Advanced Python Programming

Book Description

Create distributed applications with clever design patterns to solve complex problems

Key Features

• Set up and run distributed algorithms on a cluster using Dask and PySpark
• Master skills to accurately implement concurrency in your code
• Gain practical experience of Python design patterns with real-world examples

Book Description

This Learning Path shows you how to leverage the power of both native and third-party Python libraries for building robust and responsive applications. You will learn about profilers and reactive programming, concurrency and parallelism, as well as tools for making your apps quick and efficient. You will discover how to write code for parallel architectures using TensorFlow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark. With the knowledge of how Python design patterns work, you will be able to clone objects, secure interfaces, dynamically choose algorithms, and accomplish much more in high performance computing.

By the end of this Learning Path, you will have the skills and confidence to build engaging models that quickly offer efficient solutions to your problems.

This Learning Path includes content from the following Packt products:

• Python High Performance – Second Edition by Gabriele Lanaro

• Mastering Concurrency in Python by Quan Nguyen

• Mastering Python Design Patterns by Sakis Kasampalis

What you will learn

• Use NumPy and pandas to import and manipulate datasets
• Achieve native performance with Cython and Numba
• Write asynchronous code using asyncio and RxPy
• Design highly scalable programs with application scaffolding
• Explore abstract methods to maintain data consistency
• Clone objects using the prototype pattern
• Use the adapter pattern to make incompatible interfaces compatible
• Employ the strategy pattern to dynamically choose an algorithm

Who this book is for

This Learning Path is specially designed for Python developers who want to build high-performance applications and learn about single core and multi-core programming, distributed concurrency, and Python design patterns. Some experience with Python programming language will help you get the most out of this Learning Path.

Table of Contents

1. Benchmarking and Profiling
2. Pure Python Optimizations
3. Fast Array Operations with NumPy and Pandas
4. C Performance with Cython
5. Exploring Compilers
6. Implementing Concurrency
7. Parallel Processing
8. Advanced Introduction to Concurrent and Parallel Programming
9. Amdahl’s Law
10. Working with Threads in Python
11. Using the with Statement in Threads
12. Concurrent Web Requests
13. Working with Processes in Python
14. Reduction Operators in Processes
15. Concurrent Image Processing
16. Introduction to Asynchronous Programming
17. Implementing Asynchronous Programming in Python
18. Building Communication Channels with asyncio
19. Deadlocks
20. Starvation
21. Race Conditions
22. The Global Interpreter Lock
23. The Factory Pattern
24. The Builder Pattern
25. Other Creational Patterns
26. The Adapter Pattern
27. The Decorator Pattern
28. The Bridge Pattern
29. The Facade Pattern
30. Other Structural Patterns
31. The Chain of Responsibility Pattern
32. The Command Pattern
33. The Observer Pattern

中文：

书名：Advanced Python Programming

使用巧妙的设计模式创建分布式应用程序以解决复杂问题

Key Features

• 使用DASK和PySpark在群集上设置和运行分布式算法
• 掌握在代码中准确实现并发的技能
• 通过真实世界的示例获得Python设计模式的实践经验

Book Description

本学习途径向您展示了如何利用本机和第三方Python库的强大功能来构建健壮且响应迅速的应用程序。您将了解分析器和反应式编程、并发性和并行性，以及使您的应用程序快速高效的工具。您将了解如何使用TensorFlow和Theano为并行体系结构编写代码，并使用DASK和PySpark等技术使用计算机集群进行大规模计算。通过了解Python设计模式的工作原理，您将能够克隆对象、保护接口、动态选择算法，并在高性能计算中完成更多任务。

在本学习路径结束时，您将有能力和信心构建引人入胜的模型，快速为您的问题提供有效的解决方案。

本学习途径包括来自以下Packt产品的内容：

·Python High Performance&#8211；Gabriele Lanaro第二版

• Mastering Concurrency in Python by Quan Nguyen

·掌握Sakis Kasampalis的Python设计模式

你将学到什么

• 使用NumPy和Pandas导入和操作数据集
• Achieve native performance with Cython and Numba
• 使用Ayncio和RxPy编写异步代码
• 利用应用程序脚手架设计高度可扩展的程序
• 探索维护数据一致性的抽象方法
• 使用原型模式克隆对象
• 使用适配器模式使不兼容的接口兼容
• 采用策略模式动态选择算法

Who this book is for

本学习路径是专门为希望构建高性能应用程序并了解单核和多核编程、分布式并发和Python设计模式的Python开发人员设计的。一些使用Python编程语言的经验将帮助您最大限度地利用本学习途径。

Table of Contents

1. 基准测试和评测
2. Pure Python Optimizations
3. 使用NumPy和Pandas进行快速数组运算
4. C Performance with Cython
5. Exploring Compilers
6. Implementing Concurrency
7. 并行处理
8. 并发和并行编程高级入门
9. Amdahl’s Law
10. 在Python中使用线程
11. Using the with Statement in Threads
12. Concurrent Web Requests
13. Working with Processes in Python
14. 进程中的约简算子
15. 并行图像处理
16. Introduction to Asynchronous Programming
17. 在Python语言中实现异步编程
18. 用Asyncio构建沟通渠道
19. Deadlocks
20. Starvation
21. Race Conditions
22. 全球口译员之锁
23. 工厂模式
24. 构建器模式
25. Other Creational Patterns
26. 适配器模式
27. The Decorator Pattern
28. The Bridge Pattern
29. The Facade Pattern
30. Other Structural Patterns
31. The Chain of Responsibility Pattern
32. The Command Pattern
33. The Observer Pattern