Pair relationships

Optional · figures and composition · V31 · 8 MIN

Explore several pairwise relationships in one figure.

Exercises within this concept

FollowFollow the techniqueCurrent exercise
ChangeAdapt a requirement
TransferCombine earlier skills

Understand the idea

pairplot creates its own Figure: distributions on the diagonal and pairwise relationships off the diagonal.

Illustration · not the exercise output

A small example

For two variables, the grid shows each distribution and the relationship twice with swapped axes.

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.pairplot(data=df, vars=["hours", "score"], hue="club", diag_kind="hist")
g.figure.tight_layout()
plt.show()

What each part does

g = sns.pairplot(...): create a whole grid and keep its handle
vars=[...]: choose numeric columns
diag_kind="hist": histogram on the diagonal
g.figure: the grid’s Figure

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 pairplot for hours and score, hue=club, diag_kind="hist".
This is a figure-level exception: do not create fig, ax first.
Finish with g.figure.tight_layout() and plt.show().

Hint

pairplot owns its Figure; do not create an extra empty Figure first.

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.pairplot(data=df, vars=["hours", "score"], hue="club", diag_kind="hist")
g.figure.tight_layout()
plt.show()

Your task · Follow

Using df, call pairplot for hours and score, hue=club, diag_kind="hist".
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.