Colorblind-friendly palettes in Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of color palette display code

What will be the output of this code snippet that uses a colorblind-friendly palette from matplotlib?

Matplotlib

import matplotlib.pyplot as plt
import seaborn as sns

palette = sns.color_palette("colorblind")
plt.figure(figsize=(6, 1))
plt.imshow([palette], aspect='auto')
plt.axis('off')
plt.show()

A A horizontal bar showing 8 distinct colors suitable for colorblind viewers

B A vertical bar showing 6 colors with random shades of blue

C A scatter plot with points colored in rainbow colors

D An error because 'colorblind' is not a valid palette name in seaborn

💡 Hint

Think about what sns.color_palette("colorblind") returns and how imshow displays colors.

The 'colorblind' palette in seaborn provides 8 distinct colors designed for colorblind accessibility. The code displays these colors as a horizontal bar using imshow.

❓ data_output

intermediate

Number of colors in a colorblind-friendly palette

How many colors are in the default matplotlib 'tab10' palette, which is also considered colorblind-friendly?

Matplotlib

import matplotlib.pyplot as plt
palette = plt.get_cmap('tab10')
num_colors = palette.N
print(num_colors)

A 10

B 8

C 12

D 6

💡 Hint

Check the attribute that gives the number of colors in the colormap object.

The 'tab10' colormap in matplotlib contains exactly 10 distinct colors, designed to be distinguishable and colorblind-friendly.

❓ visualization

advanced

Identify the colorblind-friendly palette used

Given this code, which colorblind-friendly palette is being used to plot the bars?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

values = [5, 7, 3, 8, 6]
colors = plt.get_cmap('tab20c').colors[:5]
plt.bar(range(5), values, color=colors)
plt.show()

A colorblind

B tab20c

C viridis

D Set1

💡 Hint

Look at the colormap name used in plt.get_cmap.

The code uses the 'tab20c' colormap from matplotlib, which is a colorblind-friendly palette with 20 colors in a cyclic pattern.

🔧 Debug

advanced

Why does this colorblind palette code raise an error?

This code raises an error. What is the cause?

Matplotlib

import matplotlib.pyplot as plt
import seaborn as sns

palette = sns.color_palette("colorblind", 10)
plt.figure(figsize=(6, 1))
plt.imshow([palette], aspect='auto')
plt.axis('off')
plt.show()

A plt.axis('off') is invalid syntax

B The 'colorblind' palette name is misspelled

C imshow cannot display color palettes

D The 'colorblind' palette only supports 8 colors, so requesting 10 causes an error

💡 Hint

Check the allowed number of colors for the 'colorblind' palette in seaborn.

The 'colorblind' palette in seaborn has a fixed set of 8 colors. Requesting 10 colors causes a ValueError.

🚀 Application

expert

Choosing a colorblind-friendly palette for a scatter plot

You want to plot a scatter plot with 7 categories, ensuring colors are distinguishable for colorblind viewers. Which palette should you choose from matplotlib?

A 'Pastel1' because it has soft colors but only 5 distinct colors

B 'viridis' because it is a sequential palette with smooth color changes

C 'tab10' because it has 10 distinct colors designed for colorblind accessibility

D 'Accent' because it has only 4 colors and is not colorblind-friendly

💡 Hint

Consider the number of categories and colorblind accessibility of palettes.

The 'tab10' palette has 10 distinct colors and is designed to be colorblind-friendly, making it suitable for 7 categories.

Practice

1. What is the main purpose of using a colorblind-friendly palette in matplotlib charts?

easy

A. To reduce the file size of the chart image

B. To add more colors to the chart for decoration

C. To make charts easier to read for people with color vision differences

D. To speed up the chart rendering process

Solution

1. Step 1: Understand colorblind-friendly palettes

   These palettes are designed to help people with color vision differences distinguish chart elements clearly.

2. Step 2: Identify the main goal

   The goal is to improve chart readability and accessibility for everyone, especially those with colorblindness.

3. Final Answer:

   To make charts easier to read for people with color vision differences -> Option C

4. Quick Check:

   Accessibility = C [OK]

Hint: Think about accessibility and readability for all viewers [OK]

Common Mistakes:

• Confusing decoration with accessibility
• Thinking it affects file size or speed
• Assuming it adds random colors

2. Which of the following is the correct way to set a colorblind-friendly palette using seaborn with matplotlib?

easy

A. sns.set_palette('colorblind')

B. plt.color_palette('colorblind')

C. sns.colorblind_palette()

D. plt.set_palette('colorblind')

Solution

1. Step 1: Recall seaborn palette setting syntax

   Seaborn uses sns.set_palette() to set the color palette globally.

