Book Image

Applying Math with Python

By : Sam Morley
Book Image

Applying Math with Python

By: Sam Morley

Overview of this book

Python, one of the world's most popular programming languages, has a number of powerful packages to help you tackle complex mathematical problems in a simple and efficient way. These core capabilities help programmers pave the way for building exciting applications in various domains, such as machine learning and data science, using knowledge in the computational mathematics domain. The book teaches you how to solve problems faced in a wide variety of mathematical fields, including calculus, probability, statistics and data science, graph theory, optimization, and geometry. You'll start by developing core skills and learning about packages covered in Python’s scientific stack, including NumPy, SciPy, and Matplotlib. As you advance, you'll get to grips with more advanced topics of calculus, probability, and networks (graph theory). After you gain a solid understanding of these topics, you'll discover 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 (12 chapters)

Creating directed and weighted networks

Simple networks, such as those described in the previous recipes, are useful for describing networks where the direction of an edge is unimportant and where the edges carry equal weight. In practice, most networks carry additional information, such as weights or direction.

In this recipe, we will create a directed and weighted network and explore some of the basic properties of such networks.

Getting ready

For this recipe, we will need the NetworkX package, imported under the name nx (as usual), the Matplotlib pyplot module imported as plt, and the NumPy package imported as np.

How to do it...

The following steps outline how to create a directed network with weights, as well as how to explore some of the properties and techniques we discussed in the previous recipes:

  1. To create a directed network, we use the DiGraph class from NetworkX rather than the simple Graph class: