rust-numpy
polars
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rust-numpy | polars | |
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10 | 144 | |
988 | 25,298 | |
3.8% | 5.7% | |
6.7 | 10.0 | |
5 days ago | 5 days ago | |
Rust | Rust | |
BSD 2-clause "Simplified" License | MIT License |
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rust-numpy
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Numba: A High Performance Python Compiler
On the contrary, it can use and interface with numpy quite easily: https://github.com/PyO3/rust-numpy
- Carefully exploring Rust as a Python developer
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Hmm
Once I figured out the right tools, it was easy. Its just "maturin new". It automatically converts python floats and strings. Numpy arrays come through as a special Pyarray type, that you need to unwrap, but that's just one builtin function. Using pyo3, maturin and numpy, https://github.com/PyO3/rust-numpy it's fairly easy.
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Man, I love this language.
If I'm understanding this documentation correctly then you may be able to pass the numpy array directly with func(df['col'].to_numpy) which may save some conversion.
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[D] Is Rust stable/mature enough to be used for production ML? Is making Rust-based python wrappers a good choice for performance heavy uses and internal ML dependencies in 2021?
Otherwise, though, Rust is an excellent choice. The many advantages of Rust (great package manager, memory safety, modern language features, ...) are already well documented so I won't repeat them here. Specifically for writing Python libraries, check out PyO3, maturin, and rust-numpy, which allow for seamless integration with the Python scientific computing ecosystem. Dockerizing/packaging is a non-issue, with the aforementioned libraries you can easily publish Rust libraries as pip packages or compile them from source as part of your docker build. We have several successful production deployments of Rust code at OpenAI, and I have personally found it to be a joy to work with.
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Writing Rust libraries for the Python scientific computing ecosystem
Integration with numpy uses the rust-numpy crate: Example of method that accepts numpy arrays as arguments Example of a method that returns a numpy array to Python (this performs a copy, there ought to be a way to avoid it but the current implementation has been plenty fast for my use case so far)
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Feasibility of Using a Python Image Super Resolution Library in My Rust App
This example maybe helpful.
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Julia is the better language for extending Python
Given that it's via pyO3, you could even pass the numpy arrays using https://github.com/PyO3/rust-numpy and get ndarrays at the other side.
Same no copy, slightly more user friendly approach.
Further criticism of the actual approach - even if we didn't do zero copy, there's no preallocation for the vector despite the size being known upfront, and nested vectors are very slow by default.
So you could speed up the entire thing by passing it to ndarray, and then running a single call to sum over the 2D array you'd find at the other end. (https://docs.rs/ndarray/0.15.1/ndarray/struct.ArrayBase.html...)
- PyO3: Rust Bindings for the Python Interpreter
polars
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Polars
- handling of categoricals in polars seemed a little underbaked, though my main complaint, that categories cannot be pre-defined, seems to have been recently addressed: https://github.com/pola-rs/polars/issues/10705
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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:
- Segunda linguagem
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Summing columns in remote Parquet files using DuckDB
Looks like somebody requested it after reading your TIL. https://github.com/pola-rs/polars/issues/12493#issuecomment-...
It will be in the next release. (later today?)
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What are you rewriting in rust?
I am a maintainer for a dataframe interface called polars
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[Crowdsourcing] Is there any code you really wished used named function arguments?
For example with polars, the python library extensively uses named arguments, but in rust we have to use either a builder pattern or macros. The builder pattern tends to be much more verbose than the named argument equivalent. There is currently a draft PR implementing python style named arguments for some of the most common functions.
- Polars cookbook (Jupyter)
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Working with Rust
Seeing a lot of great libraries coming out with python bindings in the data world e.g delta-rs Polars. I see it growing in this space as a C++ alternative
What are some alternatives?
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 🚀
modin - Modin: Scale your Pandas workflows by changing a single line of code
arrow-datafusion - Apache Arrow DataFusion SQL Query Engine
DataFrames.jl - In-memory tabular data in Julia
datatable - A Python package for manipulating 2-dimensional tabular data structures
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
julia - The Julia Programming Language
RustPython - A Python Interpreter written in Rust
db-benchmark - reproducible benchmark of database-like ops
rayon - Rayon: A data parallelism library for Rust
hdf5-rust - HDF5 for Rust
tidypolars - Tidy interface to polars