Python for Data Analysis: Step-By-Step with Projects - Relationship of Two Features (2)

The video tutorial explores various data visualization techniques using Seaborn. It starts with histograms and the use of the hue parameter to add categorical features. The tutorial then covers line and bar charts, explaining how Seaborn aggregates data using mean values. Advanced plotting techniques using the cat plot function are demonstrated, including box, violin, strip, and swarm plots. Finally, the tutorial shows how to visualize relationships between two categorical features using count plots.

In what scenarios would you prefer to use a line chart over a bar chart?

How can we visualize two categorical features using Seaborn?

What does the count plot show when using the PM 2.5 category and quarter as features?

What are the key takeaways from the lesson on visualizing relationships between features?