Can you recall from our last lesson how we used Seaborn to make our plots more aesthetically pleasing? We'll continue our journey with Seaborn in this lesson, but this time, we'll explore a different type of visualization - histograms.

Histograms are powerful graphical representations that allow us to inspect the underlying frequency distribution (shape) of a continuous or discrete data set. This is particularly useful when we want to visualize the distribution of a variable over a range of values.

Why is understanding the data distribution important, you might ask? In the field of data analytics and statistics, most statistical tests and models assume certain data distribution patterns. Histograms, therefore, are ways for us to validate these assumptions. In other words, knowing our data well sets the stage for more complex analyses later on.

This lesson will take you further into Seaborn's capabilities. We'll cover how to create and customize histograms, offering a sharper lens to inspect our Titanic dataset.

Let's illustrate a histogram using the passenger ages (age) from titanic_df. As we saw in our previous lessons, there were a variety of ages amongst the passengers that should make for an interesting distribution.

Seaborn provides a function called histplot for creating histograms. Here's the basic syntax: