Scatter Plot

A scatter plot places one point per pair of values (x, y), so you can see at a glance whether two variables move together. McKinney uses this exact technique to check relationships in economic data — for example, plotting the change in money supply (m1) against the change in unemployment (unemp) to look for a pattern.

diagram Reading a scatter plot mermaid

  flowchart LR
  A["Two numeric columns"] --> B["One point per row: (x, y)"]
  B --> C{"Points trend together?"}
  C -->|Up-right| D["Positive relationship"]
  C -->|Down-right| E["Negative relationship"]
  C -->|Scattered| F["Little/no relationship"]

Each point is one observation; the overall pattern of points reveals the relationship between two variables.

Basic scatter plot

Scatter

import matplotlib.pyplot as plt
 
x = [1, 2, 3, 4, 5, 6, 7]
y = [50, 55, 65, 70, 75, 78, 85]
 
plt.figure(figsize=(7, 4))
plt.scatter(x, y)
plt.title("Hours vs score")
plt.xlabel("Hours")
plt.ylabel("Score")
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()

Handle overplotting

Use transparency:

Alpha

import matplotlib.pyplot as plt
 
plt.figure(figsize=(7, 4))
plt.scatter(x, y, alpha=0.6)
plt.title("Scatter with alpha")
plt.tight_layout()
plt.show()

Annotate points

Annotate

import matplotlib.pyplot as plt
 
plt.figure(figsize=(7, 4))
plt.scatter(x, y)
 
plt.annotate("top", xy=(7, 85), xytext=(6, 88), arrowprops={"arrowstyle": "->"})
 
plt.title("Annotated scatter")
plt.tight_layout()
plt.show()

Visualize it

Each dot below is one (x, y) observation — watch the cloud of points build up and notice the upward trend:

sketch A scatter plot shows one point per observation p5.js

Every (x, y) pair becomes one dot; the pattern of dots reveals whether the two variables relate.

🧪 Try It Yourself

Exercise 1 – Draw a scatter plot

Exercise 2 – Control transparency

Exercise 3 – Annotate a point

Continue to Pie Chart to see how (and when) to show parts of a whole.

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