Mastering Python

Master the art of writing beautiful and powerful Python by using all of the features that Python 3.5 offers

Mastering Python

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Rick van Hattem

16 customer reviews
Master the art of writing beautiful and powerful Python by using all of the features that Python 3.5 offers
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Book Details

ISBN 139781785289729
Paperback486 pages

Book Description

Python is a dynamic programming language. It is known for its high readability and hence it is often the first language learned by new programmers. Python being multi-paradigm, it can be used to achieve the same thing in different ways and it is compatible across different platforms. Even if you find writing Python code easy, writing code that is efficient, easy to maintain, and reuse is not so straightforward.

This book is an authoritative guide that will help you learn new advanced methods in a clear and contextualised way. It starts off by creating a project-specific environment using venv, introducing you to different Pythonic syntax and common pitfalls before moving on to cover the functional features in Python. It covers how to create different decorators, generators, and metaclasses. It also introduces you to functools.wraps and coroutines and how they work. Later on you will learn to use asyncio module for asynchronous clients and servers. You will also get familiar with different testing systems such as py.test, doctest, and unittest, and debugging tools such as Python debugger and faulthandler. You will learn to optimize application performance so that it works efficiently across multiple machines and Python versions. Finally, it will teach you how to access C functions with a simple Python call. By the end of the book, you will be able to write more advanced scripts and take on bigger challenges.

Table of Contents

Chapter 1: Getting Started – One Environment per Project
Creating a virtual Python environment using venv
Bootstrapping pip using ensurepip
Installing C/C++ packages
Summary
Chapter 2: Pythonic Syntax, Common Pitfalls, and Style Guide
Code style – or what is Pythonic code?
Common pitfalls
Summary
Chapter 3: Containers and Collections – Storing Data the Right Way
Time complexity – the big O notation
Core collections
Advanced collections
Summary
Chapter 4: Functional Programming – Readability Versus Brevity
Functional programming
list comprehensions
dict comprehensions
set comprehensions
lambda functions
functools
itertools
Summary
Chapter 5: Decorators – Enabling Code Reuse by Decorating
Decorating functions
Decorating class functions
Decorating classes
Useful decorators
Summary
Chapter 6: Generators and Coroutines – Infinity, One Step at a Time
What are generators?
Coroutines
Summary
Chapter 7: Async IO – Multithreading without Threads
Introducing the asyncio library
Summary
Chapter 8: Metaclasses – Making Classes (Not Instances) Smarter
Dynamically creating classes
Abstract classes using collections.abc
Automatically registering a plugin system
Order of operations when instantiating classes
Storing class attributes in definition order
Summary
Chapter 9: Documentation – How to Use Sphinx and reStructuredText
The reStructuredText syntax
The Sphinx documentation generator
Documenting code
Summary
Chapter 10: Testing and Logging – Preparing for Bugs
Using examples as tests with doctest
Testing with py.test
Mock objects
Logging
Summary
Chapter 11: Debugging – Solving the Bugs
Non-interactive debugging
Interactive debugging
Summary
Chapter 12: Performance – Tracking and Reducing Your Memory and CPU Usage
What is performance?
Timeit – comparing code snippet performance
cProfile – finding the slowest components
Line profiler
Improving performance
Memory usage
Performance monitoring
Summary
Chapter 13: Multiprocessing – When a Single CPU Core Is Not Enough
Multithreading versus multiprocessing
Hyper-threading versus physical CPU cores
Creating a pool of workers
Sharing data between processes
Remote processes
Summary
Chapter 14: Extensions in C/C++, System Calls, and C/C++ Libraries
Introduction
Calling C/C++ with ctypes
CFFI
Native C/C++ extensions
Summary
Chapter 15: Packaging – Creating Your Own Libraries or Applications
Installing packages
Setup parameters
Packages
Entry points
Package data
Testing packages
C/C++ extensions
Wheels – the new eggs
Summary

What You Will Learn

  • Create a virtualenv and start a new project
  • Understand how and when to use the functional programming paradigm
  • Get familiar with the different ways the decorators can be written in
  • Understand the power of generators and coroutines without digressing into lambda calculus
  • Create metaclasses and how it makes working with Python far easier
  • Generate HTML documentation out of documents and code using Sphinx
  • Learn how to track and optimize application performance, both memory and cpu
  • Use the multiprocessing library, not just locally but also across multiple machines
  • Get a basic understanding of packaging and creating your own libraries/applications

Authors

Table of Contents

Chapter 1: Getting Started – One Environment per Project
Creating a virtual Python environment using venv
Bootstrapping pip using ensurepip
Installing C/C++ packages
Summary
Chapter 2: Pythonic Syntax, Common Pitfalls, and Style Guide
Code style – or what is Pythonic code?
Common pitfalls
Summary
Chapter 3: Containers and Collections – Storing Data the Right Way
Time complexity – the big O notation
Core collections
Advanced collections
Summary
Chapter 4: Functional Programming – Readability Versus Brevity
Functional programming
list comprehensions
dict comprehensions
set comprehensions
lambda functions
functools
itertools
Summary
Chapter 5: Decorators – Enabling Code Reuse by Decorating
Decorating functions
Decorating class functions
Decorating classes
Useful decorators
Summary
Chapter 6: Generators and Coroutines – Infinity, One Step at a Time
What are generators?
Coroutines
Summary
Chapter 7: Async IO – Multithreading without Threads
Introducing the asyncio library
Summary
Chapter 8: Metaclasses – Making Classes (Not Instances) Smarter
Dynamically creating classes
Abstract classes using collections.abc
Automatically registering a plugin system
Order of operations when instantiating classes
Storing class attributes in definition order
Summary
Chapter 9: Documentation – How to Use Sphinx and reStructuredText
The reStructuredText syntax
The Sphinx documentation generator
Documenting code
Summary
Chapter 10: Testing and Logging – Preparing for Bugs
Using examples as tests with doctest
Testing with py.test
Mock objects
Logging
Summary
Chapter 11: Debugging – Solving the Bugs
Non-interactive debugging
Interactive debugging
Summary
Chapter 12: Performance – Tracking and Reducing Your Memory and CPU Usage
What is performance?
Timeit – comparing code snippet performance
cProfile – finding the slowest components
Line profiler
Improving performance
Memory usage
Performance monitoring
Summary
Chapter 13: Multiprocessing – When a Single CPU Core Is Not Enough
Multithreading versus multiprocessing
Hyper-threading versus physical CPU cores
Creating a pool of workers
Sharing data between processes
Remote processes
Summary
Chapter 14: Extensions in C/C++, System Calls, and C/C++ Libraries
Introduction
Calling C/C++ with ctypes
CFFI
Native C/C++ extensions
Summary
Chapter 15: Packaging – Creating Your Own Libraries or Applications
Installing packages
Setup parameters
Packages
Entry points
Package data
Testing packages
C/C++ extensions
Wheels – the new eggs
Summary

Book Details

ISBN 139781785289729
Paperback486 pages
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