u6570\u636e\uff0c\u4ee5\u4fbf\u8fdb\u884c\u8fdb\u4e00\u6b65\u7684\u5206\u6790\u3002\u4f7f\u7528Selenium\u548cBeautifulSoup\u5e93\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u8f7b\u677e\u5730\u6293\u53d6\u7f51\u9875\u6570\u636e\u3002\u4f8b\u5982\uff0c\u4f7f\u7528Selenium\u53ef\u4ee5\u81ea\u52a8\u5316\u6d4f\u89c8\u5668\u64cd\u4f5c\uff0c\u800c\u4f7f\u7528BeautifulSoup\u53ef\u4ee5\u89e3\u6790HTML\u6587\u6863\u5e76\u63d0\u53d6\u6240\u9700\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n<p><h4>2\u3001\u81ea\u52a8\u5316\u62a5\u544a\u751f\u6210<\/h4>\n<\/p>\n<p><p>\u81ea\u52a8\u5316\u62a5\u544a\u751f\u6210\u662f\u6307\u4f7f\u7528Python\u751f\u6210\u5404\u79cd\u62a5\u544a\uff0c\u5982\u5ba2\u6237\u62a5\u544a\u3001\u8d22\u52a1\u62a5\u544a\u548c\u98ce\u9669\u8bc4\u4f30\u62a5\u544a\u7b49\u3002\u4f7f\u7528Python\u7684\u62a5\u544a\u751f\u6210\u5e93\uff0c\u5982ReportLab\u548cMatplotlib\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u8f7b\u677e\u5730\u751f\u6210\u5404\u79cd\u683c\u5f0f\u7684\u62a5\u544a\u3002\u4f8b\u5982\uff0c\u4f7f\u7528ReportLab\u53ef\u4ee5\u751f\u6210PDF\u683c\u5f0f\u7684\u62a5\u544a\uff0c\u800c\u4f7f\u7528Matplotlib\u53ef\u4ee5\u751f\u6210\u56fe\u8868\u3002<\/p>\n<\/p>\n<p><h3>\u56db\u3001\u5ba2\u6237\u670d\u52a1\u4f18\u5316<\/h3>\n<\/p>\n<p><p><strong>\u5ba2\u6237\u670d\u52a1\u4f18\u5316\u662f\u4fdd\u9669\u5173\u8054\u4e2d\u7684\u91cd\u8981\u5e94\u7528\u4e4b\u4e00<\/strong>\u3002\u901a\u8fc7\u4f7f\u7528Python\u7684\u81ea\u7136\u8bed\u8a00\u5904\u7406\u5e93\uff0c\u5982NLTK\u548cSpaCy\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u4f18\u5316\u5ba2\u6237\u670d\u52a1\uff0c\u63d0\u9ad8\u5ba2\u6237\u6ee1\u610f\u5ea6\u548c\u5fe0\u8bda\u5ea6\u3002<\/p>\n<\/p>\n<p><h4>1\u3001\u804a\u5929\u673a\u5668\u4eba<\/h4>\n<\/p>\n<p><p>\u804a\u5929\u673a\u5668\u4eba\u662f\u6307\u4f7f\u7528\u81ea\u7136\u8bed\u8a00\u5904\u7406\u6280\u672f\u4e0e\u5ba2\u6237\u8fdb\u884c\u4e92\u52a8\u7684\u673a\u5668\u4eba\u3002\u4f7f\u7528NLTK\u548cSpaCy\u5e93\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u6784\u5efa\u667a\u80fd\u804a\u5929\u673a\u5668\u4eba\uff0c\u56de\u7b54\u5ba2\u6237\u7684\u5e38\u89c1\u95ee\u9898\uff0c\u63d0\u4f9b\u5b9e\u65f6\u5e2e\u52a9\u3002\u4f8b\u5982\uff0c\u4f7f\u7528SpaCy\u53ef\u4ee5\u8fdb\u884c\u6587\u672c\u7684\u5206\u8bcd\u3001