Multiple subplots

Explain the evidence · V24 · 8 MIN

Manage two charts on one Figure.

Exercises within this concept

FollowFollow the techniqueCurrent exercise
ChangeAdapt a requirement
TransferChoose and combine

Understand the idea

subplots creates several plotting areas on one Figure. Send each chart to its intended Axes.

Illustration · not the exercise output

A small example

subplots(1, 2) creates left and right panels; subplots(2, 1) creates upper and lower panels.

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

fig, axes = plt.subplots(1, 2, figsize=(10, 4))
sns.histplot(data=df, x="hours", bins=4, ax=axes[0])
sns.scatterplot(data=df, x="hours", y="score", ax=axes[1])
axes[0].set(title="Distribution", xlabel="hours", ylabel="Count")
axes[1].set(title="Relationship", xlabel="hours", ylabel="score")
fig.tight_layout()
plt.show()

What each part does

fig, axes = plt.subplots(1, 2, figsize=(10, 4)): two plotting areas
axes[0]: first Axes
ax=axes[1]: draw on the second Axes

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, create a two-panel Figure: a 4-bin histogram of hours on the left and a scatter of hours against score on the right.
Use panel titles "Distribution" and "Relationship", x labels "hours", and y labels "Count" and "score".
Finish the Figure with tight_layout and show.
Use: subplots().

Hint

Pass each chart its own axes[0] or axes[1]; finish the whole Figure once.

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

fig, axes = plt.subplots(1, 2, figsize=(10, 4))
sns.histplot(data=df, x="hours", bins=4, ax=axes[0])
sns.scatterplot(data=df, x="hours", y="score", ax=axes[1])
axes[0].set(title="Distribution", xlabel="hours", ylabel="Count")
axes[1].set(title="Relationship", xlabel="hours", ylabel="score")
fig.tight_layout()
plt.show()

Your task · Follow

Using df, create a two-panel Figure: a 4-bin histogram of hours on the left and a scatter of hours against score on the right.
Use panel titles "Distribution" and "Relationship", x labels "hours", and y labels "Count" and "score".
Finish the Figure with tight_layout and show.
Use: subplots().

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

fig, axes = plt.subplots(____, ____, figsize=(10, 4))
# Histogram on axes[0], scatter on axes[1]
____
# Label each panel
axes[0].set(____)
axes[1].set(____)
fig.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.