Book Image

Applying Math with Python - Second Edition

By : Sam Morley
Book Image

Applying Math with Python - Second Edition

By: Sam Morley

Overview of this book

The updated edition of Applying Math with Python will help you solve complex problems in a wide variety of mathematical fields in simple and efficient ways. Old recipes have been revised for new libraries and several recipes have been added to demonstrate new tools such as JAX. You'll start by refreshing your knowledge of several core mathematical fields and learn about packages covered in Python's scientific stack, including NumPy, SciPy, and Matplotlib. As you progress, you'll gradually get to grips with more advanced topics of calculus, probability, and networks (graph theory). Once you’ve developed a solid base in these topics, you’ll have the confidence to set out on math adventures with Python as you explore Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code. By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.
Table of Contents (13 chapters)

Finding the shortest paths in a network

A common problem where networks make an appearance is in the problem of finding the shortest – or perhaps more precisely, the highest reward – route between two nodes in a network. For instance, this could be the shortest distance between two cities, where the nodes represent the cities, and the edges are roads connecting pairs of cities. In this case, the weights of the edges would be their lengths.

In this recipe, we will find the shortest path between two nodes in a network with weights.

Getting ready

For this recipe, we will need the NetworkX package imported, as usual, under the nx alias, the Matplotlib pyplot module imported as plt, and a random number generator object from NumPy:

from numpy.random import default_rng
rng = default_rng(12345) # seed for reproducibility

How to do it...

Follow these steps to find the shortest path between two nodes in a network:

  1. First, we will create a random network...