The current top comment (sibling to the one I’m replying to) argues that keeping Python environments across actuaries/users computers up to date is too difficult.
This is nicely solved by using R server.
I’ve worked in an R server shop, and the experience is really nice. You log on to the server in chrome or Firefox and the browser window basically becomes RStudio and all calculations are done on the server and all code and data also lives on the server which is a huge bonus in terms of data protection. No copies are floating around on peoples laptops and if Johnny is sick and forgot to push his code to git - no worries, it’s all on the r studio server.
I don’t now of a nearly as good Python solution. I think Conda suggests using jupyter lab, and while that is a great environment it’s not great if it’s all you can use.
The big problem with notebooks is that you don't have a real REPL. This prevents one from single step debugging and tracing. This is one area where RStudio is much, much better.
The trouble is that so many of the younger DS people are focused on Python, that it makes financial sense to just deal with all its problems. There's also a lot more programming tools (though less statistical modelling tools).
You do have access to a repl when using jupyter notebooks.
You can hook a notebook or a repl to an existing kernel. I always have a command line attached to my notebooks. When using jupyter lab I attach the build-in terminal and place it at the bottom. When using notebooks I attach it from my terminal.
The experience in Rstudio is still better imho. It’s also a more mature text editor and ide than jupyter.
Ok fair enough, I only used notebooks when I can't avoid it. I'm pretty sure you don't get a repl by default though, is there an involved set up in jupyter?
This is nicely solved by using R server.
I’ve worked in an R server shop, and the experience is really nice. You log on to the server in chrome or Firefox and the browser window basically becomes RStudio and all calculations are done on the server and all code and data also lives on the server which is a huge bonus in terms of data protection. No copies are floating around on peoples laptops and if Johnny is sick and forgot to push his code to git - no worries, it’s all on the r studio server.
I don’t now of a nearly as good Python solution. I think Conda suggests using jupyter lab, and while that is a great environment it’s not great if it’s all you can use.