Style sheets (ggplot, seaborn, dark_background) in Matplotlib - Mini Project: Build & Apply

Using Style Sheets in Matplotlib for Better Plots

📖 Scenario: You are a data analyst preparing visual reports. You want your charts to look nice and clear by using different style themes.

🎯 Goal: Learn how to apply different style sheets like ggplot, seaborn, and dark_background in Matplotlib to change the look of your plots easily.

📋 What You'll Learn

Create a simple dataset with sales numbers for three products.

Set a style sheet variable to choose the plot style.

Plot a bar chart using the chosen style.

Display the plot to see the style effect.

💡 Why This Matters

🌍 Real World

Data analysts and scientists often use style sheets to make their charts look professional and easier to understand.

💼 Career

Knowing how to quickly change plot styles helps you prepare reports and presentations that look polished and clear.

1

Create the sales data dictionary

Create a dictionary called sales with these exact entries: 'Product A': 150, 'Product B': 200, 'Product C': 300.

Matplotlib

# Create the sales dictionary with exact values
# Your code here

Hint

Use curly braces {} to create the dictionary and separate items with commas.

2

Set the style sheet variable

Create a variable called style and set it to the string 'ggplot'.

Matplotlib

sales = {'Product A': 150, 'Product B': 200, 'Product C': 300}
# Set the style variable to 'ggplot'
# Your code here

Hint

Assign the string 'ggplot' to the variable style.

3

Plot the bar chart using the style

Import matplotlib.pyplot as plt. Use plt.style.use(style) to apply the style. Then plot a bar chart with plt.bar using the keys and values from sales.

Matplotlib

sales = {'Product A': 150, 'Product B': 200, 'Product C': 300}
style = 'ggplot'
# Import matplotlib.pyplot as plt
# Apply the style using plt.style.use(style)
# Plot a bar chart with sales keys and values
# Your code here

Hint

Remember to import matplotlib.pyplot as plt before using it.

4

Display the plot

Use plt.show() to display the bar chart with the chosen style.

Matplotlib

import matplotlib.pyplot as plt

sales = {'Product A': 150, 'Product B': 200, 'Product C': 300}
style = 'ggplot'

plt.style.use(style)
plt.bar(sales.keys(), sales.values())
# Display the plot
# Your code here

Hint

Call plt.show() to open the plot window.

Practice

1. What does the plt.style.use('ggplot') command do in matplotlib?

easy

A. It changes the plot style to look like ggplot2 from R.

B. It creates a new plot with ggplot data.

C. It saves the plot as a ggplot file.

D. It resets the plot style to default.

Solution

1. Step 1: Understand plt.style.use()

   This function sets the style for all plots that follow.

2. Step 2: Recognize 'ggplot' style

   The 'ggplot' style mimics the look of the ggplot2 library from R, changing colors and grid lines.

3. Final Answer:

   It changes the plot style to look like ggplot2 from R. -> Option A

4. Quick Check:

   Style 'ggplot' changes plot appearance [OK]

Hint: Remember: plt.style.use sets the plot's look [OK]

Common Mistakes:

• Thinking it creates or saves plots
• Confusing style setting with data loading
• Assuming it resets style

2. Which of the following is the correct way to apply the 'dark_background' style in matplotlib?

easy

A. plt.style.use('dark_background')

B. plt.use.style('dark_background')

C. plt.style('dark_background')

D. plt.set_style('dark_background')

Solution

1. Step 1: Recall the syntax for applying styles

   The correct function is plt.style.use() with the style name as a string.

2. Step 2: Check each option

   Only plt.style.use('dark_background') matches the correct syntax: plt.style.use('dark_background').

