My PR to add PEP 723 (inline script metadata) support to `pip` has caught the attention of some of the maintainers, so I've dusted it off.
https://github.com/pypa/pip/pull/13052
Probably too late to get it into the October release, but I'm glad that there weren't any knee-jerk reactions about the basic approach being wrong (or rejection of the feature)
Please boost 🙏 💚 Follow up to the above, and a request to the amazing #Python community, if you want to critique my use of release-please , I'd love your feedback! https://github.com/b-long/opentdf-python-sdk/blob/main/.github/workflows/release-please.yaml
🎟️ In-person & online tickets: https://ti.to/defna/djangocon-us-2025
#DjangoCon #Python #Django
Been working on a huge PR to merge Flask's request and app context into one. No change from a user perspective, but way simpler internally. It may uncover some test patterns that were already problematic, but weren't from our docs. Would be great if a few people could test their app with this branch! I'll probably merge in a week if nothing comes up. https://github.com/pallets/flask/pull/5812 #Python #Flask
Been tinkering with #uv a bit more today. Really enjoying it.
🎟️ In-person & online tickets: https://ti.to/defna/djangocon-us-2025
#DjangoCon #Python #Django
🚀 I just released a Python package that can make your coding workflow a lot smoother!
With omga-cli, you can:
Run quick tests on your files right from the command line
Ask coding questions and get AI-powered answers
Generate new code snippets
Run tests and even auto-fix your code
It’s like having a coding assistant directly in your terminal. ⚡
📦 Install from PyPI: 👉 https://pypi.org/project/omga-cli
🌐 Project Page: 👉 https://ispoori.github.io/omga-cli
💻 GitHub Repo: 👉 https://github.com/ispoori/omga-cli
Learning operations and doing math in python is bringing back my dying synapses of certain math knowledge from high-school...
When a tuple is passed to startswith, it will check whether any of the strings within the tuple are prefixes.
Read more 👉 https://trey.io/dley4p
Ray and Dask are Python libraries that help data scientists work faster with parallel processing. Dask excels at scalable data analysis with familiar pandas-like syntax, perfect for large datasets and ETL tasks. Ray shines in distributed ML training, hyperparameter tuning and model serving with built-in libraries like Ray Tune and Ray Serve. Choose Dask for data processing; Ray for ML pipelines. #DataScience #Python #MachineLearning #BigData #Ray #Dask #DataProcessing #ML https://www.kdnuggets.com/ray-or-dask-a-practical-guide-for-data-scientists
Learn to write cleaner Python code with these 10 practical techniques: use dataclasses instead of dicts, implement enums for fixed choices, leverage pathlib over os.path, write pure functions, add proper docstrings, and handle exceptions specifically. Transform from "it works" to truly maintainable code. #Python #CleanCode #Programming #SoftwareDevelopment #CodingTips #PythonTips #Developer https://www.kdnuggets.com/stop-writing-messy-python-a-clean-code-crash-course
Polars offers a blazing fast alternative to Pandas with 3-22x performance improvements on common operations. Built in Rust with automatic parallelisation, it uses familiar DataFrame syntax but thinks in expressions rather than columns. Migration can be gradual - start with data loading for immediate wins, then adopt lazy evaluation for full pipeline optimisation. #Polars #Pandas #DataScience #Python #Performance #DataAnalysis #MachineLearning #BigData https://www.kdnuggets.com/polars-for-pandas-users-a-blazing-fast-dataframe-alternative