{"id":988119,"date":"2024-12-27T07:59:26","date_gmt":"2024-12-26T23:59:26","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/988119.html"},"modified":"2024-12-27T07:59:28","modified_gmt":"2024-12-26T23:59:28","slug":"python%e5%a6%82%e4%bd%95%e7%bb%9f%e8%ae%a1%e7%bc%ba%e5%a4%b1%e5%80%bc","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/988119.html","title":{"rendered":"python\u5982\u4f55\u7edf\u8ba1\u7f3a\u5931\u503c"},"content":{"rendered":"

\"python\u5982\u4f55\u7edf\u8ba1\u7f3a\u5931\u503c\"<\/p>\n

Python\u7edf\u8ba1\u7f3a\u5931\u503c\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5305\u62ec\u4f7f\u7528pandas\u5e93\u7684isnull()\u548csum()\u51fd\u6570\u3001info()\u65b9\u6cd5\u3001\u4ee5\u53ca\u66f4\u9ad8\u7ea7\u7684\u53ef\u89c6\u5316\u5de5\u5177\u5982missingno\u5e93\u7b49\u3002\u9996\u5148\uff0c\u4f60\u53ef\u4ee5\u901a\u8fc7pandas\u5e93\u7684isnull()\u65b9\u6cd5\u8bc6\u522b\u6570\u636e\u6846\u4e2d\u7684\u7f3a\u5931\u503c\uff0c\u518d\u7ed3\u5408sum()\u65b9\u6cd5\u7edf\u8ba1\u6bcf\u4e00\u5217\u7684\u7f3a\u5931\u503c\u6570\u91cf\u3002\u6b64\u65b9\u6cd5\u7b80\u5355\u76f4\u89c2\uff0c\u9002\u5408\u521d\u5b66\u8005\u4f7f\u7528\u3002<\/strong><\/p>\n<\/p>\n

\u5bf9\u4e8e\u8be6\u7ec6\u7684\u7edf\u8ba1\u548c\u5904\u7406\u7f3a\u5931\u503c\uff0c\u4ee5\u4e0b\u662f\u4e00\u4e9b\u65b9\u6cd5\u548c\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528Pandas\u7edf\u8ba1\u7f3a\u5931\u503c<\/p>\n<\/p>\n

Pandas\u662fPython\u4e2d\u7528\u4e8e\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u7684\u5f3a\u5927\u5e93\u3002\u5b83\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u68c0\u6d4b\u548c\u5904\u7406\u7f3a\u5931\u503c\u3002<\/p>\n<\/p>\n

    \n
  1. \u4f7f\u7528isnull()\u548csum()\u51fd\u6570<\/strong><\/li>\n<\/ol>\n

    \u901a\u8fc7isnull()\u65b9\u6cd5\uff0c\u53ef\u4ee5\u5c06\u6570\u636e\u6846\u4e2d\u7f3a\u5931\u503c\u7684\u4f4d\u7f6e\u6807\u8bb0\u4e3aTrue\uff0c\u800c\u5176\u4ed6\u4f4d\u7f6e\u6807\u8bb0\u4e3aFalse\u3002\u7136\u540e\uff0c\u4f7f\u7528sum()\u65b9\u6cd5\u53ef\u4ee5\u7edf\u8ba1\u6bcf\u5217\u7684\u7f3a\u5931\u503c\u6570\u91cf\u3002<\/p>\n<\/p>\n

    import pandas as pd<\/p>\n
    \u793a\u4f8b\u6570\u636e\u6846<\/strong><\/h2>\n
    data = {'A': [1, 2, None], 'B': [4, None, 6]}<\/p>\n
    df = pd.DataFrame(data)<\/p>\n
    \u7edf\u8ba1\u7f3a\u5931\u503c<\/strong><\/h2>\n
    missing_values = df.isnull().sum()<\/p>\n
    print(missing_values)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0cisnull()<\/code>\u65b9\u6cd5\u7528\u4e8e\u8bc6\u522b\u7f3a\u5931\u503c\uff0csum()<\/code>\u65b9\u6cd5\u7528\u4e8e\u7edf\u8ba1\u6bcf\u4e00\u5217\u4e2d\u7f3a\u5931\u503c\u7684\u6570\u91cf\u3002<\/p>\n<\/p>\n
