python sklearn decision tree classifier feature_importances_ with feature names when using continuous values
I think you are mixing two different things here.  


The feature_importance_ - this is an array which reflects how much each of the model's original features contributes to overall classification quality.  
The features positions in the tree - this is a mere representation of the decision rules made in each step in the tree. A feature position(s) in the tree in terms of importance is not so trivial.  


There are some potential heuristics for understanding the relation between the two. If a feature dosn't appear in the tree it has 0 importance and generally the higher the feature is in the tree the more important it is (assuming its being compared to another feature on the same branch).