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.

Dask是一个原生的并行分析工具，旨在与你已经使用的库无缝集成，包括Pandas、NumPy和Scikit-Learn。有了Dask，你就可以使用你已经拥有的工具，对巨大的数据集进行压缩和处理。而《用Python和Dask实现数据科学》是您在不改变工作方式的情况下将Dask用于您的数据项目的指南!

关于技术

一个高效的数据管道对于数据科学项目的成功意味着一切。Dask是一个灵活的Python并行计算库，可以轻松建立直观的工作流程，以摄入和分析大型分布式数据集。Dask提供了动态任务调度和并行集合，扩展了NumPy、Pandas和Scikit-learn的功能，使用户能够轻松地将其代码从一台笔记本电脑扩展到数百台机器的集群。

关于这本书

使用Python和Dask实现数据科学》教你建立能够处理大量数据集的可扩展项目。在认识了Dask框架之后，你将分析纽约市停车券数据库中的数据，并使用DataFrames来简化你的过程。然后，你将使用Dask-ML创建机器学习模型，构建交互式可视化，并使用AWS和Docker构建集群。

里面的内容

- 处理大型、结构化和非结构化的数据集

- 使用Seaborn和Datashader进行可视化

- 实现你自己的算法

- 使用Dask Distributed构建分布式应用

- 打包和部署Dask应用程序

关于读者

适用于有使用Python和PyData栈经验的数据科学家和开发人员。

关于作者

Jesse Daniel是一位经验丰富的Python开发者。他在丹佛大学教授Python用于数据科学，并在一家位于丹佛的媒体技术公司领导一个数据科学家团队。