Data Visualization for Food Scientists

Data visualization is essential to communicate complex information in a form that can be more readily interpreted and understood by humans. As the sciences become increasingly data-intensive, good visualization practices are needed to assist interpretation by scientists, industry professionals, and the general public. The food sciences are no exception: visualization methods assist interpretation of everything from nutritional information to biological, chemical, and physical data about food and food processes. This Jupyter book is designed to give an introduction to data visualization and some applications in the food sciences.

Throughout this Jupyter book we will focus on creating data visualizations with Python. There are many different ways to create plots in Python and we will not discuss all of them here. Instead, we will see how to create most of the basic plots using the very popular seaborn library. Following this overview, we will learn how to use matplotlib (which seaborn also uses in the background) to modify and customize new plots or to customize plots created with seaborn.

This module requires at least a basic understanding of Python to complete the exercises.

Tools and resources used in this book

1. Python packages

   • Seaborn [Waskom, 2021]

   • Matplotlib [Hunter, 2007]

   • Plotly [Inc., 2015]

   • Pandas [Wes McKinney, 2010]

2. Datasets and other Web resources

   • Earth Microbiome Project [Thompson et al., 2017]

   • Wine quality

   • Chocolate ratings

   • Nutritional properties of cereals

   • From Data to Viz [From Data to Viz, 2023]

Acknowledgements

This project was funded through the Innovedum initiative of ETH Zürich.