dasklisted

# Dask ## Overview Dask is a Python library for parallel and distributed computing that enables three critical capabilities: - **Larger-than-memory execution** on single machines for data exceeding available RAM - **Parallel processing** for improved computational speed across multiple cores - **Distributed computation** supporting terabyte-scale datasets across multiple machines Dask scales from laptops (processing ~100 GiB) to clusters (processing ~100 TiB) while maintaining familiar Python APIs. **Current upstream:** dask **2026.3.0** (PyPI, March 2026). Docs: [docs.dask.org](https://docs.dask.org/en/stable/). Since **2025.1.0**, the expression-based DataFrame API with query planning is the only implementation — do not install `dask-expr` separately or set `dataframe.query-planning: False`. ## Quick Start ### Installation ```bash uv pip install "dask>=2025.1" ``` For a typical pandas/NumPy workflow with the distributed scheduler and dashboard: ```bash uv pip install "dask[complete]" ``` Remote object storage (S3, GCS, Azure): ```bash uv pip install s3fs # s3:// paths uv pip install gcsfs # gs:// paths ``` Requires **Python 3.10+** (3.9 support dropped in 2024.12). DataFrame I/O requires **PyArrow 16+** (as of dask 2026.1.2). ## When to Use This Skill This skill should be used when: - Process datasets that exceed available RAM - Scale pandas or NumPy operations to larger datasets - Parallelize computations for performance improvements - Process multiple f