R's ggplot2 follows The Grammar of Graphics. The closest and best implementation of it in Python is Altair (https://altair-viz.github.io). It's a fairly young library, but is very simple to use, and can do really powerful things (see the last chart here https://altair-viz.github.io/case_studies/exploring-weather....)

Bokeh and Plotly produce dynamic, javascript based visualizations. ggplot is for static images, which matplotlib is the obvious counterpart in Python. There are notable packages built on top of matplotlib including seaborn and plotnine.

Seaborn.

It is fabulous. I use it regularly. https://seaborn.pydata.org/

I would say hands down, plotnine. It...

* is a port of ggplot2 to python

* has a very active maintainer

I've been testing out porting a bunch of analyses from R to python, and it has been very easy to swap in for ggplot2. The biggest downside I can see is that with ggplot2 it's easy to convert to interactive using plotly (but there isn't that kind of support for plotnine yet).

https://github.com/has2k1/plotnine