How to process categorical and real features separately in Pipeline sklearn?

Question

Machine learning

I just started practicing in DataScience and learned about such a useful thing as Pipeline. I practice on the Titanic dataset. I want to separately process categorical features (i.e. encode them through OneHotEncoding), normalize real features and discard unnecessary features (and also fill in the gaps). How can I do this through a sequence of pipelines?