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  3. Introduction to Data Visualization with Seaborn

Exercise

Create and interpret a box plot

Let's continue using the student_data dataset. In an earlier exercise, we explored the relationship between studying and final grade by using a bar plot to compare the average final grade ("G3") among students in different categories of "study_time".

In this exercise, we'll try using a box plot look at this relationship instead. As a reminder, to create a box plot you'll need to use the catplot() function and specify the name of the categorical variable to put on the x-axis (x=____), the name of the quantitative variable to summarize on the y-axis (y=____), the pandas DataFrame to use (data=____), and the type of plot (kind="box").

We have already imported matplotlib.pyplot as plt and seaborn as sns.

Instructions 1/2

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  • Use sns.catplot() and the student_data DataFrame to create a box plot with "study_time" on the x-axis and "G3" on the y-axis. Set the ordering of the categories to study_time_order.