This timeline is a bit aggressive for Ruby. The core of my career using Ruby was 10 years ago, and I would still call it "darling" at that point, though the rumblings were beginning. Node.js was first released almost exactly 10 years ago, and (from my perspective) over the next few years marked the first real exodus from people who had previously been all in on Ruby. I missed out on that one because I thought node.js seemed like an immature answer to a question I wasn't asking. But over the next few years, I became increasingly disillusioned with writing big software in a language without good static analysis, and was on board with the mindshare (if not actual employment) exodus toward languages like Go and Rust. So I would say it was more like 5-7 years ago that you started seeing more criticism than love for Ruby in places like this.

And yeah, as other commenters have said, Python is back due to data science / machine learning. (Though I'm hoping there will be a wave of adoption of tools like Julia and Swift for this in the near future; good static analysis will be nice for data science for all the same reasons it is nice for other kinds of software.)

Python's relevance got renewed by the ML frameworks (Tensorflow) and notebooks (Jupyter).

This. Jupyter notebooks, Matplotlib, Numpy, Spyder...all that made Python big again as ML and AI became the new hotness recently and the killer app for Python.

Outside AI, Python is a really good scripting language for both Linux and Windows. My entire industry seems to run off of Python for process automation and analysis. It really is a lingua franca in this space.

I agree. During my Maths + CS studies I've been using mainly MatLab and R, Andrew Ng's famous Machine Learning intro was also taught in MatLab/ Octave. But using just one „proper” general purposing language like Python instead makes way more sense, especially when building software from scratch. Need a custom ERP? Build one with django. A Web Server? Go with flask. Integrating some ML pipelines? Easy.