Book Image

Expert Python Programming - Third Edition

By : Michał Jaworski, Tarek Ziadé
Book Image

Expert Python Programming - Third Edition

By: Michał Jaworski, Tarek Ziadé

Overview of this book

Python is a dynamic programming language that's used in a wide range of domains thanks to its simple yet powerful nature. Although writing Python code is easy, making it readable, reusable, and easy to maintain is challenging. Complete with best practices, useful tools, and standards implemented by professional Python developers, the third edition of Expert Python Programming will help you overcome this challenge. The book will start by taking you through the new features in Python 3.7. You'll then learn the advanced components of Python syntax, in addition to understanding how to apply concepts of various programming paradigms, including object-oriented programming, functional programming, and event-driven programming. This book will also guide you through learning the naming best practices, writing your own distributable Python packages, and getting up to speed with automated ways to deploy your software on remote servers. You’ll discover how to create useful Python extensions with C, C++, Cython, and CFFI. Furthermore, studying about code management tools, writing clear documentation, and exploring test-driven development will help you write clean code. By the end of the book, you will have become an expert in writing efficient and maintainable Python code.

Related Content you might be interested in

Current Title:

Small book image
Expert Python Programming - Third Edition
Michał Jaworski | Tarek Ziadé
Apr, 2019 | 646 pages
No titles found
Table of Contents (25 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Section 1: Before You Start
Section 1: Before You Start
2
Current Status of Python
Current Status of Python
Technical requirements
Where are we now and where we are going to?
Why and how Python changes
Being up-to-date with changes ­by following PEP documents
Python 3 adoption at the time of writing this book
The main differences between Python 3 and Python 2
Not only CPython
Useful resources
Summary
3
Modern Python Development Environments
Modern Python Development Environments
Technical requirements
Installing additional Python packages using pip
Isolating the runtime environment
Python's venv
System-level environment isolation
Popular productivity tools
Summary
4
Section 2: Python Craftsmanship
Section 2: Python Craftsmanship
5
Modern Syntax Elements - Below the Class Level
Modern Syntax Elements - Below the Class Level
Technical requirements
Python's built-in types
Supplemental data types and containers
Advanced syntax
Functional-style features of Python
Function and variable annotations
Other syntax elements you may not know of yet
Summary
6
Modern Syntax Elements - Above the Class Level
Modern Syntax Elements - Above the Class Level
Technical requirements
The protocols of the Python language – dunder methods and attributes
Reducing boilerplate with data classes
Subclassing built-in types
MRO and accessing methods from superclasses
Advanced attribute access patterns
Summary
7
Elements of Metaprogramming
Elements of Metaprogramming
Technical requirements
What is metaprogramming?
Summary
8
Choosing Good Names
Choosing Good Names
Technical requirements
PEP 8 and naming best practices
Naming styles
The naming guide
Best practices for arguments
Class names
Module and package names
Useful tools
Summary
9
Writing a Package
Writing a Package
Technical requirements
Creating a package
Namespace packages
Uploading a package
Standalone executables
Summary
10
Deploying the Code
Deploying the Code
Technical requirements
The Twelve-Factor App
Various approaches to deployment automation
Your own package index or index mirror
Common conventions and practices
Code instrumentation and monitoring
Summary
11
Python Extensions in Other Languages
Python Extensions in Other Languages
Technical requirements
Differentiating between the C and C++ languages
The need to use extensions
Writing extensions
Challenges with using extensions
Interfacing with dynamic libraries without extensions
Summary
12
Section 3: Quality over Quantity
Section 3: Quality over Quantity
13
Managing Code
Managing Code
Technical requirements
Working with a version control system
Setting up continuous development processes
Summary
14
Documenting Your Project
Documenting Your Project
Technical requirements
The seven rules of technical writing
Documentation as code
Popular documentation generators for Python libraries
Documenting web APIs
Building a well-organized documentation system
Summary
15
Test-Driven Development
Test-Driven Development
Technical requirements
I don't test
I do test
Summary
16
Section 4: Need for Speed
Section 4: Need for Speed
17
Optimization - Principles and Profiling Techniques
Optimization - Principles and Profiling Techniques
Technical requirements
The three rules of optimization
Optimization strategy
Finding bottlenecks
Summary
18
Optimization - Some Powerful Techniques
Optimization - Some Powerful Techniques
Technical requirements
Defining complexity
Reducing complexity by choosing proper data structures
Using collections
Using architectural trade-offs
Caching
Summary
19
Concurrency
Concurrency
Technical requirements
Why concurrency?
Multithreading
Multiprocessing
Asynchronous programming
Summary
20
Section 5: Technical Architecture
Section 5: Technical Architecture
21
Event-Driven and Signal Programming
Event-Driven and Signal Programming
Technical requirements
What exactly is event-driven programming?
Various styles of event-driven programming
Event-driven architectures
Summary
22
Useful Design Patterns
Useful Design Patterns
Technical requirements
Creational patterns
Structural patterns
Behavioral patterns
Summary
23
reStructuredText Primer
reStructuredText Primer
reStructuredText
24
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think

Class names

The name of a class has to be concise, precise, and descriptive. A common practice is to use a suffix that informs about its type or nature, for example:

  • SQLEngine
  • MimeTypes
  • StringWidget
  • TestCase

For base or abstract classes, a Base or Abstract prefix can be used as follows:

  • BaseCookie
  • AbstractFormatter

The most important thing is to be consistent with the class attributes. For example, try to avoid redundancy between the class and its attributes' names as follows:

>>> SMTP.smtp_send()  # redundant information in the namespace 
>>> SMTP.send()       # more readable and mnemonic

Let's take a look at module and package names in the next section.

Previous Section
End of Section 7
Next Section