Python Hub Weekly Digest for 2024-10-20
This week in Python, Pyinstrument v5.0 was released, offering flamegraphs for Python. Otter Wiki, a minimalistic wiki powered by Python, Markdown, and git, was also introduced. The Python 3.13.0 major release brought several new features, including an improved interactive interpreter and a preliminary JIT. In articles, the use of Python's ctypes for SystemV shared memory functions was discussed, and UV 0.3.0, a Python packaging tool, was highlighted. New projects included gptme, an agent for your terminal, and NanoCube, for fast OLAP-style point queries on Pandas DataFrames. Have a great week and happy coding!
💖 Most Popular
Pyinstrument v5.0 - flamegraphs for Python!
Otter Wiki: A minimalistic wiki powered by Python, Markdown and git
Python and SysV shared memory
The article explains how to use Python's ctypes to wrap SystemV shared memory functions (like shmat, shmget) for interprocess communication on systems restricted to Python 3.7. The author demonstrates creating, reading, writing, and destroying shared memory segments through Python, noting that while this approach isn't needed in Python 3.8+ due to built-in abstractions, it's useful in re...
CSnakes
A tool for embedding Python into .NET projects.
Niquests
Niquests is a simple, yet elegant, HTTP library. It is a drop-in replacement for Requests, which is under feature freeze.
📖 Articles
Python 3.13.0
The newest major release of Python introduces several new features including an improved interactive interpreter, an experimental free-threaded build mode, and a preliminary JIT, along with various optimizations and changes to the standard library.
Python Hub Weekly Digest for 2024-10-13
Programming and poetry (not Python's tool)
uv IS the Future of Python Packaging
The video discusses the recent release of UV 0.3.0, a Python packaging tool that aims to streamline the development workflow by integrating features that allow it to serve as a comprehensive solution for managing Python projects. The presenter highlights its speed, ease of use, and potential to replace existing tools, while also addressing current limitations and areas for improvement in...
Python 3.13 and the Latest Trends: A Developer’s Guide to 2025
Learn about the exciting new features in Python 3.13. Get insider insights into the latest updates and learn about the plans for Python 3.14.
Django + Postgres: The Hunt for Long Running Queries
Using django-pgactivity for application-level monitoring of database queries.
TypedDicts are better than you think
This post explains how Python’s TypedDict can enhance code clarity and maintainability by enabling more precise type annotations in dictionaries. It discusses how TypedDict ensures type safety and helps with early error detection in dynamic programming environments.
Django dashboard
give your django dashboard a new modern skin with new features,
Mobile responsive and customizable on top of tailwindcss
Scaling AI-Based Data Processing with Hugging Face + Dask
The article demonstrates how to scale AI-based data processing using Hugging Face and Dask, progressing from processing 100 rows locally with pandas to handling 211 million rows across multiple GPUs in the cloud. It showcases the use of Dask for distributed computing, enabling efficient data loading, preprocessing of large datasets, and parallel model inference, with a practical example ...
In the Making of Python Fitter and Faster
How Python's recent performance improvements work under the hood.
Python client for the $20 Colmi R02 smart ring
Switching from virtualenvwrapper to direnv, Starship, and uv
Earlier this week I considered whether I should finally switch away from virtualenvwrapper to using ...
Django: Introducing Djade, a template formatter
Happy DjangoCon US 2024 to you.
Whilst I am not there, I have adopted the spirit of the season and ...
⚙️ Projects
gptme
Your agent in your terminal, equipped with local tools: writes code, uses the terminal, browses the web, vision.
NanoCube
Lightning fast OLAP-style point queries on Pandas DataFrames.
Splink
Fast, accurate and scalable probabilistic data linkage with support for multiple SQL backends.
👾 Reddits
I Understand Machine Learning with Numpy and PyTorch Better Since I Started Focusing on the Basics
Project by Ruslan Keba. Since 2012. Powered by Python. Made in 🇺🇦Ukraine.