I have released a package that can help implementing nested cross validation in Python (for the moment, it only works for binary classifiers). If you want to check it out, it's here:

https://github.com/JaimeArboleda/nestedcvtraining

It's my first Python package, so any comments, suggestions or critics will be more than welcome!!

I post it as an answer because nested cross validation is performed inside the main function and you don't have to take care of how to implement it. Anyway, the code is visible in my github account, so that you can check how I implemented it if you are curious. Also, the readme explain it in detail (although, maybe, not very clearly!). 

After the function is called, you get a model (that in fact will be a pipeline if there is a post-process to perform) and a complete report of what happened inside, so that you can assess the estimates of the different scoring functions. It comes with many options thay may be enough for a lot of common settings, I hope.

I don't test with a completely separated test set, because the outer procedure is meant to do this repeatedly and you can assess the quality of the model. However, if you have any doubt I'll be more than happy to try to answer it.