# Preface

Several years ago, I stumbled across the Dakar Rally website for the first time, and with it the live results service showing timing and event results data for each stage of the rally. I'd been tinkering with motorsport data visulisation for some time, so it was natural for me to have a poke around the website to see what I might be able to with the Dakar data.

Over the years, I've occasionally revisited my original code, played with it a little more, tried to keep the scrapers\* that extract the data from the Dakar Rally website working, and so on. Much of the code I write is written using Jupyter notebooks, a document format that supports a blend of text, code and code outputs and that encourages a "narrative" form of software or data analysis development. Publishing tools such as Jupyter Book, MyST and Quarto make it relateively easy to publish collections of notebooks as HTML books, e-books, and PDFs. And so, with another new year upon us, I thought it was about time I wrote up a quick how to guide for how to get started with working with Dakar Rally data.

So if you're into motorsport and you've been wondering how code might be used in data analysis, how you can create your own data visualisations, or how you might be able to use data as a the basis for telling stories about an event, this guide may be of some interest to you.

*Tony Hirst*  
*Apse Heath, January, 2025.*


\* A *scraper* is a tool, or piece of software code, for literally "scraping" data, in a raw form, from a website or document, such as a PDF document, so that it can be manipulated and analysed elsewhere.