Avoid misleading charts

Make a useful report · V36 · 8 MIN

Make the scale and selection honest.

Exercises within this concept

FollowFollow the techniqueCurrent exercise
ChangeAdapt a requirement
TransferCombine earlier skills

Understand the idea

Bar length represents magnitude, so use a zero baseline. Choose an order that supports the comparison without changing the values.

Illustration · not the exercise output

A small example

Values 4 and 5 differ by 25%. A y-axis beginning at 3 exaggerates their apparent bar-length ratio.

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))
means = df.groupby("flavour")["price"].mean().sort_values(ascending=False)
ax.bar(means.index, means.values)
ax.set_ylim(bottom=0)
ax.set(title="Candy shop", xlabel="flavour", ylabel="Mean price")
fig.tight_layout()
plt.show()

What each part does

sort_values(ascending=False): a meaningful category order
ax.set_ylim(bottom=0): honest bar-length baseline

Other choices for later exercises

alpha=: transparency for overlapping points
order=[...]: set a deliberate category order

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, compare mean price by flavour as bars, highest mean first.
Start the y-axis at zero so lengths represent the price comparison fairly.
Chart: title "Candy shop"; x "flavour"; y "Mean 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))
means = df.groupby("flavour")["price"].mean().sort_values(ascending=False)
ax.bar(means.index, means.values)
ax.set_ylim(bottom=0)
ax.set(title="Candy shop", xlabel="flavour", ylabel="Mean price")
fig.tight_layout()
plt.show()

Your task · Follow

Using df, compare mean price by flavour as bars, highest mean first.
Start the y-axis at zero so lengths represent the price comparison fairly.
Chart: title "Candy shop"; x "flavour"; y "Mean 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))
means = df.groupby("flavour")["price"].mean().sort_values(ascending=False)
ax.bar(means.index, means.values)
ax.set_ylim(bottom=____)
ax.set(title="Candy shop", xlabel="flavour", ylabel="Mean 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.