Chapter 1: Data Structure in Archaeology using Python

Welcome to the first chapter of Introduction to Computational Archaeology. In this chapter, we lay the groundwork for how archaeologists can use Python to represent and work with structured data. Whether you’re organizing field notes, cataloguing artifacts, or preparing data for statistical analysis, understanding how to structure and relate data is an essential first step.

What You’ll Learn

You will be introduced to the core mathematical concepts of sets, relations, and cardinality, and how these can be represented and manipulated in Python using basic data structures like lists, sets, and dictionaries. You’ll also begin creating simple visualizations to explore your data.

Why It Matters in Archaeology

Archaeological data is often complex and deeply relational — one object might be linked to multiple features, time periods, or material categories. Understanding how to express these relationships computationally allows you to organize excavation data, digitize typologies, and eventually run more advanced analyses.

  Example: Imagine needing to manage a digital catalog of thousands of artifacts from multiple trench layers, each with their own attributes (e.g., material, date, condition). Sets and relations will help you efficiently structure this dataset and prepare it for analysis.

What You’ll Be Able to Do

  Understand the basics of sets, relations, and how data is structured
Create and manipulate simple data structures in Python
Use these structures to represent archaeological information such as artifact collections, stratigraphic relationships, or excavation unit records
Generate basic bar charts and scatterplots to begin visualizing your dataset

Chapter Structure

This chapter includes four key sections followed by a hands-on tutorial using a real-world dataset related to archaeological finds. A short activity at the end helps you apply what you’ve learned in a focused task.

🧭 Activity Preview

At the end of this chapter, you will work on a tutorial where you’ll visualize artifact distributions across excavation layers. This will prepare you for deeper forms of analysis in later chapters.