Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Mastering Python
  • Table Of Contents Toc
  • Feedback & Rating feedback
Mastering Python

Mastering Python

By : Rick Hattem
4.6 (65)
close
close
Mastering Python

Mastering Python

4.6 (65)
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 (21 chapters)
close
close
19
Other Books You May Enjoy
20
Index

Time complexity – the big O notation

Before we can begin with this chapter, there is a simple notation that you need to understand. This chapter heavily uses the big O notation to indicate the time complexity for an operation. Feel free to skip this paragraph if you are already familiar with this notation. While this notation sounds really complicated, the concept is actually quite simple.

When we say that a function takes O(1) time, it means that it generally only takes 1 step to execute. Similarly, a function with O(n) would take n steps to execute, where n is generally the size of the object. This time complexity is just a basic indication of what to expect when executing the code, as it is generally what matters most.

The purpose of this system is to indicate the approximate performance of an operation; this is separate from code speed but it is still relevant. A piece of code that executes a single step 1000 times faster but needs O(2**n) steps to execute will still be slower...

Visually different images
CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Mastering Python
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist download Download options font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon