Secret Recipes of the Python Ninja

Secret Recipes of the Python Ninja

Overview of this book

This book covers the unexplored secrets of Python, delve into its depths, and uncover its mysteries. You’ll unearth secrets related to the implementation of the standard library, by looking at how modules actually work. You’ll understand the implementation of collections, decimals, and fraction modules. If you haven’t used decorators, coroutines, and generator functions much before, as you make your way through the recipes, you’ll learn what you’ve been missing out on. We’ll cover internal special methods in detail, so you understand what they are and how they can be used to improve the engineering decisions you make. Next, you’ll explore the CPython interpreter, which is a treasure trove of secret hacks that not many programmers are aware of. We’ll take you through the depths of the PyPy project, where you’ll come across several exciting ways that you can improve speed and concurrency. Finally, we’ll take time to explore the PEPs of the latest versions to discover some interesting hacks.

Title Page

Packt Upsell

Foreword

Contributors

Preface

Free Chapter

Working with Python Modules

Introduction

Using and importing modules and namespaces

Implementing virtual Python environments

Python package installation options

Utilizing requirement files and resolving conflicts

Using local patches and constraint files

Working with packages

Creating wheels and bundles

Comparing source code to bytecode

How to create and reference module packages

Operating system-specific binaries

How to upload programs to PyPI

Project packaging

Uploading to PyPI

Utilizing the Python Interpreter

Introduction

Launching Python environments

Utilizing Python command options

Working with environment variables

Making scripts executable

Modifying interactive interpreter startup

Alternative Python implementations

Installing Python on Windows

Using the Windows Python launcher

Embedding Python with other applications

Using alternative Python shells – IPython

Using alternative Python shells – bpython

Using alternative Python shells – DreamPie

Working with Decorators

Introduction

Reviewing functions

Introducing decorators

Using function decorators

Using class decorators

Examples of decorators

Using the decorators module

Using Python Collections

Introduction

Reviewing containers

Implementing namedtuple

Implementing deque

Implementing ChainMap

Implementing Counters

Implementing OrderedDict

Implementing defaultdict

Implementing UserDict

Implementing UserList

Implementing UserString

Improving Python collections

Looking at the collections – extended module

Generators, Coroutines, and Parallel Processing

How iteration works in Python

Using the itertools module

Using generator functions

Simulating multithreading with coroutines

When to use parallel processing

Forking processes

How to implement multithreading

How to implement multiprocessing

Working with Python's Math Module

Using the math module's functions and constants

Working with complex numbers

Improving decimal numbers

Increasing accuracy with fractions

Working with random numbers

Using the secrets module

Implementing basic statistics

Improving functionality with comath

Improving Python Performance with PyPy

Introduction

What is PyPy?

What is RPython?

Some real-world examples

Python Enhancement Proposals

Introduction

What are PEPs?

PEP 556 – Threaded garbage collection

PEP 554 – Multiple subinterpreters

PEP 551 – Security transparency

PEP 543 – Unified TLS API

Documenting with LyX

Introduction

Python documentation tools and techniques

Inline comments and the dir command

Using docstrings

Using PyDoc help

HTML reports

Using reStructuredText files

Using the Sphinx documentation program

Using LaTeX and LyX document preparation programs

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How iteration works in Python

In Python, an iterator is an object that represents a stream of data. While iterators are available for containers, sequences in particular always support iteration.

Iterators have the __next__() method available (or the built-in next() function). Calling next() multiple times returns successive items from the data stream. When no more items are available, a StopIteration exception is thrown.

Any class can use an iterator by defining a container.__iter__() method. This method returns an iterator object, typically just self. This object is necessary to support the iterator protocol. Different types of iteration can be supported, with each one providing a specific iterator request. For example, a tree structure could support both breadth-first and depth-first traversals.

The iterator protocol mentioned previously actually comprises two methods: iterator.__next__() and iterator.__iter__(). (Notice that __iter__() has a different class compared to the one above.)

As...

Secret Recipes of the Python Ninja

Secret Recipes of the Python Ninja

Overview of this book

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