Chapter 1, Benchmark and Profiling, will teach you how to assess the performance of Python programs and practical strategies on how to identify and isolate the slow sections of your code.
Chapter 2, Pure Python Optimizations, discusses how to improve your running times by order of magnitudes using the efficient data structures and algorithms available in the Python standard library and pure-Python third-party modules.
Chapter 3, Fast Array Operations with NumPy and Pandas, is a guide to the NumPy and Pandas packages. Mastery of these packages will allow you to implement fast numerical algorithms with an expressive, concise interface.
Chapter 4, C Performance with Cython, is a tutorial on Cython, a language that uses a Python-compatible syntax to generate efficient C code.
Chapter 5, Exploring Compilers, covers tools that can be used to compile Python to efficient machine code. The chapter will teach you how to use Numba, an optimizing compiler for Python functions, and PyPy, an alternative interpreter that can execute and optimize Python programs on the fly.
Chapter 6, Implementing Concurrency, is a guide to asynchronous and reactive programming. We will learn about key terms and concepts, and demonstrate how to write clean, concurrent code using the asyncio and RxPy frameworks.
Chapter 7, Parallel Processing, is an introduction to parallel programming on multi-core processors and GPUs. In this chapter, you will learn to achieve parallelism using the multiprocessing module and by expressing your code using Theano and Tensorflow.
Chapter 8, Advanced Introduction to Concurrent and Parallel Programming, introduces you to the concept of concurrency, and demonstrates an instance in which concurrent programming can improve significantly the speed of a Python program.
Chapter 9, Amdahl's Law, takes a theoretical approach and discusses the limitations of concurrency in improving the speed of applications. We will take a look at what concurrency truly provides and how we can best incorporate it.
Chapter 10, Working with Threads in Python, introduces the formal definition of threading and covers a different approach to implementing threading in a Python program. In this chapter, we will also discuss a major element in concurrent programming—the concept of synchronization.
Chapter 11, Using the with Statement in Threads, combines the concept of context management with threading in the overall context of concurrent programming in Python. We will be introduced to the main idea behind context management and how it is used in various programming practices, including threading.
Chapter 12, Concurrent Web Requests, covers one of the main applications of concurrent programming: web scraping. It also covers the concept of web scraping, along with other relevant elements, before discussing how threading can be applied to web scraping programs in order to achieve significant speedup.
Chapter 13, Working with Processes in Python, shows the formal definition of multiprocessing and how Python supports it. We will also learn more about the key differences between threading and multiprocessing, which are often confused with one another.
Chapter 14, Reduction Operators in Processes, pairs the concepts of reduction operations and multiprocessing together as a concurrent programming practice. This chapter will go over the theoretical foundation of reduction operations and how it is relevant to multiprocessing as well as programming in general.
Chapter 15, Concurrent Image Processing, goes into a specific application of concurrency: image processing. The basic ideas behind image processing, in addition to some of the most common processing techniques, are discussed. We will, of course, see how concurrency, specifically multiprocessing, can speed up the task of image processing.
Chapter 16, Introduction to Asynchronous Programming, considers the formal concept of asynchronous programming as one of the three major concurrent programming models aside from threading and multiprocessing. We will learn how asynchronous programming is fundamentally different from the two mentioned, but can still speedup concurrent applications.
Chapter 17, Implementing Asynchronous Programming in Python, goes in depth into the API that Python provides to facilitate asynchronous programming. Specifically, we will learn about the asyncio module, which is the main tool for implementing asynchronous programming in Python, and the general structure of an asynchronous application.
Chapter 18, Building Communication Channels with asyncio, combines the knowledge obtained regarding asynchronous programming covered in previous chapters with the topic of network communication. Specifically, we will look into using the aiohttp module as a tool to make asynchronous HTTP requests to web servers, as well as the aiofile module that implements asynchronous file reading/writing.
Chapter 19, Deadlocks, introduces the first of the problems that are commonly faced in concurrent programming. We will learn about the classical dining philosophers problem as an example of how deadlocks can cause concurrent programs to stop functioning. This chapter will also cover a number of potential approaches to deadlocks as well as relevant concepts, such as livelocks and distributed deadlocks.
Chapter 20, Starvation, considers another common problem in concurrent applications. The chapter uses the narrative of the classical readers-writers problem to explain the concept of starvation and its causes. We will, of course, also discuss potential solutions to these problems via hands-on examples in Python.
Chapter 21, Race Conditions, addresses arguably the most well-known concurrency problem: race conditions. We will also discuss the concept of a critical section, which is an essential element in the context of race conditions specifically, and concurrent programming in general. The chapter will then cover mutual exclusion as a potential solution for this problem.
Chapter 22, The Global Interpreter Lock, introduces the infamous GIL, which is considered the biggest challenge in concurrent programming in Python. We will learn about the reason behind GIL's implementation and the problems that it raises. This chapter concludes with some thoughts regarding how Python programmers and developers should think about and interact with the GIL.
Chapter 23, The Factory Pattern, will teach you how to use the Factory design pattern (Factory Method and Abstract Factory) to initialize objects, and also covers the benefits of using the Factory design pattern instead of direct object instantiation.
Chapter 24, The Builder Pattern, will teach you how to simplify the object creation process for cases typically composed of several related objects. We will review real-world examples and use cases, and then implement the builder pattern in developing a pizza-ordering application.
Chapter 25, Other Creational Patterns, will teach you how to handle other object creation situations with techniques such as creating a new object that is a full copy (hence, named clone) of an existing object, a technique offered by the Prototype pattern. You will also learn about the Singleton pattern.
Chapter 26, The Adapter Pattern, will teach you how to make your existing code compatible with a foreign interface (for example, an external library) with minimal changes. Specifically, we will we see how you can achieve interface conformance using the adapter pattern without modifying the source code of the incompatible model.
Chapter 27, The Decorator Pattern, will teach you how to enhance the functionality of an object without using inheritance. We will mention several categories of cross-cutting concerns, and specifically demonstrate memoization in this view. We will also describe how decorators can help us to keep our functions clean, without sacrificing performance.
Chapter 28, The Bridge Pattern, will teach you how to externalize an object's implementation details from its class hierarchy to another object class hierarchy. This chapter encourages the idea of preferring composition over inheritance.
Chapter 29, The Facade Pattern, will teach you how to create a single entry point to hide the complexity of a system. We will cover the basic use cases of facade and an implementation of the interface used by a multiserver operating system.
Chapter 30, Other Structural Patterns, will teach you the Flyweight, Model-View-Controller and Proxy patterns. With the Flyweight pattern, you will learn to reuse objects from an object pool to improve the memory usage and possibly the performance of your applications. The Model-View-Controller (MVC) pattern is used in application development (desktop, web) to improve maintainability by avoiding mixing the business logic with the user interface. And with the Proxy pattern, you provide a special object that acts as a surrogate or placeholder for another object to control access to it and reduce complexity and/or improve performance.
Chapter 31, The Chain of Responsibility Pattern, will teach another technique to improve the maintainability of your applications by avoiding mixing the business logic with the user interface.
Chapter 32, The Command Pattern, will teach you how to encapsulate operations (such as undo, copy, paste) as objects, to improve your application. Among the advantages of this technique, the object that invokes the command is decoupled from the object that performs it.
Chapter 33, The Observer Pattern, will teach you how to send a request to multiple receivers.
