Dask
A comprehensive skill catalog for AI agents
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Dask parallel computing library. Use for scaling pandas.
SKILL.md
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Dask
Dask scales Python. It looks like Pandas/NumPy but runs on clusters. 2025 updates focus on High Performance Shuffle and GPU integration.
When to Use
- Big Data: When data > RAM but < BigQuery scale.
- Cluster Computing: Utilizing a Kubernetes cluster for Python functions.
- Xarray: Backend for geospatial data.
Core Concepts
Collections
dask.dataframe, dask.array, dask.bag.
Scheduler
Decides where to run tasks (Local Threads, Processes, or Distributed Cluster).
Dashboard
Real-time visualization of task progress (port 8787).
Best Practices (2025)
Do:
- Use
dask-expr: The new query optimization engine for Dask DataFrames. - Use Parquet: CSVs are distinctively slow in distributed settings.
Don't:
- Don't use for small data: The overhead of the scheduler makes it slower than Pandas for <1GB.