{"id":1000381,"date":"2024-12-27T09:49:35","date_gmt":"2024-12-27T01:49:35","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1000381.html"},"modified":"2024-12-27T09:49:38","modified_gmt":"2024-12-27T01:49:38","slug":"%e5%a6%82%e4%bd%95%e8%ae%ad%e7%bb%83%e6%95%b0%e6%8d%ae%e9%9b%86python","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1000381.html","title":{"rendered":"\u5982\u4f55\u8bad\u7ec3\u6570\u636e\u96c6python"},"content":{"rendered":"

\"\u5982\u4f55\u8bad\u7ec3\u6570\u636e\u96c6python\"<\/p>\n

\u8bad\u7ec3\u6570\u636e\u96c6Python\u7684\u5173\u952e\u6b65\u9aa4\u5305\u62ec\uff1a\u6570\u636e\u51c6\u5907\u3001\u6570\u636e\u6e05\u6d17\u3001\u7279\u5f81\u9009\u62e9\u3001\u6a21\u578b\u9009\u62e9\u3001\u6a21\u578b\u8bad\u7ec3\u3001\u6a21\u578b\u8bc4\u4f30\u548c\u6a21\u578b\u8c03\u4f18\u3002<\/strong>\u5728\u8fd9\u7bc7\u6587\u7ae0\u4e2d\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u8ba8\u8bba\u5176\u4e2d\u7684\u6bcf\u4e00\u4e2a\u6b65\u9aa4\uff0c\u5e76\u63d0\u4f9b\u76f8\u5e94\u7684Python\u4ee3\u7801\u793a\u4f8b\u3002\u7279\u522b\u5730\uff0c\u6211\u4eec\u5c06\u91cd\u70b9\u8bb2\u8ff0\u6570\u636e\u6e05\u6d17\u7684\u91cd\u8981\u6027\uff0c\u56e0\u4e3a\u8fd9\u662f\u786e\u4fdd\u6a21\u578b\u6027\u80fd\u7684\u57fa\u7840\u3002<\/p>\n<\/p>\n

\u6570\u636e\u51c6\u5907\u662f\u6307\u6536\u96c6\u548c\u7ec4\u7ec7\u6570\u636e\uff0c\u4ee5\u4fbf\u7528\u4e8e\u673a\u5668\u5b66\u4e60<\/a>\u6a21\u578b\u3002\u6570\u636e\u901a\u5e38\u6765\u81ea\u591a\u79cd\u6765\u6e90\uff0c\u4f8b\u5982\u6570\u636e\u5e93\u3001CSV\u6587\u4ef6\u3001API\u7b49\u3002\u4e3a\u4e86\u6709\u6548\u5730\u8bad\u7ec3\u6a21\u578b\uff0c\u6570\u636e\u9700\u8981\u7ecf\u8fc7\u6e05\u6d17\u3001\u9884\u5904\u7406\u548c\u8f6c\u6362\uff0c\u4ee5\u786e\u4fdd\u5176\u8d28\u91cf\u548c\u4e00\u81f4\u6027\u3002\u8fd9\u4e00\u6b65\u9aa4\u81f3\u5173\u91cd\u8981\uff0c\u56e0\u4e3a\u6570\u636e\u7684\u8d28\u91cf\u76f4\u63a5\u5f71\u54cd\u5230\u6a21\u578b\u7684\u6027\u80fd\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u6570\u636e\u51c6\u5907<\/p>\n<\/p>\n

\u6570\u636e\u51c6\u5907\u662f\u8bad\u7ec3\u6570\u636e\u96c6\u7684\u9996\u8981\u6b65\u9aa4\uff0c\u5b83\u5305\u62ec\u6536\u96c6\u6570\u636e\u3001\u5bfc\u5165\u6570\u636e\u4ee5\u53ca\u521d\u6b65\u67e5\u770b\u6570\u636e\u7684\u7ed3\u6784\u548c\u5185\u5bb9\u3002\u5728Python\u4e2d\uff0c\u6211\u4eec\u901a\u5e38\u4f7f\u7528Pandas\u5e93\u6765\u5904\u7406\u6570\u636e\u3002<\/p>\n<\/p>\n

    \n
  1. \u6570\u636e\u6536\u96c6\u4e0e\u5bfc\u5165<\/strong><\/li>\n<\/ol>\n

    \u5728\u6570\u636e\u79d1\u5b66\u9879\u76ee\u4e2d\uff0c\u6570\u636e\u7684\u6765\u6e90\u591a\u79cd\u591a\u6837\u3002\u53ef\u80fd\u9700\u8981\u4ece\u6570\u636e\u5e93\u4e2d\u63d0\u53d6\u3001\u4eceCSV\u6587\u4ef6\u4e2d\u8bfb\u53d6\u3001\u6216\u8005\u901a\u8fc7API\u83b7\u53d6\u6570\u636e\u3002\u4f7f\u7528Pandas\u5e93\u7684read_csv<\/code>\u3001read_sql<\/code>\u7b49\u51fd\u6570\uff0c\u53ef\u4ee5\u8f7b\u677e\u5bfc\u5165\u6570\u636e\u3002<\/p>\n<\/p>\n

