dask
Dask - Scalable Parallel Computing
Dask provides high-level collections (Arrays, DataFrames, Bags) that mimic the APIs of NumPy and pandas but operate in parallel on data sets that are larger than memory.
When to Use
- Processing datasets that don't fit in RAM (Out-of-core computing).
- Speeding up computations by using all available CPU cores.
- Parallelizing custom Python functions or complex workflows (dask.delayed).
- Scaling machine learning pipelines to large clusters.
- Handling large-scale arrays in physics, climate science, or imaging.
- Analyzing massive log files or unstructured data (dask.bag).
Reference Documentation
Official docs: https://docs.dask.org/
Dask Examples: https://examples.dask.org/
Search patterns: dask.dataframe, dask.array, dask.delayed, client.compute, dask.distributed
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