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I don't necessarily disagree with you, but regarding Python winning data science, I think that's in large part to a) Anaconda creating a single cohesive alterna

I don't necessarily disagree with you, but regarding Python winning data science, I think that's in large part to a) Anaconda creating a single cohesive alternative ecosystem, shunned by engineers but loved by data scientists who just want to get things done, and b) the fact that with a couple of major libraries/frameworks you can get most things done, Pandas, Numpy, Sklearn, maybe a few others, meaning there's less dependency management to do overall.

rsyring 2y ago
As a thought experiment, imagine someone had built Anaconda for C and then, instead of making Python APIs out of those mostly C libraries, just left the interface as C. Same packaging solution, same libraries, but the user has to learn C. Or swap C for Go even. No memory management but still more esoteric syntax and typing, interfaces, etc. required. How far does that get? The tools you mention were built on/around Python because it was realistic to ask technical but non-programmer users to learn to use Python. The lack of explicit typing and more "conversation" nature of Python syntax is, IMO, what makes that possible. So Python "wins" and other tooling is built on/around it to make it even easier for non-programmers to operate in the ecosystem, which solidifies it's position even more.