\u8bcd\u6027\u6807\u6ce8\u548c\u547d\u540d\u5b9e\u4f53\u8bc6\u522b\uff0c\u4ece\u800c\u7406\u89e3\u5ba2\u6237\u7684\u610f\u56fe\u3002<\/p>\n<\/p>\n<p><h4>2\u3001\u60c5\u611f\u5206\u6790<\/h4>\n<\/p>\n<p><p>\u60c5\u611f\u5206\u6790\u662f\u6307\u5206\u6790\u5ba2\u6237\u53cd\u9988\u4e2d\u7684\u60c5\u611f\uff0c\u4ee5\u4fbf\u4e86\u89e3\u5ba2\u6237\u7684\u6ee1\u610f\u5ea6\u548c\u60c5\u7eea\u3002\u4f7f\u7528Python\u7684\u81ea\u7136\u8bed\u8a00\u5904\u7406\u5e93\uff0c\u5982TextBlob\u548cVADER\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u8fdb\u884c\u60c5\u611f\u5206\u6790\u3002\u4f8b\u5982\uff0c\u4f7f\u7528TextBlob\u53ef\u4ee5\u8ba1\u7b97\u6587\u672c\u7684\u60c5\u611f\u6781\u6027\u548c\u4e3b\u89c2\u6027\uff0c\u4ece\u800c\u8bc4\u4f30\u5ba2\u6237\u7684\u60c5\u611f\u3002<\/p>\n<\/p>\n<p><h3>\u4e94\u3001\u98ce\u9669\u8bc4\u4f30<\/h3>\n<\/p>\n<p><p><strong>\u98ce\u9669\u8bc4\u4f30\u662f\u4fdd\u9669\u5173\u8054\u4e2d\u81f3\u5173\u91cd\u8981\u7684\u4e00\u73af<\/strong>\u3002\u901a\u8fc7\u4f7f\u7528Python\u7684\u7edf\u8ba1\u5206\u6790\u5e93\uff0c\u5982StatsModels\u548cSciPy\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u8fdb\u884c\u98ce\u9669\u8bc4\u4f30\uff0c\u4ee5\u8bc6\u522b\u548c\u7ba1\u7406\u6f5c\u5728\u7684\u98ce\u9669\u3002<\/p>\n<\/p>\n<p><h4>1\u3001\u7edf\u8ba1\u5206\u6790<\/h4>\n<\/p>\n<p><p>\u7edf\u8ba1\u5206\u6790\u662f\u6307\u4f7f\u7528\u7edf\u8ba1\u65b9\u6cd5\u5206\u6790\u6570\u636e\uff0c\u4ee5\u53d1\u73b0\u6570\u636e\u4e2d\u7684\u6a21\u5f0f\u548c\u5173\u7cfb\u3002\u4f7f\u7528StatsModels\u5e93\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u8fdb\u884c\u5404\u79cd\u7edf\u8ba1\u5206\u6790\uff0c\u5982\u56de\u5f52\u5206\u6790\u3001\u5047\u8bbe\u68c0\u9a8c\u548c\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u3002\u4f8b\u5982\uff0c\u4f7f\u7528<code>OLS<\/code>\u7c7b\u53ef\u4ee5\u8fdb\u884c\u666e\u901a\u6700\u5c0f\u4e8c\u4e58\u56de\u5f52\u5206\u6790\uff0c\u800c\u4f7f\u7528<code>ttest_ind<\/code>\u51fd\u6570\u53ef\u4ee5\u8fdb\u884c\u72ec\u7acb\u6837\u672ct\u68c0\u9a8c\u3002<\/p>\n<\/p>\n<p><h4>2\u3001\u98ce\u9669\u5efa\u6a21<\/h4>\n<\/p>\n<p><p>\u98ce\u9669\u5efa\u6a21\u662f\u6307\u6784\u5efa\u6570\u5b66\u6a21\u578b\uff0c\u4ee5\u8bc4\u4f30\u548c\u7ba1\u7406\u98ce\u9669\u3002\u4f7f\u7528Python\u7684\u673a\u5668\u5b66\u4e60\u5e93\uff0c\u5982