这不需要循环。

# Create and fit selector

selector = SelectKBest(f_classif, k=5)
selector.fit(features_df, target)

# Get columns to keep and create new dataframe with those only

cols = selector.get_support(indices=True)
features_df_new = features_df.iloc[:,cols]

对我来说，这段代码运行良好，而且更像是“Python”：

mask = select_k_best_classifier.get_support()
new_features = features_dataframe.columns[mask]

您可以执行以下操作：

mask = select_k_best_classifier.get_support() #list of booleans
new_features = [] # The list of your K best features

for bool, feature in zip(mask, feature_names):
    if bool:
        new_features.append(feature)

然后更改要素的名称：

dataframe = pd.DataFrame(fit_transofrmed_features, columns=new_features)

下面的代码将帮助你找到前K个特征及其F-分数。令，X是Pandas Dataframe ，其列是所有特征，y是类标签列表。

import pandas as pd
from sklearn.feature_selection import SelectKBest, f_classif

# Suppose, we select 5 features with top 5 Fisher scores

selector = SelectKBest(f_classif, k = 5)

# New dataframe with the selected features for later use in the classifier. fit() method works too, if you want only the feature names and their corresponding scores

X_new = selector.fit_transform(X, y)
names = X.columns.values[selector.get_support()]
scores = selector.scores_[selector.get_support()]
names_scores = list(zip(names, scores))
ns_df = pd.DataFrame(data = names_scores, columns=['Feat_names', 'F_Scores'])

# Sort the dataframe for better visualization

ns_df_sorted = ns_df.sort_values(['F_Scores', 'Feat_names'], ascending = [False, True])
print(ns_df_sorted)

根据chi 2选择最佳10个特征;

from sklearn.feature_selection import SelectKBest, chi2

KBest = SelectKBest(chi2, k=10).fit(X, y)

使用get_support（）获取功能

f = KBest.get_support(1) #the most important features

创建名为X_new的新df;

X_new = X[X.columns[f]] # final features`

在Scikit-learn 1.0中，转换器有get_feature_names_out方法，这意味着你可以写

dataframe = pd.DataFrame(fit_transformed_features, columns=transformer.get_features_names_out())

还有另一种替代方法，然而，该方法不如上述解决方案快。

# Use the selector to retrieve the best features

X_new = select_k_best_classifier.fit_transform(train[feature_cols],train['is_attributed'])

# Get back the kept features as a DataFrame with dropped columns as all 0s

selected_features = pd.DataFrame(select_k_best_classifier.inverse_transform(X_new),
                            index=train.index,
                            columns= feature_cols)
selected_columns = selected_features.columns[selected_features.var() !=0]

# Fit the SelectKBest instance

select_k_best_classifier = SelectKBest(score_func=f_classif, k=5).fit(features_dataframe, targeted_class)

# Extract the required features

new_features  = select_k_best_classifier.get_feature_names_out(features_names)