numexpr
ruff
numexpr | ruff | |
---|---|---|
4 | 97 | |
2,143 | 26,725 | |
0.7% | 4.7% | |
8.2 | 10.0 | |
about 1 month ago | 6 days ago | |
Python | Rust | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
numexpr
-
Making Python 100x faster with less than 100 lines of Rust
You can just slap numexpr on top of it to compile this line on the fly.
https://github.com/pydata/numexpr
- Extending Python with Rust
-
[D] How to avoid CPU bottlenecking in PyTorch - training slowed by augmentations and data loading?
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
-
Selection in pandas using query
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
-
Ruff: The Extensible Python Linter
Ruff is an open-source Python linter created by Astral Sh that stands out for its impressive speed, adaptability, and wide-ranging features.
-
Introducing Tapyr: Create and Deploy Enterprise-Ready PyShiny Dashboards with Ease
Leverage Python Tools: Tapyr takes advantage of Python’s ecosystem tools, including ruff, pytest, and others.
-
Ask HN: High quality Python scripts or small libraries to learn from
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
-
Enhance Your Project Quality with These Top Python Libraries
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.
-
Ask HN: What interesting project ideas you've got but have no time to work on?
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).
-
Rye: A Vision Continued
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.
- An fast Python linter and code formatter, written in Rust
-
Smooth Packaging: Flowing from Source to PyPi with GitLab Pipelines
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.
- Show HN: Marimo – an open-source reactive notebook for Python
-
AST-grep(sg) is a CLI tool for code structural search, lint, and rewriting
I confess I stole the pip recipe from Charlie :D
https://github.com/astral-sh/ruff/blob/main/.github/workflow...
What are some alternatives?
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
black - The uncompromising Python code formatter
pygfx - A python render engine running on wgpu.
mypy - Optional static typing for Python
greptimedb - An open-source, cloud-native, distributed time-series database with PromQL/SQL/Python supported. Available on GreptimeCloud.
pyright - Static Type Checker for Python
jnumpy - Writing Python C extensions in Julia within 5 minutes.
Pylint - It's not just a linter that annoys you!
jsmpeg - MPEG1 Video Decoder in JavaScript
Flake8 - flake8 is a python tool that glues together pycodestyle, pyflakes, mccabe, and third-party plugins to check the style and quality of some python code.
poly-match - Source for the "Making Python 100x faster with less than 100 lines of Rust" blog post
pre-commit - A framework for managing and maintaining multi-language pre-commit hooks.