    import pandas as pd<\/p>\n
    \u4eceCSV\u6587\u4ef6\u4e2d\u8bfb\u53d6\u6570\u636e<\/strong><\/h2>\n
    data = pd.read_csv('data.csv')<\/p>\n
    \u67e5\u770b\u6570\u636e\u7684\u524d\u51e0\u884c<\/strong><\/h2>\n
    print(data.head())<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u6570\u636e\u63a2\u7d22<\/strong><\/li>\n<\/ol>\n
      \u6570\u636e\u63a2\u7d22\u662f\u4e86\u89e3\u6570\u636e\u96c6\u7ed3\u6784\u548c\u5185\u5bb9\u7684\u91cd\u8981\u6b65\u9aa4\u3002\u901a\u8fc7\u4f7f\u7528Pandas\u63d0\u4f9b\u7684\u51fd\u6570\uff0c\u6211\u4eec\u53ef\u4ee5\u5feb\u901f\u67e5\u770b\u6570\u636e\u7684\u57fa\u672c\u4fe1\u606f\uff0c\u4f8b\u5982\u6570\u636e\u7c7b\u578b\u3001\u7f3a\u5931\u503c\u60c5\u51b5\u3001\u63cf\u8ff0\u6027\u7edf\u8ba1\u7b49\u3002<\/p>\n<\/p>\n
      # \u67e5\u770b\u6570\u636e\u7c7b\u578b\u548c\u7f3a\u5931\u503c<\/p>\n
      print(data.info())<\/p>\n
      \u67e5\u770b\u6570\u636e\u7684\u57fa\u672c\u7edf\u8ba1\u4fe1\u606f<\/strong><\/h2>\n
      print(data.describe())<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u4e8c\u3001\u6570\u636e\u6e05\u6d17<\/p>\n<\/p>\n
      \u6570\u636e\u6e05\u6d17\u662f\u786e\u4fdd\u6570\u636e\u8d28\u91cf\u7684\u91cd\u8981\u6b65\u9aa4\u3002\u5305\u62ec\u5904\u7406\u7f3a\u5931\u503c\u3001\u53bb\u9664\u91cd\u590d\u6570\u636e\u3001\u4fee\u6b63\u5f02\u5e38\u503c\u7b49\u3002\u6570\u636e\u6e05\u6d17\u7684\u8d28\u91cf\u76f4\u63a5\u5f71\u54cd\u6a21\u578b\u7684\u6027\u80fd\uff0c\u56e0\u6b64\u9700\u8981\u7279\u522b\u91cd\u89c6\u3002<\/p>\n<\/p>\n
        \n
      1. \u5904\u7406\u7f3a\u5931\u503c<\/strong><\/li>\n<\/ol>\n
        \u7f3a\u5931\u503c\u662f\u6570\u636e\u6e05\u6d17\u4e2d\u5e38\u89c1\u7684\u95ee\u9898\u3002\u53ef\u4ee5\u901a\u8fc7\u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c\u6216\u5217\u3001\u7528\u5747\u503c\/\u4e2d\u4f4d\u6570\/\u4f17\u6570\u586b\u5145\u7f3a\u5931\u503c\u7b49\u65b9\u6cd5\u8fdb\u884c\u5904\u7406\u3002<\/p>\n<\/p>\n
        # \u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c<\/p>\n
        data_cleaned = data.dropna()<\/p>\n
        \u7528\u5747\u503c\u586b\u5145\u7f3a\u5931\u503c<\/strong><\/h2>\n
        data_filled = data.fillna(data.mean())<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        1. \u53bb\u9664\u91cd\u590d\u6570\u636e<\/strong><\/li>\n<\/ol>\n
          \u91cd\u590d\u6570\u636e\u53ef\u80fd\u5bfc\u81f4\u6a21\u578b\u504f\u5dee\uff0c\u9700\u8981\u5728\u6570\u636e\u6e05\u6d17\u8fc7\u7a0b\u4e2d\u53bb\u9664\u3002<\/p>\n<\/p>\n
          # \u53bb\u9664\u91cd\u590d\u884c<\/p>\n
          data_no_duplicates = data.drop_duplicates()<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u4e09\u3001\u7279\u5f81\u9009\u62e9<\/p>\n<\/p>\n