Scikit-learn\u548cTensorFlow\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u6784\u5efa\u590d\u6742\u7684\u98ce\u9669\u6a21\u578b\u3002\u4f8b\u5982\uff0c\u4f7f\u7528Scikit-learn\u7684<code>LogisticRegression<\/code>\u7c7b\u53ef\u4ee5\u6784\u5efa\u903b\u8f91\u56de\u5f52\u6a21\u578b\uff0c\u7528\u4e8e\u9884\u6d4b\u5ba2\u6237\u7684\u8fdd\u7ea6\u98ce\u9669\u3002<\/p>\n<\/p>\n<p><h3>\u516d\u3001\u6570\u636e\u53ef\u89c6\u5316<\/h3>\n<\/p>\n<p><p><strong>\u6570\u636e\u53ef\u89c6\u5316\u662f\u4fdd\u9669\u5173\u8054\u4e2d\u7684\u91cd\u8981\u5de5\u5177<\/strong>\u3002\u901a\u8fc7\u4f7f\u7528Python\u7684\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u5982Matplotlib\u548cSeaborn\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u4ee5\u56fe\u5f62\u7684\u5f62\u5f0f\u5c55\u793a\u6570\u636e\uff0c\u5e2e\u52a9\u7ba1\u7406\u5c42\u505a\u51fa\u660e\u667a\u7684\u51b3\u7b56\u3002<\/p>\n<\/p>\n<p><h4>1\u3001\u56fe\u8868\u751f\u6210<\/h4>\n<\/p>\n<p><p>\u56fe\u8868\u751f\u6210\u662f\u6307\u4f7f\u7528\u56fe\u5f62\u5c55\u793a\u6570\u636e\uff0c\u4ee5\u4fbf\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u4e2d\u7684\u6a21\u5f0f\u548c\u5173\u7cfb\u3002\u4f7f\u7528Matplotlib\u548cSeaborn\u5e93\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u751f\u6210\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\uff0c\u5982\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u548c\u6563\u70b9\u56fe\u7b49\u3002\u4f8b\u5982\uff0c\u4f7f\u7528Matplotlib\u7684<code>plot<\/code>\u51fd\u6570\u53ef\u4ee5\u751f\u6210\u6298\u7ebf\u56fe\uff0c\u800c\u4f7f\u7528Seaborn\u7684<code>barplot<\/code>\u51fd\u6570\u53ef\u4ee5\u751f\u6210\u67f1\u72b6\u56fe\u3002<\/p>\n<\/p>\n<p><h4>2\u3001\u4ea4\u4e92\u5f0f\u53ef\u89c6\u5316<\/h4>\n<\/p>\n<p><p>\u4ea4\u4e92\u5f0f\u53ef\u89c6\u5316\u662f\u6307\u751f\u6210\u53ef\u4ee5\u4e0e\u7528\u6237\u4e92\u52a8\u7684\u56fe\u8868\uff0c\u4ee5\u4fbf\u7528\u6237\u53ef\u4ee5\u66f4\u6df1\u5165\u5730\u63a2\u7d22\u6570\u636e\u3002\u4f7f\u7528Python\u7684\u4ea4\u4e92\u5f0f\u53ef\u89c6\u5316\u5e93\uff0c\u5982Plotly\u548cBokeh\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u751f\u6210\u4ea4\u4e92\u5f0f\u56fe\u8868\u3002\u4f8b\u5982\uff0c\u4f7f\u7528Plotly\u7684<code>scatter<\/code>\u51fd\u6570\u53ef\u4ee5\u751f\u6210\u4ea4\u4e92\u5f0f\u6563\u70b9\u56fe\uff0c\u800c\u4f7f\u7528Bokeh\u7684<code>figure<\/code>\u51fd\u6570\u53ef\u4ee5\u751f\u6210\u4ea4\u4e92\u5f0f\u6298\u7ebf\u56fe\u3002<\/p>\n<\/p>\n<p><h3>\u4e03\u3001\u5b9a\u4ef7\u7b56\u7565\u4f18\u5316<\/h3>\n<\/p>\n<p><p><strong>\u5b9a\u4ef7\u7b56\u7565\u4f18\u5316\u662f\u4fdd\u9669\u5173\u8054\u4e2d\u7684\u5173\u952e\u5e94\u7528<\/strong>\u3002\u901a\u8fc7\u4f7f\u7528Python\u7684\u4f18\u5316\u5e93\uff0c\u5982SciPy\u548cCVXPY\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u4f18\u5316\u5b9a\u4ef7\u7b56\u7565\uff0c\u4ee5\u786e\u4fdd\u76c8\u5229\u80fd\u529b\u548c\u5e02\u573a\u7ade\u4e89\u529b\u3002<\/p>\n<\/p>\n<p><h4>1\u3001\u7ebf\u6027\u4f18\u5316<\/h4>\n<\/p>\n<p><p>\u7ebf\u6027\u4f18\u5316\u662f\u6307\u5728\u7ea6\u675f\u6761\u4ef6\u4e0b\uff0c\u4f18\u5316\u7ebf\u6027\u76ee\u6807\u51fd\u6570\u3002\u4f7f\u7528SciPy\u7684<code>linprog<\/code>\u51fd\u6570\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u89e3\u51b3\u7ebf\u6027\u4f18\u5316\u95ee\u9898\u3002\u4f8b\u5982\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u4f7f\u7528\u7ebf\u6027\u4f18\u5316\u6765\u786e\u5b9a\u6700\u4f73\u7684\u4fdd\u9669\u5b9a\u4ef7\u7b56\u7565\uff0c\u4ee5\u6700\u5927\u5316\u5229\u6da6\u3002<\/p>\n<\/p>\n<p><h4>2\u3001\u975e\u7ebf\u6027\u4f18\u5316<\/h4>\n<\/p>\n<p><p>\u975e\u7ebf\u6027\u4f18\u5316\u662f\u6307\u5728\u7ea6\u675f\u6761\u4ef6\u4e0b\uff0c\u4f18\u5316\u975e\u7ebf\u6027\u76ee\u6807\u51fd\u6570\u3002\u4f7f\u7528SciPy\u7684<code>minimize<\/code>\u51fd\u6570\u548cCVXPY\u5e93\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u89e3\u51b3\u975e\u7ebf\u6027\u4f18\u5316\u95ee\u9898\u3002\u4f8b\u5982\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u4f7f\u7528\u975e\u7ebf\u6027\u4f18\u5316\u6765\u786e\u5b9a\u6700\u4f18\u7684\u6295\u8d44\u7ec4\u5408\uff0c\u4ee5\u6700\u5927\u5316\u56de\u62a5\u3002<\/p>\n<\/p>\n<p><h3>\u516b\u3001\u6b3a\u8bc8\u68c0\u6d4b<\/h3>\n<\/p>\n<p><p><strong>\u6b3a\u8bc8\u68c0\u6d4b\u662f\u4fdd\u9669\u5173\u8054\u4e2d\u7684\u91cd\u8981\u5e94\u7528<\/strong>\u3002\u901a\u8fc7\u4f7f\u7528Python\u7684\u5f02\u5e38\u68c0\u6d4b\u5e93\uff0c\u5982PyOD\u548cScikit-learn\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u68c0\u6d4b\u548c\u9884\u9632\u6b3a\u8bc8\u884c\u4e3a\uff0c\u51cf\u5c11\u635f\u5931\u3002<\/p>\n<\/p>\n<p><h4>1\u3001\u5f02\u5e38\u68