rfe.py

from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression

rfe_selector = RFE(estimator=LogisticRegression(), n_features_to_select=10, step=1, importance_getter='auto')
rfe_selector.fit(X, y)

# The best set of features that are selcted, denoted as True, False
print(selector.support_)
# All selected features are marked 1, the unselected redundant features ranked in increasing order
print(selector.ranking_)

# Get a mask, or integer index, of the features selected
rfe_support = rfe_selector.get_support()
rfe_feature = X.loc[:,rfe_support].columns.tolist()
print(str(len(rfe_feature)), 'selected features')