+1 to that. Add on top of it that by design, a lot of Python coders are not professional programmers, and you get yourself a better model of why migrating was so long. People coded an untested unstructured project that worked on their machine. It was enough to get the job done, and was a big reason they choose this language in the first place. But migrating that is not fun. Plus they didn't understand their own system, so touching it on such large scale seemed overwhelming, and hence the "it's too hard".

It's about risk. Python is a scripting language. That means that it's often auxiliary to a primary programming language. Your project might be in C++ but you use Python to package it or generate some assets, etc. Do you really care what language your auxiliary scripts are written in? No. Are you going to spend any amount of time converting them to Python 3 syntax? No. Your boss doesn't care, and you don't care. It would only end badly.

Scripting is only one use of python, it's also a math language that is used for implementing servers, machine learning and statistical analytics, data processing pipelines, and more. Numpy/Pandas allows fast math operations because it makes python the interface for operations implemented on c-arrays underneath the surface. For many many people, python is the primary programming language. The ability to use the same language for scripting as for your primary application is part of the charm of python.

The existence of the cloud means that you can scale wide instead of scaling tall, which makes developer time and complexity management more valuable than hardware efficiency. Python is optimized for primary applications in exactly that kind of environment, where the performance characteristics of something like C++ don't make up for the clumsiness of the language.