Book Image

Mastering Python

By : Rick van Hattem
Book Image

Mastering Python

By: Rick van Hattem

Overview of this book

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.

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Mastering Python
Rick van Hattem
Apr, 2016 | 486 pages
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Table of Contents (22 chapters)
Mastering Python
Mastering Python
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Getting Started – One Environment per Project
Getting Started – One Environment per Project
Creating a virtual Python environment using venv
Bootstrapping pip using ensurepip
Installing C/C++ packages
Summary
2
Pythonic Syntax, Common Pitfalls, and Style Guide
Pythonic Syntax, Common Pitfalls, and Style Guide
Code style – or what is Pythonic code?
Common pitfalls
Summary
3
Containers and Collections – Storing Data the Right Way
Containers and Collections – Storing Data the Right Way
Time complexity – the big O notation
Core collections
Advanced collections
Summary
4
Functional Programming – Readability Versus Brevity
Functional Programming – Readability Versus Brevity
Functional programming
list comprehensions
dict comprehensions
set comprehensions
lambda functions
functools
itertools
Summary
5
Decorators – Enabling Code Reuse by Decorating
Decorators – Enabling Code Reuse by Decorating
Decorating functions
Decorating class functions
Decorating classes
Useful decorators
Summary
6
Generators and Coroutines – Infinity, One Step at a Time
Generators and Coroutines – Infinity, One Step at a Time
What are generators?
Coroutines
Summary
7
Async IO – Multithreading without Threads
Async IO – Multithreading without Threads
Introducing the asyncio library
Summary
8
Metaclasses – Making Classes (Not Instances) Smarter
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
9
Documentation – How to Use Sphinx and reStructuredText
Documentation – How to Use Sphinx and reStructuredText
The reStructuredText syntax
The Sphinx documentation generator
Documenting code
Summary
10
Testing and Logging – Preparing for Bugs
Testing and Logging – Preparing for Bugs
Using examples as tests with doctest
Testing with py.test
Mock objects
Logging
Summary
11
Debugging – Solving the Bugs
Debugging – Solving the Bugs
Non-interactive debugging
Interactive debugging
Summary
12
Performance – Tracking and Reducing Your Memory and CPU Usage
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
13
Multiprocessing – When a Single CPU Core Is Not Enough
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
14
Extensions in C/C++, System Calls, and C/C++ Libraries
Extensions in C/C++, System Calls, and C/C++ Libraries
Introduction
Calling C/C++ with ctypes
CFFI
Native C/C++ extensions
Summary
15
Packaging – Creating Your Own Libraries or Applications
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
Index
Index

Improving performance

Much can be said about performance optimization, but truthfully, if you have read the entire book up to this point, you know most of the Python-specific techniques to write fast code. The most important factor in application performance will always be the choice of algorithms, and by extension, the data structures. Searching for an item within list is almost always a worse idea than searching for an item in dict or set.

Using the right algorithm

Within any application, the right choice of algorithm is by far the most important performance characteristic, which is why I am repeating it to illustrate the results of a bad choice:

In [1]: a = list(range(1000000))

In [2]: b = dict.fromkeys(range(1000000))

In [3]: %timeit 'x' in a
10 loops, best of 3: 20.5 ms per loop

In [4]: %timeit 'x' in b
10000000 loops, best of 3: 41.6 ns per loop

Checking whether an item is within a list is an O(n) operation and checking whether an item is within a dict is an O(1) operation. A huge difference...

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