How do I optimize Dask memory usage?

The skill provides guidance on choosing appropriate chunk sizes (target ~100MB), avoiding local data loading before handing to Dask, and utilizing the dashboard to identify memory leaks.

When should I use Dask instead of standard pandas?

You should switch to Dask when your dataset exceeds your available RAM or when pandas operations become a performance bottleneck that requires multi-core parallelization.

Does this skill support distributed clusters?

Yes, it provides implementation patterns for both local multi-core execution and scaling out to distributed clusters using Dask's distributed scheduler.

What are the best file formats to use with Dask?

While Dask supports CSV and JSON, it performs optimally with parallel-friendly, columnar formats like Parquet for tabular data and Zarr or HDF5 for array data.

Dask Parallel Computing

Name: Dask Parallel Computing
Author: pur3v4d3r

bypur3v4d3r

•

データサイエンスとML

Scales Python, pandas, and NumPy workflows across multiple cores or clusters for larger-than-memory datasets.

概要

This skill enables Claude to implement parallel and distributed computing patterns using Dask. It provides specific guidance for scaling data science workloads, allowing users to process datasets that exceed available RAM by using parallel DataFrames, Arrays, and Bags. Whether you are building complex ETL pipelines, performing heavy scientific computations on multi-dimensional arrays, or parallelizing custom Python workflows with Futures, this skill ensures best practices for memory management, task scheduling, and performance optimization.

主な機能

Task-based parallelization using Dask Futures for custom, dynamic workflows
1 GitHub stars
Distributed Arrays for large-scale NumPy computations and linear algebra
Functional processing of unstructured data via Dask Bags for logs and JSON
Parallel DataFrames for scaling pandas-like operations to massive datasets
Optimization strategies for thread, process, and distributed scheduling

ユースケース

Accelerating scientific simulations and array manipulations via parallel chunking
Processing multi-gigabyte CSV or Parquet datasets that don't fit in local RAM
Building high-performance ETL pipelines for cleaning and transforming massive log files

概要

主な機能

Task-based parallelization using Dask Futures for custom, dynamic workflows
1 GitHub stars
Distributed Arrays for large-scale NumPy computations and linear algebra
Functional processing of unstructured data via Dask Bags for logs and JSON
Parallel DataFrames for scaling pandas-like operations to massive datasets
Optimization strategies for thread, process, and distributed scheduling

ユースケース

Accelerating scientific simulations and array manipulations via parallel chunking
Processing multi-gigabyte CSV or Parquet datasets that don't fit in local RAM
Building high-performance ETL pipelines for cleaning and transforming massive log files