Chapter 1, Getting Started – One Environment per Project, introduces virtual Python environments using virtualenv or venv to isolate the packages in your Python projects.

Chapter 2, Pythonic Syntax, Common Pitfalls, and Style Guide, explains what Pythonic code is and how to write code that is Pythonic and adheres to the Python philosophy.

Chapter 3, Containers and Collections – Storing Data the Right Way, is where we use the many containers and collections bundled with Python to create code that is fast and readable.

Chapter 4, Functional Programming – Readability Versus Brevity, covers functional programming techniques such as list/dict/set comprehensions and lambda statements that are available in Python. Additionally, it illustrates their similarities with the mathematical principles involved.

Chapter 5, Decorators – Enabling Code Reuse by Decorating, explains not only how to create your own function/class decorators, but also how internal decorators such as property, staticmethod, and classmethod work.

Chapter 6, Generators and Coroutines – Infinity, One Step at a Time, shows how generators and coroutines can be used to lazily evaluate structures of infinite size.

Chapter 7, Async IO – Multithreading without Threads, demonstrates the usage of asynchronous functions using async def and await so that external resources no longer stall your Python processes.

Chapter 8, Metaclasses – Making Classes (Not Instances) Smarter, goes deeper into the creation of classes and how class behavior can be completely modified.

Chapter 9, Documentation – How to Use Sphinx and reStructuredText, shows how you can make Sphinx automatically document your code with very little effort. Additionally, it shows how the Napoleon syntax can be used to document function arguments in a way that is legible both in the code and the documentation.

Chapter 10, Testing and Logging – Preparing for Bugs, explains how code can be tested and how logging can be added to enable easy debugging in case bugs occur at a later time.

Chapter 11, Debugging – Solving the Bugs, demonstrates several methods of hunting down bugs with the use of tracing, logging, and interactive debugging.

Chapter 12, Performance – Tracking and Reducing Your Memory and CPU Usage, shows several methods of measuring and improving CPU and memory usage.

Chapter 13, Multiprocessing – When a Single CPU Core Is Not Enough, illustrates that the multiprocessing library can be used to execute your code, not just on multiple processors but even on multiple machines.

Chapter 14, Extensions in C/C++, System Calls, and C/C++ Libraries, covers the calling of C/C++ functions for both interoperability and performance using Ctypes, CFFI, and native C/C++.

Chapter 15, Packaging – Creating Your Own Libraries or Applications, demonstrates the usage of setuptools and setup.py to build and deploy packages on the Python Package Index (PyPI).

The only hard requirement for this book is a Python interpreter. A Python 3.5 or newer interpreter is recommended, but many of the code examples will function in older Python versions, such as 2.7, with a simple from __future__ import print_statement added at the top of the file.

Additionally, Chapter 14, Extensions in C/C++, System Calls, and C/C++ Libraries requires a C/C++ compiler, such as GCC, Visual Studio, or XCode. A Linux machine is by far the easiest to execute the C/C++ examples, but these should function on Windows and OS X machines without too much effort as well.

If you are beyond the absolute Python beginner level, then this book is for you. Even if you are already an expert Python programmer, I guarantee that you will find some useful techniques and insights in this book.

At the very least, it will allow Python 2 programmers to learn a lot more about the new features introduced in Python 3, and specifically Python 3.5.

Basic proficiency in Python is required as the installation of Python interpreters and the basic Python syntax are not covered.

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "It should be noted that the type() function has another use as well."

A block of code is set as follows:

import abc
import importlib


class Plugins(abc.ABCMeta):
    plugins = dict()

    def __new__(metaclass, name, bases, namespace):
        cls = abc.ABCMeta.__new__(
            metaclass, name, bases, namespace)

Any command-line input or output is written as follows where the >>> indicate the Python console and the # indicates a regular Linux/Unix shell:

>>> class Spam(object):
…     eggs = 'my eggs'

>>> Spam = type('Spam', (object,), dict(eggs='my eggs'))

Feedback from our readers is always welcome. Let us know what you think about this book—what you liked or disliked. Reader feedback is important for us as it helps us develop titles that you will really get the most out of.

To send us general feedback, simply e-mail <[email protected]>, and mention the book's title in the subject of your message.

If there is a topic that you have expertise in and you are interested in either writing or contributing to a book, see our author guide at www.packtpub.com/authors.

Now that you are the proud owner of a Packt book, we have a number of things to help you to get the most from your purchase.