I didn't have this choice because I was forced to move from R to Python:
It depends on your environment: When you are embedded in an engineer department, working technical group or something similar than Python is more feasible.
When you are surrounded by scientists and especially statisticians, stay with R.
PS: R offers keras and tensorflow as well though it is implemented under the hood of python. Only very advanced stuff will make you need Python.
Though I'm getting more and more used to Python, the synthax in R is easier. And though each package has its own, it is somehow consistent while Python is not..
And ggplot is so strong. Python has a clone (plotnine) but it lacks several (important) features. In principle you can do nearly as much as in R but especially visualization and data wrangling is much easier in R. Thus, the most famous Python library, pandas, is a clone of R.
PSS: Advanced statistics aims definitely at R. Python offers a lot of everyday tools and methods for a data scientist but it will never reach those >13,000 packages R provides. For example, I had to do an inverse regression and python doesn't offer this. In R you can choose between several confidence tests and whether it is linear or nonlinear.
The same goes to mixed models: It is implemented in python but it is so basic there I can't realize how this can be sufficient for someone.