You would be surprised at the number of Excel plots in the literature.
Most astrophysicists use IDL or IRAF, I've never seen anyone use Matlab. The benefits are tons of functions specifically tailored for astro data analysis. Python is gathering momentum though, there are libraries like sunpy and astropy. Plenty of IDL fanatics are floored when you can show them just how easy it is to process data with the Numpy stack.
I'm _not_ an astrophysicist, but I work with a lot of them. I use Jupyter notebook daily. It's the perfect balance between REPL python and standalone scripts. My typical workflow is to hack something together in a notebook, which lets you iterate very quickly, then once I'm happy I freeze the code into a module.
Haha, I forgot about IDL. My very first coding project as an undergrad was in IDL, until one day a new grad student showed up to the lab and said, "WTF? Do this in python."
When looking at plots in papers, there are always little giveaways for what program it was made in. Plot has horizontal gridlines, but no vertical gridlines: Excel. Plot is typeset with Arial, size 4: Matlab. Plot looks like it was sent through a fax a few times: IDL.
Undergrad astrophysics student here. The research classes at my institution are all taught in python. I am writing my senior thesis in python, and my advisor and his grad student do most of their work in c++ + python.
IDL is not that bad, assuming that you really understood when it generates an implicit loop and that int is just 16bit. (And double check, I wrote a few scripts that happily read the first 32000 lines of a file multiple times...)
I'm an astrophysics/CS student, and IDL has been on life support at my university for the last 3 years. Everything is either done through Fortran 90 or C/C++ for simulations code, and the data processing is done through Python with some combination of GNUplot/matplotlib/yt, or maybe R.
Oh good God how could I forget about Fortran and GNUplot. F90 though? Luxury. All your code should be in F77 :P . I've worked on a few image reduction pipelines which were all in C++.
The two that I'm familiar with are mathematica and matlab.
Mathematica is a lisp for representing and manipulating mathematical expressions combined with an IDE that knows 2D layout (so you can write expressions like you would on paper) and a massive integrated library of mathematical routines. The "gateway drug" is its ability to symbolically integrate, differentiate, factor, simplify expressions, solve equations, interactively plot without explicitly sampling, etc. Then you discover that all it's "heavy lifting" capabilities are integrated with each other -- i.e. you could use a piece-wise implicit surface to define boundary conditions for a differential equation, solve it with finite element on 20 different tessellation levels, and compare the results using a norm built out of an interpolator and integrator to check for convergence. All in a handful of lines of code where you only have to worry about high-level details rather than dozens of for loops and hundreds of lines of glue. I really don't think there's anything comparable in the open source ecosystem yet, but I'd love to be wrong (yes, I know about SAGE).
Matlab is relatively unremarkable as a language -- it's not a lisp, it deals with matrices of floats not expressions, and its only competitive language feature is the eponymous set of linear algebra primitives that CS-trained language lawyers tend to roll their eyes at but that really do make a difference for the scientists and engineers who use it day-to-day. The killer value proposition, though, is its collection of libraries. They're not symbolic like what you would find in Mathematica but they're usually more extensive and relentlessly practical. Sometimes that means speed, sometimes that means features which cater to your particular obscure workflow, sometimes it means integration, but it always seems to result in a decision along the lines of "I could spend a day munging python libraries A, B, C, and D together, or I could open matlab which already has a package and a GUI for it."
Python, Julia, and R do many things very well. They can beat mathematica/matlab in a number of areas but there are still huge swaths of math/science/engineering where they're just not competitive. That goes double when you take into account legacy code. It's changing slowly, but science is a highly competitive environment which is not keen on rewarding contributions of this sort, so it could be quite a while before they catch up.
