#### 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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# Technical requirements

The main plotting package for Python is Matplotlib, which can be installed using your favorite package manager, such as pip:

```          python3.8 -m pip install matplotlib
```

This will install the most recent version of Matplotlib, which, at the time of writing this book, is version 3.2.1.

Matplotlib contains numerous sub-packages, but the main user interface is the matplotlib.pyplotpackage, which, by convention, is imported under the pltalias. This is achieved using the following import statement:

`import matplotlib.pyplot as plt`

Many of the recipes in this chapter also require NumPy, which, as usual, is imported under the npalias.

The code for this chapter can be found in the Chapter 02 folder of the GitHub repository at https://github.com/PacktPublishing/Applying-Math-with-Python/tree/master/Chapter%2002.

Check out the following video to see the Code in Action: https://bit.ly/2ZOSuhs.