Density curve

Optional · distribution detail · V05 · 8 MIN

Understand a smoothed view of a distribution.

Exercises within this concept

FollowFollow the techniqueCurrent exercise
ChangeAdapt a requirement
TransferCombine earlier skills

Understand the idea

A kernel density estimate (KDE) smooths observations into a curve. Its height is density, not a count or a probability at one exact value.

Illustration · not the exercise output

A small example

Increasing bw_adjust from 1 to 2 makes bumps wider and the combined curve smoother; it does not add observations.

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.kdeplot(data=df, x="hours", bw_adjust=1, cut=0, ax=ax)
ax.set(title="Study club", xlabel="hours", ylabel="Density")
fig.tight_layout()
plt.show()

What each part does

sns.kdeplot: a smoothed density estimate
bw_adjust=1: default bandwidth multiplier
cut=0: stay within observed endpoints

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 sns.kdeplot for hours, with bw_adjust=1 and cut=0.
Chart: title "Study club"; x "hours"; y "Density".

Hint

Density is not a count. cut=0 prevents the curve extending beyond observed values.

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.kdeplot(data=df, x="hours", bw_adjust=1, cut=0, ax=ax)
ax.set(title="Study club", xlabel="hours", ylabel="Density")
fig.tight_layout()
plt.show()

Your task · Follow

Using df, call sns.kdeplot for hours, with bw_adjust=1 and cut=0.
Chart: title "Study club"; x "hours"; y "Density".

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))
# Build the requested chart
____
# Add the labels from Your task
ax.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.