Factors I personally think led to Python's popularity: 1) Perl kind of shooting itself in the foot 20 years ago and Python becoming the de facto scripting lang

Factors I personally think led to Python's popularity: 1) Perl kind of shooting itself in the foot 20 years ago and Python becoming the de facto scripting language for Linux distributions that needed to do anything more complicated than was suitable for shell scripts but didn't require entirely new compiled software projects. 2) The above meant Python is almost always available and a good tool to have handy if you need to do something one-off and simple but more complicated than what you can do with a built-in calculator app. For instance, ever curious if you can pull the exponents off of x509 certificates and manually verify signatures by hand? Pretty easy to do in Python. 3) The C API and compiled modules made it possible to link against pre-existing BLAS implementations, and the extensible syntax and user-defined operators made it possible to mimic the style of MATLAB and R. Thus, Python became a popular choice as a lingua franca for engineers, scientists, and stats geeks who just wanted to do some data exploration or modeling and weren't trying to create shippable software. 4) MIT decided to make Python its primary teaching language in the early 2000s or so and a lot of CS programs in the US followed suit. 5) It became possible at some point to write Microsoft Office macros in Python, giving marginally technical business types a nice option to learn that was more broadly useful than VB script to automate their own workflows. Why it ever became so popular among actual software developers I have a harder time answering, but for research, exploratory work, prototyping, scripting, workflow automation, it's as good as anything else you can come up with, usually already available, and it has an extremely "batteries included" standard library that means you probably don't need to worry about the kind of ecosystem dependency hell you're envisioning here. Possibly some factors include the rise of LeetCode, as Python's "executable pseudocode" style means it is very easy to find or translate examples of algorithm implementations into Python solutions for learning, and the fact that a large trend of the post big data era is trying to turn exploratory data analysis pipelining tasks into real software, along with people who used to brand themselves as "data scientists" deciding to become software developers instead, and already knowing Python. Python also gives you a pretty good first order approximation of a solution when you want to turn some researcher's data model into a service, provided your app is also written in Python. This has become far less important these days with data APIs, ML APIs, standardized formats for model serialization, but previously, a very popular solution to the so-called "two language problem" was just making Python fast enough to let it be both languages itself rather than trying to add web app frameworks to Julia.