      \n
    1. \u4f7f\u7528info()\u65b9\u6cd5<\/strong><\/li>\n<\/ol>\n
      info()<\/code>\u65b9\u6cd5\u53ef\u4ee5\u63d0\u4f9b\u6570\u636e\u6846\u7684\u7b80\u8981\u4fe1\u606f\uff0c\u5305\u62ec\u6bcf\u5217\u7684\u6570\u636e\u7c7b\u578b\u548c\u975e\u7a7a\u503c\u7684\u6570\u91cf\uff0c\u4ece\u800c\u95f4\u63a5\u5e2e\u52a9\u6211\u4eec\u8bc6\u522b\u7f3a\u5931\u503c\u3002<\/p>\n<\/p>\n
      df.info()<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u901a\u8fc7\u89c2\u5bdf\u8f93\u51fa\u7ed3\u679c\uff0c\u4f60\u53ef\u4ee5\u4e86\u89e3\u5230\u6bcf\u4e00\u5217\u7684\u7f3a\u5931\u503c\u60c5\u51b5\u3002<\/p>\n<\/p>\n
      \u4e8c\u3001\u53ef\u89c6\u5316\u7f3a\u5931\u503c<\/p>\n<\/p>\n
      \u53ef\u89c6\u5316\u5de5\u5177\u53ef\u4ee5\u66f4\u76f4\u89c2\u5730\u5e2e\u52a9\u6211\u4eec\u4e86\u89e3\u6570\u636e\u4e2d\u7684\u7f3a\u5931\u503c\u60c5\u51b5\u3002missingno\u662f\u4e00\u4e2a\u4e13\u95e8\u7528\u4e8e\u5904\u7406\u7f3a\u5931\u503c\u7684\u53ef\u89c6\u5316\u5de5\u5177\u3002<\/p>\n<\/p>\n
        \n
      1. \u4f7f\u7528missingno\u5e93<\/strong><\/li>\n<\/ol>\n
        missingno\u5e93\u53ef\u4ee5\u521b\u5efa\u77e9\u9635\u56fe\u3001\u6761\u5f62\u56fe\u548c\u70ed\u529b\u56fe\u7b49\u591a\u79cd\u56fe\u5f62\uff0c\u5e2e\u52a9\u6211\u4eec\u76f4\u89c2\u5730\u89c2\u5bdf\u6570\u636e\u7f3a\u5931\u60c5\u51b5\u3002<\/p>\n<\/p>\n
        import missingno as msno<\/p>\n
        \u7ed8\u5236\u7f3a\u5931\u503c\u77e9\u9635\u56fe<\/strong><\/h2>\n
        msno.matrix(df)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u53ef\u4ee5\u5feb\u901f\u8bc6\u522b\u6570\u636e\u96c6\u4e2d\u7f3a\u5931\u503c\u7684\u5206\u5e03\u60c5\u51b5\u3002<\/p>\n<\/p>\n
        \u4e09\u3001\u5904\u7406\u7f3a\u5931\u503c<\/p>\n<\/p>\n
        \u7edf\u8ba1\u5b8c\u7f3a\u5931\u503c\u540e\uff0c\u63a5\u4e0b\u6765\u5c31\u662f\u5904\u7406\u8fd9\u4e9b\u7f3a\u5931\u503c\u3002\u6839\u636e\u5177\u4f53\u60c5\u51b5\uff0c\u5e38\u89c1\u7684\u5904\u7406\u65b9\u6cd5\u5305\u62ec\u5220\u9664\u7f3a\u5931\u503c\u3001\u586b\u5145\u7f3a\u5931\u503c\u7b49\u3002<\/p>\n<\/p>\n
          \n
        1. \u5220\u9664\u7f3a\u5931\u503c<\/strong><\/li>\n<\/ol>\n
          \u5982\u679c\u7f3a\u5931\u503c\u7684\u6bd4\u4f8b\u8f83\u5c0f\uff0c\u53ef\u4ee5\u9009\u62e9\u76f4\u63a5\u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c\u6216\u5217\u3002<\/p>\n<\/p>\n
          # \u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c<\/p>\n
          df_dropped = df.dropna()<\/p>\n
          <\/code><\/pre>\n<\/p>\n
            \n
          1. \u586b\u5145\u7f3a\u5931\u503c<\/strong><\/li>\n<\/ol>\n
