I'm a R&D scientist in private industry, and so I don't write commercial software, but have used programming as a tool throughout my career. For me, betting on Python meant migrating almost all of my technical work to Python based tools.

Oddly enough it started at home. I was a long time Visual Basic programmer, but I decided to try Linux on my home computer, and needed a way to write simple programs. Going a step further, I decided that I wanted my tools to be open source and platform independent. After evaluating a few tools, I settled on Python and started playing with it. I tried out things like Maxima and Octave as well.

A few months later, a big crisis landed in my lap at work, and I decided to use Python to develop the solution. I was amazed by how productive it seemed to be, and I decided to sweep more projects towards the event horizon of Python.

I've also climbed aboard the "reproducible science" movement. Though I don't publish my work, reproducibility is just as much of a problem within a closed setting. Jupyter notebooks have made it much easier for me to dredge up an old project and figure out what I did, months or even years later.

It means focusing on learning the toolchains and ecosystem in my free time so that I was really productive with the technology.