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Speed Up Your Python with Rust

You're reading from   Speed Up Your Python with Rust Optimize Python performance by creating Python pip modules in Rust with PyO3

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Product type Paperback
Published in Jan 2022
Publisher Packt
ISBN-13 9781801811446
Length 384 pages
Edition 1st Edition
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Author (1):
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Maxwell Flitton Maxwell Flitton
Author Profile Icon Maxwell Flitton
Maxwell Flitton
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Table of Contents (16) Chapters Close

Preface 1. Section 1: Getting to Understand Rust
2. Chapter 1: An Introduction to Rust from a Python Perspective FREE CHAPTER 3. Chapter 2: Structuring Code in Rust 4. Chapter 3: Understanding Concurrency 5. Section 2: Fusing Rust with Python
6. Chapter 4: Building pip Modules in Python 7. Chapter 5: Creating a Rust Interface for Our pip Module 8. Chapter 6: Working with Python Objects in Rust 9. Chapter 7: Using Python Modules with Rust 10. Chapter 8: Structuring an End-to-End Python Package in Rust 11. Section 3: Infusing Rust into a Web Application
12. Chapter 9: Structuring a Python Flask App for Rust 13. Chapter 10: Injecting Rust into a Python Flask App 14. Chapter 11: Best Practices for Integrating Rust 15. Other Books You May Enjoy

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...

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