All good points. I think the network effects are more important than they were in the past, because the network has gotten bigger. Today, new graduates make sure to put Python on their resumes, even if they've barely touched it. A colleague who left for another job thanked me for encouraging him to learn Python. Contrast that with 13 years ago, roughly when I switched to Python. My colleagues thought it was some weird toy, and encouraged me to learn C# instead. The past 13 years have also seen most programmers get over their aversion to open-source tools.

It's true, as mentioned in another comment, that repeatability is important. But at least in experimental science, the repeatability of an experiment is a bigger hurdle than making the same code run twice. I use Python code to control my experiments, and the ability to read my way through a complex workflow is quite valuable.

One thing I like is being able to bodge together a huge blob of data and metadata into a dict that's easy to store and unpack. That encourages me to keep more experimental data and metadata, and use it later.

Python and its libraries have sprawled to the point where it's anything but simple.