Book Image

Speed Up Your Python with Rust

By : Maxwell Flitton
5 (2)
Book Image

Speed Up Your Python with Rust

5 (2)
By: Maxwell Flitton

Overview of this book

Python has made software development easier, but it falls short in several areas including memory management that lead to poor performance and security. Rust, on the other hand, provides memory safety without using a garbage collector, which means that with its low memory footprint, you can build high-performant and secure apps relatively easily. However, rewriting everything in Rust can be expensive and risky as there might not be package support in Rust for the problem being solved. This is where Python bindings and pip come in. This book will help you, as a Python developer, to start using Rust in your Python projects without having to manage a separate Rust server or application. Seeing as you'll already understand concepts like functions and loops, this book covers the quirks of Rust such as memory management to code Rust in a productive and structured manner. You'll explore the PyO3 crate to fuse Rust code with Python, learn how to package your fused Rust code in a pip package, and then deploy a Python Flask application in Docker that uses a private Rust pip module. Finally, you'll get to grips with advanced Rust binding topics such as inspecting Python objects and modules in Rust. By the end of this Rust book, you'll be able to develop safe and high-performant applications with better concurrency support.
Table of Contents (16 chapters)
1
Section 1: Getting to Understand Rust
5
Section 2: Fusing Rust with Python
11
Section 3: Infusing Rust into a Web Application

Exploring NumPy

Before we start using NumPy in our own modules, we must explore what NumPy is and how to use it. NumPy is a third-party computational Python package that enables us to perform calculations on lists. NumPy is mainly written in the C language, meaning that it will be faster than pure Python. In this section, we will have to assess whether our NumPy implementation beats a Rust implementation that is imported into Python.  

Adding vectors in NumPy

NumPy enables us to build vectors that we can loop through and apply functions to. We can also perform operations between vectors. We can demonstrate the power of NumPy by adding items of each vector together, as seen here:

[0, 1, 2, 3, 4]
[0, 1, 2, 3, 4]
---------------
[0, 2, 4, 6, 8]

To achieve this, we initially need to import modules by running the following code:

import time
import numpy as np
import matplotlib.pyplot as plt

With this, we can build a numpy_function NumPy function that creates...