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

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

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

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

Hint

Use plt.show() to see the colorbar.

Practice

1. What is the main purpose of using a discrete colorbar in a matplotlib plot?

easy

A. To increase the size of the plot figure

B. To create a continuous gradient of colors for smooth data

C. To add labels to the x-axis of a plot

D. To show data with distinct color steps for clear category separation

Solution

1. Step 1: Understand colorbar types

   Continuous colorbars show smooth color changes, while discrete colorbars use fixed color steps.

2. Step 2: Identify the purpose of discrete colorbars

   Discrete colorbars help separate data into clear categories using distinct colors.

3. Final Answer:

   To show data with distinct color steps for clear category separation -> Option D

4. Quick Check:

   Discrete colorbar = distinct color steps [OK]

Hint: Discrete colorbars separate categories with fixed colors [OK]

Common Mistakes:

• Confusing discrete with continuous colorbars
• Thinking colorbars change plot size
• Mixing colorbar purpose with axis labeling

2. Which of the following code snippets correctly creates a discrete colorbar with 3 color steps using matplotlib?

easy

A. cmap = ListedColormap(['red', 'green', 'blue'])
norm = Normalize(0, 3)

B. cmap = LinearSegmentedColormap(['red', 'green', 'blue'])
norm = Normalize(0, 3)

C. cmap = ListedColormap(['red', 'green', 'blue'])
norm = BoundaryNorm([0, 1, 2, 3], cmap.N)

D. cmap = LinearSegmentedColormap(['red', 'green', 'blue'])
norm = BoundaryNorm([0, 1, 2, 3], cmap.N)

Solution

1. Step 1: Identify correct colormap for discrete colors

   ListedColormap is used for discrete colors, LinearSegmentedColormap is for continuous gradients.

2. Step 2: Check normalization method

   BoundaryNorm with boundaries defines discrete steps; Normalize is for continuous scaling.

3. Final Answer:

   cmap = ListedColormap(['red', 'green', 'blue'])\nnorm = BoundaryNorm([0, 1, 2, 3], cmap.N) -> Option C

4. Quick Check:

   ListedColormap + BoundaryNorm = discrete colorbar [OK]

Hint: Use ListedColormap + BoundaryNorm for discrete colorbars [OK]

Common Mistakes:

• Using Normalize instead of BoundaryNorm
• Using LinearSegmentedColormap for discrete colors
• Incorrect boundary list length

3. What will be the output color for a value of 1.5 in this discrete colorbar setup?

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

cmap = ListedColormap(['yellow', 'orange', 'red'])
norm = BoundaryNorm([0, 1, 2, 3], cmap.N)
color = cmap(norm(1.5))
print(color)

medium

A. (1.0, 1.0, 0.0, 1.0) # yellow color

B. (1.0, 0.647, 0.0, 1.0) # orange color

C. (1.0, 0.0, 0.0, 1.0) # red color

D. (0.5, 0.5, 0.5, 1.0) # gray color

Solution

1. Step 1: Understand BoundaryNorm mapping

   BoundaryNorm maps values 0-1 to first color (yellow), 1-2 to second (orange), 2-3 to third (red).

2. Step 2: Find color for value 1.5

   1.5 lies between 1 and 2, so it maps to the second color: orange (RGB approx (1.0, 0.647, 0.0, 1.0)).

3. Final Answer:

   (1.0, 0.647, 0.0, 1.0) # orange color -> Option B

4. Quick Check:

   Value 1.5 in 1-2 range = orange [OK]

Hint: Check which boundary interval the value falls into [OK]

Common Mistakes:

• Choosing first or third color incorrectly
• Confusing Normalize with BoundaryNorm behavior
• Misreading boundary intervals

4. Identify the error in this code that tries to create a discrete colorbar with 4 colors:

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

cmap = ListedColormap(['blue', 'green', 'yellow', 'red'])
norm = BoundaryNorm([0, 1, 2, 3], cmap.N)
plt.colorbar(plt.cm.ScalarMappable(norm=norm, cmap=cmap))
plt.show()

medium

A. Boundary list length should be one more than number of colors

B. ListedColormap cannot have more than 3 colors

C. plt.colorbar requires a continuous colormap

D. ScalarMappable is not compatible with BoundaryNorm

Solution

1. Step 1: Check boundary list length

   BoundaryNorm boundaries must have length = number of colors + 1 to define intervals correctly.

2. Step 2: Compare boundaries and colors

   Here, 4 colors but only 4 boundaries given; should be 5 boundaries like [0,1,2,3,4].

3. Final Answer:

   Boundary list length should be one more than number of colors -> Option A

4. Quick Check:

   Boundaries = colors + 1 [OK]

Hint: Boundaries count = colors count + 1 for discrete colorbars [OK]

Common Mistakes:

• Using equal boundaries and colors length
• Thinking ScalarMappable is incompatible
• Believing ListedColormap limits colors

5. You have data with 5 categories labeled 0 to 4. You want a discrete colorbar with distinct colors for each category and labels shown on the colorbar ticks. Which approach correctly achieves this in matplotlib?

hard

A. Use ListedColormap with 5 colors, BoundaryNorm with boundaries [0,1,2,3,4,5], and set colorbar ticks at [0.5,1.5,2.5,3.5,4.5] with labels ['0','1','2','3','4']

B. Use LinearSegmentedColormap with 5 colors and Normalize from 0 to 4, then set colorbar ticks at [0,1,2,3,4]

C. Use ListedColormap with 4 colors, BoundaryNorm with boundaries [0,1,2,3,4], and set colorbar ticks at [0,1,2,3]

D. Use ListedColormap with 5 colors, Normalize from 0 to 5, and set colorbar ticks at [0,1,2,3,4]

Solution

1. Step 1: Set colormap and boundaries for 5 categories

   ListedColormap with 5 colors matches 5 categories; BoundaryNorm boundaries must be length 6 for 5 intervals.

2. Step 2: Position ticks between boundaries

   Ticks at midpoints like 0.5,1.5,... label categories clearly on discrete colorbar.

3. Final Answer:

   Use ListedColormap with 5 colors, BoundaryNorm with boundaries [0,1,2,3,4,5], and set colorbar ticks at [0.5,1.5,2.5,3.5,4.5] with labels ['0','1','2','3','4'] -> Option A

4. Quick Check:

   Boundaries + mid-ticks label discrete categories [OK]

Hint: Use boundaries + midpoints for ticks to label categories [OK]

Common Mistakes:

• Using continuous colormap for discrete data
• Setting ticks at boundaries instead of midpoints
• Mismatching number of colors and boundaries