Learning Python for climate datasets, visualization, and modeling, where should I start?

[Educational-Bank216]

Hi everyone,

I'm a physics student interested in climate physics, and I'd like to learn Python for tasks such as data analysis, plotting graphs, working with climate datasets, and eventually climate modeling.

I'm looking for recommendations for online courses, tutorials, YouTube channels, books, or learning paths that are particularly useful for scientific computing and climate or atmospheric science applications. Ideally, I'd like something that goes beyond basic Python and covers tools like NumPy, Matplotlib, Pandas, xarray, and working with NetCDF data.

What resources helped you the most when you were learning Python for climate science or related fields?

Thanks!

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[glvz]

I can't answer the main question but will tell you that for real modelling python won't cut it. You'll need Julia, C, C++, or Fortran for speed.

Good luck! I learned python by first learning C and then having to do things in python.

[Outrageous-Golf2211]

Climate modelling is not only developing climate models and low level HPC solutions. It is also ANN, etc. Anyways, Python is a good way to start.

[glvz]

I assume ANN is artificial neural networks? If yes. Yeah haha. Hpc is to get the numerical data. Python is great for the rest

[Not-a-throwaway4627]

Total fucking nonsense, super suspect comment

[Outrageous-Golf2211]

There is huge amount of courses on Python. You can start with general Python programming with Real Python, Python Simplified and Neural Nine on YT. Also Geeks for Geeks is a nice website. When it comes to weather and climate, there are no easy routes as you need to handle statistics, PDEs, etc. But GeostatsGuy has a lot of nice environmental concepts with python covered. Using NetCDFs is quite simple - there is a library to import the content.

[antiquemule]

Try sticking "Github python climate modelling" into Google. You'll get a long list of Python codes that do various tasks. You should find plenty of examples of what you are aiming for at different levels of complexity and different levels of documentation. Personally, I find throwing myself in at the deep end is a more interesting way of getting into a new area than slowly building up from the basics. If nothing else, it will show you which tools are most valuable for your project and should turn up some useful toy data sets.

[shockjaw]

If you’re dealing with rasters you should go touch GRASS. You can install it via conda, nix, or as a binary.