python - Know feature names after imputation -

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i run sk-learn classifier on pandas dataframe (x). since data missing, use sk-learn's imputer this:

imp=imputer(strategy='mean',axis=0) x=imp.fit_transform(x)

after doing however, number of features decreased, presumably because imputer gets rids of empty columns.

that's fine, except imputer transforms dataframe numpy ndarray, , lose column/feature names. need them later on identify important features (with clf.feature_importances_).

how can know names of features in clf.feature_importances_, if of columns of initial dataframe have been dropped imputer?

you can this:

invalid_mask = np.isnan(imp.statistics_) valid_mask = np.logical_not(invalid_mask) valid_idx, = np.where(valid_mask)

now have old indexes (indexes these columns had in matrix x) valid columns. can feature names these indexes list of feature names of old x.


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