{"id":1041702,"date":"2024-12-31T12:51:19","date_gmt":"2024-12-31T04:51:19","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1041702.html"},"modified":"2024-12-31T12:51:21","modified_gmt":"2024-12-31T04:51:21","slug":"python%e5%a6%82%e4%bd%95uci%e4%b8%8b%e8%bd%bd%e4%b8%8b%e6%9d%a5%e7%9a%84%e6%95%b0%e6%8d%ae%e9%9b%86","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1041702.html","title":{"rendered":"python\u5982\u4f55uci\u4e0b\u8f7d\u4e0b\u6765\u7684\u6570\u636e\u96c6"},"content":{"rendered":"

\"python\u5982\u4f55uci\u4e0b\u8f7d\u4e0b\u6765\u7684\u6570\u636e\u96c6\"<\/p>\n

Python \u4f7f\u7528 UCI \u6570\u636e\u96c6\u7684\u6b65\u9aa4<\/strong><\/p>\n<\/p>\n

\u5728Python\u4e2d\u4f7f\u7528UCI\u6570\u636e\u96c6\u7684\u6b65\u9aa4\u5305\u62ec\uff1a\u4e0b\u8f7d\u6570\u636e\u3001\u8bfb\u53d6\u6570\u636e\u3001\u9884\u5904\u7406\u6570\u636e\u3001\u5206\u6790\u6570\u636e\u3001\u53ef\u89c6\u5316\u6570\u636e\u3002\u8fd9\u4e9b\u6b65\u9aa4\u53ef\u4ee5\u5e2e\u52a9\u4f60\u5feb\u901f\u83b7\u53d6\u548c\u4f7f\u7528UCI\u6570\u636e\u96c6\u3002\u4e0b\u8f7d\u6570\u636e\u3001\u8bfb\u53d6\u6570\u636e\u3001\u9884\u5904\u7406\u6570\u636e\u3001\u5206\u6790\u6570\u636e\u3001\u53ef\u89c6\u5316\u6570\u636e<\/strong>\u662f\u5173\u952e\u6b65\u9aa4\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u5b8c\u6210\u8fd9\u4e9b\u6b65\u9aa4\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4e0b\u8f7d\u6570\u636e<\/h3>\n<\/p>\n

\u5728\u4f7f\u7528UCI\u6570\u636e\u96c6\u4e4b\u524d\uff0c\u4f60\u9700\u8981\u5148\u4e0b\u8f7d\u6570\u636e\u3002UCI\u673a\u5668\u5b66\u4e60<\/a>\u5e93\u4e2d\u5305\u542b\u4e86\u5927\u91cf\u4e0d\u540c\u7c7b\u578b\u7684\u6570\u636e\u96c6\u3002\u4f60\u53ef\u4ee5\u8bbf\u95eeUCI\u673a\u5668\u5b66\u4e60\u5e93\u5b98\u65b9\u7f51\u7ad9\uff0c\u901a\u8fc7\u6d4f\u89c8\u548c\u9009\u62e9\u5408\u9002\u7684\u6570\u636e\u96c6\u8fdb\u884c\u4e0b\u8f7d\u3002\u901a\u5e38\uff0cUCI\u6570\u636e\u96c6\u7684\u4e0b\u8f7d\u94fe\u63a5\u4f1a\u63d0\u4f9bCSV\u683c\u5f0f\u7684\u6570\u636e\u6587\u4ef6\uff0c\u8fd9\u4e9b\u6587\u4ef6\u53ef\u4ee5\u76f4\u63a5\u7528\u4e8e\u5206\u6790\u3002<\/p>\n<\/p>\n

    \n
  1. \n

    \u8bbf\u95eeUCI\u673a\u5668\u5b66\u4e60\u5e93\u7f51\u7ad9<\/strong>\uff1a\u6253\u5f00UCI\u673a\u5668\u5b66\u4e60\u5e93\u7f51\u7ad9\uff08https:\/\/archive.ics.uci.edu\/ml\/index.php\uff09\uff0c\u6d4f\u89c8\u6570\u636e\u96c6\u76ee\u5f55\uff0c\u9009\u62e9\u4f60\u611f\u5174\u8da3\u7684\u6570\u636e\u96c6\u3002<\/a><\/p>\n<\/p>\n<\/li>\n

