The most comprehensive coverage of Dask to date, with real-world examples that made a difference in my daily 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.

We interviewed Jesse as a part of our Six Questions series. Check it out here.