c0\u6d4b<\/h4>\n<\/p>\n<p><p>\u5f02\u5e38\u68c0\u6d4b\u662f\u6307\u8bc6\u522b\u6570\u636e\u4e2d\u7684\u5f02\u5e38\u6a21\u5f0f\uff0c\u4ee5\u68c0\u6d4b\u6f5c\u5728\u7684\u6b3a\u8bc8\u884c\u4e3a\u3002\u4f7f\u7528PyOD\u548cScikit-learn\u5e93\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u8fdb\u884c\u5f02\u5e38\u68c0\u6d4b\u3002\u4f8b\u5982\uff0c\u4f7f\u7528Scikit-learn\u7684<code>IsolationForest<\/code>\u7c7b\u53ef\u4ee5\u6784\u5efa\u5b64\u7acb\u68ee\u6797\u6a21\u578b\uff0c\u7528\u4e8e\u68c0\u6d4b\u5f02\u5e38\u6570\u636e\u3002<\/p>\n<\/p>\n<p><h4>2\u3001\u5206\u7c7b\u6a21\u578b<\/h4>\n<\/p>\n<p><p>\u5206\u7c7b\u6a21\u578b\u662f\u6307\u5c06\u6570\u636e\u5206\u4e3a\u4e0d\u540c\u7684\u7c7b\u522b\uff0c\u4ee5\u8bc6\u522b\u6b3a\u8bc8\u884c\u4e3a\u3002\u4f7f\u7528Python\u7684\u673a\u5668\u5b66\u4e60\u5e93\uff0c\u5982Scikit-learn\u548cTensorFlow\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u6784\u5efa\u5206\u7c7b\u6a21\u578b\u3002\u4f8b\u5982\uff0c\u4f7f\u7528Scikit-learn\u7684<code>LogisticRegression<\/code>\u7c7b\u53ef\u4ee5\u6784\u5efa\u903b\u8f91\u56de\u5f52\u6a21\u578b\uff0c\u7528\u4e8e\u5206\u7c7b\u6b3a\u8bc8\u548c\u975e\u6b3a\u8bc8\u884c\u4e3a\u3002<\/p>\n<\/p>\n<p><h3>\u4e5d\u3001\u5ba2\u6237\u7ec6\u5206<\/h3>\n<\/p>\n<p><p><strong>\u5ba2\u6237\u7ec6\u5206\u662f\u4fdd\u9669\u5173\u8054\u4e2d\u7684\u91cd\u8981\u7b56\u7565<\/strong>\u3002\u901a\u8fc7\u4f7f\u7528Python\u7684\u805a\u7c7b\u5e93\uff0c\u5982Scikit-learn\u548cKMeans\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u5bf9\u5ba2\u6237\u8fdb\u884c\u7ec6\u5206\uff0c\u4ee5\u5236\u5b9a\u9488\u5bf9\u6027\u7684\u8425\u9500\u7b56\u7565\u548c\u670d\u52a1\u3002<\/p>\n<\/p>\n<p><h4>1\u3001\u805a\u7c7b\u5206\u6790<\/h4>\n<\/p>\n<p><p>\u805a\u7c7b\u5206\u6790\u662f\u6307\u5c06\u6570\u636e\u5206\u4e3a\u591a\u4e2a\u7ec4\uff0c\u4ee5\u4fbf\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u4e2d\u7684\u6a21\u5f0f\u3002\u4f7f\u7528Scikit-learn\u5e93\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u8fdb\u884c\u805a\u7c7b\u5206\u6790\u3002\u4f8b\u5982\uff0c\u4f7f\u7528<code>KMeans<\/code>\u7c7b\u53ef\u4ee5\u8fdb\u884cK-means\u805a\u7c7b\uff0c\u5c06\u5ba2\u6237\u5206\u4e3a\u591a\u4e2a\u7ec6\u5206\u5e02\u573a\u3002<\/p>\n<\/p>\n<p><h4>2\u3001\u5ba2\u6237\u753b\u50cf<\/h4>\n<\/p>\n<p><p>\u5ba2\u6237