  2. \n

    \u4e0b\u8f7d\u6570\u636e\u6587\u4ef6<\/strong>\uff1a\u9009\u62e9\u6570\u636e\u96c6\u540e\uff0c\u627e\u5230\u6570\u636e\u6587\u4ef6\u7684\u4e0b\u8f7d\u94fe\u63a5\uff0c\u901a\u5e38\u662fCSV\u6216\u5176\u4ed6\u683c\u5f0f\u7684\u6587\u4ef6\uff0c\u70b9\u51fb\u94fe\u63a5\u4e0b\u8f7d\u6570\u636e\u6587\u4ef6\u5230\u672c\u5730\u3002<\/p>\n<\/p>\n<\/li>\n

  3. \n

    \u4fdd\u5b58\u6570\u636e\u6587\u4ef6<\/strong>\uff1a\u5c06\u4e0b\u8f7d\u7684\u6570\u636e\u6587\u4ef6\u4fdd\u5b58\u5230\u4f60\u9879\u76ee\u7684\u5de5\u4f5c\u76ee\u5f55\u4e2d\uff0c\u4ee5\u4fbf\u540e\u7eed\u8bfb\u53d6\u548c\u5904\u7406\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n

    \u4e8c\u3001\u8bfb\u53d6\u6570\u636e<\/h3>\n<\/p>\n

    \u4e0b\u8f7d\u6570\u636e\u6587\u4ef6\u540e\uff0c\u4f60\u9700\u8981\u4f7f\u7528Python\u8bfb\u53d6\u6570\u636e\u3002\u901a\u5e38\u4f7f\u7528pandas\u5e93\u6765\u8bfb\u53d6CSV\u6587\u4ef6\uff0c\u56e0\u4e3apandas\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u64cd\u4f5c\u548c\u5206\u6790\u529f\u80fd\u3002<\/p>\n<\/p>\n

      \n
    1. \n

      \u5bfc\u5165pandas\u5e93<\/strong>\uff1a\u5728Python\u811a\u672c\u6216Jupyter Notebook\u4e2d\u5bfc\u5165pandas\u5e93\u3002<\/p>\n<\/p>\n

