Joint relationships

Optional · figures and composition · V32 · 8 MIN

Connect a relationship to its marginal distributions.

Exercises within this concept

FollowFollow the techniqueCurrent exercise
ChangeAdapt a requirement
TransferCombine earlier skills

Understand the idea

jointplot creates a central relationship plot with a distribution along each margin, all on its own Figure.

Illustration · not the exercise output

A small example

A central scatter pairs hours and score; the top margin describes hours and the side margin describes score.

Follow the code

Apply the idea to the supplied table. Read from top to bottom; the final line displays the result.

import matplotlib.pyplot as plt
import seaborn as sns

g = sns.jointplot(data=df, x="hours", y="score", kind="scatter")
g.figure.tight_layout()
plt.show()

What each part does

g = sns.jointplot(...): create a relationship and its margins
kind="scatter": central scatter plot

Your inputs

The editable setup on the right creates df. Run executes the setup and your work from top to bottom.

Study club · 8 synthetic rows
student	club	hours	score	group
Ari	Art	1	52	A
Bo	Code	3	71	A
Cy	Art	2	65	B
Dee	Code	4	82	B
Eli	Art	5	89	A
Flo	Code	3	76	A
Gus	Art	6	93	B
Han	Code	2	61	B

Your task · Follow

Using df, call jointplot with x=hours, y=score, kind="scatter".
This is a figure-level exception: do not create fig, ax first.
Finish with g.figure.tight_layout() and plt.show().

Hint

jointplot owns a Figure with a central Axes and marginal Axes.

Reveal solution

One way to do it. Keep any supplied setup in the editor and use this in the Your work section.

import matplotlib.pyplot as plt
import seaborn as sns

g = sns.jointplot(data=df, x="hours", y="score", kind="scatter")
g.figure.tight_layout()
plt.show()

Your task · Follow

Using df, call jointplot with x=hours, y=score, kind="scatter".
This is a figure-level exception: do not create fig, ax first.
Finish with g.figure.tight_layout() and plt.show().

YOUR PYTHON

Tab: indent · Shift+Tab: outdent · Esc, then Tab: leave editor

# Supplied setup — editable
import pandas as pd

data = {
    "student": ["Ari", "Bo", "Cy", "Dee", "Eli", "Flo", "Gus", "Han"],
    "club": ["Art", "Code", "Art", "Code", "Art", "Code", "Art", "Code"],
    "hours": [1, 3, 2, 4, 5, 3, 6, 2],
    "score": [52, 71, 65, 82, 89, 76, 93, 61],
    "group": ["A", "A", "B", "B", "A", "A", "B", "B"],
}

df = pd.DataFrame(data)

# Your work
import matplotlib.pyplot as plt
import seaborn as sns

# Create the figure-level chart
g = ____
g.figure.tight_layout()
plt.show()

Each run executes all editor code in a fresh Python session. Display charts with plt.show().

Python starts when you open a lesson.

Output

Run your code to see what Python returns.