Data Science with Python and Dask
Authors: Jesse C. Daniel
ISBN-10: 1617295604
ISBN-13: 9781617295607
Edition 版本: 1
Released: 2019-07-30
Print Length 页数: 296 pages
Book Description
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!
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 from a single laptop to a cluster of hundreds of machines with ease.
Data Science with Python and Dask teaches you to build scalable projects that can handle massive datasets. After meeting the Dask framework,you’ll analyze data in the NYC Parking Ticket database and use DataFrames to streamline your process. Then,you’ll create machine learning models using Dask-ML,build interactive visualizations,and build clusters using AWS and Docker.
What’s inside
Working with large,structured and unstructured datasets
Visualization with Seaborn and Datashader
Implementing your own algorithms
Building distributed apps with Dask Distributed
Packaging and deploying Dask apps
contents
Dedication
preface
acknowledgments
about this book
about the author
about the cover illustration
Part 1: The building blocks of scalable computing
Chapter 1: Why scalable computing matters
Chapter 2: Introducing Dask
Part 2: Working with structured data using Dask DataFrames
Chapter 3: Introducing Dask DataFrames
Chapter 4: Loading data into DataFrames
Chapter 5: Cleaning and transforming DataFrames
Chapter 6: Summarizing and analyzing DataFrames
Chapter 7: Visualizing DataFrames with Seaborn
Chapter 8: Visualizing location data with Datashader
Part 3: Extending and deploying Dask
Chapter 9: Working with Bags and Arrays
Chapter 10: Machine learning with Dask-ML
Chapter 11: Scaling and deploying Dask
appendix Software installation
Index
