Django Query Optimization, Marimo, Self-contained Python scripts with uv and more

with some more interesting news, articles, packages and projects

Apr 05, 2025

PyOhio CFP is Open

The CFP is currently Open through April 27th.

There’s currently no standard for creating an immutable record, like a lock file, that defines both direct and indirect dependencies for a virtual environment. This PEP proposes a new file format to ensure reproducible installations in Python environments.

Where to Find the PSF Online

Articles

How to build Hot Module Replacement in Python

Hot-reloading in Python can be slow since the entire server process must be restarted from scratch. The Tach package speeds this up by building a dependency map. Evan Doyle explained how it works with an interesting example.

# Evan Doyle

Django Query Optimization - Defer, Only, and Exclude

We all know that SELECT * FROM table_name is less efficient than selecting specific columns. The same applies to Django ORM, where you can optimize application performance using defer() and only(). If you want to learn how to use them, check out this article by Nik Tomazic.

# Nik Tomazic

Reinventing notebooks as reusable Python programs

If you use Jupyter notebooks often, you’ve probably faced issues like small edits leading to big Git diffs or difficulties reusing code across notebooks. Marimo is a good alternative that tackles these problems. In this article, Akshay, Myles and Dylan explained how Marimo solves these issues.

# Akshay, Myles and Dylan

Self-contained Python scripts with uv

If you wrote a script and need to run it, installing dependencies and setting up a virtual environment can feel like extra effort for a small script. A neat alternative is using # /// script tags (Inline script metadata) in the Python script. Tucker Beck shared a great example in this article, demonstrating how he did it using uv.

# Tucker Beck

Implementation of all RL algorithms in a simpler way

Fareed Khan shared a repository with Jupyter notebooks that provide a clear understanding of RL algorithms like PPO, SAC, A3C and more. Each notebook includes both theory and code to help you understand the concepts.