#### 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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# Finding interior points

One problem with working with two-dimensional figures in a programming environment is that you can't possibly store all the points that lie within the figure. Instead, we usually store far fewer points that represent the figure in some way. In most cases, this will be a number of points (connected by lines) that describe the boundary of the figure. This is efficient in terms of memory and makes it easy to visualize them on screen using Matplotlib Patches, for example. However, this approach makes it more difficult to determine whether a point, or another figure, lies within a given figure. This is a crucial question in many geometric problems.

In this recipe, we will learn how to represent geometric figures and determine whether a point lies within a figure or not.

`import matplotlib as mplimport...`