Map variables visually

Compare observations · V03 · 8 MIN

Use colour to compare categories in a scatter chart.

Exercises within this concept

FollowFollow the techniqueCurrent exercise
ChangeAdapt a requirement
TransferCombine earlier skills

Understand the idea

A visual mapping links a column to how each observation appears. A fixed appearance gives all observations the same style.

Illustration · not the exercise output

A small example

hue="club" assigns different colours to clubs. color="blue" makes every point blue.

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, ax = plt.subplots(figsize=(6, 4))
sns.scatterplot(data=df, x="hours", y="score", hue="club", ax=ax)
ax.set(title="Study club", xlabel="hours", ylabel="score")
fig.tight_layout()
plt.show()

What each part does

data=df: the table supplying all mappings
x="hours", y="score": horizontal and vertical variables
hue="club": colour by category

Other choices for later exercises

style="club": redundant shapes for accessibility
size="hours": map magnitude to marker area

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, compare hours and score with club mapped to colour.
Keep point sizes and marker shapes constant.
Use: scatterplot().
Chart: title "Study club"; x "hours"; y "score".

Hint

Choose the observations first, then map the requested measurements to the chart.

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, ax = plt.subplots(figsize=(6, 4))
sns.scatterplot(data=df, x="hours", y="score", hue="club", ax=ax)
ax.set(title="Study club", xlabel="hours", ylabel="score")
fig.tight_layout()
plt.show()

Your task · Follow

Using df, compare hours and score with club mapped to colour.
Keep point sizes and marker shapes constant.
Use: scatterplot().
Chart: title "Study club"; x "hours"; y "score".

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, ax = plt.subplots(figsize=(6, 4))
sns.scatterplot(data=df, x="hours", y="score", hue=____, ax=ax)
ax.set(title="Study club", xlabel="hours", ylabel="score")
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.