I have been using Julia for building, solving, and simulating computational models for a few years now, but my empirical (“data science”) work has remained in legacy languages – Python for data wrangling and plotting, Stata for proper econometrics.
I want to see if I can transition to Julia for those tasks as well, partly in search of one-language elegance and partly because there are things I dislike about both of those alternatives (pandas syntax and constant changes are infuriating, while Stata is proprietary and essentially anti-thetical to well-written modular code).
But while I’ve gotten generally familiar with DataFrames.jl, I’m not sure what kind of workflow I should develop and what auxillary packages I should invest my time in learning.
Should I use base DataFrames.jl? DataFramesMeta.jl? Tidier.jl? What do most people use these days?
My current Python workflow is usually – play around with stuff in a Jupyter notebook, then move backend-type code into py files while continuing to use Jupyter as a frontend.
Should I replicate this workflow exactly with Julia or is there a better alternative? Should I use Jupyter or Pluto.jl?
I understand that my questions are asking for opinions, and one may just be tempted to answer, “try out all approaches and see what you’re most comfortable with.” But I’d like to speed the process along by learning the workflow that most others use. I also know that there are similar threads on here from a few years ago, but my understanding is that the toolkit has evolved substantially, so those threads may be outdated.
Thanks in advance!