Python for Data Analysis: Step-By-Step with Projects - Relationship of Multiple Features

This lesson explores visualizing relationships between more than two features using Seaborn. It covers using parameters like hue, col, and style to create histograms, box plots, bar plots, strip plots, pair plots, and scatter plots. The video demonstrates how to visualize data with multiple features, such as temperature, pressure, and PM 2.5, across different quarters and years. It emphasizes the importance of focusing on the question at hand and keeping visualizations simple and effective.

What is the primary purpose of using the hue parameter in Seaborn plots?

• To add a legend to the plot

• To change the plot type

• To color the plot based on a feature

• To adjust the size of the plot

In the Seaborn distplot example, what does the col parameter do?

• It adjusts the size of the plot

• It defines subsets to plot separately

• It changes the color of the plot

• It adds a title to the plot

How does a box plot differ from a bar plot in Seaborn?

• Both show the distribution

• A box plot shows the mean, while a bar plot shows the distribution

• A box plot shows the distribution, while a bar plot shows the mean

• Both show the mean

What is the default plot type when using Seaborn's catplot function?

• Box plot

• Scatter plot

• Bar plot

• Strip plot

In a pair plot, what does the diagonal line typically represent?

• A scatter plot

• A line plot

• A histogram

• A bar plot

What additional feature can be added to a scatter plot to represent another variable?

• Col

• Hue

• Size

• All of the above

Why is it important to keep visualizations simple and effective?

• To reduce the file size

• To ensure they are easy to interpret

• To make them look more professional

• To use fewer colors

What does the size parameter in a scatter plot control?

• The size of the dots

• The shape of the dots

• The position of the dots

• The color of the dots

How can you make differences among categories more obvious in a scatter plot?

• By using different colors

• By using different styles

• All of the above

• By using different sizes

What is a potential downside of using too many features in a single plot?

• It can make the plot larger in size

• It can make the plot more colorful

• It can make the plot harder to interpret

• It can make the plot more accurate