rust-dataframe
A Rust DataFrame implementation, built on Apache Arrow (by nevi-me)
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 🚀 (by vaexio)
| rust-dataframe | vaex | |
|---|---|---|
| 1 | 7 | |
| 287 | 8,171 | |
| - | 0.1% | |
| 0.8 | 5.4 | |
| over 3 years ago | 28 days ago | |
| Rust | Python | |
| Apache License 2.0 | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
rust-dataframe
Posts with mentions or reviews of rust-dataframe.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-03-13.
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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.
vaex
Posts with mentions or reviews of vaex.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-06-03.
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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 !
What are some alternatives?
When comparing rust-dataframe and vaex you can also consider the following projects:
data.table - R's data.table package extends data.frame:
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust