{"id":960743,"date":"2024-12-27T03:52:28","date_gmt":"2024-12-26T19:52:28","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/960743.html"},"modified":"2024-12-27T03:52:30","modified_gmt":"2024-12-26T19:52:30","slug":"python%e5%bc%82%e5%b8%b8%e5%80%bc%e5%a6%82%e4%bd%95%e5%8e%bb%e9%99%a4","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/960743.html","title":{"rendered":"python\u5f02\u5e38\u503c\u5982\u4f55\u53bb\u9664"},"content":{"rendered":"

\"python\u5f02\u5e38\u503c\u5982\u4f55\u53bb\u9664\"<\/p>\n

\u5728Python\u4e2d\u53bb\u9664\u5f02\u5e38\u503c\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528\u7edf\u8ba1\u65b9\u6cd5\u8bc6\u522b\u5f02\u5e38\u503c\u3001\u5229\u7528Z-score\u3001IQR\u65b9\u6cd5\u68c0\u6d4b\u5f02\u5e38\u503c\u3001\u901a\u8fc7\u6570\u636e\u53ef\u89c6\u5316\u5de5\u5177\u8bc6\u522b\u5f02\u5e38\u503c\u3001\u5e94\u7528\u673a\u5668\u5b66\u4e60<\/a>\u7b97\u6cd5\u68c0\u6d4b\u5f02\u5e38\u503c\u3002<\/strong>\u5176\u4e2d\uff0c\u4f7f\u7528\u7edf\u8ba1\u65b9\u6cd5\u8bc6\u522b\u5f02\u5e38\u503c\u662f\u5e38\u7528\u7684\u65b9\u6cd5\u4e4b\u4e00\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u8ba1\u7b97\u6570\u636e\u96c6\u7684\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\uff0c\u7136\u540e\u5c06\u6570\u636e\u70b9\u4e0e\u5e73\u5747\u503c\u7684\u504f\u5dee\u8d85\u8fc7\u4e00\u5b9a\u500d\u6570\u7684\u6807\u51c6\u5dee\u7684\u70b9\u8bc6\u522b\u4e3a\u5f02\u5e38\u503c\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u4e9b\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528\u7edf\u8ba1\u65b9\u6cd5\u8bc6\u522b\u5f02\u5e38\u503c<\/p>\n<\/p>\n

\u4f7f\u7528\u7edf\u8ba1\u65b9\u6cd5\u53bb\u9664\u5f02\u5e38\u503c\u662f\u4e00\u79cd\u7b80\u5355\u4e14\u6709\u6548\u7684\u65b9\u6cd5\u3002\u901a\u5e38\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u8ba1\u7b97\u6570\u636e\u96c6\u7684\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\uff0c\u7136\u540e\u8bc6\u522b\u51fa\u504f\u79bb\u5e73\u5747\u503c\u8d85\u8fc7\u4e00\u5b9a\u500d\u6570\u6807\u51c6\u5dee\u7684\u70b9\u4f5c\u4e3a\u5f02\u5e38\u503c\u3002<\/p>\n<\/p>\n

    \n
  1. \u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee<\/strong><\/li>\n<\/ol>\n

    \u5728\u6570\u636e\u96c6\u4e2d\uff0c\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\u662f\u4e24\u4e2a\u91cd\u8981\u7684\u7edf\u8ba1\u91cf\u3002\u5e73\u5747\u503c\u8868\u793a\u6570\u636e\u7684\u4e2d\u5fc3\u4f4d\u7f6e\uff0c\u800c\u6807\u51c6\u5dee\u8868\u793a\u6570\u636e\u7684\u79bb\u6563\u7a0b\u5ea6\u3002\u901a\u8fc7\u8ba1\u7b97\u6bcf\u4e2a\u6570\u636e\u70b9\u4e0e\u5e73\u5747\u503c\u7684\u504f\u5dee\uff0c\u5e76\u4e0e\u6807\u51c6\u5dee\u8fdb\u884c\u6bd4\u8f83\uff0c\u53ef\u4ee5\u6709\u6548\u8bc6\u522b\u5f02\u5e38\u503c\u3002<\/p>\n<\/p>\n

    import numpy as np<\/p>\n
    def remove_outliers(data, threshold=3):<\/p>\n
        mean = np.mean(data)<\/p>\n
        std = np.std(data)<\/p>\n
        filtered_data = [x for x in data if abs(x - mean) <= threshold * std]<\/p>\n
        return filtered_data<\/p>\n
    data = [10, 12, 12, 13, 12, 20, 100, 12, 13, 12]<\/p>\n
    filtered_data = remove_outliers(data)<\/p>\n
