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Data science with Python and Dask

Author: Jesse C Daniel
Publisher: Shelter Island, NY : Manning Publications Co., [2019] ©2019
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
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, enabling users to scale their code  Read more...
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Document Type: Book
All Authors / Contributors: Jesse C Daniel
ISBN: 9781617295607 1617295604
OCLC Number: 1110436512
Notes: Includes index.
Description: xvii, 276 pages : illustrations ; 24 cm
Contents: Part 1. The building blocks of scalable computing. Why scalable computing matters --
Introducing Dask --
Part 2. Working with structured data using Dask DataFrames. Introducing Dask DataFrames --
Loading data into DataFrames --
Cleaning and transforming DataFrames --
Summarizing and analyzing DataFrames --
Visualizing DataFrames with Seaborn --
Visualizing location data with Datashader --
Part 3. Extending and deploying Dask. Working with bags and arrays --
Machine learning with Dask-ML --
Scaling and deploying Dask.
Responsibility: Jesse C. Daniel.

Abstract:

Large datasets tend to be distributed, non-uniform, and prone to change. Teaching readers how to build distributed data projects that can handle huge amounts of data, this edition introduces Dask  Read more...

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