Styling and themes in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this seaborn plot style code?

Consider the following Python code using seaborn and matplotlib to set a style and plot a simple line chart. What style is applied to the plot background?

Data Analysis Python

import seaborn as sns
import matplotlib.pyplot as plt

sns.set_style('darkgrid')
plt.plot([1, 2, 3], [4, 5, 6])
plt.show()

A The plot has a white background with grid lines.

B The plot has a dark background with grid lines.

C The plot has a white background without grid lines.

D The plot has a dark background without grid lines.

💡 Hint

Check seaborn's documentation for the 'darkgrid' style description.

The 'darkgrid' style in seaborn applies a white background with gray grid lines to the plot.

❓ data_output

intermediate

What is the color of bars in this matplotlib bar chart with a custom style?

Given this code snippet, what color will the bars be in the resulting bar chart?

Data Analysis Python

import matplotlib.pyplot as plt
plt.style.use('ggplot')
plt.bar(['A', 'B', 'C'], [5, 7, 3])
plt.show()

A Blue bars with white grid lines.

B Red bars with gray grid lines.

C Gray bars with white grid lines.

D Green bars with white grid lines.

💡 Hint

The 'ggplot' style uses a gray color palette for bars.

The 'ggplot' style in matplotlib uses gray bars and white grid lines by default.

❓ visualization

advanced

Which option produces a seaborn heatmap with the 'white' style applied?

You want to create a heatmap with a white background and no grid lines using seaborn. Which code snippet correctly applies the 'white' style before plotting?

Data Analysis Python

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
data = np.random.rand(5,5)

A

sns.set_style('dark')
sns.heatmap(data)
plt.show()

B

sns.set_style('white')
sns.heatmap(data)
plt.show()

C

sns.set_style('whitegrid')
sns.heatmap(data)
plt.show()

D

sns.set_style('ticks')
sns.heatmap(data)
plt.show()

💡 Hint

The 'white' style removes grid lines and sets a white background.

Using sns.set_style('white') sets a white background with no grid lines, ideal for clean heatmaps.

🧠 Conceptual

advanced

What does the matplotlib style context manager do?

What is the effect of using matplotlib's style.context in this code snippet?

Data Analysis Python

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

A Both plots use the 'seaborn-dark' style.

B Neither plot uses the 'seaborn-dark' style.

C Only the second plot uses the 'seaborn-dark' style; the first uses default style.

D Only the first plot uses the 'seaborn-dark' style; the second uses default style.

💡 Hint

The style.context applies style only inside the with block.

The style.context temporarily applies the style only inside the with block; outside it reverts to default.

🔧 Debug

expert

Why does this seaborn style setting not change the plot appearance?

This code tries to set a seaborn style but the plot looks unchanged. What is the reason?

Data Analysis Python

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

A plt.style.use('classic') overrides sns.set_style('darkgrid'), so the plot uses 'classic' style.

B sns.set_style('darkgrid') overrides plt.style.use('classic'), so the plot uses 'darkgrid' style.

C Both styles combine, resulting in a mixed style plot.

D Neither style is applied because they conflict, so default style is used.

💡 Hint

The last style setting before plotting takes effect.

plt.style.use('classic') is called after sns.set_style, so it overrides the seaborn style.

Practice

1. What is the main purpose of using styling and themes in data visualization?

easy

A. To change the data values automatically

B. To increase the size of the dataset

C. To speed up data processing

D. To make charts look better and easier to understand

Solution

1. Step 1: Understand the role of styling in charts

   Styling and themes improve the visual appeal and clarity of charts.

2. Step 2: Identify the correct purpose

   They help viewers understand data better by making charts clearer and more attractive.

3. Final Answer:

   To make charts look better and easier to understand -> Option D

4. Quick Check:

   Styling improves chart clarity [OK]

Hint: Styling improves look and clarity of charts [OK]

Common Mistakes:

• Confusing styling with data processing
• Thinking styling changes data values
• Assuming styling increases dataset size

2. Which of the following is the correct way to apply a style named 'ggplot' using matplotlib in Python?

easy

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

B. plt.style('ggplot')

C. style.use('ggplot')

D. plt.use.style('ggplot')

Solution

1. Step 1: Recall matplotlib style syntax

   The correct syntax to apply a style is plt.style.use('style_name').

2. Step 2: Match the correct option

   plt.style.use('ggplot') matches this syntax exactly for 'ggplot'.

3. Final Answer:

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

4. Quick Check:

   Use plt.style.use('style') to apply style [OK]

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

Common Mistakes:

• Using plt.style('style') without .use
• Calling style.use without plt prefix
• Incorrect method order like plt.use.style

3. What will be the effect of running this code snippet?

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

medium

A. A line plot with seaborn-dark style applied

B. A line plot with default matplotlib style

C. Syntax error due to wrong style name

D. No plot will be shown

Solution

1. Step 1: Understand plt.style.use('seaborn-dark')

   This line sets the plot style to 'seaborn-dark', changing colors and grid style.

2. Step 2: Analyze the plot commands

   plt.plot creates a line plot, plt.show displays it with the applied style.

3. Final Answer:

   A line plot with seaborn-dark style applied -> Option A

4. Quick Check:

   Style changes plot look [OK]

Hint: plt.style.use sets style before plotting [OK]

Common Mistakes:

• Assuming default style is used after plt.style.use
• Thinking style name causes syntax error
• Expecting no plot to show without plt.show

4. Identify the error in this code that tries to apply a style and plot data:

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

medium

A. plt.plot arguments are reversed

B. Wrong style name 'dark_background'

C. Missing parentheses in plt.show call

D. plt.style.use should be called after plt.plot

Solution

1. Step 1: Check plt.show usage

   plt.show is a function and needs parentheses to execute.

2. Step 2: Verify style name and plot arguments

   'dark_background' is a valid style, and plot arguments are correctly ordered.

3. Final Answer:

   Missing parentheses in plt.show call -> Option C

4. Quick Check:

   plt.show() needs parentheses to display plot [OK]

Hint: Always call plt.show() with parentheses [OK]

Common Mistakes:

• Forgetting parentheses on plt.show
• Assuming style name is invalid without checking
• Thinking plot arguments order causes error

5. You want to create a bar chart with a style that uses a dark background and grid lines. Which code snippet correctly applies the style and plots the data?

import matplotlib.pyplot as plt
data = [5, 7, 3]
labels = ['A', 'B', 'C']
# Choose the correct code to apply style and plot

hard

A. plt.bar(labels, data)
plt.style.use('dark_background')
plt.show()

B. plt.style.use('dark_background')
plt.bar(labels, data)
plt.show()

C. plt.style.use('ggplot')
plt.bar(labels, data)
plt.show()

D. plt.style('dark_background')
plt.bar(labels, data)
plt.show()

Solution

1. Step 1: Apply style before plotting

   Style must be set before plotting to affect the chart appearance.

2. Step 2: Check style name and plotting method

   'dark_background' is the correct style for dark theme with grid lines; plt.bar plots bar chart correctly.

3. Step 3: Verify plt.show() usage

   plt.show() is called with parentheses to display the plot.

4. Final Answer:

   plt.style.use('dark_background') plt.bar(labels, data) plt.show() -> Option B

5. Quick Check:

   Set style before plot, use plt.show() [OK]

Hint: Set style before plotting, call plt.show() with () [OK]

Common Mistakes:

• Setting style after plotting
• Using plt.style instead of plt.style.use
• Forgetting parentheses on plt.show