            \u5bf9\u4e8e\u4e0d\u80fd\u5220\u9664\u7684\u60c5\u51b5\uff0c\u53ef\u4ee5\u4f7f\u7528\u5747\u503c\u3001\u4e2d\u4f4d\u6570\u3001\u4f17\u6570\u6216\u5176\u4ed6\u81ea\u5b9a\u4e49\u503c\u6765\u586b\u5145\u7f3a\u5931\u503c\u3002<\/p>\n<\/p>\n
            # \u4f7f\u7528\u5747\u503c\u586b\u5145\u7f3a\u5931\u503c<\/p>\n
            df_filled = df.fillna(df.mean())<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u56db\u3001\u5229\u7528\u63d2\u503c\u65b9\u6cd5\u5904\u7406\u7f3a\u5931\u503c<\/p>\n<\/p>\n
            \u63d2\u503c\u6cd5\u662f\u4e00\u79cd\u901a\u8fc7\u5df2\u77e5\u6570\u636e\u70b9\u6765\u4f30\u7b97<\/a>\u672a\u77e5\u6570\u636e\u70b9\u7684\u6570\u5b66\u65b9\u6cd5\u3002\u5728\u6570\u636e\u5904\u7406\u4e2d\uff0c\u63d2\u503c\u6cd5\u5e38\u7528\u4e8e\u5904\u7406\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u7f3a\u5931\u503c\u3002<\/p>\n<\/p>\n
              \n
            1. \u7ebf\u6027\u63d2\u503c<\/strong><\/li>\n<\/ol>\n
              \u7ebf\u6027\u63d2\u503c\u662f\u4e00\u79cd\u7b80\u5355\u4e14\u5e38\u7528\u7684\u65b9\u6cd5\uff0c\u901a\u8fc7\u4e24\u4e2a\u5df2\u77e5\u70b9\u4e4b\u95f4\u7684\u7ebf\u6027\u5173\u7cfb\u6765\u4f30\u7b97\u7f3a\u5931\u503c\u3002<\/p>\n<\/p>\n
              # \u7ebf\u6027\u63d2\u503c<\/p>\n
              df_interpolated = df.interpolate()<\/p>\n
              <\/code><\/pre>\n<\/p>\n
                \n
              1. \u591a\u9879\u5f0f\u63d2\u503c<\/strong><\/li>\n<\/ol>\n
                \u5bf9\u4e8e\u975e\u7ebf\u6027\u6570\u636e\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u9879\u5f0f\u63d2\u503c\u65b9\u6cd5\u3002\u53ef\u4ee5\u901a\u8fc7\u6307\u5b9a\u591a\u9879\u5f0f\u7684\u9636\u6570\u6765\u63a7\u5236\u63d2\u503c\u7684\u7075\u6d3b\u6027\u3002<\/p>\n<\/p>\n
                # \u591a\u9879\u5f0f\u63d2\u503c<\/p>\n
                df_poly_interpolated = df.interpolate(method='polynomial', order=2)<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                \u4e94\u3001\u5229\u7528\u673a\u5668\u5b66\u4e60<\/a>\u6a21\u578b\u5904\u7406\u7f3a\u5931\u503c<\/p>\n<\/p>\n
                \u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u4f7f\u7528\u673a\u5668\u5b66\u4e60\u6a21\u578b\u9884\u6d4b\u7f3a\u5931\u503c\u53ef\u80fd\u662f\u4e00\u4e2a\u6709\u6548\u7684\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
                  \n
                1. K-\u8fd1\u90bb\u586b\u5145<\/strong><\/li>\n<\/ol>\n
                  K-\u8fd1\u90bb\u7b97\u6cd5\uff08KNN\uff09\u662f\u4e00\u79cd\u7528\u4e8e\u5206\u7c7b\u548c\u56de\u5f52\u7684\u975e\u53c2\u6570\u65b9\u6cd5\u3002\u53ef\u4ee5\u4f7f\u7528KNN\u6765\u9884\u6d4b\u7f3a\u5931\u503c\u3002<\/p>\n<\/p>\n
                  from sklearn.impute import KNNImputer<\/p>\n
