Histogram

Choose and build · V04 · 8 MIN

See how values fall into bins.

Exercises within this concept

FollowFollow the techniqueCurrent exercise
ChangeAdapt a requirement
TransferChoose and combine

Understand the idea

A histogram divides the numeric axis into intervals and counts observations inside each one.

Illustration · not the exercise output

A small example

With values [1, 2, 3, 7] and edges [0, 4, 8], the bars have heights 3 and 1.

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.histplot(data=df, x="hours", bins=4, ax=ax)
ax.set(title="Study club", xlabel="hours", ylabel="Count")
fig.tight_layout()
plt.show()

What each part does

sns.histplot: count values inside bins
bins=4: four intervals

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.histplot for hours with 4 bins.
Chart: title "Study club"; x "hours"; y "Count".

Hint

Bins group a numeric measurement; the vertical height here is a count.

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.histplot(data=df, x="hours", bins=4, ax=ax)
ax.set(title="Study club", xlabel="hours", ylabel="Count")
fig.tight_layout()
plt.show()

Your task · Follow

Using df, call sns.histplot for hours with 4 bins.
Chart: title "Study club"; x "hours"; y "Count".

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.