Discrete colorbars in Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of discrete colorbar with 3 colors

What will be the number of color segments in the colorbar produced by this code?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import BoundaryNorm

cmap = plt.get_cmap('viridis', 3)
bounds = [0, 1, 2, 3]
norm = BoundaryNorm(bounds, cmap.N)

fig, ax = plt.subplots(figsize=(4, 1))
fig.subplots_adjust(bottom=0.5)
cb = plt.colorbar(plt.cm.ScalarMappable(norm=norm, cmap=cmap), cax=ax, boundaries=bounds, ticks=bounds)
plt.show()

A 3

B 4

C 2

D 1

💡 Hint

Count the number of intervals defined by the bounds.

The colorbar has 3 discrete colors because the bounds define 3 intervals: [0-1), [1-2), and [2-3).

❓ data_output

intermediate

Values mapped to colors using BoundaryNorm

Given the following code, what is the color index assigned to the value 2.5?

Matplotlib

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

bounds = [0, 1, 2, 3, 4]
cmap = plt.get_cmap('plasma', 4)
norm = BoundaryNorm(bounds, cmap.N)

value = 2.5
color_index = norm(value)
print(color_index)

A 0

B 2

C 3

D 1

💡 Hint

Check which interval 2.5 falls into between the bounds.

The value 2.5 lies between 2 and 3, which is the third interval (index 2, zero-based).

❓ visualization

advanced

Identify the correct discrete colorbar visualization

Which option shows the correct discrete colorbar with 5 colors and boundaries at [0, 10, 20, 30, 40, 50]?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import BoundaryNorm

cmap = plt.get_cmap('coolwarm', 5)
bounds = [0, 10, 20, 30, 40, 50]
norm = BoundaryNorm(bounds, cmap.N)

fig, ax = plt.subplots(figsize=(6, 1))
fig.subplots_adjust(bottom=0.5)
cb = plt.colorbar(plt.cm.ScalarMappable(norm=norm, cmap=cmap), cax=ax, boundaries=bounds, ticks=bounds)
plt.show()

A Colorbar with 6 color blocks and boundaries at 0, 8, 16, 24, 32, 40, 48

B Colorbar with smooth gradient blending colors continuously from 0 to 50

C Colorbar with 4 color blocks and boundaries at 0, 12, 24, 36, 48

D Colorbar with 5 distinct color blocks separated by clear boundaries at 0,10,20,30,40,50

💡 Hint

Discrete colorbars show distinct color blocks matching the number of intervals.

The code creates a discrete colorbar with 5 colors and boundaries exactly at the specified points, producing 5 distinct color blocks.

🔧 Debug

advanced

Error raised by incorrect BoundaryNorm usage

What error will this code raise?

Matplotlib

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

bounds = [0, 1, 2]
cmap = plt.get_cmap('viridis', 3)
norm = BoundaryNorm(bounds, 4)

value = 1.5
color_index = norm(value)
print(color_index)

A ValueError: Number of colors, N, must be one less than number of boundaries

B TypeError: 'BoundaryNorm' object is not callable

C IndexError: color index out of range

D No error, prints 1

💡 Hint

Check the relationship between cmap.N and the length of bounds.

BoundaryNorm expects the number of colors (N) to be one less than the number of boundaries. Here, N=4 but bounds length is 3, causing a ValueError.

🚀 Application

expert

Creating a discrete colorbar for categorical data

You have 4 categories labeled 'A', 'B', 'C', 'D' and want a discrete colorbar with colors red, green, blue, and yellow respectively. Which code snippet correctly creates this discrete colorbar?

A

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

colors = ['red', 'green', 'blue', 'yellow']
cmap = ListedColormap(colors)

fig, ax = plt.subplots(figsize=(6, 1))
fig.subplots_adjust(bottom=0.5)
cb = plt.colorbar(plt.cm.ScalarMappable(cmap=cmap), cax=ax)
cb.set_ticks([0,1,2,3])
cb.ax.set_xticklabels(['A', 'B', 'C', 'D'])
plt.show()

B

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

colors = ['red', 'green', 'blue', 'yellow']
cmap = plt.get_cmap('tab10', 4)
bounds = [0, 1, 2, 3, 4]
norm = BoundaryNorm(bounds, cmap.N)

fig, ax = plt.subplots(figsize=(6, 1))
fig.subplots_adjust(bottom=0.5)
cb = plt.colorbar(plt.cm.ScalarMappable(norm=norm, cmap=cmap), cax=ax, boundaries=bounds, ticks=bounds)
plt.show()

C

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

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

fig, ax = plt.subplots(figsize=(6, 1))
fig.subplots_adjust(bottom=0.5)
cb = plt.colorbar(plt.cm.ScalarMappable(norm=norm, cmap=cmap), cax=ax, boundaries=bounds, ticks=[0.5,1.5,2.5,3.5])
cb.ax.set_xticklabels(['A', 'B', 'C', 'D'])
plt.show()

D

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

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

fig, ax = plt.subplots(figsize=(6, 1))
fig.subplots_adjust(bottom=0.5)
cb = plt.colorbar(plt.cm.ScalarMappable(norm=norm, cmap=cmap), cax=ax, boundaries=bounds, ticks=bounds)
cb.ax.set_xticklabels(['A', 'B', 'C', 'D'])
plt.show()

💡 Hint

Use ListedColormap with BoundaryNorm and set ticks at midpoints for labels.

Option C correctly uses ListedColormap with BoundaryNorm and sets ticks at midpoints (0.5,1.5,2.5,3.5) to label categories properly. Other options either miss norm or have incorrect ticks.