#### 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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# Validating data

Data is often presented in a raw form and might contain anomalies or incorrect or malformed data, which will obviously present a problem for later processing and analysis. It is usually a good idea to build a validation step into a processing pipeline. Fortunately, the Cerberus package provides a lightweight and easy to use validation tool for Python.

For validation, we have to define a schema, which is a technical description of what the data should look like and the checks that should be performed on the data. For example, we can check the type and place bounds of the maximum and minimum values. Cerberus validators can also perform type conversions during the validation step, which allows us to plug data loaded directly from CSV files into the validator.

In this recipe, we will learn how to use Cerberus to validate data loaded from a CSV file.