          \u7279\u5f81\u9009\u62e9\u662f\u4ece\u6570\u636e\u96c6\u4e2d\u9009\u62e9\u6700\u6709\u7528\u7684\u7279\u5f81\u7528\u4e8e\u6a21\u578b\u8bad\u7ec3\u7684\u8fc7\u7a0b\u3002\u7279\u5f81\u9009\u62e9\u53ef\u4ee5\u51cf\u5c11\u6a21\u578b\u7684\u590d\u6742\u5ea6\uff0c\u63d0\u9ad8\u6a21\u578b\u7684\u6027\u80fd\u3002<\/p>\n<\/p>\n
            \n
          1. \u76f8\u5173\u6027\u5206\u6790<\/strong><\/li>\n<\/ol>\n
            \u901a\u8fc7\u8ba1\u7b97\u7279\u5f81\u4e0e\u76ee\u6807\u53d8\u91cf\u4e4b\u95f4\u7684\u76f8\u5173\u6027\uff0c\u53ef\u4ee5\u9009\u62e9\u4e0e\u76ee\u6807\u53d8\u91cf\u76f8\u5173\u6027\u8f83\u9ad8\u7684\u7279\u5f81\u3002<\/p>\n<\/p>\n
            # \u8ba1\u7b97\u76f8\u5173\u6027\u77e9\u9635<\/p>\n
            correlation_matrix = data.corr()<\/p>\n
            \u9009\u62e9\u76f8\u5173\u6027\u8f83\u9ad8\u7684\u7279\u5f81<\/strong><\/h2>\n
            print(correlation_matrix['target_variable'].sort_values(ascending=False))<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. \u4f7f\u7528\u7279\u5f81\u9009\u62e9\u7b97\u6cd5<\/strong><\/li>\n<\/ol>\n
              \u53ef\u4ee5\u4f7f\u7528\u8bf8\u5982RFE\uff08\u9012\u5f52\u7279\u5f81\u6d88\u9664\uff09\u3001LASSO\u7b49\u7b97\u6cd5\u6765\u81ea\u52a8\u9009\u62e9\u7279\u5f81\u3002<\/p>\n<\/p>\n
              from sklearn.feature_selection import RFE<\/p>\n
              from sklearn.linear_model import LogisticRegression<\/p>\n
              \u521b\u5efa\u903b\u8f91\u56de\u5f52\u6a21\u578b<\/strong><\/h2>\n
              model = LogisticRegression()<\/p>\n
              \u9012\u5f52\u7279\u5f81\u6d88\u9664<\/strong><\/h2>\n
              rfe = RFE(model, 5)<\/p>\n
              fit = rfe.fit(data, target)<\/p>\n
              \u67e5\u770b\u9009\u62e9\u7684\u7279\u5f81<\/strong><\/h2>\n
              print(fit.support_)<\/p>\n
              print(fit.ranking_)<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u56db\u3001\u6a21\u578b\u9009\u62e9<\/p>\n<\/p>\n
              \u6a21\u578b\u9009\u62e9\u662f\u6839\u636e\u95ee\u9898\u7c7b\u578b\u9009\u62e9\u5408\u9002\u7684\u7b97\u6cd5\u6765\u8bad\u7ec3\u6a21\u578b\u7684\u8fc7\u7a0b\u3002\u4e0d\u540c\u7684\u673a\u5668\u5b66\u4e60\u95ee\u9898\uff08\u5982\u5206\u7c7b\u3001\u56de\u5f52\u3001\u805a\u7c7b\u7b49\uff09\u9700\u8981\u4f7f\u7528\u4e0d\u540c\u7684\u7b97\u6cd5\u3002<\/p>\n<\/p>\n
                \n
              1. \u5206\u7c7b\u95ee\u9898<\/strong><\/li>\n<\/ol>\n
                \u5bf9\u4e8e\u5206\u7c7b\u95ee\u9898\uff0c\u53ef\u4ee5\u9009\u62e9\u4f7f\u7528\u51b3\u7b56\u6811\u3001\u968f\u673a\u68ee\u6797\u3001\u652f\u6301\u5411\u91cf\u673a\uff08SVM\uff09\u3001\u903b\u8f91\u56de\u5f52\u7b49\u7b97\u6cd5\u3002<\/p>\n<\/p>\n
                from sklearn.model_selection import trAI<\/a>n_test_split<\/p>\n
                from sklearn.ensemble import RandomForestClassifier<\/p>\n
                \u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/strong><\/h2>\n
                X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.2, random_state=42)<\/p>\n
                \u521b\u5efa\u5e76\u8bad\u7ec3\u968f\u673a\u68ee\u6797\u6a21\u578b<\/strong><\/h2>\n
                model = RandomForestClassifier(n_estimators=100)<\/p>\n
                model.fit(X_train, y_train)<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                  \n
                1. \u56de\u5f52\u95ee\u9898<\/strong><\/li>\n<\/ol>\n
                  \u5bf9\u4e8e\u56de\u5f52\u95ee\u9898\uff0c\u53ef\u4ee5\u9009\u62e9\u4f7f\u7528\u7ebf\u6027\u56de\u5f52\u3001\u51b3\u7b56\u6811\u56de\u5f52\u3001\u968f\u673a\u68ee\u6797\u56de\u5f52\u7b49\u7b97\u6cd5\u3002<\/p>\n<\/p>\n
                  from sklearn.ensemble import RandomForestRegressor<\/p>\n
                  \u521b\u5efa\u5e76\u8bad\u7ec3\u968f\u673a\u68ee\u6797\u56de\u5f52\u6a21\u578b<\/strong><\/h2>\n
                  model = RandomForestRegressor(n_estimators=100)<\/p>\n
                  model.fit(X_train, y_train)<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u4e94\u3001\u6a21\u578b\u8bad\u7ec3<\/p>\n<\/p>\n
                  \u6a21\u578b\u8bad\u7ec3\u662f\u6307\u5c06\u9009\u62e9\u7684\u7b97\u6cd5\u5e94\u7528\u4e8e\u8bad\u7ec3\u6570\u636e\uff0c\u4ee5\u751f\u6210\u9884\u6d4b\u6a21\u578b\u7684\u8fc7\u7a0b\u3002\u8bad\u7ec3\u8fc7\u7a0b\u9700\u8981\u8c03\u6574\u6a21\u578b\u53c2\u6570\uff0c\u4ee5\u4f7f\u5176\u5728\u8bad\u7ec3\u6570\u636e\u4e0a\u8868\u73b0\u6700\u4f73\u3002<\/p>\n<\/p>\n
                    \n
                  1. \u8bad\u7ec3\u6a21\u578b<\/strong><\/li>\n<\/ol>\n
                    \u4f7f\u7528\u8bad\u7ec3\u6570\u636e\u5bf9\u6a21\u578b\u8fdb\u884c\u8bad\u7ec3\uff0c\u5e76\u901a\u8fc7\u8c03\u6574\u53c2\u6570\u63d0\u9ad8\u6a21\u578b\u6027\u80fd\u3002<\/p>\n<\/p>\n
                    # \u8bad\u7ec3\u6a21\u578b<\/p>\n
                    model.fit(X_train, y_train)<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                      \n
                    1. \u4fdd\u5b58\u6a21\u578b<\/strong><\/li>\n<\/ol>\n
                      \u4e3a\u4e86\u5728\u672a\u6765\u4f7f\u7528\u8bad\u7ec3\u597d\u7684\u6a21\u578b\uff0c\u53ef\u4ee5\u5c06\u5176\u4fdd\u5b58\u5230\u78c1\u76d8\u3002<\/p>\n<\/p>\n
                      import joblib<\/p>\n
                      \u4fdd\u5b58\u6a21\u578b<\/strong><\/h2>\n
                      joblib.dump(model, 'model.pkl')<\/p>\n
                      <\/code><\/pre>\n<\/p>\n
                      \u516d\u3001\u6a21\u578b\u8bc4\u4f30<\/p>\n<\/p>\n
                      \u6a21\u578b\u8bc4\u4f30\u662f\u6307\u4f7f\u7528\u6d4b\u8bd5\u6570\u636e\u96c6\u6765\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\u3002\u5e38\u7528\u7684\u8bc4\u4f30\u6307\u6807\u5305\u62ec\u51c6\u786e\u7387\u3001\u7cbe\u786e\u7387\u3001\u53ec\u56de\u7387\u3001F1-score\u7b49\u3002<\/p>\n<\/p>\n
                        \n
                      1. \u8bc4\u4f30\u5206\u7c7b\u6a21\u578b<\/strong><\/li>\n<\/ol>\n