\u753b\u50cf\u662f\u6307\u901a\u8fc7\u5206\u6790\u5ba2\u6237\u6570\u636e\uff0c\u6784\u5efa\u5ba2\u6237\u7684\u8be6\u7ec6\u63cf\u8ff0\u3002\u4f7f\u7528Python\u7684\u5206\u6790\u5e93\uff0c\u5982Pandas\u548cSeaborn\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u6784\u5efa\u5ba2\u6237\u753b\u50cf\u3002\u4f8b\u5982\uff0c\u4f7f\u7528Pandas\u53ef\u4ee5\u5bf9\u5ba2\u6237\u6570\u636e\u8fdb\u884c\u7edf\u8ba1\u5206\u6790\uff0c\u800c\u4f7f\u7528Seaborn\u53ef\u4ee5\u751f\u6210\u5ba2\u6237\u753b\u50cf\u7684\u53ef\u89c6\u5316\u56fe\u8868\u3002<\/p>\n<\/p>\n<p><h3>\u5341\u3001\u4fdd\u9669\u4ea7\u54c1\u5f00\u53d1<\/h3>\n<\/p>\n<p><p><strong>\u4fdd\u9669\u4ea7\u54c1\u5f00\u53d1\u662f\u4fdd\u9669\u5173\u8054\u4e2d\u7684\u521b\u65b0\u5e94\u7528<\/strong>\u3002\u901a\u8fc7\u4f7f\u7528Python\u7684\u6a21\u62df\u5e93\uff0c\u5982SimPy\u548cNumPy\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u5f00\u53d1\u548c\u6d4b\u8bd5\u65b0\u7684\u4fdd\u9669\u4ea7\u54c1\uff0c\u4ee5\u6ee1\u8db3\u5e02\u573a\u9700\u6c42\u3002<\/p>\n<\/p>\n<p><h4>1\u3001\u6a21\u62df\u6d4b\u8bd5<\/h4>\n<\/p>\n<p><p>\u6a21\u62df\u6d4b\u8bd5\u662f\u6307\u901a\u8fc7\u6a21\u62df\u73b0\u5b9e\u4e16\u754c\u7684\u60c5\u51b5\uff0c\u6d4b\u8bd5\u4fdd\u9669\u4ea7\u54c1\u7684\u6027\u80fd\u3002\u4f7f\u7528SimPy\u5e93\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u8fdb\u884c\u79bb\u6563\u4e8b\u4ef6\u6a21\u62df\u3002\u4f8b\u5982\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u4f7f\u7528SimPy\u6a21\u62df\u5ba2\u6237\u7684\u7d22\u8d54\u8fc7\u7a0b\uff0c\u4ee5\u8bc4\u4f30\u65b0\u4fdd\u9669\u4ea7\u54c1\u7684\u53ef\u884c\u6027\u3002<\/p>\n<\/p>\n<p><h4>2\u3001\u4ea7\u54c1\u4f18\u5316<\/h4>\n<\/p>\n<p><p>\u4ea7\u54c1\u4f18\u5316\u662f\u6307\u901a\u8fc7\u6570\u636e\u5206\u6790\u548c\u5efa\u6a21\uff0c\u4f18\u5316\u4fdd\u9669\u4ea7\u54c1\u7684\u8bbe\u8ba1\u548c\u5b9a\u4ef7\u3002\u4f7f\u7528Python\u7684\u4f18\u5316\u5e93\uff0c\u5982SciPy\u548cCVXPY\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u8fdb\u884c\u4ea7\u54c1\u4f18\u5316\u3002\u4f8b\u5982\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u4f7f\u7528\u4f18\u5316\u6280\u672f\u6765\u786e\u5b9a\u6700\u4f73\u7684\u4fdd\u9669\u4ea7\u54c1\u7ec4\u5408\uff0c\u4ee5\u6700\u5927\u5316\u5e02\u573a\u4efd\u989d\u548c\u5229\u6da6\u3002<\/p>\n<\/p>\n<p><p>\u901a\u8