2. Step 2: Identify the correct palette name

   The palette name for colorblind-friendly colors is exactly 'colorblind'.

3. Final Answer:

   sns.set_palette('colorblind') -> Option A

4. Quick Check:

   Seaborn set_palette with 'colorblind' = B [OK]

Hint: Use sns.set_palette('colorblind') to apply palette [OK]

Common Mistakes:

• Using plt instead of sns for palette setting
• Calling a non-existent function sns.colorblind_palette()
• Using wrong function names like plt.set_palette

3. What will be the output of the following code snippet?

import matplotlib.pyplot as plt
import seaborn as sns
sns.set_palette('colorblind')
colors = sns.color_palette()
print(colors[0])

medium

A. (0.0, 0.0, 0.0)

B. (0.0, 0.45, 0.70)

C. (1.0, 0.0, 0.0)

D. Error: palette not found

Solution

1. Step 1: Understand sns.set_palette and sns.color_palette

   Setting 'colorblind' palette changes the default colors to a known colorblind-friendly set. Calling sns.color_palette() returns the current palette colors.

2. Step 2: Identify the first color in 'colorblind' palette

   The first color in seaborn's 'colorblind' palette is approximately (0.0, 0.45, 0.70), a blue shade.

3. Final Answer:

   (0.0, 0.45, 0.70) -> Option B

4. Quick Check:

   First color in 'colorblind' palette = A [OK]

Hint: Remember 'colorblind' palette starts with blue (0.0, 0.45, 0.7) [OK]

Common Mistakes:

• Expecting black or red as first color
• Confusing palette names causing error
• Not calling sns.set_palette before sns.color_palette

4. Identify the error in this code that tries to apply a colorblind-friendly palette:

import seaborn as sns
import matplotlib.pyplot as plt
sns.set_palette('colorblind')
plt.plot([1, 2, 3], [4, 5, 6], color='colorblind')
plt.show()

medium

A. Using 'colorblind' as a single color in plt.plot is invalid

B. sns.set_palette should be called after plt.plot

C. Missing import for matplotlib.colors

D. plt.show() is missing parentheses

Solution

1. Step 1: Analyze plt.plot color argument

   The color parameter expects a single color value, not a palette name.

2. Step 2: Understand how palettes are applied

   Palettes set default colors for multiple plots, but you cannot use the palette name as a color string directly.

3. Final Answer:

   Using 'colorblind' as a single color in plt.plot is invalid -> Option A

4. Quick Check:

   Palette name ≠ single color string [OK]

Hint: Palette sets defaults; don't use palette name as color string [OK]

Common Mistakes:

• Thinking palette name can be used as a color string
• Wrong order of sns.set_palette and plotting
• Forgetting plt.show() parentheses

5. You want to create a bar chart with 5 bars using matplotlib and ensure it is colorblind-friendly. Which code snippet correctly applies a colorblind-friendly palette and plots the bars with different colors?

hard

A. import seaborn as sns
import matplotlib.pyplot as plt
plt.bar(range(5), [1,2,3,4,5])
sns.set_palette('colorblind')
plt.show()

B. import matplotlib.pyplot as plt
plt.bar(range(5), [1,2,3,4,5], color='colorblind')
plt.show()

C. import seaborn as sns
import matplotlib.pyplot as plt
colors = sns.set_palette('colorblind')
plt.bar(range(5), [1,2,3,4,5], color=colors)
plt.show()

D. import seaborn as sns
import matplotlib.pyplot as plt
sns.set_palette('colorblind')
colors = sns.color_palette()
plt.bar(range(5), [1,2,3,4,5], color=colors)
plt.show()

Solution

1. Step 1: Apply the colorblind palette correctly

   Use sns.set_palette('colorblind') to set the palette globally, then get the colors with sns.color_palette().

2. Step 2: Use the colors list in plt.bar

   Pass the list of colors to the color parameter to color each bar differently.

3. Final Answer:

   The code that sets the palette, retrieves the colors list, and passes it to plt.bar color parameter -> Option D

4. Quick Check:

   Set palette + use colors list = A [OK]

Hint: Set palette, get colors list, pass to color parameter [OK]

Common Mistakes:

• Passing palette name as color string
• Assigning sns.set_palette() return to colors
• Not passing colors list to bar plot