vaex
rust-dataframe
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| vaex | rust-dataframe | |
|---|---|---|
| 7 | 1 | |
| 8,173 | 287 | |
| 0.4% | - | |
| 6.0 | 0.8 | |
| 15 days ago | over 3 years ago | |
| Python | Rust | |
| MIT License | Apache License 2.0 |
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.
vaex
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preprocessing millions of records - how to speed up the processing
Try vaex, vaex, using lazy evaluation and parallel calculations, you should be fine.
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High performance (for the consumer) time series storage?
I'd recommend QuestDB. Worked with it multiple times for different algorithmic trading needs and it didn't disappoint. If you want to load data fast, I'd recommend this Python library.
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Python Pandas vs Dask for csv file reading
How about vaex?
- Polars: Lightning-fast DataFrame library for Rust and Python
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For stocks, what historical data do you store and how do you store it?
You might find vaex (https://github.com/vaexio/vaex) interesting if you work with HDF5.
- I wrote one of the fastest DataFrame libraries
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A Hybrid Apache Arrow/Numpy DataFrame with Vaex Version 4.0
My guess is that should be possible, feel free to hop onto https://github.com/vaexio/vaex/discussions !
rust-dataframe
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I wrote one of the fastest DataFrame libraries
>Rust DataFrame implementation, built on Apache Arrow
https://github.com/nevi-me/rust-dataframe
A bit less mature/feature-complete than polars last time I looked. Does not seem to do anything with on-disk spillover from what I can see. But if you wanted to use Arrow to do that, nevi-me's crate may be a good place to start.
What are some alternatives?
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
data.table - R's data.table package extends data.frame:
minimal-pandas-api-for-polars - pip install minimal-pandas-api-for-polars
TypedTables.jl - Simple, fast, column-based storage for data analysis in Julia
visidata - A terminal spreadsheet multitool for discovering and arranging data
gsir-te - Getting Started in R -- Tinyverse Edition
umap - Uniform Manifold Approximation and Projection
lance - Modern columnar data format for ML and LLMs implemented in Rust. Convert from parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, with more integrations coming..
db-benchmark - reproducible benchmark of database-like ops
ballista - Distributed compute platform implemented in Rust, and powered by Apache Arrow.