scipy

If you say the installation story for the scipy stack, that is less a matter of Python packaging and more a matter of the scipy stack.

And of course, you can fire up a classifier in SciPy without knowing any of this at all.

Getting SciPy/NumPy/Pandas etc working on a regular Windows machine was a huge pain compared to just installing a single application.

There are a ton of libraries in the python ecosystem that are perfect for data science (numpy, scipy, scikit-learn, etc).

K-d trees are even built in to SciPy, which the author probably already has installed (Scikit-Learn also has an implementation).

The scipy.sparse module has some key stuff implemented in pure Python, and doesn't interoperate properly with the rest of the PyData ecosystem.

There were various big missing pieces I remember (database drivers, then web frameworks, then scipy).

I actually do most of my numerical stuff in the Python/NumPy/SciPy stack, and perhaps that is a better choice than Matlab.

Tons of packages like numpy, scipy, scikit-image, etc.

I think numpy and scipy[1] beg to differ.

I'm being a bit contrarian to give contrast to the endless C/Python/JS answers, but Matlab does have a lot of libraries that aren't yet in the NumPy/SciPy stack.

Even then, down in the guts NumPy and SciPy are Fortran - you kind of need to know that stuff if you want to be a wizard and not a glue sticker.