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.

List all Python tutorials.