I often wonder what inspires folks to start from scratch in the face of a gigantic ecosystem like the one that Python brings with it, which will also keep improving.
https://julialang.org/blog/2012/02/why-we-created-julia
https://discourse.julialang.org/t/julia-motivation-why-weren...
I don't expect anyone who has not spent a lot of time with Matlab to "get" it.
Has the language improved dramatically in the last few decade?
I don't think it is likely for a software engineer to understand Matlab's niche and effectiveness. It comes from a different direction.
A ball point pen, a paint brush and a piece of drafting graphite all have their uses.
It also has the drawback that most of its users generate write-only code, so everyone that learns it also learns to write code that way.
https://cheatsheets.quantecon.org/
You can go as far as to say that Python is verbose in many cases, and non-intuitive in others (A @ B?).
That said, you would never want to write a webapp in MATLAB, so as people expand from "math scripting" to "programming" they run into MATLAB issues which have absolutely no good solution. This is where the Python pickup comes from: it's still decent for scientific computing, but it also is an actual programming language. However, Julia keeps the nice syntax of MATLAB in the mathematical domain, keeps the technical computing focus of its community, adds some speed, and also is a general-purpose languages where webservers etc. are being written. In that sense, Julia is a really good fit for people looking to ditch MATLAB.
(A lot of MATLAB's bad syntax was bolted on later. It started as MATrix LAB, and later became a programming language. You can easily see the elegance of its initial design, and the terrible choices when extending it.)
[A, B]
That said, you get most of what you want with libraries. In NumPy you won't write
[aRow + bRow for aRow, bRow in zip(aMat, bMat)]
Also, Python is mostly not used for mathematical work, and programmers tend to assume matrices are scary or only useful for mathematical work, so it has a "boring" syntax more suited for operating on single items at a time, with lots of loops.
https://cheatsheets.quantecon.org/
Python works, but it's far from elegant in this domain.
I think that is a necessary trade-off for Python as a "general-purpose" programming language. I had used MATLAB and IDL quite intensively before I moved on to Python and R. When writing MATLAB, I felt like a scientist and did not have to bother with programming practices, like coding style, unit tests, writing functions instead of scripts, etc. But Python forces me to think like both a scientist and a programmer. (For example, every time you write `np.array([1, 2, 3])` instead of `[1, 2, 3]` it reminds you that array operation is not a free lunch offered by the language; it comes from the NumPy library. Also, it keeps the namespace pure.) I personally like this way better. But I also agree that not everyone likes it. (In my institution, researchers are kinda split half-and-half between Python and MATLAB.)
Also, if I'm just restricted to using Pandas on a laptop or small server instance, then loading in a several gigabyte csv file can really tax the memory.