                  \u4f7f\u7528KNN\u586b\u5145\u7f3a\u5931\u503c<\/strong><\/h2>\n
                  imputer = KNNImputer(n_neighbors=2)<\/p>\n
                  df_knn_imputed = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                    \n
                  1. \u4f7f\u7528\u56de\u5f52\u6a21\u578b<\/strong><\/li>\n<\/ol>\n
                    \u53ef\u4ee5\u8bad\u7ec3\u4e00\u4e2a\u56de\u5f52\u6a21\u578b\u6765\u9884\u6d4b\u7f3a\u5931\u503c\u3002\u5177\u4f53\u6b65\u9aa4\u5305\u62ec\uff1a<\/p>\n<\/p>\n
                      \n
                    • \u5c06\u975e\u7f3a\u5931\u503c\u4f5c\u4e3a\u8bad\u7ec3\u96c6<\/li>\n
                    • \u8bad\u7ec3\u56de\u5f52\u6a21\u578b<\/li>\n
                    • \u4f7f\u7528\u6a21\u578b\u9884\u6d4b\u7f3a\u5931\u503c<\/li>\n<\/ul>\n
                      \u516d\u3001\u603b\u7ed3<\/p>\n<\/p>\n
                      \u5904\u7406\u7f3a\u5931\u503c\u662f\u6570\u636e\u9884\u5904\u7406\u4e2d\u91cd\u8981\u7684\u4e00\u6b65\u3002\u6839\u636e\u5177\u4f53\u60c5\u51b5\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u6765\u5904\u7406\u7f3a\u5931\u503c\uff0c\u53ef\u4ee5\u63d0\u9ad8\u6570\u636e\u5206\u6790\u548c\u5efa\u6a21\u7684\u8d28\u91cf\u3002\u5728\u4f7f\u7528\u8fd9\u4e9b\u65b9\u6cd5\u65f6\uff0c\u8981\u8003\u8651\u6570\u636e\u7684\u7279\u6027\u548c\u4e1a\u52a1\u9700\u6c42\uff0c\u9009\u62e9\u6700\u9002\u5408\u7684\u65b9\u6cd5\u3002\u65e0\u8bba\u662f\u7b80\u5355\u7684\u5220\u9664\u3001\u586b\u5145\uff0c\u8fd8\u662f\u590d\u6742\u7684\u63d2\u503c\u548c\u673a\u5668\u5b66\u4e60\u65b9\u6cd5\uff0c\u90fd\u6709\u5176\u9002\u7528\u7684\u573a\u666f\u548c\u9650\u5236\u3002\u901a\u8fc7\u5408\u7406\u7684\u5904\u7406\uff0c\u53ef\u4ee5\u5927\u5e45\u5ea6\u63d0\u9ad8\u6570\u636e\u5206\u6790\u7ed3\u679c\u7684\u53ef\u9760\u6027\u548c\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n
                      \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                       \u5982\u4f55\u4f7f\u7528Python\u8bc6\u522b\u6570\u636e\u96c6\u4e2d\u7684\u7f3a\u5931\u503c\uff1f<\/strong>
                      \u5728Python\u4e2d\uff0c\u4f7f\u7528Pandas\u5e93\u53ef\u4ee5\u8f7b\u677e\u8bc6\u522b\u6570\u636e\u96c6\u4e2d\u7684\u7f3a\u5931\u503c\u3002\u901a\u8fc7\u8c03\u7528isnull()<\/code>\u51fd\u6570\uff0c\u53ef\u4ee5\u751f\u6210\u4e00\u4e2a\u5e03\u5c14\u503c\u7684\u6570\u636e\u6846\uff0c\u663e\u793a\u6bcf\u4e2a\u503c\u662f\u5426\u4e3a\u7f3a\u5931\u503c\u3002\u63a5\u7740\uff0c\u7ed3\u5408sum()<\/code>\u51fd\u6570\uff0c\u53ef\u4ee5\u7edf\u8ba1\u6bcf\u5217\u7f3a\u5931\u503c\u7684\u6570\u91cf\u3002\u4f8b\u5982\uff0cdata.isnull().sum()<\/code>\u5c06\u8fd4\u56de\u6bcf\u4e00\u5217\u7f3a\u5931\u503c\u7684\u603b\u6570\u3002<\/p>\n
