Book Image

Applying Math with Python - Second Edition

By : Sam Morley
Book Image

Applying Math with Python - Second Edition

By: Sam Morley

Overview of this book

The updated edition of Applying Math with Python will help you solve complex problems in a wide variety of mathematical fields in simple and efficient ways. Old recipes have been revised for new libraries and several recipes have been added to demonstrate new tools such as JAX. You'll start by refreshing your knowledge of several core mathematical fields and learn about packages covered in Python's scientific stack, including NumPy, SciPy, and Matplotlib. As you progress, you'll gradually get to grips with more advanced topics of calculus, probability, and networks (graph theory). Once you’ve developed a solid base in these topics, you’ll have the confidence to set out on math adventures with Python as you explore 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.
Table of Contents (13 chapters)

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 on 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.

Getting ready

For this recipe, we need to import the csv module from the Python Standard Library (https://docs...