Easy is not good enough. The language has to be productive. Firstly, let me admit that the python is pretty high in this metric. Furthermore, I'd probably agree that ten years ago, it was arguably the most productive widely used programming language, given quality of ecosystem in both libraries (numpy, sqlalchemy, django, etc), tooling (jupyter, venv, conda) and ubiquity.
But times have changed, FP influenced statically typed languages made a lot of progress. To me it's not obvious anymore that dynamic typing gives any serious advantages in productivity vs say Kotlin/Scala/C#/F# or even modern Java. Sure, Python is still "simpler", but it's not a productivity argument anymore, but a social argument - "quicker to teach", "non-specialist can use it", etc. There are still niches Python rules supreme due to simply overwhelming library support, like ML and numeric methods, but not for general computing. The main issue though is that contrary to static side making huge strides in productivity, Python made very little progress on its Achilles' heel - speed. Thirty times slower and GIL is just not good in 2024. Hence the rise of Go and alternative "pythons" like Julia and Mojo.