Pygame VS NumPy

pygame never used it, but its popular python library, heard good things about it. i suggest if you are not trying to make resource intensive game, and you want to learn python which is a really valuable skill.

Pygame is a framework of python modules and libraries for writing games. It is modular and great for learning the basics of the Python programming language.

There two parts to sprite class. One is in cython and the there one in python. Would have to go through code to see which one their still using. Every test I have run. Sprite and Group will handle more images. With out frame rate dropping below 60.

To code a game, at first you should be familiar with some basic CS concepts and DSA. All of the languages above have modules and frameworks for games. I would suggest considering to start learning Python with DSA and then pygame (A Python library for creating games).

Pygame uses SDL_ttf under the hood to work with fonts. If you’re just wanting to load your own font you can just use pygame.font.Font() and point it to a ttf file. If you’re wanting to mess with the font object or do something else with the raw font data, you might want to check out https://github.com/pygame/pygame/blob/main/src_c/font.c and https://github.com/libsdl-org/SDL_ttf/blob/main/SDL_ttf.c to get an idea of what pygame and SDL are doing under the surface.

What happened here? https://github.com/pygame/pygame/issues/1476

In NumPy with @, dot() or matmul():

In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.

Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:

numpy: A library for scientific computing in Python

Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.

I was looking at all the big projects like numpy, pytorch, flask, etc.

I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]