Data Science with Python Matplotlib and Seaborn Visualization Questions and Answers

Question 1

A data analyst has created a line plot using Matplotlib's `plt.plot()` but finds that the plot is not displayed when the script is run.
Which of the following functions must be called to render and display the plot?

Accepted Answer

plt.show()

Answer

plt.render()

Answer

plt.draw()

Answer

plt.display()

Question 2

You are tasked with visualizing the relationship between two continuous variables, 'total_bill' and 'tip', from a dataset. Additionally, you want to see the distribution of each of these variables individually along the axes of the same plot. Which Seaborn function is best suited for this purpose?

Accepted Answer

sns.jointplot()

Answer

sns.pairplot()

Answer

sns.scatterplot()

Answer

sns.boxplot()

Question 3

After generating a scatter plot with Matplotlib, you need to add a descriptive title to the plot and label the x-axis and y-axis. Which of the following code snippets correctly accomplishes this?

Accepted Answer

plt.title('My Title'); plt.xlabel('X-Axis'); plt.ylabel('Y-Axis')

Answer

plot.title('My Title'); plot.xlabel('X-Axis'); plot.ylabel('Y-Axis')

Answer

plt.set_title('My Title'); plt.set_xlabel('X-Axis'); plt.set_ylabel('Y-Axis')

Answer

plt.figure(title='My Title', xlabel='X-Axis', ylabel='Y-Axis')

Question 4

Which of the following statements best describes a primary advantage of using Seaborn over Matplotlib?

Accepted Answer

Seaborn is a high-level interface that creates visually appealing statistical plots with less code.

Answer

Seaborn provides more low-level control over every individual plot element.

Answer

Seaborn is required for creating basic plots like line charts and bar charts.

Answer

Seaborn offers faster rendering performance for simple visualizations.

Question 5

A data scientist needs to compare four different data distributions side-by-side in a single figure, arranged in a 2x2 grid. Which Matplotlib function is most commonly used to create the figure and the grid of subplots simultaneously?

Accepted Answer

plt.subplots(2, 2)

Answer

plt.grid(2, 2)

Answer

plt.figure(subplots=4)

Answer

plt.add_subplots(4)

Question 6

To ensure a consistent and professional look for all visualizations in a report, you want to apply a specific color scheme to all subsequent Seaborn plots. Which function should be used to set a default color palette, such as 'colorblind'?

Accepted Answer

sns.set_palette('colorblind')

Answer

sns.color_palette('colorblind')

Answer

sns.set(palette='colorblind')

Answer

sns.style(palette='colorblind')