Как использовать Garbage Collection для управления потреблением памяти

Django: Clean up unused code with Vulture

Introduction to Autoencoders

Python⇒Speed: The easiest way to speed up Python with Rust

Real Python: Profiling in Python: How to Find Performance Bottlenecks

Unlocking Performance: A Guide to Async Support in Django

This guide provides valuable insights and practical tips for new and experienced developers to leverage async programming in Django for non-blocking operations, improved scalability, and enhanced responsiveness.

Compiling typed Python

CLI Tools Hidden in the Python Standard Library

There are several modules in Python that are directly callable from the command line, including the ability to gzip and pretty print JSON. This article introduces you to what is available and how Simon discovered them.

Django: Flush out test flakiness by randomly ordering QuerySets

Netflix Movies and Series Recommendation Systems

Python⇒Speed: When NumPy is too slow

If you’re doing numeric calculations, NumPy is a lot faster than than plain Python—but sometimes that’s not enough. What should you do when your NumPy-based code is too slow? Your first thought might be parallelism, but that should probably be the last thing you consider. There are many speedups you can do before parallelism becomes helpful, from algorithmic improvements to working around NumPy’s architectural limitations. Let’s see why NumPy can be slow, and then some solutions to help speed up your code even more.

Bullet Proofing Django Models

Рекомендаци по составлению моделей в DJango

Stack Abuse: Guide to Interfaces in Python

How to Flatten a List of Lists in Python

Или посмотреть на funcy библиотеку

Building Search DSLs with Django

What happens when there are too many fields for a UI to search on? Search DSLs can give a user more granular access to searching without exposing an overly complicated interface.

Django Views: The Right Way

An opinionated guide on how to write views in Django by one of the core Django devs.

Dependency Tracking for Python Function Calls

Tracking the code and data accessed by a function call can be used to draw dependency graphs, for debugging and profiling, and for cache invalidation. This article shows you a variety ways of doing it, as well as some initial ideas that don’t work very well.

Faster Python 3.13 Plan

This brief outline highlights the plan for the faster CPython project for the 3.13 release. Includes PEP 669, PEP 554, improved memory management, and more.

Python's raise: Effectively Raising Exceptions in Your Code

In this tutorial, you'll learn how to raise exceptions in Python, which will improve your ability to efficiently handle errors and exceptional situations in your code. This way, you'll write more reliable, robust, and maintainable code.

Pro-Tip – pytest fixtures are magic!