    print(filtered_data)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u5b9a\u4e49\u4e86\u4e00\u4e2a\u51fd\u6570remove_outliers<\/code>\uff0c\u5b83\u63a5\u53d7\u4e00\u4e2a\u6570\u636e\u5217\u8868\u548c\u4e00\u4e2a\u9608\u503c\u53c2\u6570threshold<\/code>\u3002\u901a\u8fc7\u8ba1\u7b97\u6570\u636e\u7684\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\uff0c\u6211\u4eec\u53ef\u4ee5\u8fc7\u6ee4\u6389\u90a3\u4e9b\u504f\u79bb\u5e73\u5747\u503c\u8d85\u8fc7\u9608\u503c\u500d\u6807\u51c6\u5dee\u7684\u5f02\u5e38\u503c\u3002<\/p>\n<\/p>\n
      \n
    1. Z-score\u65b9\u6cd5<\/strong><\/li>\n<\/ol>\n
      Z-score\u662f\u8861\u91cf\u6570\u636e\u70b9\u504f\u79bb\u5747\u503c\u7a0b\u5ea6\u7684\u6807\u51c6\u5316\u6307\u6807\u3002\u901a\u8fc7\u8ba1\u7b97\u6570\u636e\u70b9\u7684Z-score\uff0c\u6211\u4eec\u53ef\u4ee5\u8bc6\u522b\u51fa\u90a3\u4e9bZ-score\u503c\u8f83\u5927\u7684\u5f02\u5e38\u503c\u3002<\/p>\n<\/p>\n
      from scipy.stats import zscore<\/p>\n
      data = [10, 12, 12, 13, 12, 20, 100, 12, 13, 12]<\/p>\n
      z_scores = zscore(data)<\/p>\n
      filtered_data = [x for i, x in enumerate(data) if abs(z_scores[i]) < 3]<\/p>\n
      print(filtered_data)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528scipy.stats<\/code>\u6a21\u5757\u4e2d\u7684zscore<\/code>\u51fd\u6570\u8ba1\u7b97\u6570\u636e\u7684Z-score\uff0c\u7136\u540e\u8fc7\u6ee4\u6389Z-score\u7edd\u5bf9\u503c\u5927\u4e8e3\u7684\u5f02\u5e38\u503c\u3002<\/p>\n<\/p>\n
      \u4e8c\u3001\u5229\u7528IQR\u65b9\u6cd5\u68c0\u6d4b\u5f02\u5e38\u503c<\/p>\n<\/p>\n
      IQR\uff08Interquartile Range\uff09\u65b9\u6cd5\u662f\u57fa\u4e8e\u5206\u4f4d\u6570\u7684\u5f02\u5e38\u503c\u68c0\u6d4b\u65b9\u6cd5\uff0c\u901a\u8fc7\u8ba1\u7b97\u6570\u636e\u96c6\u7684\u56db\u5206\u4f4d\u6570\u95f4\u8ddd\uff0c\u53ef\u4ee5\u6709\u6548\u8bc6\u522b\u51fa\u5f02\u5e38\u503c\u3002<\/p>\n<\/p>\n
        \n
      1. \u8ba1\u7b97IQR<\/strong><\/li>\n<\/ol>\n
        IQR\u662f\u6570\u636e\u96c6\u4e2d\u7b2c75\u767e\u5206\u4f4d\u6570\uff08Q3\uff09\u4e0e\u7b2c25\u767e\u5206\u4f4d\u6570\uff08Q1\uff09\u4e4b\u95f4\u7684\u8ddd\u79bb\u3002\u901a\u5e38\uff0c\u6211\u4eec\u5c06\u4f4e\u4e8eQ1 – 1.5 * IQR\u6216\u9ad8\u4e8eQ3 + 1.5 * IQR\u7684\u6570\u636e\u70b9\u8bc6\u522b\u4e3a\u5f02\u5e38\u503c\u3002<\/p>\n<\/p>\n
        def remove_outliers_iqr(data):<\/p>\n
            q1 = np.percentile(data, 25)<\/p>\n
            q3 = np.percentile(data, 75)<\/p>\n
            iqr = q3 - q1<\/p>\n
            lower_bound = q1 - 1.5 * iqr<\/p>\n
            upper_bound = q3 + 1.5 * iqr<\/p>\n
            filtered_data = [x for x in data if lower_bound <= x <= upper_bound]<\/p>\n
            return filtered_data<\/p>\n
        data = [10, 12, 12, 13, 12, 20, 100, 12, 13, 12]<\/p>\n
        filtered_data = remove_outliers_iqr(data)<\/p>\n
        print(filtered_data)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        1. \u5e94\u7528IQR\u65b9\u6cd5<\/strong><\/li>\n<\/ol>\n
