Determine most important features per class in binary classification?

The only good answer i found was this :

https://stackoverflow.com/questions/33118361/determine-most-important-feature-per-class

But the above answer doesn’t work in binary classification because there is only one i, so instead i used the top most positive weights to detect most important features for class 1, and most negative ones for class 0 :

myclassifier = SGDClassifier(loss='log',  penalty='l1', l1_ratio=0.9, learning_rate='optimal' , shuffle=False, fit_intercept=True , verbose=True)
myclassifier.fit(X_train, Y_train)
        
top_indices_class1 = np.argsort(myclassifier.coef_[0])[-100:]

for x in top_indices_class1: 

    print(feature_list[int(x)]  , myclassifier.coef_[0][int(x)]  )

top_indices_class0 = np.argsort(myclassifier.coef_[0])[:100]

for x in top_indices_class0: 

    print(feature_list[int(x)] , myclassifier.coef_[0][int(x)])

Is this a good method to find the most important features for each class? is there any better way to do this?