3. Final Answer:

   plt.style.use('dark_background') -> Option A

4. Quick Check:

   Correct syntax is plt.style.use('style_name') [OK]

Hint: Use plt.style.use('style_name') to apply styles [OK]

Common Mistakes:

• Using plt.style() instead of plt.style.use()
• Swapping method order like plt.use.style()
• Using non-existent plt.set_style()

3. What will be the background color of the plot after running this code?

import matplotlib.pyplot as plt
plt.style.use('dark_background')
plt.plot([1, 2, 3], [4, 5, 6])
plt.show()

medium

A. White background with black grid lines

B. Black background with white grid lines

C. Gray background with no grid lines

D. Blue background with colored grid lines

Solution

1. Step 1: Identify the style used

   The code uses plt.style.use('dark_background'), which sets a dark theme.

2. Step 2: Understand dark_background style

   This style sets the plot background to black and grid lines to white or light colors for contrast.

3. Final Answer:

   Black background with white grid lines -> Option B

4. Quick Check:

   'dark_background' means dark plot background [OK]

Hint: dark_background style means black background [OK]

Common Mistakes:

• Assuming default white background
• Confusing grid line colors
• Thinking dark_background changes data colors only

4. Identify the error in this code snippet:

import matplotlib.pyplot as plt
plt.style.use('seaborn')
plt.plot([1, 2, 3], [3, 2, 1])
plt.show()

It raises an error: AttributeError: module 'matplotlib.pyplot' has no attribute 'style'

medium

A. plt.plot() is used incorrectly

B. The style name 'seaborn' is misspelled

C. matplotlib version is too old and lacks style module

D. plt.show() is missing parentheses

Solution

1. Step 1: Understand the error message

   The error says plt has no attribute 'style', meaning the style module is missing.

2. Step 2: Check possible causes

   This usually happens if matplotlib version is old and does not support style sheets.

3. Final Answer:

   matplotlib version is too old and lacks style module -> Option C

4. Quick Check:

   AttributeError on plt.style means old matplotlib [OK]

Hint: AttributeError on plt.style means update matplotlib [OK]

Common Mistakes:

• Assuming typo in style name
• Blaming plt.plot() syntax
• Forgetting plt.show() needs parentheses

5. You want to create two plots side by side: the first with 'ggplot' style and the second with 'seaborn' style. Which code correctly applies different styles to each subplot?

hard

A. plt.style.use('ggplot')
plt.subplot(1, 2, 1)
plt.plot([1,2])
plt.subplot(1, 2, 2)
plt.plot([2,1])
plt.style.use('seaborn')
plt.show()

B. plt.subplot(1, 2, 1)
plt.plot([1,2])
plt.subplot(1, 2, 2)
plt.plot([2,1])
plt.style.use(['ggplot', 'seaborn'])
plt.show()

C. plt.subplot(1, 2, 1)
plt.style.use('ggplot')
plt.plot([1,2])
plt.subplot(1, 2, 2)
plt.style.use('seaborn')
plt.plot([2,1])
plt.show()

D. with plt.style.context('ggplot'):
plt.subplot(1, 2, 1)
plt.plot([1,2])
with plt.style.context('seaborn'):
plt.subplot(1, 2, 2)
plt.plot([2,1])
plt.show()

Solution

1. Step 1: Understand style application scope

   plt.style.use() sets global style, affecting all plots after it.

2. Step 2: Use plt.style.context() for local styles

   To apply different styles to subplots, use plt.style.context() as a context manager around each plot.

3. Step 3: Check options

   Only with plt.style.context('ggplot'): plt.subplot(1, 2, 1) plt.plot([1,2]) with plt.style.context('seaborn'): plt.subplot(1, 2, 2) plt.plot([2,1]) plt.show() uses plt.style.context() correctly to apply different styles to each subplot.

4. Final Answer:

   Use plt.style.context() blocks for different subplot styles. -> Option D

5. Quick Check:

   Use style.context() for local style changes [OK]

Hint: Use plt.style.context() to apply styles locally [OK]

Common Mistakes:

• Using plt.style.use() multiple times globally
• Trying to pass list of styles to plt.style.use()
• Not using context manager for local styles