Use of one-hot encoder to build decision trees
Based on my tests, One-hot encoding results in continuous variables (amounts, in my case) having been assigned higher feature importance. 
Also, a single level of a categorical variable must meet a very high bar in order to be selected for splitting early in the tree building. This apparently degrades predictive performance (I didn't see this consequence mentioned in any post, unfortunately). 
I will investigate for other approaches.
I need to build decision trees on categorical data. 
I understood that scikit-learn was only able to deal with numerical values, and the recommended approach is then to use on-hot encoding, preferrably using the Panda Dummies. 
So, I build a sample dataset where all attributes and labels are categorical. At this stage, I try to understand how to 'one-hot' encode to be able to use sklearn, but the documentation does not address this case. 
Could eventually give me a quick example or a link to some material for beginners ?