What this book covers
Chapter 1, Getting Started – One Environment per Project, demonstrates several options for managing Python versions, virtual environments, and package dependencies.
Chapter 2, Interactive Python Interpreters, explores Python interpreter options. Python’s default interpreter is perfectly functional, but better alternatives are available. With a few modifications or a replacement, you can get auto-completion, syntax highlighting, and graphical output.
Chapter 3, Pythonic Syntax and Common Pitfalls, discusses Pythonic coding, which is the art of writing beautiful and readable Python code. This chapter is not the holy grail, but it is filled with tips and best practices to achieve something along those lines.
Chapter 4, Pythonic Design Patterns, continues on the theme of Chapter 3. Writing Pythonic code is not just about code style, but also about using the right design patterns and data structures. This chapter tells you about the data structures available and their performance characteristics.
Chapter 5, Functional Programming – Readability Versus Brevity, covers functional programming. Functional programming is considered a bit of a black art by some, but when applied correctly it can be a really powerful tool that makes code reuse trivial. It is probably as close to the underlying mathematics as you can get within programming.
Chapter 6, Decorators – Enabling Code Reuse by Decorating, discusses decorators, an amazing tool for reusing a method. With decorators, you can wrap functions and classes with some other function to modify their parameters and return values – an extremely useful tool.
Chapter 7, Generators and Coroutines – Infinity, One Step at a Time, discusses generators. Lists and tuples are fantastic if you already know that you are going to use every element, but the faster alternative is to only calculate the elements you actually need. That is what a generator does for you: generate items on demand.
Chapter 8, Metaclasses – Making Classes (not Instances) Smarter, explores metaclasses, the classes that make other classes. It is a magic you rarely need, but it does have practical uses cases such as plugin systems.
Chapter 9, Documentation – How to Use Sphinx and reStructuredText, gives you some documentation-related tips. Writing documentation might not be the favorite activity for most programmers, but it is useful. This chapter shows you how to make that easier by using Sphinx and reStructuredText to generate large portions automatically.
Chapter 10, Testing and Logging – Preparing for Bugs, covers how to implement tests and logging to prevent and detect bugs. Bugs are inevitable and by using logging, we can trace the cause. Often, bugs can be prevented by using tests.
Chapter 11, Debugging – Solving the Bugs, builds on Chapter 10. The previous chapter helped us find the bugs; now we need to solve them. Debuggers can be a huge help when hunting down difficult bugs, and this chapter shows you several debugging options.
Chapter 12, Performance – Tracking and Reducing Your Memory and CPU Usage, discusses the performance of your code. A common problem programmers have is trying to optimize code that does not need it, a fun but generally futile exercise. This chapter helps you find the code that needs to be optimized.
Chapter 13, asyncio – Multithreading without Threads, covers asyncio. Waiting for external resources such as network resources is the most common bottleneck for applications. With asyncio, we can stop waiting for those bottlenecks and switch to another task instead.
Chapter 14, Multiprocessing – When a Single CPU Core Is Not Enough, discusses performance from a different perspective. With multiprocessing, we can use multiple processors (even remotely) in parallel. When your processor is the bottleneck, this can help a lot.
Chapter 15, Scientific Python and Plotting, covers the most important libraries for scientific computing. Python has become the language of choice for scientific purposes.
Chapter 16, Artificial Intelligence, shows many AI algorithms and the libraries available for implementing them. In addition to being the language of choice for scientific purposes, most AI libraries are currently being built using Python as well.
Chapter 17, Extensions in C/C++, System Calls, and C/C++ Libraries, shows you how to use existing C/C++ libraries from Python, which not only allows reuse but can also speed up execution greatly. Python is a wonderful language, but it is often not the fastest solution.
Chapter 18, Packaging – Creating Your Own Libraries or Applications, will help you package your code into a fully functioning Python package that others can use. After building your wonderful new library, you might want to share it with the world.