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发布于 : Apr 24, 2019
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Apr 2019
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Data scientists and engineers often use libraries such as pandas to perform ad hoc data analysis. Although expressive and powerful, these libraries have one critical limitation: they only work on a single CPU and don't provide horizontal scalability for large data sets. Dask, however, includes a lightweight, high-performance scheduler that can scale from a laptop to a cluster of machines. And because it works with NumPy, pandas and Scikit-learn, Dask looks promising for further assessment.
