Panther
polars
Panther | polars | |
---|---|---|
2 | 144 | |
213 | 26,378 | |
- | 3.4% | |
4.3 | 10.0 | |
over 1 year ago | 3 days ago | |
Rust | Rust | |
MIT License | GNU General Public License v3.0 or later |
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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.
Panther
- Panther: A high performance Python technical analysis library written in Rust using PyO3 and rust-numpy. 9x faster than pandas alone!
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Man, I love this language.
I recently started learning rust and decided to make a python library with PyO3 and NDArray as a first project. With the name Panther, the library was supposed to be an implementation of stock technical indicators (EMA, SMA, RSI, Ect). I added a few functions, and decided to do some speed tests with the pandas way of calculating these indicators. I was shocked to see that my code was about 9x faster on average than pandas calculations. I know this is expected when using a low level language like rust in python, but I'm amazed none the less. Especially as someone new to rust, the fact I could get these "advertised" results with rust in python without having to do crazy optimizations is crazy to me. Plus, something about writing low-level code and getting these results in python is very satisfying. The best part though? The process to get these results wasn't even hard! The cargo packages I used had great documentation and the compiler?! Actually helpful! With reference material on errors too! Officially done geeking out about Rust haha but love this language and love this community. Hoping to get more involved with OS stuff. What project is everyone working on? Anything cool?
polars
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Why Python's Integer Division Floors (2010)
This is because 0.1 is in actuality the floating point value value 0.1000000000000000055511151231257827021181583404541015625, and thus 1 divided by it is ever so slightly smaller than 10. Nevertheless, fpround(1 / fpround(1 / 10)) = 10 exactly.
I found out about this recently because in Polars I defined a // b for floats to be (a / b).floor(), which does return 10 for this computation. Since Python's correctly-rounded division is rather expensive, I chose to stick to this (more context: https://github.com/pola-rs/polars/issues/14596#issuecomment-...).
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Polars
https://github.com/pola-rs/polars/releases/tag/py-0.19.0
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Stuff I Learned during Hanukkah of Data 2023
That turned out to be related to pola-rs/polars#11912, and this linked comment provided a deceptively simple solution - use PARSE_DECLTYPES when creating the connection:
- Polars 0.20 Released
- Segunda linguagem
- Polars: Dataframes powered by a multithreaded query engine, written in Rust
- Summing columns in remote Parquet files using DuckDB
- Polars 0.34 is released. (A query engine focussing on DataFrame front ends)
What are some alternatives?
boing - A safe wrapper over libui-ng-sys.
vaex - Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀
lazerpay-rust-sdk - Lazerpay SDK for Rust 🦀
modin - Modin: Scale your Pandas workflows by changing a single line of code
tendie-factory - Tendie-Factory is a work in progress application that seeks to track the stocks mentioned in the wallstreetbets subreddit.
datafusion - Apache DataFusion SQL Query Engine
docs.rs - crates.io documentation generator
DataFrames.jl - In-memory tabular data in Julia
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
datatable - A Python package for manipulating 2-dimensional tabular data structures
ZenithTA - A high performance python technical analysis library written in Rust and the Numpy C API.
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing