Notice how the article load_penguins() example starts neatly after all the messy parts of data science are done and stops right before the next pain starts.

It lives in a sterile, idealized world.

Python is a great language for data science in practice because it turns out data science is also:

   - gluing a lot of data sources

   - cleaning up a ton of terribly shaped data

   - validation and error handling

   - I/O, networking, and format conversion

   - emboarding non-programmers into programming

   - wrapping a lot of compiled languages' libs or plugging system

   - prototyping stuff and exposing that prototype to some people

   - turning prototypes into more permanent projects

There are other languages excellent at some of those, or some of the other things, but rarely good at most. And because humanity is vast, diverse, and constantly renewing, being the second best at those is eventually always winning.

Because whoever you are, you will be annoyed at not having the best experience at task X. But you would be mortified if you had the worst experience at doing task Y and Z. And task X, Y, and Z change depending on who you ask.

And you want to get things done, while days have 24 hours.

As usual, to understand the Python phenomenon, you have to see the whole picture. Not your little corner of the bubble. Not the ideal world in your head either. Life is not a maths problem with a clearly laid out premise and an elegant answer.

That's the same debate about why PHP won the web in 2000 no matter the size of the spaghetti plate, why Windows stayed used for so long despite it being terrible, why people keep using iphones after all the abuses, etc. There is more to it than the use case you have every day. People have needs you don't haven't thought about.

So it's not "let the language war begin". It's, "dude, get more experience, go work with accountants, ngos, govs and logistic chains, go work in china, africa and south america, go from a startup to schools to corporate, satisfy the geeks, the artists and the business people, than we'll talk".