Python Tutorial : Adding a third variable with hue

Want to learn more? Take the full course at https://learn.datacamp.com/courses/introduction-to-data-visualization-with-seaborn at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- We saw in the last lesson that a really nice advantage of Seaborn is that it works well with Pandas DataFrames. In this lesson, we'll see another big advantage that Seaborn offers: the ability to quickly add a third variable to your plots by adding color. To showcase this cool feature in Seaborn, we'll be using Seaborn's built-in tips dataset. You can access it by using the "load dataset" function in Seaborn and passing in the name of the dataset. These are the first five rows of the tips dataset. This dataset contains one row for each table served at a restaurant and has information about things like the bill amount, how many people were at the table, and when the table was served. Let's explore the relationship between the "total_bill" and "tip" columns using a scatter plot. Here is the code to generate it. The total bill per table (in dollars) is on the x-axis, and the total tip (in dollars) is on the y-axis. We can see from this plot that larger bills are associated with larger tips. What if we want to see which of the data points are smokers versus non-smokers? Seaborn makes this super easy. You can set the "hue" parameter equal to the DataFrame column name "smoker" and then Seaborn will automatically color each point by whether they are a smoker. Plus, it will add a legend to the plot automatically! If you don't want to use Pandas, you can set it equal to a list of values instead of a column name. Hue also allows you to assert more control over the ordering and coloring of each value. The "hue order" parameter takes in a list of values and will set the order of the values in the plot accordingly. Notice how the legend for smoker now lists "yes" before "no". You can also control the colors assigned to each value usin