I really don't relate to this. When I was in high school and college and for the first few years of my career, Python was
not "a very good tool for that particular job". A number of the Ruby programmers I first knew and respected were using Ruby for doing computational / scientific computing with more iteration and velocity than they could achieve with Fortran, C, or C++ (the common scientific languages at the time). There was no reason back then to think that Python as a language was any better suited for this purpose than Ruby or Perl or any other language really. (And what we now call data scientists were already mostly doing this with R.)
It was indeed through a significant amount of writing and advocacy and persuasion that Python - through numpy and scipy and pandas - won out here.
For a time it looked like it was going to fade, as it was too slow and all the "big" tooling was built on the jvm instead. But it got faster and people made more interfaces to underlying faster components, and people did more writing and advocacy and persuasion, and now it's mostly python still, with a bunch of native or jvm code under the hood sometimes.
But none of this happened just through python being clearly better for this niche. People always write and discuss and advocate for the tools they like. It's normal. It's same as it ever was.