Matplotlib Scatter Charts

last modified February 25, 2025

Matplotlib is a powerful Python library for creating static, animated, and interactive visualizations. Scatter charts are used to visualize the relationship between two variables. This tutorial covers how to create various types of scatter charts using Matplotlib.

Scatter charts are ideal for identifying trends, correlations, and outliers in data. Matplotlib provides a flexible and easy-to-use interface for creating scatter charts with customizations.

Basic Scatter Chart

This example demonstrates how to create a basic scatter chart.

basic_scatter_chart.py

import matplotlib.pyplot as plt

# Data
x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]

# Create a scatter chart
plt.scatter(x, y)

# Add labels and title
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.title("Basic Scatter Chart")

# Display the chart
plt.show()

The plt.scatter() function is used to create a scatter chart. The plt.show() function displays the chart.

Customizing Scatter Charts

This example demonstrates how to customize scatter charts with colors, sizes, and markers.

custom_scatter_chart.py

import matplotlib.pyplot as plt

# Data
x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]
sizes = [100, 200, 300, 400, 500]  # Marker sizes
colors = ['red', 'green', 'blue', 'purple', 'orange']  # Marker colors

# Create a scatter chart with custom styles
plt.scatter(x, y, s=sizes, c=colors, alpha=0.6, edgecolors="black")

# Add labels and title
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.title("Custom Scatter Chart")

# Display the chart
plt.show()

The s, c, alpha, and edgecolors parameters are used to customize the appearance of the markers.

Scatter Chart with Color Mapping

This example shows how to use color mapping to represent a third variable.

color_mapping_scatter.py

import matplotlib.pyplot as plt
import numpy as np

# Data
x = np.random.rand(50)
y = np.random.rand(50)
colors = np.random.rand(50)  # Third variable for color mapping
sizes = 1000 * np.random.rand(50)  # Third variable for size mapping

# Create a scatter chart with color mapping
plt.scatter(x, y, c=colors, s=sizes, alpha=0.6, cmap="viridis")

# Add a colorbar
plt.colorbar()

# Add labels and title
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.title("Scatter Chart with Color Mapping")

# Display the chart
plt.show()

The cmap parameter is used to apply a colormap to the markers. The plt.colorbar() function adds a colorbar to the chart.

Scatter Chart with Regression Line

This example demonstrates how to add a regression line to a scatter chart.

scatter_with_regression.py

import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import linregress

# Data
x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]

# Create a scatter chart
plt.scatter(x, y)

# Add a regression line
slope, intercept, r_value, p_value, std_err = linregress(x, y)
plt.plot(x, slope * np.array(x) + intercept, color="red", label="Regression Line")

# Add labels, title, and legend
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.title("Scatter Chart with Regression Line")
plt.legend()

# Display the chart
plt.show()

The linregress() function from scipy.stats is used to calculate the regression line. The plt.plot() function adds the regression line to the chart.

3D Scatter Chart

This example demonstrates how to create a 3D scatter chart.

3d_scatter_chart.py

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np

# Data
x = np.random.rand(50)
y = np.random.rand(50)
z = np.random.rand(50)

# Create a 3D scatter chart
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
ax.scatter(x, y, z)

# Add labels and title
ax.set_xlabel("X-axis")
ax.set_ylabel("Y-axis")
ax.set_zlabel("Z-axis")
ax.set_title("3D Scatter Chart")

# Display the chart
plt.show()

The mpl_toolkits.mplot3d module is used to create 3D scatter charts. The projection="3d" parameter enables 3D plotting.

Best Practices for Scatter Charts

Label Axes Clearly: Always label the X and Y axes to make the chart understandable.
Use Color Mapping: Use color mapping to represent a third variable effectively.
Choose Appropriate Markers: Use markers that are easy to distinguish and interpret.
Limit Data Points: Avoid cluttering the chart with too many data points.

Source

Matplotlib Scatter Chart Documentation

In this article, we have explored various types of scatter charts using Matplotlib, including basic, customized, color-mapped, regression, and 3D scatter charts.

Author

My name is Jan Bodnar, and I am a passionate programmer with extensive programming experience. I have been writing programming articles since 2007. To date, I have authored over 1,400 articles and 8 e-books. I possess more than ten years of experience in teaching programming.

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