Colorblind-friendly palettes in Matplotlib - Mini Project: Build & Apply

Colorblind-friendly palettes

📖 Scenario: You are working on a data visualization project. You want to make sure your charts are easy to understand for everyone, including people who are colorblind. Using colorblind-friendly palettes helps make your visuals clear and accessible.

🎯 Goal: Create a simple bar chart using a colorblind-friendly palette from matplotlib. You will first set up the data, then choose the palette, apply it to the bars, and finally display the chart.

📋 What You'll Learn

Create a dictionary called fruits with these exact entries: 'Apples': 10, 'Bananas': 15, 'Cherries': 7, 'Dates': 12

Create a variable called palette and set it to the colorblind-friendly palette 'tab10' from matplotlib

Use a for loop with variables fruit, count to iterate over fruits.items() and plot bars with colors from palette

Use plt.show() to display the bar chart

💡 Why This Matters

🌍 Real World

Colorblind-friendly palettes help make charts readable for people with color vision differences, improving accessibility in reports and presentations.

💼 Career

Data scientists and analysts must create visuals that communicate clearly to all audiences, including those with colorblindness.

1

DATA SETUP: Create the fruit count dictionary

Create a dictionary called fruits with these exact entries: 'Apples': 10, 'Bananas': 15, 'Cherries': 7, 'Dates': 12.

Matplotlib

# Create the fruits dictionary with counts
# Your code here

Hint

Use curly braces {} to create the dictionary with the exact fruit names and counts.

2

CONFIGURATION: Set the colorblind-friendly palette

Import matplotlib.pyplot as plt. Then create a variable called palette and set it to the colorblind-friendly palette 'tab10' from matplotlib.

Matplotlib

fruits = {'Apples': 10, 'Bananas': 15, 'Cherries': 7, 'Dates': 12}
# Import matplotlib.pyplot as plt and set palette to 'tab10'
# Your code here

Hint

Use plt.get_cmap('tab10') to get the colorblind-friendly palette.

3

CORE LOGIC: Plot bars using the colorblind-friendly palette

Use a for loop with variables fruit and count to iterate over fruits.items(). Plot bars with plt.bar() using colors from palette by indexing with the loop index.

Matplotlib

import matplotlib.pyplot as plt

fruits = {'Apples': 10, 'Bananas': 15, 'Cherries': 7, 'Dates': 12}

palette = plt.get_cmap('tab10')

# Use a for loop to plot bars with colors from palette
# Your code here

Hint

Use enumerate(fruits.items()) to get the index i and the fruit and count. Use palette(i / (len(fruits) - 1)) for the bar color.

4

OUTPUT: Display the bar chart

Use plt.show() to display the bar chart with the colorblind-friendly palette applied.

Matplotlib

import matplotlib.pyplot as plt

fruits = {'Apples': 10, 'Bananas': 15, 'Cherries': 7, 'Dates': 12}

palette = plt.get_cmap('tab10')

for i, (fruit, count) in enumerate(fruits.items()):
    plt.bar(fruit, count, color=palette(i / (len(fruits) - 1)))

# Display the plot
# Your code here

Hint

Call plt.show() to open the window with the bar chart.

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