Scatter Plots

Visualize the relationship between two continuous variables

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Explanation

Scatter plots show how two numerical variables relate to each other. Each point = one observation.

python

fig, ax = plt.subplots()
ax.scatter(df['height'], df['weight'],
           alpha=0.5, color='steelblue', s=30)
ax.set_xlabel('Height (cm)')
ax.set_ylabel('Weight (kg)')
ax.set_title('Height vs Weight')
plt.show()

Key parameters:

s — marker size (default 20)
c — color (can be an array to color-code by a third variable)
alpha — transparency (essential for overlapping points)
cmap — colormap when c is numeric ('viridis', 'coolwarm', 'Reds')

Color by a third variable:

python scatter = ax.scatter(x, y, c=df['age'], cmap='viridis', s=40) fig.colorbar(scatter, label='Age')

When to use scatter vs line:

Scatter: no inherent order between points (height vs weight)
Line: points have a meaningful order (time series)

Examples

Color-coded scatter plot

c= accepts a list of colors to color each point individually

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(0)
n = 100
x = np.random.rand(n) * 100  # study hours × 10
y = x * 0.8 + np.random.randn(n) * 10  # scores
grade = np.where(y > 70, 'Pass', 'Fail')
colors = np.where(y > 70, 'green', 'red')

fig, ax = plt.subplots()
ax.scatter(x, y, c=colors, alpha=0.6)
ax.set_xlabel('Study Hours')
ax.set_ylabel('Score')
ax.set_title('Study Hours vs Score')

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