Style sheets (ggplot, seaborn, dark_background) in Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ visualization

intermediate

Effect of ggplot style on a line plot

You run the following code to plot a simple line graph using matplotlib with the ggplot style applied. What will be the main visible difference compared to the default style?

Matplotlib

import matplotlib.pyplot as plt
plt.style.use('ggplot')
plt.plot([1, 2, 3, 4], [10, 20, 25, 30])
plt.show()

A The plot will have a gray background with white grid lines and red plot lines.

B The plot will have no grid and blue plot lines on a white background.

C The plot will have a dark background with bright colored lines and no grid.

D The plot will have a white grid with red lines and a light gray background.

💡 Hint

Think about how ggplot style changes the background and grid colors.

The ggplot style in matplotlib sets a gray background with white grid lines and red plot lines by default. This mimics the ggplot2 style from R.

❓ Predict Output

intermediate

Output colors with dark_background style

What color will the line plot be when using the dark_background style with the following code?

Matplotlib

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

A The line will be blue on a gray background.

B The line will be black on a white background.

C The line will be white on a black background.

D The line will be red on a white background.

💡 Hint

Consider what the dark_background style does to the figure background and default line colors.

The dark_background style sets the figure background to black and the default line color to white for good contrast.

❓ data_output

advanced

Number of grid lines with seaborn style

Using the seaborn style, you plot a line graph with x values from 0 to 4 and y values as their squares. How many vertical grid lines will appear by default?

Matplotlib

import matplotlib.pyplot as plt
plt.style.use('seaborn')
plt.plot(range(5), [x**2 for x in range(5)])
plt.show()

A No vertical grid lines

B 5 vertical grid lines

C 4 vertical grid lines

D 6 vertical grid lines

💡 Hint

Check how matplotlib sets ticks and grids by default with seaborn style.

By default, matplotlib places ticks at the edges and between points, so for 5 x values, 6 vertical grid lines appear with seaborn style.

🧠 Conceptual

advanced

Understanding style sheet impact on plot elements

Which of the following statements about matplotlib style sheets ggplot, seaborn, and dark_background is correct?

A <code>seaborn</code> style adds a white grid on a light gray background and changes font sizes.

B <code>ggplot</code> style uses a dark background and bright colored lines by default.

C <code>dark_background</code> style sets a white background with black grid lines.

D <code>seaborn</code> style disables grid lines by default.

💡 Hint

Recall the visual themes each style sheet applies.

The seaborn style applies a light gray background with white grid lines and adjusts font sizes for clarity. The other options incorrectly describe the styles.

🔧 Debug

expert

Why does this style sheet code raise an error?

You run this code but get a FileNotFoundError. What is the cause?

Matplotlib

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

A The plot data lists have different lengths causing an error.

B The style name 'seaborn-dark' does not exist in matplotlib's default styles.

C The matplotlib module is not imported correctly.

D The plot command is missing a required argument.

💡 Hint

Check the exact style sheet names available in matplotlib.

The style seaborn-dark is not a valid default style in matplotlib, so trying to use it raises a FileNotFoundError.

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