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.
Deze oefening maakt deel uit van de cursus
Introduction to Data Visualization with Seaborn
Praktische interactieve oefening
Probeer deze oefening eens door deze voorbeeldcode in te vullen.
# Specify the category ordering
study_time_order = ["<2 hours", "2 to 5 hours",
"5 to 10 hours", ">10 hours"]
# Create a box plot and set the order of the categories
# Show plot
plt.show()