Box plot

Compare observations · V11 · 8 MIN

Read a median and interquartile spread.

Exercises within this concept

FollowFollow the techniqueCurrent exercise
ChangeAdapt a requirement
TransferChoose and combine

Understand the idea

A box plot summarizes ordered values: the box covers the middle half and the line inside marks the median.

Illustration · not the exercise output

A small example

With Q1=10 and Q3=14, IQR=4. The usual fences are 4 and 20; whiskers end at observed values inside them.

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.boxplot(data=df, x="flavour", y="price", ax=ax)
ax.set(title="Candy shop", xlabel="flavour", ylabel="price")
fig.tight_layout()
plt.show()

What each part does

sns.boxplot: compact group distributions
IQR: upper quartile minus lower quartile

Your inputs

The editable setup on the right creates df. Run executes the setup and your work from top to bottom.

Candy shop · 6 synthetic rows
candy	flavour	price	rating	shelf
Gummy Bear	fruity	1.2	4.1	A
Choco Pop	chocolate	2.1	4.6	B
Mint Bite	mint	1.5	3.8	A
Berry Loop	fruity	2.8	4.4	B
Cocoa Cube	chocolate	3.4	4.9	A
Lemon Drop	fruity	1.8	4	B

Your task · Follow

Using df, call sns.boxplot with flavour on x and price on y.
Chart: title "Candy shop"; x "flavour"; y "price".

Hint

Whiskers stop at observed values within the fences; flagged points are not automatically errors.

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.boxplot(data=df, x="flavour", y="price", ax=ax)
ax.set(title="Candy shop", xlabel="flavour", ylabel="price")
fig.tight_layout()
plt.show()

Your task · Follow

Using df, call sns.boxplot with flavour on x and price on y.
Chart: title "Candy shop"; x "flavour"; y "price".

YOUR PYTHON

Tab: indent · Shift+Tab: outdent · Esc, then Tab: leave editor

# Supplied setup — editable
import pandas as pd

data = {
    "candy": [
        "Gummy Bear",
        "Choco Pop",
        "Mint Bite",
        "Berry Loop",
        "Cocoa Cube",
        "Lemon Drop",
    ],
    "flavour": ["fruity", "chocolate", "mint", "fruity", "chocolate", "fruity"],
    "price": [1.2, 2.1, 1.5, 2.8, 3.4, 1.8],
    "rating": [4.1, 4.6, 3.8, 4.4, 4.9, 4],
    "shelf": ["A", "B", "A", "B", "A", "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.