Data Science at Scale with Python and Dask
- Format: Pocket
- Antall sider: 296
- Språk: Engelsk
- Forlag/Utgiver: SD Books
- EAN: 9781617295607
- Utgivelsesår: 2019
- Bidragsyter: Daniel, Jesse
529,-
Summary
Dask is a native parallel analytics tool designed to integrate seamlessly with the libraries you''re already using, including Pandas, NumPy, and Scikit-Learn. With Dask you can crunch and work with huge datasets, using the tools you already have. And Data Science with Python and Dask is your guide to using Dask for your data projects without changing the way you work!
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. You''ll find registration instructions inside the print book.
About the Technology
An efficient data pipeline means everything for the success of a data science project. Dask is a flexible library for parallel computing in Python that makes it easy to build intuitive workflows for ingesting and analyzing large, distributed datasets. Dask provides dynamic task scheduling and parallel collections that extend the functionality of NumPy, Pandas, and Scikit-learn,
Dask is a native parallel analytics tool designed to integrate seamlessly with the libraries you''re already using, including Pandas, NumPy, and Scikit-Learn. With Dask you can crunch and work with huge datasets, using the tools you already have. And Data Science with Python and Dask is your guide to using Dask for your data projects without changing the way you work!
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. You''ll find registration instructions inside the print book.
About the Technology
An efficient data pipeline means everything for the success of a data science project. Dask is a flexible library for parallel computing in Python that makes it easy to build intuitive workflows for ingesting and analyzing large, distributed datasets. Dask provides dynamic task scheduling and parallel collections that extend the functionality of NumPy, Pandas, and Scikit-learn,