Choose detail to suit the sample

Optional · distribution detail · V15 · 8 MIN

Choose detail to suit the sample

Prefer observed points when groups are too small for tail summaries.

Exercises within this concept

FollowFollow the techniqueCurrent exercise
ChangeAdapt the workflow
TransferCombine earlier skills

Understand the idea

With only a few observations per group, show the actual measurements. A complex distribution shape can imply more evidence than is available.

Illustration · not the exercise output

A small example

For a group containing weights 3 and 7, two visible points communicate both known values directly.

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

What each part does

sns.stripplot(..., jitter=False): show raw observations at their category positions
sns.boxplot(...): summarise the same observations with a box
sns.boxenplot(...): nested quantiles for larger samples; not needed for this tiny table

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, inspect every price by flavour with a strip plot and jitter=False.
The catalogue is too small to justify detailed tail estimates.
Use: stripplot().
Chart: title "Candy shop"; x "flavour"; y "price".

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

Your task · Follow

Using df, inspect every price by flavour with a strip plot and jitter=False.
The catalogue is too small to justify detailed tail estimates.
Use: stripplot().
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))
sns.____(data=df, x="flavour", y="price", jitter=False, ax=ax)
ax.set(title="Candy shop", xlabel="flavour", ylabel="price")
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.