      import pandas as pd<\/p>\n
      <\/code><\/pre>\n<\/p>\n<\/li>\n
    2. \n
      \u8bfb\u53d6CSV\u6587\u4ef6<\/strong>\uff1a\u4f7f\u7528pandas\u7684read_csv<\/code>\u51fd\u6570\u8bfb\u53d6CSV\u6587\u4ef6\u3002\u786e\u4fdd\u6307\u5b9a\u6587\u4ef6\u8def\u5f84\u6b63\u786e\u3002<\/p>\n<\/p>\n
      data = pd.read_csv('path\/to\/your\/dataset.csv')<\/p>\n
      <\/code><\/pre>\n<\/p>\n<\/li>\n
    3. \n
      \u67e5\u770b\u6570\u636e<\/strong>\uff1a\u8bfb\u53d6\u6570\u636e\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528head()<\/code>\u65b9\u6cd5\u67e5\u770b\u524d\u51e0\u884c\u6570\u636e\u3002<\/p>\n<\/p>\n
      print(data.head())<\/p>\n
      <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
      \u4e09\u3001\u9884\u5904\u7406\u6570\u636e<\/h3>\n<\/p>\n
      \u8bfb\u53d6\u6570\u636e\u540e\uff0c\u53ef\u80fd\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u9884\u5904\u7406\u3002\u6570\u636e\u9884\u5904\u7406\u6b65\u9aa4\u5305\u62ec\u5904\u7406\u7f3a\u5931\u503c\u3001\u6570\u636e\u6807\u51c6\u5316\u3001\u7279\u5f81\u9009\u62e9\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u6570\u636e\u9884\u5904\u7406\u6b65\u9aa4\u3002<\/p>\n<\/p>\n
        \n
      1. \n
        \u68c0\u67e5\u7f3a\u5931\u503c<\/strong>\uff1a\u68c0\u67e5\u6570\u636e\u96c6\u4e2d\u662f\u5426\u5b58\u5728\u7f3a\u5931\u503c\uff0c\u5e76\u91c7\u53d6\u9002\u5f53\u7684\u5904\u7406\u63aa\u65bd\uff08\u5982\u5220\u9664\u7f3a\u5931\u503c\u6216\u586b\u8865\u7f3a\u5931\u503c\uff09\u3002<\/p>\n<\/p>\n
        print(data.isnull().sum())<\/p>\n
        <\/code><\/pre>\n<\/p>\n<\/li>\n
      2. \n
        \u5220\u9664\u7f3a\u5931\u503c<\/strong>\uff1a\u5982\u679c\u7f3a\u5931\u503c\u8f83\u591a\uff0c\u53ef\u4ee5\u9009\u62e9\u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c\u3002<\/p>\n<\/p>\n
        data.dropna(inplace=True)<\/p>\n
        <\/code><\/pre>\n<\/p>\n<\/li>\n
      3. \n
        \u586b\u8865\u7f3a\u5931\u503c<\/strong>\uff1a\u5982\u679c\u7f3a\u5931\u503c\u8f83\u5c11\uff0c\u53ef\u4ee5\u9009\u62e9\u586b\u8865\u7f3a\u5931\u503c\u3002<\/p>\n<\/p>\n
        data.fillna(data.mean(), inplace=True)<\/p>\n
        <\/code><\/pre>\n<\/p>\n<\/li>\n
      4. \n
        \u6570\u636e\u6807\u51c6\u5316<\/strong>\uff1a\u5bf9\u6570\u636e\u8fdb\u884c\u6807\u51c6\u5316\u5904\u7406\uff0c\u4f7f\u5176\u7b26\u5408\u7279\u5b9a\u7684\u8303\u56f4\u6216\u5206\u5e03\u3002<\/p>\n<\/p>\n
        from sklearn.preprocessing import StandardScaler<\/p>\n
        scaler = StandardScaler()<\/p>\n
        data_scaled = scaler.fit_transform(data)<\/p>\n
        <\/code><\/pre>\n<\/p>\n<\/li>\n
      5. \n
        \u7279\u5f81\u9009\u62e9<\/strong>\uff1a\u9009\u62e9\u5bf9\u6a21\u578b\u6709\u7528\u7684\u7279\u5f81\uff0c\u5220\u9664\u5197\u4f59\u6216\u65e0\u5173\u7684\u7279\u5f81\u3002<\/p>\n<\/p>\n
        data = data[['feature1', 'feature2', 'feature3']]<\/p>\n
        <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
        \u56db\u3001\u5206\u6790\u6570\u636e<\/h3>\n<\/p>\n
        \u6570\u636e\u9884\u5904\u7406\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u6790\u3002\u6570\u636e\u5206\u6790\u6b65\u9aa4\u5305\u62ec\u63cf\u8ff0\u6027\u7edf\u8ba1\u5206\u6790\u3001\u76f8\u5173\u6027\u5206\u6790\u3001\u6570\u636e\u53ef\u89c6\u5316\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u6570\u636e\u5206\u6790\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
