Point estimates

Optional · distribution detail · V10 · 8 MIN

Show a group estimate with uncertainty.

Exercises within this concept

FollowFollow the techniqueCurrent exercise
ChangeAdapt a requirement
TransferCombine earlier skills

Understand the idea

A point plot marks one summary per category. An interval can describe spread of observations or uncertainty of the summary; these are different claims.

Illustration · not the exercise output

A small example

A mean of 10 with standard deviation 2 gets an SD interval from 8 to 12. This is not a confidence interval.

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.pointplot(data=df, x="flavour", y="price", errorbar="sd", capsize=0.15, ax=ax)
ax.set(title="Candy shop", xlabel="flavour", ylabel="price")
fig.tight_layout()
plt.show()

What each part does

sns.pointplot: a point for each estimate
errorbar="sd": spread of observed values
capsize=0.15: caps at interval ends

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.pointplot with flavour on x and price on y, using errorbar="sd" and capsize=0.15.
Chart: title "Candy shop"; x "flavour"; y "price".

Hint

One SD describes spread of observations, not a confidence interval for the mean.

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.pointplot(data=df, x="flavour", y="price", errorbar="sd", capsize=0.15, ax=ax)
ax.set(title="Candy shop", xlabel="flavour", ylabel="price")
fig.tight_layout()
plt.show()

Your task · Follow

Using df, call sns.pointplot with flavour on x and price on y, using errorbar="sd" and capsize=0.15.
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.