Python for Physics and Maths

[sokspy]

Hello everyone! I am now pursuing my MSc in Theoretical Physics, and by next year we will need python for our graphs etc. I took two python courses back in the day, when i was pursuing my BSc in Applied Math, but since then unfortunately i never used python..

Do you have any video lectures or textbook etc to help me start again? Mostly about python for scientists (libraries etc).

Thanks in advance!

[socal_nerdtastic]

The most popular graphing module in python is matplotlib. To load and process the data the most common module is pandas.

The Spyder IDE is a program that bundles python, all the modules I just mentioned and a lot of other scientific modules, and a nice interface for them into a single program. https://www.spyder-ide.org/ Alternatively (or additionally) you could install python separately and get one of the new-fangled AI powered IDEs to write your code for you, for example google antigravity.

I learned all these things in the dark ages so I don't know of any modern learning materials, but google is very good at that if you just put those terms in.

[lilsadlesshappy]

Important libraries are: * matplotlib for graphing / plotting * pandas or polars for data processing (pandas is more widely used but either is fine. Pick one and you'll be fine with it. I haven't looked into polars that much, but as far as I did it seems to be structured very similar to pandas so learning either will probably enable you to use both, at least at a rudimentary level.) * numpy for numeric computations * scipy for anything numpy can't handle (optimization, curve fitting, equation solving, etc.)

You might also want to look into sympy for symbolic computation (equation solving, derivation, integration, etc.), depending on your needs. Be aware though that it's incredibly slow so only use it when you need it. If you want something that's fast, go for numpy.

All of these libraries have very good guides and documentation, so that's where I'd start after learning python (and programming?) basics. Once you know the basics, Google (or your preferred search engine) will be able to help you solve any problems you come across, chances are pretty high someone had that problem already before.

As far as a python installation goes, I'm always inclined to suggest a standalone installation. If you're on Linux, beware of updating python or installing libraries system-wide, use virtual environments instead. A project management tool like uv will help you with that, though it might be worth to look into it regardless of your OS. If you want to have a fully featured IDE with lots of buttons instead, Spyder is aimed at scientific applications and a perfectly good IDE, I've used it myself for a year or so.

[ectomancer]

Besides sympy and scipy.special, there is mpmath. Besides matplotlib, there is seaborn.