                        \u5bf9\u4e8e\u5206\u7c7b\u6a21\u578b\uff0c\u53ef\u4ee5\u4f7f\u7528\u6df7\u6dc6\u77e9\u9635\u3001\u51c6\u786e\u7387\u3001\u7cbe\u786e\u7387\u3001\u53ec\u56de\u7387\u3001F1-score\u7b49\u6307\u6807\u8fdb\u884c\u8bc4\u4f30\u3002<\/p>\n<\/p>\n
                        from sklearn.metrics import classification_report, confusion_matrix<\/p>\n
                        \u9884\u6d4b\u6d4b\u8bd5\u96c6<\/strong><\/h2>\n
                        y_pred = model.predict(X_test)<\/p>\n
                        \u6253\u5370\u6df7\u6dc6\u77e9\u9635\u548c\u5206\u7c7b\u62a5\u544a<\/strong><\/h2>\n
                        print(confusion_matrix(y_test, y_pred))<\/p>\n
                        print(classification_report(y_test, y_pred))<\/p>\n
                        <\/code><\/pre>\n<\/p>\n
                          \n
                        1. \u8bc4\u4f30\u56de\u5f52\u6a21\u578b<\/strong><\/li>\n<\/ol>\n
                          \u5bf9\u4e8e\u56de\u5f52\u6a21\u578b\uff0c\u53ef\u4ee5\u4f7f\u7528\u5747\u65b9\u8bef\u5dee\uff08MSE\uff09\u3001\u5747\u65b9\u6839\u8bef\u5dee\uff08RMSE\uff09\u3001R^2\u7b49\u6307\u6807\u8fdb\u884c\u8bc4\u4f30\u3002<\/p>\n<\/p>\n
                          from sklearn.metrics import mean_squared_error, r2_score<\/p>\n
                          \u9884\u6d4b\u6d4b\u8bd5\u96c6<\/strong><\/h2>\n
                          y_pred = model.predict(X_test)<\/p>\n
                          \u6253\u5370\u5747\u65b9\u8bef\u5dee\u548cR^2<\/strong><\/h2>\n
                          print("MSE:", mean_squared_error(y_test, y_pred))<\/p>\n
                          print("R^2:", r2_score(y_test, y_pred))<\/p>\n
                          <\/code><\/pre>\n<\/p>\n
                          \u4e03\u3001\u6a21\u578b\u8c03\u4f18<\/p>\n<\/p>\n
                          \u6a21\u578b\u8c03\u4f18\u662f\u6307\u901a\u8fc7\u8c03\u6574\u6a21\u578b\u7684\u8d85\u53c2\u6570\u6765\u63d0\u9ad8\u6a21\u578b\u7684\u6027\u80fd\u3002\u5e38\u7528\u7684\u65b9\u6cd5\u6709\u7f51\u683c\u641c\u7d22\uff08Grid Search\uff09\u548c\u968f\u673a\u641c\u7d22\uff08Random Search\uff09\u3002<\/p>\n<\/p>\n
                            \n
                          1. \u7f51\u683c\u641c\u7d22<\/strong><\/li>\n<\/ol>\n
                            \u901a\u8fc7\u7f51\u683c\u641c\u7d22\uff0c\u53ef\u4ee5\u5c1d\u8bd5\u4e0d\u540c\u7684\u53c2\u6570\u7ec4\u5408\uff0c\u4ee5\u627e\u5230\u6700\u4f73\u7684\u6a21\u578b\u53c2\u6570\u3002<\/p>\n<\/p>\n
                            from sklearn.model_selection import GridSearchCV<\/p>\n
                            \u5b9a\u4e49\u53c2\u6570\u7f51\u683c<\/strong><\/h2>\n
                            param_grid = {<\/p>\n
                                'n_estimators': [50, 100, 200],<\/p>\n
                                'max_depth': [None, 10, 20, 30]<\/p>\n
                            }<\/p>\n
                            \u521b\u5efa\u7f51\u683c\u641c\u7d22\u5bf9\u8c61<\/strong><\/h2>\n
