这是一本面向Python程序员的，全面介绍Python Web框架Flask的书。关于本书的详细介绍、相关资源等更多信息可以访问本书的官方主页http://helloflask.com/book了解。
• 国内首本Flask著作，在内容上涵盖完整的Flask Web开发学习路径，在实践上包含完整的Flask Web程序开发流程。同时兼容Python2 .7和Python3.6。
• 内容最新，基于Flask 1.0.2版本，所有的Python库、前端框架和JavaScript库均使用最新版本。
• 内容全面，除了基础知识，还包含Web API、全文搜索、国际化和本地化、第三方登录、AJAX、WebSocket、源码分析、扩展开发等实用内容。
• 实战性强，包含8个示例程序，5个实战项目和1个Flask扩展。实战项目包括留言板、个人博客、图片社交网站、待办事项程序和聊天室，分别对应不同的知识点，难度递增。
• 5个实战项目的Git标签经过精心设置，每个标签都对应新增的某个程序功能。
• 全书所有章节均经过精心设计和编排，力求满足不同水平读者的需要，并且让读者可以循序渐进的掌握Flask开发知识和技巧。
• 通过丰富的图表和代码示例讲解复杂知识点，尽量做到通俗易懂。
本书共16章，分为三部分。
第一部分 基础篇（第1~6章）
通过大量的程序实例详细介绍了Flask的所有基础知识，同时在每章的后面又提供了一些进阶技巧，供进阶读者学习。
第二部分 实战篇（第7~11章）
通过5个真实的项目案例来串接和阐释不同的知识点，难度逐渐递增。这5个案例分别为：留言板SayHello、个人博客Bluelog、图片社交网站Albumy、待办事项程序Todoism和聊天室CatChat。
第三部分 进阶篇（第12~16章）
首先介绍了Flask程序的部署流程：测试、性能优化和部署上线；然后通过一个真实的实例讲解了Flask扩展的开发，最后以源代码为切入点深入剖析了Flask的实现原理与主要工作机制。

Summary
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!
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. You'll find registration instructions inside the print book.
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

If you’re among the Python developers put off by Asyncio’s complexity, it’s time to take another look. Asyncio is complicated because it aims to solve problems in concurrent network programming for both framework developers and end-user developers like you. The features you need to consider are a small subset of the whole Asyncio API, but picking out the right features is the tricky part. That’s where this practical book comes in.
Veteran Python developer Caleb Hattingh helps end-user developers gain a basic understanding of Asyncio’s building blocks—enough to get you started writing simple event-based programs. You’ll learn why Asyncio offers a safer alternative to preemptive multitasking (threading) and how this API provides a simple way to support thousands of simultaneous socket connections.
Get a critical comparison of Asyncio and threading for concurrent network programming
Take an Asyncio walkthrough, including a quickstart guide to help you hit the ground looping with event-based programming
Learn the difference between Asyncio features for end-user developers and those for framework developers
Understand Asyncio’s new async/await language syntax, including coroutines and task and future APIs
Get detailed case studies (with code) of some popular Asyncio-compatible third-party libraries

Classic Computer Science Problems in Python sharpens your CS problem-solving skills with time-tested scenarios, exercises, and algorithms, using Python. You'll tackle dozens of coding challenges, ranging from simple tasks like binary search algorithms to clustering data using k-means. You'll especially enjoy the feeling of satisfaction as you crack problems that connect computer science to the real-world concerns of apps, data, performance, and even nailing your next job interview!
what's inside
Search algorithms
Common techniques for graphs
Neural networks
Genetic algorithms
Adversarial search
Uses type hints throughout
Covers Python 3.7