I couldn't agree more. I'm fluent in languages like Julia, and MATLAB. I'm 90% fluent in R and prefer data.table over dplyr but working in both is easy enough. The past few months I've been fully transitioning to Python. And while base Python I find to be extremely elegant, typical data science and scientific computing workflows are a headache. There aren't just 1-2 packages to choose from for each use, every package has it's own syntax, keeping track of Pandas Series vs DataFrames is confusion. Want fast differentiable code? Then rewrite everything in numpy in JAX which requires its own tricks.
What Python desperately needs is a coordinated effort for a core data science /scientific computing stack with a unified framework.
In my opinion, if it weren't for Python's extensive use in Industry and package ecosystem, Julia would be the language of choice for nearly all data science and scientific computing uses.
Financial economist and Julia aficionado here looking to transition to academia to industry if you're ever hiring. If you have any feedback /tips it would be much appreciated. Email and website in bio.
From an economic perspective, dynamic pricing maximizing total welfare. Those with the highest person values get to see the concert, without the middle-man making money.
What Python desperately needs is a coordinated effort for a core data science /scientific computing stack with a unified framework.
In my opinion, if it weren't for Python's extensive use in Industry and package ecosystem, Julia would be the language of choice for nearly all data science and scientific computing uses.