Learn how to seize the power of Python’s dataclasses, editable installs, virtual environments, and new free-threaded build. Plus other great reads for Python developers.
Some Python features are so useful they feel almost illegal, or like they’re granting you superpowers. We have dataclasses to take the hassle out of writing classes, editable installs to speed up package development, virtual environments to simplify dependency management, and a free-threaded Python build to improve parallelism. Want to seize the power of all of these great features? Just follow the links below.
Top picks for Python readers on InfoWorld
How to use Python dataclasses
If you’re sick of writing the same boilerplate time and again for your Python classes, try dataclasses. They boil down that boilerplate into a few basic instructions.
How to use editable installs for Python packages
Sharing locally installed packages across multiple projects is not only easy — it also gives you a way to update those shared packages without having to reinstall them. Editable installs make this possible.
How to use virtual environments in Python
It’s perhaps the ultimate Python superpower. Virtual environments give each Python program its own little universe of packages and settings. And they have never been easier to work with than they are now.
4 tips for getting started with free-threaded Python
Exponentially faster Python programs are now within reach thanks to the new free-threaded build. Just make sure that your programs can be made faster with threading, and that you learn the right level of abstraction for using threads.
More good reads and Python updates elsewhere
Pyrefly 1.3.0 has arrived
The wicked-fast type checker for Python now includes better type inference, along with support for frameworks like Django, SQLAlchemy, and PyTorch and improved support for NumPy and JAX — all of which have historically lacked good support in Python type checkers.
Teaching NumPy’s ufuncs new tricks
NumPy’s universal functions, or “ufuncs”, provide vectorized operations across arrays at high speed. This blog post provides a brief introduction to ufuncs and unpacks how some new capabilities and performance improvements in ufuncs were implemented.
Working to make Python lazy
The new lazy imports feature in Python 3.15 speeds up programs that include imports that aren’t used immediately. This blog post by Henry Schreiner explores just how much of a speedup you can expect with some important PyPI packages.
Official documentation of the time complexity of Python objects
The official Python documentation now includes a nicely detailed page covering the time complexity of Python’s built-in types (lists, dicts, tuples, etc.) and their various operations (append, insert, delete, etc.). It’s a handy resource for beginners and old hands alike who want to choose the most efficient tool for a given job.
