Apache Arrow
ruff
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Apache Arrow | ruff | |
---|---|---|
75 | 95 | |
13,523 | 26,504 | |
2.5% | 8.1% | |
10.0 | 10.0 | |
3 days ago | 6 days ago | |
C++ | Rust | |
Apache License 2.0 | 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.
Apache Arrow
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How moving from Pandas to Polars made me write better code without writing better code
In comes Polars: a brand new dataframe library, or how the author Ritchie Vink describes it... a query engine with a dataframe frontend. Polars is built on top of the Arrow memory format and is written in Rust, which is a modern performant and memory-safe systems programming language similar to C/C++.
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From slow to SIMD: A Go optimization story
I learned yesterday about GoLang's assembler https://go.dev/doc/asm - after browsing how arrow is implemented for different languages (my experience is mainly C/C++) - https://github.com/apache/arrow/tree/main/go/arrow/math - there are bunch of .S ("asm" files) and I'm still not able to comprehend how these work exactly (I guess it'll take more reading) - it seems very peculiar.
The last time I've used inlined assembly was back in Turbo/Borland Pascal, then bit in Visual Studio (32-bit), until they got disabled. Then did very little gcc with their more strict specification (while the former you had to know how the ABI worked, the latter too - but it was specced out).
Anyway - I wasn't expecting to find this in "Go" :) But I guess you can always start with .go code then produce assembly (-S) then optimize it, or find/hire someone to do it.
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Time Series Analysis with Polars
One is related to the heritage of being built around the NumPy library, which is great for processing numerical data, but becomes an issue as soon as the data is anything else. Pandas 2.0 has started to bring in Arrow, but it's not yet the standard (you have to opt-in and according to the developers it's going to stay that way for the foreseeable future). Also, pandas's Arrow-based features are not yet entirely on par with its NumPy-based features. Polars was built around Arrow from the get go. This makes it very powerful when it comes to exchanging data with other languages and reducing the number of in-memory copying operations, thus leading to better performance.
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TXR Lisp
IMO a good first step would be to use the txr FFI to write a library for Apache arrow: https://arrow.apache.org/
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3D desktop Game Engine scriptable in Python
https://www.reddit.com/r/O3DE/comments/rdvxhx/why_python/ :
> Python is used for scripting the editor only, not in-game behaviors.
> For implementing entity behaviors the only out of box ways are C++, ScriptCanvas (visual scripting) or Lua. Python is currently not available for implementing game logic.
C++, Lua, and Python all implement CFFI (C Foreign Function Interface) for remote function and method calls.
"Using CFFI for embedding" https://cffi.readthedocs.io/en/latest/embedding.html :
> You can use CFFI to generate C code which exports the API of your choice to any C application that wants to link with this C code. This API, which you define yourself, ends up as the API of a .so/.dll/.dylib library—or you can statically link it within a larger application.
Apache Arrow already supports C, C++, Python, Rust, Go and has C GLib support Lua:
https://github.com/apache/arrow/tree/main/c_glib/example/lua :
> Arrow Lua example: All example codes use LGI to use Arrow GLib based bindings
pyarrow.from_numpy_dtype:
- Show HN: Udsv.js – A faster CSV parser in 5KB (min)
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Interacting with Amazon S3 using AWS Data Wrangler (awswrangler) SDK for Pandas: A Comprehensive Guide
AWS Data Wrangler is a Python library that simplifies the process of interacting with various AWS services, built on top of some useful data tools and open-source projects such as Pandas, Apache Arrow and Boto3. It offers streamlined functions to connect to, retrieve, transform, and load data from AWS services, with a strong focus on Amazon S3.
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Cap'n Proto 1.0
Worker should really adopt Apache Arrow, which has a much bigger ecosystem.
https://github.com/apache/arrow
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C++ Jobs - Q3 2023
Apache Arrow
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Wheel fails for pyarrow installation
I am aware of the fact that there are other posts about this issue but none of the ideas to solve it worked for me or sometimes none were found. The issue was discussed in the wheel git hub last December and seems to be solved but then it seems like I'm installing the wrong version? I simply used pip3 install pyarrow, is that wrong?
ruff
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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
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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.
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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).
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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
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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
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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...
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Embracing Modern Python for Web Development
Ruff is an emerging tool in the Python ecosystem that describes itself as "an extremely fast Python linter and code formatter, written in Rust".
- Ruff: An fast Python linter and code formatter, written in Rust
What are some alternatives?
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
black - The uncompromising Python code formatter
h5py - HDF5 for Python -- The h5py package is a Pythonic interface to the HDF5 binary data format.
mypy - Optional static typing for Python
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
pyright - Static Type Checker for Python
FlatBuffers - FlatBuffers: Memory Efficient Serialization Library
Pylint - It's not just a linter that annoys you!
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
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.
ClickHouse - ClickHouse® is a free analytics DBMS for big data
pre-commit - A framework for managing and maintaining multi-language pre-commit hooks.