                            grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=5)<\/p>\n
                            \u6267\u884c\u7f51\u683c\u641c\u7d22<\/strong><\/h2>\n
                            grid_search.fit(X_train, y_train)<\/p>\n
                            \u6253\u5370\u6700\u4f73\u53c2\u6570<\/strong><\/h2>\n
                            print("Best parameters found: ", grid_search.best_params_)<\/p>\n
                            <\/code><\/pre>\n<\/p>\n
                              \n
                            1. \u968f\u673a\u641c\u7d22<\/strong><\/li>\n<\/ol>\n
                              \u968f\u673a\u641c\u7d22\u901a\u8fc7\u5728\u53c2\u6570\u7a7a\u95f4\u4e2d\u968f\u673a\u91c7\u6837\u53c2\u6570\u7ec4\u5408\uff0c\u6bd4\u7f51\u683c\u641c\u7d22\u66f4\u5feb\u3002<\/p>\n<\/p>\n
                              from sklearn.model_selection import RandomizedSearchCV<\/p>\n
                              \u521b\u5efa\u968f\u673a\u641c\u7d22\u5bf9\u8c61<\/strong><\/h2>\n
                              random_search = RandomizedSearchCV(estimator=model, param_distributions=param_grid, n_iter=10, cv=5)<\/p>\n
                              \u6267\u884c\u968f\u673a\u641c\u7d22<\/strong><\/h2>\n
                              random_search.fit(X_train, y_train)<\/p>\n
                              \u6253\u5370\u6700\u4f73\u53c2\u6570<\/strong><\/h2>\n
                              print("Best parameters found: ", random_search.best_params_)<\/p>\n
                              <\/code><\/pre>\n<\/p>\n
                              \u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u6709\u6548\u5730\u4f7f\u7528Python\u5bf9\u6570\u636e\u96c6\u8fdb\u884c\u8bad\u7ec3\uff0c\u4ece\u800c\u6784\u5efa\u9ad8\u6027\u80fd\u7684\u673a\u5668\u5b66\u4e60\u6a21\u578b\u3002\u6bcf\u4e2a\u6b65\u9aa4\u90fd\u662f\u81f3\u5173\u91cd\u8981\u7684\uff0c\u9700\u8981\u6839\u636e\u5177\u4f53\u95ee\u9898\u8fdb\u884c\u8c03\u6574\u548c\u4f18\u5316\u3002<\/p>\n<\/p>\n
                              \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                               \u5982\u4f55\u9009\u62e9\u5408\u9002\u7684\u6570\u636e\u96c6\u8fdb\u884c\u8bad\u7ec3\uff1f<\/strong>
                              \u9009\u62e9\u5408\u9002\u7684\u6570\u636e\u96c6\u662f\u8bad\u7ec3\u6a21\u578b\u7684\u5173\u952e\u3002\u9996\u5148\uff0c\u660e\u786e\u4f60\u7684\u9879\u76ee\u76ee\u6807\u548c\u9700\u6c42\uff0c\u9009\u62e9\u4e0e\u4e4b\u76f8\u5173\u7684\u9886\u57df\u6570\u636e\u3002\u6b64\u5916\uff0c\u6570\u636e\u96c6\u7684\u89c4\u6a21\u3001\u8d28\u91cf\u548c\u591a\u6837\u6027\u4e5f\u975e\u5e38\u91cd\u8981\u3002\u786e\u4fdd\u6570\u636e\u96c6\u5305\u542b\u8db3\u591f\u7684\u6837\u672c\uff0c\u4ee5\u4fbf\u6a21\u578b\u80fd\u591f\u5b66\u4e60\u5230\u6709\u610f\u4e49\u7684\u7279\u5f81\u3002\u540c\u65f6\uff0c\u68c0\u67e5\u6570\u636e\u96c6\u662f\u5426\u6807\u6ce8\u51c6\u786e\uff0c\u907f\u514d\u56e0\u9519\u8bef\u6807\u7b7e\u5f71\u54cd\u6a21\u578b\u6027\u80fd\u3002<\/p>\n