          \n
        1. \n
          \u63cf\u8ff0\u6027\u7edf\u8ba1\u5206\u6790<\/strong>\uff1a\u8ba1\u7b97\u6570\u636e\u7684\u57fa\u672c\u7edf\u8ba1\u91cf\uff08\u5982\u5747\u503c\u3001\u6807\u51c6\u5dee\u3001\u4e2d\u4f4d\u6570\u7b49\uff09\u3002<\/p>\n<\/p>\n
          print(data.describe())<\/p>\n
          <\/code><\/pre>\n<\/p>\n<\/li>\n
        2. \n
          \u76f8\u5173\u6027\u5206\u6790<\/strong>\uff1a\u8ba1\u7b97\u6570\u636e\u7279\u5f81\u4e4b\u95f4\u7684\u76f8\u5173\u6027\u3002<\/p>\n<\/p>\n
          correlation_matrix = data.corr()<\/p>\n
          print(correlation_matrix)<\/p>\n
          <\/code><\/pre>\n<\/p>\n<\/li>\n
        3. \n
          \u6570\u636e\u53ef\u89c6\u5316<\/strong>\uff1a\u4f7f\u7528\u53ef\u89c6\u5316\u5de5\u5177\uff08\u5982Matplotlib\u6216Seaborn\uff09\u5bf9\u6570\u636e\u8fdb\u884c\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
          import matplotlib.pyplot as plt<\/p>\n
          import seaborn as sns<\/p>\n
          sns.heatmap(correlation_matrix, annot=True)<\/p>\n
          plt.show()<\/p>\n
          <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
          \u4e94\u3001\u53ef\u89c6\u5316\u6570\u636e<\/h3>\n<\/p>\n
          \u53ef\u89c6\u5316\u662f\u6570\u636e\u5206\u6790\u7684\u91cd\u8981\u90e8\u5206\uff0c\u53ef\u4ee5\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u6570\u636e\u53ef\u89c6\u5316\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
            \n
          1. \n
            \u6563\u70b9\u56fe<\/strong>\uff1a\u7528\u4e8e\u663e\u793a\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u3002<\/p>\n<\/p>\n
            sns.scatterplot(x='feature1', y='feature2', data=data)<\/p>\n
            plt.show()<\/p>\n
            <\/code><\/pre>\n<\/p>\n<\/li>\n
          2. \n
            \u76f4\u65b9\u56fe<\/strong>\uff1a\u7528\u4e8e\u663e\u793a\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u3002<\/p>\n<\/p>\n
            data['feature1'].hist(bins=50)<\/p>\n
            plt.show()<\/p>\n
            <\/code><\/pre>\n<\/p>\n<\/li>\n
          3. \n
            \u7bb1\u7ebf\u56fe<\/strong>\uff1a\u7528\u4e8e\u663e\u793a\u6570\u636e\u7684\u5206\u5e03\u53ca\u5f02\u5e38\u503c\u3002<\/p>\n<\/p>\n
            sns.boxplot(x='feature1', data=data)<\/p>\n
            plt.show()<\/p>\n
            <\/code><\/pre>\n<\/p>\n<\/li>\n
          4. \n
            \u70ed\u529b\u56fe<\/strong>\uff1a\u7528\u4e8e\u663e\u793a\u53d8\u91cf\u4e4b\u95f4\u7684\u76f8\u5173\u6027\u3002<\/p>\n<\/p>\n
            sns.heatmap(correlation_matrix, annot=True)<\/p>\n
            plt.show()<\/p>\n
            <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
            \u516d\u3001\u5b9e\u9645\u6848\u4f8b\u5206\u6790<\/h3>\n<\/p>\n
            \u4e3a\u4e86\u66f4\u597d\u5730\u7406\u89e3\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u6211\u4eec\u4ee5\u4e00\u4e2a\u5b9e\u9645\u6848\u4f8b\u4e3a\u4f8b\uff0c\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528UCI\u6570\u636e\u96c6\u8fdb\u884c\u6570\u636e\u5206\u6790\u3002\u5047\u8bbe\u6211\u4eec\u9009\u62e9UCI\u673a\u5668\u5b66\u4e60\u5e93\u4e2d\u7684\u201c\u7cd6\u5c3f\u75c5\u6570\u636e\u96c6\u201d\uff08Diabetes Dataset\uff09\uff0c\u4ee5\u4e0b\u662f\u8be6\u7ec6\u7684\u6b65\u9aa4\u3002<\/p>\n<\/p>\n
              \n
            1. \n
