gsir-te
vaex
| gsir-te | vaex | |
|---|---|---|
| 1 | 7 | |
| 230 | 8,170 | |
| - | 0.1% | |
| 0.0 | 5.4 | |
| about 5 years ago | about 1 month ago | |
| R | Python | |
| GNU General Public License v3.0 only | MIT License |
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gsir-te
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I wrote one of the fastest DataFrame libraries
I dropped dplyr in favor of data.table and never looked back.
https://github.com/eddelbuettel/gsir-te
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 !
What are some alternatives?
data.table - R's data.table package extends data.frame:
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
rust-dataframe - A Rust DataFrame implementation, built on Apache Arrow
minimal-pandas-api-for-polars - pip install minimal-pandas-api-for-polars
visidata - A terminal spreadsheet multitool for discovering and arranging data
umap - Uniform Manifold Approximation and Projection
db-benchmark - reproducible benchmark of database-like ops
dtplyr - Data table backend for dplyr
TypedTables.jl - Simple, fast, column-based storage for data analysis in Julia
explorer - Series (one-dimensional) and dataframes (two-dimensional) for fast and elegant data exploration in Elixir