                              \u5728Python\u4e2d\u5982\u4f55\u5904\u7406\u7f3a\u5931\u503c\uff1f<\/strong>
                              \u5728\u6570\u636e\u9884\u5904\u7406\u4e2d\uff0c\u7f3a\u5931\u503c\u7684\u5904\u7406\u81f3\u5173\u91cd\u8981\u3002\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u4e2d\u7684fillna()<\/code>\u51fd\u6570\u6765\u586b\u8865\u7f3a\u5931\u503c\uff0c\u4f8b\u5982\u7528\u5747\u503c\u3001\u4e2d\u4f4d\u6570\u6216\u4f17\u6570\u586b\u5145\uff1b\u4e5f\u53ef\u4ee5\u9009\u62e9\u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c\u6216\u5217\uff0c\u4f7f\u7528dropna()<\/code>\u51fd\u6570\u3002\u6839\u636e\u6570\u636e\u96c6\u7684\u7279\u70b9\u548c\u9700\u6c42\uff0c\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u6765\u5904\u7406\u7f3a\u5931\u503c\uff0c\u4ece\u800c\u63d0\u9ad8\u6a21\u578b\u7684\u8bad\u7ec3\u6548\u679c\u3002<\/p>\n
                              \u5982\u4f55\u8bc4\u4f30\u8bad\u7ec3\u6a21\u578b\u7684\u6548\u679c\uff1f<\/strong>
                              \u8bc4\u4f30\u6a21\u578b\u6548\u679c\u662f\u786e\u4fdd\u5176\u6027\u80fd\u7684\u91cd\u8981\u6b65\u9aa4\u3002\u53ef\u4ee5\u4f7f\u7528\u4ea4\u53c9\u9a8c\u8bc1\u65b9\u6cd5\uff0c\u901a\u8fc7\u5c06\u6570\u636e\u96c6\u5206\u6210\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\u6765\u9a8c\u8bc1\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\u3002\u6b64\u5916\uff0c\u5e38\u7528\u7684\u8bc4\u4f30\u6307\u6807\u5305\u62ec\u51c6\u786e\u7387\u3001\u7cbe\u786e\u7387\u3001\u53ec\u56de\u7387\u548cF1\u5206\u6570\u7b49\u3002\u5229\u7528Scikit-learn\u5e93\u63d0\u4f9b\u7684\u51fd\u6570\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8ba1\u7b97\u8fd9\u4e9b\u6307\u6807\uff0c\u5e2e\u52a9\u4f60\u5224\u65ad\u6a21\u578b\u7684\u6548\u679c\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u8bad\u7ec3\u6570\u636e\u96c6Python\u7684\u5173\u952e\u6b65\u9aa4\u5305\u62ec\uff1a\u6570\u636e\u51c6\u5907\u3001\u6570\u636e\u6e05\u6d17\u3001\u7279\u5f81\u9009\u62e9\u3001\u6a21\u578b\u9009\u62e9\u3001\u6a21\u578b\u8bad\u7ec3\u3001\u6a21\u578b\u8bc4\u4f30\u548c\u6a21\u578b\u8c03\u4f18\u3002\u5728 […]","protected":false},"author":3,"featured_media":1000394,"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\/1000381"}],"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=1000381"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1000381\/revisions"}],"predecessor-version":[{"id":1000397,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1000381\/revisions\/1000397"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1000394"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1000381"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1000381"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1000381"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}