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.