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!

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, enabling users to scale their code from a single laptop to a cluster of hundreds of machines with ease.

About the Book

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

About the Reader

For data scientists and developers with experience using Python and the PyData stack.

About the Author

Jesse Daniel

is an experienced Python developer. He taught Python for Data Science at the University of Denver and leads a team of data scientists at a Denver-based media technology company.

Table of 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

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