              \u4e0b\u8f7d\u6570\u636e<\/strong>\uff1a\u8bbf\u95eeUCI\u673a\u5668\u5b66\u4e60\u5e93\u7f51\u7ad9\uff0c\u627e\u5230\u7cd6\u5c3f\u75c5\u6570\u636e\u96c6\u7684\u4e0b\u8f7d\u94fe\u63a5\uff0c\u4e0b\u8f7d\u6570\u636e\u6587\u4ef6\u5e76\u4fdd\u5b58\u5230\u672c\u5730\u3002<\/p>\n<\/p>\n<\/li>\n
            2. \n
              \u8bfb\u53d6\u6570\u636e<\/strong>\uff1a\u4f7f\u7528pandas\u8bfb\u53d6\u6570\u636e\u6587\u4ef6\u3002<\/p>\n<\/p>\n
              data = pd.read_csv('diabetes.csv')<\/p>\n
              <\/code><\/pre>\n<\/p>\n<\/li>\n
            3. \n
              \u67e5\u770b\u6570\u636e<\/strong>\uff1a\u67e5\u770b\u6570\u636e\u7684\u524d\u51e0\u884c\u3002<\/p>\n<\/p>\n
              print(data.head())<\/p>\n
              <\/code><\/pre>\n<\/p>\n<\/li>\n
            4. \n
              \u68c0\u67e5\u7f3a\u5931\u503c<\/strong>\uff1a\u68c0\u67e5\u6570\u636e\u96c6\u4e2d\u662f\u5426\u5b58\u5728\u7f3a\u5931\u503c\u3002<\/p>\n<\/p>\n
              print(data.isnull().sum())<\/p>\n
              <\/code><\/pre>\n<\/p>\n<\/li>\n
            5. \n
              \u5220\u9664\u7f3a\u5931\u503c<\/strong>\uff1a\u5982\u679c\u5b58\u5728\u7f3a\u5931\u503c\uff0c\u53ef\u4ee5\u9009\u62e9\u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c\u3002<\/p>\n<\/p>\n
              data.dropna(inplace=True)<\/p>\n
              <\/code><\/pre>\n<\/p>\n<\/li>\n
            6. \n
              \u6570\u636e\u6807\u51c6\u5316<\/strong>\uff1a\u5bf9\u6570\u636e\u8fdb\u884c\u6807\u51c6\u5316\u5904\u7406\u3002<\/p>\n<\/p>\n
              from sklearn.preprocessing import StandardScaler<\/p>\n
              scaler = StandardScaler()<\/p>\n
              data_scaled = scaler.fit_transform(data)<\/p>\n
              <\/code><\/pre>\n<\/p>\n<\/li>\n
            7. \n
              \u63cf\u8ff0\u6027\u7edf\u8ba1\u5206\u6790<\/strong>\uff1a\u8ba1\u7b97\u6570\u636e\u7684\u57fa\u672c\u7edf\u8ba1\u91cf\u3002<\/p>\n<\/p>\n
              print(data.describe())<\/p>\n
              <\/code><\/pre>\n<\/p>\n<\/li>\n
            8. \n
              \u76f8\u5173\u6027\u5206\u6790<\/strong>\uff1a\u8ba1\u7b97\u6570\u636e\u7279\u5f81\u4e4b\u95f4\u7684\u76f8\u5173\u6027\u3002<\/p>\n<\/p>\n
              correlation_matrix = data.corr()<\/p>\n
              print(correlation_matrix)<\/p>\n
              <\/code><\/pre>\n<\/p>\n<\/li>\n
            9. \n
              \u6570\u636e\u53ef\u89c6\u5316<\/strong>\uff1a\u4f7f\u7528Seaborn\u5bf9\u6570\u636e\u8fdb\u884c\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
              sns.heatmap(correlation_matrix, annot=True)<\/p>\n
              plt.show()<\/p>\n
              <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
              \u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u5b8c\u6210\u5bf9UCI\u6570\u636e\u96c6\u7684\u4e0b\u8f7d\u3001\u8bfb\u53d6\u3001\u9884\u5904\u7406\u3001\u5206\u6790\u548c\u53ef\u89c6\u5316\u3002\u5e0c\u671b\u8fd9\u7bc7\u6587\u7ae0\u80fd\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528UCI\u6570\u636e\u96c6\u8fdb\u884c\u6570\u636e\u5206\u6790\u3002\u5982\u679c\u4f60\u6709\u4efb\u4f55\u95ee\u9898\u6216\u9700\u8981\u8fdb\u4e00\u6b65\u7684\u5e2e\u52a9\uff0c\u8bf7\u968f\u65f6\u5411\u6211\u63d0\u95ee\u3002<\/p>\n<\/p>\n
              \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
               \u5982\u4f55\u5c06UCI\u4e0b\u8f7d\u7684\u6570\u636e\u96c6\u5bfc\u5165Python\u8fdb\u884c\u5206\u6790\uff1f<\/strong>