          \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u5b9a\u4e49\u4e86\u4e00\u4e2a\u51fd\u6570remove_outliers_iqr<\/code>\uff0c\u901a\u8fc7\u8ba1\u7b97\u6570\u636e\u7684Q1\u3001Q3\u548cIQR\uff0c\u7136\u540e\u8fc7\u6ee4\u6389\u4f4e\u4e8e\u4e0b\u9650\u548c\u9ad8\u4e8e\u4e0a\u9650\u7684\u5f02\u5e38\u503c\u3002<\/p>\n<\/p>\n
          \u4e09\u3001\u901a\u8fc7\u6570\u636e\u53ef\u89c6\u5316\u5de5\u5177\u8bc6\u522b\u5f02\u5e38\u503c<\/p>\n<\/p>\n
          \u6570\u636e\u53ef\u89c6\u5316\u5de5\u5177\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u76f4\u89c2\u5730\u8bc6\u522b\u6570\u636e\u4e2d\u7684\u5f02\u5e38\u503c\u3002\u5e38\u7528\u7684\u53ef\u89c6\u5316\u5de5\u5177\u5305\u62ec\u7bb1\u7ebf\u56fe\uff08box plot\uff09\u548c\u6563\u70b9\u56fe\uff08scatter plot\uff09\u3002<\/p>\n<\/p>\n
            \n
          1. \u7bb1\u7ebf\u56fe<\/strong><\/li>\n<\/ol>\n
            \u7bb1\u7ebf\u56fe\u662f\u4e00\u79cd\u901a\u8fc7\u53ef\u89c6\u5316\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\uff0c\u5e2e\u52a9\u8bc6\u522b\u5f02\u5e38\u503c\u7684\u5de5\u5177\u3002\u5728\u7bb1\u7ebf\u56fe\u4e2d\uff0c\u6570\u636e\u7684\u4e0a\u4e0b\u56db\u5206\u4f4d\u6570\u4e4b\u95f4\u7684\u533a\u57df\u7528\u7bb1\u5b50\u8868\u793a\uff0c\u7bb1\u5b50\u5916\u7684\u6570\u636e\u70b9\u901a\u5e38\u88ab\u89c6\u4e3a\u5f02\u5e38\u503c\u3002<\/p>\n<\/p>\n
            import matplotlib.pyplot as plt<\/p>\n
            data = [10, 12, 12, 13, 12, 20, 100, 12, 13, 12]<\/p>\n
            plt.boxplot(data)<\/p>\n
            plt.show()<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. \u6563\u70b9\u56fe<\/strong><\/li>\n<\/ol>\n
              \u6563\u70b9\u56fe\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u8bc6\u522b\u6570\u636e\u4e2d\u7684\u79bb\u7fa4\u70b9\u3002\u901a\u8fc7\u89c2\u5bdf\u6570\u636e\u70b9\u7684\u5206\u5e03\uff0c\u6211\u4eec\u53ef\u4ee5\u8bc6\u522b\u51fa\u90a3\u4e9b\u660e\u663e\u504f\u79bb\u5176\u4ed6\u6570\u636e\u70b9\u7684\u5f02\u5e38\u503c\u3002<\/p>\n<\/p>\n
              plt.scatter(range(len(data)), data)<\/p>\n
              plt.show()<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u56db\u3001\u5e94\u7528\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u68c0\u6d4b\u5f02\u5e38\u503c<\/p>\n<\/p>\n
              \u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u53ef\u4ee5\u7528\u6765\u8bc6\u522b\u6570\u636e\u96c6\u4e2d\u7684\u5f02\u5e38\u503c\uff0c\u7279\u522b\u662f\u5728\u5904\u7406\u9ad8\u7ef4\u6570\u636e\u65f6\u3002<\/p>\n<\/p>\n
                \n
              1. \u5b64\u7acb\u68ee\u6797\uff08Isolation Forest\uff09<\/strong><\/li>\n<\/ol>\n
                \u5b64\u7acb\u68ee\u6797\u662f\u4e00\u79cd\u57fa\u4e8e\u51b3\u7b56\u6811\u7684\u5f02\u5e38\u503c\u68c0\u6d4b\u7b97\u6cd5\uff0c\u901a\u8fc7\u6784\u5efa\u968f\u673a\u6811\u6765\u5206\u79bb\u6570\u636e\u70b9\uff0c\u8bc6\u522b\u51fa\u90a3\u4e9b\u5bb9\u6613\u88ab\u9694\u79bb\u7684\u5f02\u5e38\u503c\u3002<\/p>\n<\/p>\n
                from sklearn.ensemble import IsolationForest<\/p>\n
                data = np.array(data).reshape(-1, 1)<\/p>\n
                clf = IsolationForest(contamination=0.1)<\/p>\n
                predictions = clf.fit_predict(data)<\/p>\n
                filtered_data = data[predictions == 1].flatten()<\/p>\n
