1.4 Visualizing Archaeological Data: Bar Charts and Scatterplots

Objective: Learn to create basic visualizations using matplotlib and pandas

In previous chapters, you learned how to structure, filter, and clean archaeological datasets. Now, we move to the next step: making data visible. In this chapter, you’ll use Python libraries matplotlib and pandas to build simple charts that help you and others better understand patterns in archaeological data.

We continue working with our fictional excavation site, Tell Logika, where you’ll visualize metal tool adoption and compare site-level excavation data. You’ll explore how different trenches reveal different distributions when visualized as bar charts or scatterplots.

1.4.1 Why Visualize Data?

Visualizations reveal patterns and trends
They help us understand comparisons and outliers
Charts communicate results clearly to others

As archaeologists, we often deal with complex and layered data — counts of artifacts by type, comparisons across time periods, or material presence by site. Visualizations turn abstract data into intuitive visuals that tell a story.

1.4.2 Python Visualization Tools

matplotlib.pyplot: Customizable, low-level plotting tool
pandas.DataFrame.plot(): Simple, high-level charting using structured data

We’ll start with matplotlib to build a bar chart and scatterplot, then show how pandas simplifies charting when your data is already organized.

1.4.3 Bar Chart – Metal Tool Use by Trench

import matplotlib.pyplot as plt

metal_use = {"TL01": 75, "TL02": 40, "TL03": 85}

plt.bar(metal_use.keys(), metal_use.values())
plt.title("Metal Tool Use by Trench")
plt.xlabel("Trench ID")
plt.ylabel("% Metal Tools")
plt.grid(True)
plt.show()

Bar charts help compare categories — in this case, the percentage of metal tools found in each trench. You can immediately see which areas had higher metal adoption.

1.4.4 Scatterplot – Site Elevation vs Metal Tool Use

elevation = [150, 160, 200]  # in meters
metal_use = [40, 75, 85]
sites = ["TL02", "TL01", "TL03"]

plt.scatter(elevation, metal_use)

for i, site in enumerate(sites):
    plt.text(elevation[i], metal_use[i], site)

plt.title("Elevation vs Metal Tool Use")
plt.xlabel("Elevation (m)")
plt.ylabel("% Metal Tools")
plt.grid(True)
plt.show()

Scatterplots reveal relationships. This chart lets you ask: Do higher elevation sites show more metal tool usage? You can see this more clearly when plotted.

🔁 Try switching axes for a different perspective:

If you’d prefer to display elevation on the Y-axis (higher elevations appear higher in the chart), you can flip the axes. This helps visually emphasize elevation as a vertical dimension, which can be more intuitive.

# Flip the axes: metal use on X, elevation on Y

plt.scatter(metal_use, elevation)

for i, site in enumerate(sites):
    plt.text(metal_use[i], elevation[i], site)

plt.title("Metal Tool Use vs Elevation")
plt.xlabel("% Metal Tools")
plt.ylabel("Elevation (m)")
plt.grid(True)
plt.show()

This variation keeps the same data but shifts how we interpret it. Use whichever layout communicates your message more clearly.

1.4.5 Quick Plotting with pandas

import pandas as pd

df = pd.DataFrame({
    "Trench": ["TL01", "TL02", "TL03"],
    "Metal_Tools": [75, 40, 85]
})

df.plot(x="Trench", y="Metal_Tools", kind="bar", legend=False)
plt.title("Metal Tool Use by Trench (pandas)")
plt.ylabel("% Metal Tools")
plt.grid(True)
plt.show()

Once your data is in a DataFrame, pandas lets you create visuals with one line of code. It’s ideal for fast exploration and prototyping.

1.4.6 Basic Plot Customization

Add titles and axis labels
Use annotations to label points clearly
Apply gridlines for readability

Simple visual touches go a long way toward clarity. Always include titles and labels to help your viewer quickly understand your visual story.

1.4.7 Which Chart Type Should You Use?

Purpose	Chart Type
Compare categories	Bar chart
Show relationships	Scatterplot
Display change over time	Line chart
Show parts of a whole	Pie chart

1.4.8 Now It’s Your Turn!

Let’s apply your knowledge with an exercise from Tell Logika.

📊 Activity: Visualizing Tool Use at Tell Logika

Scenario: Your archaeological team has collected data on metal tool use and site elevation across three trenches at Tell Logika. You need to present this data visually to help identify patterns in metal tool distribution.

Instructions:

Create a new folder: ComputationalArchaeology
Create a subfolder: chapter1
Create a Jupyter notebook named: Visualizing_Tools.ipynb

Use the following starter code:

import matplotlib.pyplot as plt
import pandas as pd

# Bar chart: metal use by trench
metal_use = {"TL01": 75, "TL02": 40, "TL03": 85}
plt.bar(metal_use.keys(), metal_use.values())
plt.title("Metal Tool Use by Trench")
plt.xlabel("Trench ID")
plt.ylabel("% Metal Tools")
plt.grid(True)
plt.show()

# Scatterplot: elevation vs metal use
elevation = [150, 160, 200]
metal_use = [40, 75, 85]
sites = ["TL02", "TL01", "TL03"]

plt.scatter(elevation, metal_use)
for i, site in enumerate(sites):
    plt.text(elevation[i], metal_use[i], site)
plt.title("Elevation vs Metal Tool Use")
plt.xlabel("Elevation (m)")
plt.ylabel("% Metal Tools")
plt.grid(True)
plt.show()

# pandas bar chart
df = pd.DataFrame({
    "Trench": ["TL01", "TL02", "TL03"],
    "Metal_Tools": [75, 40, 85]
})
df.plot(x="Trench", y="Metal_Tools", kind="bar", legend=False)
plt.title("Metal Tool Use by Trench (pandas)")
plt.ylabel("% Metal Tools")
plt.grid(True)
plt.show()

Tips:

Change trench IDs or add more data to test chart flexibility
Modify gridlines, labels, or chart colors
Use plt.savefig("filename.png") to export your charts

1.4.9 Quick Review

✅ Use plt.bar() for comparing trench categories
✅ Use plt.scatter() for visualizing relationships like elevation vs tool use
✅ Use pandas.plot() for quick DataFrame visuals
✅ Always label your axes and title your charts