How Should I Organize My Python Projects for Better Readability and Scalability?

One great approach is to dive into software engineering literature. The more you know about clean code and design patterns, the better your project structure will be. Checking out the source code of projects like scikit-learn can also be really enlightening; their architecture is top-notch, and mimicking their setup can teach you a lot.

I can't believe I'm saying this, but AI can actually help a lot with this. It can set up project folders and structures in just seconds!

We transitioned from a monolithic Django app due to scalability issues, especially with geoscaling. Now we use microservices based on Flask, and utilizing Google Cloud significantly improved our scaling capabilities. There are challenges with REST APIs, like managing too much cross-talk, but we adapt as needed, and it scales really well. For our Python projects, we maintain good test coverage and regularly update dependencies, though we don’t jump on the latest Python version immediately. Code reviews and planning are key aspects of our workflow.

Keep it simple yet structured! Begin with a clear root folder for each project and place all your source code in a dedicated package directory named after the project. Separate your concerns logically—like having models, views, and tests organized in distinct modules. Utilize `__init__.py` files to define public interfaces, and keep tests next to the code they check. For dependency management, stick to `requirements.txt` or better yet, use `pyproject.toml`. Document major choices in a README file. Generally, a flatter structure is better until you need something more complex! Taking cues from popular open-source projects can also guide your organization.