It's the same for using Python for bioinformatics. Go through the excellent official Python tutorial[0]. You won't remember everything, but you want to build some subconscious memory and muscle memory. After that, dove straight into your bioinformatics project. You will inevitably get stuckany times, there are no programmers who don't! At that point you'll need to do some research just as we all do, and you'll likely learn something new about Python in the process. The key is to let the bioinformatics motivate the Python, not the other way around.
There are many other visualization tools that work w/ jupyter such as seaborne, d3.js, etc.
If you can't find what you are looking for you can build your own on top of those kind of tools, write widgets that draw HTML in the notebook, etc.
See https://gist.github.com/afrendeiro/8fa43f074a64590c67c30ec7b...
There won't be much visualisation required in those problems explicitly, but you should reach for something like matplotlib when you need it and follow the most relevant examples you cna find for what you're visualising.
There are general Python Data Viz courses with free content[0], and I've been building machine learning courses (slightly different topic) with a platform called co:rise, who also offer basic Python Data Science courses[1]. You can find plenty of tutorial blogs via searching, or look at specialized mini-courses on Udemy[2]
[0] https://www.edx.org/course/data-science-visualization
[2] https://www.udemy.com/course/python-for-machine-learning-dat...
Just keep in mind it's a very vast field and scientists usually specialize in a tiny part of it so don't go too hard on yourself.