#### 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.
Preface
Basic Packages, Functions, and Concepts
Free Chapter
Mathematical Plotting with Matplotlib
Working with Randomness and Probability
Geometric Problems
Finding Optimal Solutions
Miscellaneous Topics
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# Computing convex hulls

A geometric figure is said to be convex if every pair of points within the figure can be joined using a straight line that is also contained within the figure. Simple examples of convex bodies include points, straight lines, squares, circles (disks), regular polygons, and so on. The geometric figure shown in Figure 8.5 is not convex since the points on the opposite sides of the hole cannot be connected by a straight line that remains inside the figure.

Convex figures are simple from a certain perspective, which means they are useful in a variety of applications. One particular problem involves finding the smallest convex set that contains a collection of points. This smallest convex set is called the convex hull of the set of points.

In this recipe, we'll learn how to find the convex hull of a set of points using the Shapely package.