              \u8981\u5c06UCI\u673a\u5668\u5b66\u4e60\u5e93\u4e0b\u8f7d\u7684\u6570\u636e\u96c6\u5bfc\u5165Python\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528pandas\u5e93\u3002\u9996\u5148\uff0c\u4e0b\u8f7d\u6570\u636e\u96c6\u5e76\u4fdd\u5b58\u4e3aCSV\u6216TXT\u683c\u5f0f\u3002\u7136\u540e\uff0c\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u8bfb\u53d6\u6570\u636e\uff1a  <\/p>\n
              import pandas as pd\n\n# \u66ff\u6362\u4e3a\u60a8\u7684\u6570\u636e\u96c6\u8def\u5f84\ndata = pd.read_csv('path\/to\/your\/dataset.csv')  \nprint(data.head())\n<\/code><\/pre>\n
              \u8fd9\u5c06\u5e2e\u52a9\u60a8\u67e5\u770b\u6570\u636e\u96c6\u7684\u524d\u51e0\u884c\uff0c\u4fbf\u4e8e\u8fdb\u884c\u540e\u7eed\u5206\u6790\u3002<\/p>\n
              UCI\u6570\u636e\u96c6\u901a\u5e38\u5305\u542b\u54ea\u4e9b\u683c\u5f0f\u7684\u6570\u636e\uff1f<\/strong>
              UCI\u6570\u636e\u96c6\u901a\u5e38\u5305\u542bCSV\u3001TXT\u6216ARFF\u683c\u5f0f\u7684\u6570\u636e\u3002CSV\u548cTXT\u662f\u6700\u5e38\u89c1\u7684\u683c\u5f0f\uff0c\u6613\u4e8e\u7528pandas\u7b49\u6570\u636e\u5206\u6790\u5e93\u8bfb\u53d6\u3002ARFF\u683c\u5f0f\u4e3b\u8981\u7528\u4e8eWeka\u7b49\u673a\u5668\u5b66\u4e60\u5de5\u5177\u3002\u5982\u679c\u60a8\u4e0b\u8f7d\u7684\u662fARFF\u683c\u5f0f\uff0c\u53ef\u4ee5\u4f7f\u7528liac-arff<\/code>\u5e93\u5c06\u5176\u8f6c\u6362\u4e3apandas DataFrame\u683c\u5f0f\u3002<\/p>\n
              \u5982\u4f55\u5904\u7406UCI\u6570\u636e\u96c6\u4e2d\u7f3a\u5931\u7684\u6570\u636e\uff1f<\/strong>
              \u5904\u7406\u7f3a\u5931\u6570\u636e\u53ef\u4ee5\u91c7\u7528\u591a\u79cd\u65b9\u6cd5\u3002\u60a8\u53ef\u4ee5\u9009\u62e9\u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c\uff0c\u6216\u8005\u4f7f\u7528\u586b\u5145\u65b9\u6cd5\u3002pandas\u63d0\u4f9b\u4e86\u591a\u79cd\u586b\u5145\u7f3a\u5931\u503c\u7684\u65b9\u6cd5\uff0c\u4f8b\u5982\u4f7f\u7528\u5747\u503c\u3001\u4e2d\u4f4d\u6570\u6216\u4f17\u6570\u586b\u5145\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a  <\/p>\n
              # \u5220\u9664\u7f3a\u5931\u503c\ndata.dropna(inplace=True)\n\n# \u4f7f\u7528\u5747\u503c\u586b\u5145\ndata.fillna(data.mean(), inplace=True)\n<\/code><\/pre>\n
              \u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u53d6\u51b3\u4e8e\u6570\u636e\u96c6\u7684\u5177\u4f53\u60c5\u51b5\u548c\u5206\u6790\u9700\u6c42\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python \u4f7f\u7528 UCI \u6570\u636e\u96c6\u7684\u6b65\u9aa4 \u5728Python\u4e2d\u4f7f\u7528UCI\u6570\u636e\u96c6\u7684\u6b65\u9aa4\u5305\u62ec\uff1a\u4e0b\u8f7d\u6570\u636e\u3001\u8bfb\u53d6\u6570\u636e\u3001\u9884\u5904 […]","protected":false},"author":3,"featured_media":1041706,"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\/1041702"}],"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=1041702"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1041702\/revisions"}],"predecessor-version":[{"id":1041707,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1041702\/revisions\/1041707"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1041706"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1041702"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1041702"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1041702"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}