Mapping column names to random forest feature importances

It is simple, I plotted it like this.

feat_importances = pd.Series(extraTree.feature_importances_, index=X.columns)
feat_importances.nlargest(15).plot(kind='barh')
plt.title("Top 15 important features")
plt.show()

A sort of generic solution would be to throw the features/importances into a dataframe and sort them before plotting:

import pandas as pd
%matplotlib inline
#do code to support model
#"data" is the X dataframe and model is the SKlearn object

feats = {} # a dict to hold feature_name: feature_importance
for feature, importance in zip(data.columns, model.feature_importances_):
    feats[feature] = importance #add the name/value pair 

importances = pd.DataFrame.from_dict(feats, orient='index').rename(columns={0: 'Gini-importance'})
importances.sort_values(by='Gini-importance').plot(kind='bar', rot=45)