Comment by analog31

I actually use straight matplotlib, or for quick-and-dirty, pyplot. Every notebook starts with the same boilerplate, turning on auto-reload, then numpy, pyplot, asdf. And then my own weird libraries, or those shared with colleagues. Occasionally OpenCV, pyserial, sympy, and other odds and ends.

I have a Python "wrapper" for every piece of lab equipment that I touch.

I'm a physicist, and I work on developing measurement equipment. My graphing needs tend to be simplistic, with a big factor being the ability to visualize something quickly and then plan the next step (or realize I screwed up and start over). I'm often the only reader of my graphs.

My work is all secret, so I don't publish, except an occasional patent. The graphing needs for patents are their own beast, arcane, and perhaps a bit repulsive.

I noticed your comment suggests a more "life science" interest, and I think those fields may place a heavier burden on visualization. So I wouldn't be shocked if the physical and life sciences had different graphing needs. I suspect pyplot has a closer vibe to what you're using, than straight matplotlib, but maybe not close enough. There have been attempts to wrap mpl in a ggplot-like interface, but I don't know how successfully.