                print(filtered_data)<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                  \n
                1. \u5c40\u90e8\u5f02\u5e38\u56e0\u5b50\uff08Local Outlier Factor\uff09<\/strong><\/li>\n<\/ol>\n
                  \u5c40\u90e8\u5f02\u5e38\u56e0\u5b50\u662f\u4e00\u79cd\u57fa\u4e8e\u5bc6\u5ea6\u7684\u5f02\u5e38\u503c\u68c0\u6d4b\u7b97\u6cd5\uff0c\u901a\u8fc7\u6bd4\u8f83\u6570\u636e\u70b9\u7684\u5bc6\u5ea6\uff0c\u8bc6\u522b\u51fa\u90a3\u4e9b\u5bc6\u5ea6\u663e\u8457\u4f4e\u4e8e\u5468\u56f4\u70b9\u7684\u5f02\u5e38\u503c\u3002<\/p>\n<\/p>\n
                  from sklearn.neighbors import LocalOutlierFactor<\/p>\n
                  clf = LocalOutlierFactor(n_neighbors=2)<\/p>\n
                  predictions = clf.fit_predict(data)<\/p>\n
                  filtered_data = data[predictions == 1].flatten()<\/p>\n
                  print(filtered_data)<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u4e94\u3001\u603b\u7ed3<\/p>\n<\/p>\n
                  \u53bb\u9664\u5f02\u5e38\u503c\u662f\u6570\u636e\u9884\u5904\u7406\u4e2d\u91cd\u8981\u7684\u4e00\u6b65\uff0c\u5b83\u80fd\u591f\u63d0\u5347\u6570\u636e\u5206\u6790\u7684\u51c6\u786e\u6027\u548c\u6a21\u578b\u7684\u6027\u80fd\u3002\u5728Python\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u65b9\u6cd5\u6765\u8bc6\u522b\u548c\u53bb\u9664\u5f02\u5e38\u503c\uff0c\u5305\u62ec\u7edf\u8ba1\u65b9\u6cd5\u3001IQR\u65b9\u6cd5\u3001\u6570\u636e\u53ef\u89c6\u5316\u5de5\u5177\u4ee5\u53ca\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u3002\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u53d6\u51b3\u4e8e\u6570\u636e\u7684\u7279\u6027\u548c\u5177\u4f53\u7684\u5e94\u7528\u573a\u666f\u3002\u65e0\u8bba\u91c7\u7528\u54ea\u79cd\u65b9\u6cd5\uff0c\u53bb\u9664\u5f02\u5e38\u503c\u7684\u76ee\u7684\u662f\u4e3a\u4e86\u63d0\u9ad8\u6570\u636e\u7684\u8d28\u91cf\u548c\u53ef\u9760\u6027\uff0c\u4ece\u800c\u4e3a\u540e\u7eed\u7684\u5206\u6790\u548c\u5efa\u6a21\u63d0\u4f9b\u66f4\u597d\u7684\u57fa\u7840\u3002<\/p>\n<\/p>\n
                  \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                   \u5982\u4f55\u8bc6\u522b\u6570\u636e\u4e2d\u7684\u5f02\u5e38\u503c\uff1f<\/strong>
                  \u8bc6\u522b\u5f02\u5e38\u503c\u901a\u5e38\u53ef\u4ee5\u901a\u8fc7\u7edf\u8ba1\u65b9\u6cd5\u8fdb\u884c\uff0c\u6bd4\u5982\u4f7f\u7528\u6807\u51c6\u5dee\u3001\u56db\u5206\u4f4d\u6570\u6216Z-score\u7b49\u3002\u6807\u51c6\u5dee\u65b9\u6cd5\u4e2d\uff0c\u5982\u679c\u4e00\u4e2a\u6570\u636e\u70b9\u7684\u503c\u8d85\u8fc7\u5747\u503c\u00b13\u4e2a\u6807\u51c6\u5dee\uff0c\u5219\u53ef\u80fd\u88ab\u8ba4\u4e3a\u662f\u5f02\u5e38\u503c\u3002\u56db\u5206\u4f4d\u6570\u65b9\u6cd5\u5219\u6839\u636e\u6570\u636e\u7684IQR\uff08\u56db\u5206\u4f4d\u8ddd\uff09\u6765\u786e\u5b9a\uff0c\u901a\u5e38\u8bbe\u5b9a\u4e3a\u4f4e\u4e8eQ1-1.5IQR\u6216\u9ad8\u4e8eQ3+1.5<\/em>IQR\u7684\u503c\u4e3a\u5f02\u5e38\u503c\u3002<\/p>\n
                  \u53bb\u9664\u5f02\u5e38\u503c\u540e\u4f1a\u5bf9\u6570\u636e\u5206\u6790\u7ed3\u679c\u4ea7\u751f\u4ec0\u4e48\u5f71\u54cd\uff1f<\/strong>
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