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Parallel Processing
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Dask Distributed Computing
dask
K-Dense-AI/scientific-agent-skills
361
Scale pandas and NumPy workflows beyond RAM using Dask for parallel and distributed computing. Ideal for processing large multi-file datasets, out-of-core DataFrames, big arrays, and unstructured data, from laptops to clusters.
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Polars: Fast DataFrame Library for Data Science
polars
sickn33/antigravity-awesome-skills
324
Polars is a lightning-fast DataFrame library designed for efficient, memory-efficient data processing in Python and Rust. Built upon Apache Arrow, it supports lazy evaluation and parallel execution, making it ideal for ETL pipelines, large-scale data transformations, and scenarios where standard Pandas performance is insufficient. It excels at handling datasets up to 100GB that fit within available RAM.
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