fc7\u4f7f\u7528Python\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u5728\u6570\u636e\u6e05\u7406\u4e0e\u5904\u7406\u3001\u9884\u6d4b\u5206\u6790\u3001\u81ea\u52a8\u5316\u4efb\u52a1\u3001\u5ba2\u6237\u670d\u52a1\u4f18\u5316\u3001\u98ce\u9669\u8bc4\u4f30\u3001\u6570\u636e\u53ef\u89c6\u5316\u3001\u5b9a\u4ef7\u7b56\u7565\u4f18\u5316\u3001\u6b3a\u8bc8\u68c0\u6d4b\u3001\u5ba2\u6237\u7ec6\u5206\u548c\u4fdd\u9669\u4ea7\u54c1\u5f00\u53d1\u7b49\u65b9\u9762\u53d6\u5f97\u663e\u8457\u7684\u6210\u679c\uff0c\u63d0\u9ad8\u6548\u7387\u548c\u7ade\u4e89\u529b\u3002Python\u7684\u5f3a\u5927\u529f\u80fd\u548c\u5e7f\u6cdb\u7684\u5e94\u7528\uff0c\u4f7f\u5176\u6210\u4e3a\u4fdd\u9669\u884c\u4e1a\u4e2d\u4e0d\u53ef\u6216\u7f3a\u7684\u5de5\u5177\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5728\u4fdd\u9669\u884c\u4e1a\u4e2d\uff0cPython\u6709\u54ea\u4e9b\u5177\u4f53\u5e94\u7528\uff1f<\/strong><br \/>Python\u5728\u4fdd\u9669\u884c\u4e1a\u7684\u5e94\u7528\u5e7f\u6cdb\uff0c\u4e3b\u8981\u5305\u62ec\u6570\u636e\u5206\u6790\u3001\u98ce\u9669\u8bc4\u4f30\u3001\u7d22\u8d54\u5904\u7406\u548c\u81ea\u52a8\u5316\u6d41\u7a0b\u7b49\u65b9\u9762\u3002\u901a\u8fc7\u4f7f\u7528Python\u7684\u5f3a\u5927\u6570\u636e\u5904\u7406\u5e93\uff0c\u5982Pandas\u548cNumPy\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u6709\u6548\u5730\u5206\u6790\u5ba2\u6237\u6570\u636e\uff0c\u9884\u6d4b\u98ce\u9669\uff0c\u5e76\u5236\u5b9a\u5408\u9002\u7684\u4fdd\u9669\u4ea7\u54c1\u3002\u540c\u65f6\uff0c\u5229\u7528\u673a\u5668\u5b66\u4e60\u6846\u67b6\uff08\u5982Scikit-learn\u548cTensorFlow\uff09\uff0c\u516c\u53f8\u80fd\u591f\u63d0\u5347\u7d22\u8d54\u5ba1\u6838\u7684\u6548\u7387\uff0c\u8bc6\u522b\u6f5c\u5728\u7684\u6b3a\u8bc8\u884c\u4e3a\u3002<\/p>\n<p><strong>\u4f7f\u7528Python\u8fdb\u884c\u4fdd\u9669\u6570\u636e\u5206\u6790\u65f6\uff0c\u5e94\u8be5\u5173\u6ce8\u54ea\u4e9b\u5173\u952e\u6307\u6807\uff1f<\/strong><br \/>\u5728\u8fdb\u884c\u4fdd\u9669\u6570\u636e\u5206\u6790\u65f6\uff0c\u5173\u952e\u6307\u6807\u5305\u62ec\u7d22\u8d54\u7387\u3001\u5ba2\u6237\u4fdd\u7559\u7387\u3001\u4fdd\u8d39\u6536\u5165\u3001\u4e8b\u6545\u9891\u7387\u548c\u635f\u5931\u6bd4\u7387\u7b49\u3002\u8fd9\u4e9b\u6307\u6807\u53ef\u4ee5\u5e2e\u52a9\u4fdd\u9669\u516c\u53f8\u8bc4\u4f30\u5176\u4e1a\u52a1\u8868\u73b0\u548c\u98ce\u9669\u6c34\u5e73\u3002\u6b64\u5916\uff0c\u5ba2\u6237\u7ec6\u5206\u548c\u884c\u4e3a\u5206\u6790\u4e5f\u662f\u91cd\u8981\u7684\u65b9\u9762\uff0c\u901a\u8fc7Python\u7684\u53ef\u89c6\u5316\u5de5\u5177\uff08\u5982Matplotlib\u548cSeaborn\uff09\uff0c\u53ef\u4ee5\u76f4\u89c2\u5730\u5448\u73b0\u6570\u636e\uff0c\u4f7f\u51b3\u7b56\u66f4\u52a0\u79d1\u5b66\u3002<\/p>\n<p><strong>Python\u5982\u4f55\u5e2e\u52a9\u4fdd\u9669\u516c\u53f8\u8fdb\u884c\u98ce\u9669\u7ba1\u7406\uff1f<\/strong><br \/>Python\u80fd\u591f\u901a\u8fc7\u6784\u5efa\u590d\u6742\u7684\u98ce\u9669\u6a21\u578b\u6765\u5e2e\u52a9\u4fdd\u9669\u516c\u53f8\u8fdb\u884c\u98ce\u9669\u7ba1\u7406\u3002\u5229\u7528\u7edf\u8ba1\u5206\u6790\u548c\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\uff0c\u4fdd\u9669\u516c\u53f8\u53ef\u4ee5\u8bc6\u522b\u548c\u91cf\u5316\u6f5c\u5728\u98ce\u9669\u56e0\u7d20\uff0c\u4ece\u800c\u5236\u5b9a\u66f4\u7cbe\u786e\u7684\u4fdd\u9669\u5b9a\u4ef7\u7b56\u7565\u3002\u6b64\u5916\uff0cPython\u8fd8\u53ef\u4ee5\u7528\u4e8e\u6a21\u62df\u4e0d\u540c\u7684\u98ce\u9669\u573a\u666f\uff0c\u5e2e\u52a9\u516c\u53f8\u8bc4\u4f30\u5176\u8d22\u52a1\u7a33\u5065\u6027\u548c\u5e94\u5bf9\u7a81\u53d1\u4e8b\u4ef6\u7684\u80fd\u529b\uff0c\u4ece\u800c\u5b9e\u73b0\u66f4\u6709\u6548\u7684\u98ce\u9669\u63a7\u5236\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u5728\u4fdd\u9669\u5173\u8054\u4e2d\u7684\u5e94\u7528\u4e3b\u8981\u5305\u62ec\u6570\u636e\u6e05\u7406\u4e0e\u5904\u7406\u3001\u9884\u6d4b\u5206\u6790\u3001\u81ea\u52a8\u5316\u4efb\u52a1\u4ee5\u53ca\u5ba2\u6237\u670d\u52a1\u4f18\u5316\u3002\u5176\u4e2d\uff0c\u6570\u636e\u6e05\u7406\u4e0e\u5904 [&hellip;]","protected":false},"author":3,"featured_media":1095722,"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\/1095717"}],"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=1095717"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1095717\/revisions"}],"predecessor-version":[{"id":1095724,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1095717\/revisions\/1095724"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1095722"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1095717"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1095717"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1095717"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}