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Mastering Python

Mastering Python

By : Rick Hattem
4 (11)
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Mastering Python

Mastering Python

4 (11)
By: Rick 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.
Table of Contents (17 chapters)
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6
6. Generators and Coroutines – Infinity, One Step at a Time
16
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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Mastering Python
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