Python Matplotlib - Creating Multiple Scatter Plots in the Same Figure

Multiple Scatter Plots in Matplotlib

When visualizing relationships between multiple datasets, plotting multiple scatter plots in the same figure can be very useful. Python's Matplotlib library provides an easy way to achieve this using the plt.scatter() function, combined with customizable markers, colors, and labels.

Example 1: Two Scatter Plots

This example shows how to plot two scatter plots in the same figure:

import matplotlib.pyplot as plt

# Data for the first scatter plot
x1 = [1, 2, 3, 4, 5]
y1 = [2, 3, 5, 7, 11]

# Data for the second scatter plot
x2 = [1, 2, 3, 4, 5]
y2 = [1, 4, 6, 8, 10]

# Create scatter plots
plt.scatter(x1, y1, color='blue', label='Dataset 1', marker='o')
plt.scatter(x2, y2, color='red', label='Dataset 2', marker='x')

# Add labels and title
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Two Scatter Plots')

# Add legend
plt.legend()

# Show plot
plt.show()

Explanation

  1. The plt.scatter() function is called twice, once for each dataset.
  2. Different color and marker parameters distinguish the datasets.
  3. The plt.legend() function is used to identify the datasets.
Matplotlib Two Scatter Plots

Example 2: More Than Two Scatter Plots

This example demonstrates how to plot three scatter plots in the same figure:

import matplotlib.pyplot as plt

# Data for three scatter plots
x1 = [1, 2, 3, 4, 5]
y1 = [2, 3, 5, 7, 11]
x2 = [1, 2, 3, 4, 5]
y2 = [1, 4, 6, 8, 10]
x3 = [1, 2, 3, 4, 5]
y3 = [5, 7, 8, 10, 15]

# Create scatter plots
plt.scatter(x1, y1, color='blue', label='Dataset 1', marker='o')
plt.scatter(x2, y2, color='red', label='Dataset 2', marker='x')
plt.scatter(x3, y3, color='green', label='Dataset 3', marker='^')

# Add labels and title
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Multiple Scatter Plots')

# Add legend
plt.legend()

# Show plot
plt.show()

Explanation

  1. Three scatter plots are created by calling plt.scatter() three times.
  2. Each dataset is assigned a unique color and marker to make it distinguishable.
  3. The legend is automatically updated to reflect all three datasets.
Matplotlib More Than Two Scatter Plots

Customization Tips

You can further customize these scatter plots by adjusting marker sizes, transparencies, or adding gridlines:

  • Use the s parameter to control marker size.
  • Set the alpha parameter for transparency.
  • Add gridlines with plt.grid(True).

Summary

In this tutorial, we covered:

  • How to create multiple scatter plots in the same figure.
  • How to customize their appearance using colors, markers, and legends.
  • Examples for both two and more than two scatter plots.

With these techniques, you can effectively visualize and compare multiple datasets in Python using Matplotlib.



Python Libraries