Book Image

Mastering Python 2E - Second Edition

By : Rick van Hattem
5 (1)
Book Image

Mastering Python 2E - Second Edition

5 (1)
By: Rick van Hattem

Overview of this book

Even if you find writing Python code easy, writing code that is efficient, maintainable, and reusable is not so straightforward. Many of Python’s capabilities are underutilized even by more experienced programmers. Mastering Python, Second Edition, is an authoritative guide to understanding advanced Python programming so you can write the highest quality code. This new edition has been extensively revised and updated with exercises, four new chapters and updates up to Python 3.10. Revisit important basics, including Pythonic style and syntax and functional programming. Avoid common mistakes made by programmers of all experience levels. Make smart decisions about the best testing and debugging tools to use, optimize your code’s performance across multiple machines and Python versions, and deploy often-forgotten Python features to your advantage. Get fully up to speed with asyncio and stretch the language even further by accessing C functions with simple Python calls. Finally, turn your new-and-improved code into packages and share them with the wider Python community. If you are a Python programmer wanting to improve your code quality and readability, this Python book will make you confident in writing high-quality scripts and taking on bigger challenges
Table of Contents (21 chapters)
19
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Index

Arrays and matrices

Matrices are at the heart of most scientific Python and artificial intelligence libraries because they are very convenient for storing a lot of related data. They are also suitable for really fast bulk processing, and calculations on them can be performed much faster than you could achieve with many separate variables. In some cases, these calculations can even be offloaded to the GPU for even faster processing.

Note that a 0D matrix is effectively a single number, a 1D matrix is a regular array, and there is no real limit to the number of dimensions you can use. It should be noted that both size and processing time quickly increase with multiple dimensions, of course.

NumPy – Fast arrays and matrices

The numpy package spawned most of the scientific Python development and is still used at the core of many of the libraries covered in this chapter and the next. The library is largely (where it matters, at least) written in C, which makes it extremely...