20 years working on Python. Two things have driven its success.

First, it's easy to learn and reasonably powerful. You can make a lot of useful things really quickly, which was true even 15 years ago. It's only gotten easier since, and the ecosystem has continued to grow.

Second, ML latched onto it. That gave Python a lot of credibility for people coming through CS programs and into industry, especially as AI use cases were picking up momentum. Think Tensorflow starting 10-ish years ago, then Pytorch, and so on.

[opinion time] Unfortunately, ML has contributed to disaster. The quality of software engineering in ML applications lags badly behind that in other segments, and there's where most Python development is happening now. The feedback loop of training Bad Python into LLMs is only making it worse. The skill floor was dropping, and now it's practically zero.

It also wasn't helped much by language design decisions. Gradual typing was a huge mistake. It would have been much better to go the Typescript route and define a superset of the language that compiles to native Python. The grafted-in typing features that rely on runtime behavior and never got an actual spec for a type system haven't helped.