numexpr VS ruff

You can just slap numexpr on top of it to compile this line on the fly.

https://github.com/pydata/numexpr

Are you doing any costly chained NumPy operations in your preprocessing? E.g. max(abs(large_ary)), this produces multiple copies of your data, https://github.com/pydata/numexpr can greatly reduce time spent with such operations

What is not entirely obvious here is that under the hood you can install a nice library called numexpr (docs, src) that exists to make calculations with large NumPy (and pandas) objects potentially much faster. When you use query or eval, this expression is passed into numexpr and optimized using its bag of tricks. Expected performance improvement can be between .95x and up to 20x, with average performance around 3-4x for typical use cases. You can read details in the docs, but essentially numexpr takes vectorized operations and makes them work in chunks that optimize for cache and CPU branch prediction. If your arrays are really large, your cache will not be hit as often. If you break your large arrays into very small pieces, your CPU won’t be as efficient.

Ruff is an open-source Python linter created by Astral Sh that stands out for its impressive speed, adaptability, and wide-ranging features.

Leverage Python Tools: Tapyr takes advantage of Python’s ecosystem tools, including ruff, pytest, and others.

I think I mention this all the time when this comes up, but I learned the most 'best practices' through using ruff.

https://docs.astral.sh/ruff/

I just installed and enabled all the rules by setting

Ruff is a Python linter that helps to identify and remove code smells. Over 700 built-in rules: Ruff includes native re-implementations of popular Flake8 plugins, like flake8-bugbear. And also built-in caching to avoid re-analyzing unchanged files.

Because the Python's "ast" modules is too slow, and lacks proper "format" feature (it has unparse but it removes comments, and forgets the current style completely). I use "ruff" a lot (https://github.com/astral-sh/ruff) which is in Rust. But I want to be able to implement fast custom linters in Go (linters that ruff / fixit lack, and Python linters lack or are too slow).

I think it’s interesting that rye uses ruff (https://github.com/astral-sh/ruff) for linting and formatting. That’s the right call, and it’s also correct to bundle that in for an integrated dev experience.

I had to guess, that’s the path that the Astral team would take as well - expand ruff’s capabilities so it can do everything a Python developer needs. So the vision that Armin is describing here might be achieved by ruff eventually. They’d have an advantage that they’re not a single person maintenance team, but the disadvantage of needing to show a return to their investors.

Adding more weight to ease of setup and configurability, the choice came down on flake8. It is easy to integrate, since its also available through pip and let’s you configure which standards you want to omit by simply stating them as a list via the --ignore switch. Moving to ruff appears quite smooth, so future updates may do so.

I confess I stole the pip recipe from Charlie :D

https://github.com/astral-sh/ruff/blob/main/.github/workflow...