#### 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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# Basic mathematical functions

Basic mathematical functions appear in many applications. For example, logarithms can be used to scale data that grows exponentially to give linear data. The exponential function and trigonometric functions are common fixtures when working with geometric information, the gamma function appears in combinatorics, and the Gaussian error function is important in statistics.

The math module in the Python Standard Library provides all of the standard mathematical functions, along with common constants and some utility functions, and it can be imported using the following command:

`import math`

Once it's imported, we can use any of the mathematical functions that are contained in this module. For instance, to find the square root of a non-negative number, we would use the sqrt function from math:

`import mathmath.sqrt(4)  #  2.0`

Attempting to use the sqrt function with a negative argument will raise a ValueError....