Discrete colorbars in Matplotlib - Mini Project: Build & Apply

Discrete colorbars

📖 Scenario: You are working on a data visualization project where you want to show different categories with distinct colors. Instead of a smooth gradient, you want a colorbar with clear, separate color blocks.

🎯 Goal: Create a discrete colorbar using matplotlib that shows exactly 4 distinct colors representing 4 categories.

📋 What You'll Learn

Create a list called colors with exactly 4 color names or hex codes.

Create a ListedColormap from the colors list and assign it to cmap.

Create a BoundaryNorm with boundaries [0, 1, 2, 3, 4] and assign it to norm.

Use matplotlib.pyplot to create a figure and axis.

Add a colorbar to the axis using matplotlib.colorbar.ColorbarBase with the cmap and norm.

Display the plot.

💡 Why This Matters

🌍 Real World

Discrete colorbars are useful when you want to show categories or groups clearly in data visualizations, such as different risk levels, types of land use, or survey responses.

💼 Career

Data scientists and analysts often need to create clear and understandable visualizations. Knowing how to make discrete colorbars helps communicate categorical data effectively.

1

Create a list of 4 colors

Create a list called colors with these exact colors: 'red', 'green', 'blue', and 'yellow'.

Matplotlib

# Create a list called colors with 'red', 'green', 'blue', 'yellow'
# Your code here

Need a hint?

Use square brackets and separate colors with commas.

2

Create a ListedColormap and BoundaryNorm

Import ListedColormap and BoundaryNorm from matplotlib.colors. Then create a ListedColormap called cmap from the colors list. Also create a BoundaryNorm called norm with boundaries [0, 1, 2, 3, 4] and number of colors equal to the length of colors.

Matplotlib

colors = ['red', 'green', 'blue', 'yellow']
# Import ListedColormap and BoundaryNorm
# Create cmap from colors
# Create norm with boundaries [0, 1, 2, 3, 4]
# Your code here

Need a hint?

Use ListedColormap(colors) and BoundaryNorm([0, 1, 2, 3, 4], ncolors=len(colors)).

3

Create figure and axis, add discrete colorbar

Import matplotlib.pyplot as plt. Create a figure and axis using plt.subplots(). Then import ColorbarBase from matplotlib.colorbar and create a colorbar on the axis using ColorbarBase with the cmap and norm. Use orientation='vertical' for the colorbar.

Matplotlib

from matplotlib.colors import ListedColormap, BoundaryNorm

colors = ['red', 'green', 'blue', 'yellow']
cmap = ListedColormap(colors)
norm = BoundaryNorm([0, 1, 2, 3, 4], ncolors=len(colors))

# Import matplotlib.pyplot as plt
# Create figure and axis
# Import ColorbarBase
# Create colorbar on axis with cmap and norm
# Your code here

Need a hint?

Use plt.subplots() and ColorbarBase with the correct arguments.

4

Display the plot

Use plt.show() to display the figure with the discrete colorbar.

Matplotlib

from matplotlib.colors import ListedColormap, BoundaryNorm
import matplotlib.pyplot as plt
from matplotlib.colorbar import ColorbarBase

colors = ['red', 'green', 'blue', 'yellow']
cmap = ListedColormap(colors)
norm = BoundaryNorm([0, 1, 2, 3, 4], ncolors=len(colors))

fig, ax = plt.subplots()
cb = ColorbarBase(ax, cmap=cmap, norm=norm, orientation='vertical')

# Display the plot
# Your code here

Need a hint?

Use plt.show() to see the colorbar.