Faster Python is now within reach, thanks to its shiny new JIT. But we also have fast dataframes thanks to Pandas, and three slick new GUI interfaces for SQLite. And then there’s Zed, the new Rust-powered contender to VS Code’s throne. It’s a busy time in the world of Python, and we’ve got all the scoops.

Top picks for Python readers on InfoWorld

How to use Pandas for data analysis in Python
For working with data tables in Python, look no further than Pandas, the fast and universal dataframe solution. With version 3 on the way, now’s the time to learn what to expect.

Get started with Python’s new native JIT
Python’s most potentially game-changing new feature (of late) is the JIT compiler. It promises to add a performance boost to code with no extra work on your part, but do the promises add up? Let’s check the benchmarks and find out.

Get a GUI for SQLite—actually, get three!
Quit poking through SQLite databases at the command line or via clunky scripts. We found three GUI interfaces that provide desktop, web, and VS Code add-ons so you can explore your data in style.

Zed: The Rust-powered challenger to VS Code
Could a platform-native IDE and editor written in Rust be the next big challenger to Visual Studio Code? This video lets you get a feel for hands-on work with Zed.

More good reads and Python updates elsewhere

PyCrucible 0.4 released
One of the best new solutions for redistributing Python apps painlessly now runs faster and can deliver much smaller downloads.

On the future of funding the Python Software Foundation
Deb Nicholson, executive director of the Python Software Foundation, talks about what it will take to keep the PSF funded in the years to come, including how software nonprofits can remain solvent in tough times.

How we made Python’s packaging library 3x faster
This is a great case study for how, among other things, Python’s new statistical profiler (in 3.15) can yield big wins by revealing unexpected bottlenecks. There’s also a nice sidebar on how to take advantage of post-3.11 regex features for performance.

Slightly off-topic: Extracting books from production language models
It’s easier than you think to trick an LLM into revealing most of a text it’s been trained on. And this trick even works with state-of-the-art production LLMs that theoretically guard against it.