                      Python\u4e2d\u6709\u54ea\u4e9b\u65b9\u6cd5\u53ef\u4ee5\u5904\u7406\u7f3a\u5931\u503c\uff1f<\/strong>
                      \u5904\u7406\u7f3a\u5931\u503c\u7684\u65b9\u6cd5\u591a\u79cd\u591a\u6837\uff0c\u5305\u62ec\u5220\u9664\u7f3a\u5931\u503c\u3001\u586b\u5145\u7f3a\u5931\u503c\u6216\u4f7f\u7528\u63d2\u503c\u6cd5\u3002\u4f7f\u7528Pandas\uff0cdropna()<\/code>\u65b9\u6cd5\u53ef\u4ee5\u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c\u6216\u5217\uff1b\u800cfillna()<\/code>\u65b9\u6cd5\u5219\u53ef\u7528\u4e8e\u7528\u7279\u5b9a\u503c\u6216\u4f7f\u7528\u5747\u503c\u3001\u4e2d\u4f4d\u6570\u7b49\u7edf\u8ba1\u91cf\u586b\u5145\u7f3a\u5931\u503c\u3002\u6b64\u5916\uff0cSciPy\u5e93\u4e2d\u7684\u63d2\u503c\u65b9\u6cd5\u4e5f\u53ef\u7528\u4e8e\u66f4\u590d\u6742\u7684\u6570\u636e\u63d2\u8865\u3002<\/p>\n
                      \u5982\u4f55\u8bc4\u4f30\u7f3a\u5931\u503c\u5bf9\u6570\u636e\u5206\u6790\u7ed3\u679c\u7684\u5f71\u54cd\uff1f<\/strong>
                      \u7f3a\u5931\u503c\u53ef\u80fd\u5bf9\u6570\u636e\u5206\u6790\u7ed3\u679c\u4ea7\u751f\u663e\u8457\u5f71\u54cd\uff0c\u56e0\u6b64\u8bc4\u4f30\u5176\u5f71\u54cd\u81f3\u5173\u91cd\u8981\u3002\u53ef\u4ee5\u901a\u8fc7\u5bf9\u6bd4\u5904\u7406\u7f3a\u5931\u503c\u524d\u540e\u7684\u6570\u636e\u7edf\u8ba1\u7279\u5f81\uff0c\u4f8b\u5982\u5747\u503c\u3001\u6807\u51c6\u5dee\u7b49\uff0c\u6765\u89c2\u5bdf\u53d8\u5316\u3002\u6b64\u5916\uff0c\u4f7f\u7528\u53ef\u89c6\u5316\u5de5\u5177\u5982Seaborn\u6216Matplotlib\u7ed8\u5236\u7f3a\u5931\u503c\u7684\u70ed\u56fe\uff0c\u53ef\u4ee5\u76f4\u89c2\u5c55\u793a\u7f3a\u5931\u503c\u7684\u5206\u5e03\uff0c\u4ece\u800c\u5e2e\u52a9\u66f4\u597d\u5730\u7406\u89e3\u5b83\u4eec\u5bf9\u5206\u6790\u7ed3\u679c\u7684\u6f5c\u5728\u5f71\u54cd\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u7edf\u8ba1\u7f3a\u5931\u503c\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5305\u62ec\u4f7f\u7528pandas\u5e93\u7684isnull()\u548csum()\u51fd\u6570\u3001info()\u65b9\u6cd5 […]","protected":false},"author":3,"featured_media":988126,"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\/988119"}],"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=988119"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/988119\/revisions"}],"predecessor-version":[{"id":988129,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/988119\/revisions\/988129"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/988126"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=988119"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=988119"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=988119"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}