Answer one numerical question

Reading the whole table · I18S · 8 MIN

Answer one numerical question

Choose a direct summary for a specific question.

Exercises within this concept

FollowFollow an average calculationCurrent exercise
ChangeChoose a total
TransferChoose a robust middle

Understand the idea

Choose the reduction that answers the question. A total, an average and a middle value describe different things.

Illustration · not the exercise output

A small example

For [2, 4, 12]: sum is 18, mean is 6 and median is 4. For [2, 4, 8, 10], median is (4 + 8) / 2 = 6.

Follow the code

Apply the idea to the supplied table. Read from top to bottom; the final line displays the result.

df["price"].mean()

What each part does

df["price"]: Choose the numeric column whose values answer the question.
.mean(): Average its known values.

Other choices for later exercises

.median(): Find the middle known value instead.
.sum() / .count(): Add known values / count known values.
.min() / .max(): Smallest / largest known value.
.quantile(0.75): The 75th percentile of known values.

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 price, calculate the mean price.
Leave the numeric result on the final line.

Hint

mean answers an average question, whereas sum answers a total question.

Reveal solution

One way to do it. Keep any supplied setup in the editor and use this in the Your work section.

df["price"].mean()

Your task · Follow

Using df price, calculate the mean price.
Leave the numeric result on the final line.

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
df["price"].____()

Each run executes all editor code in a fresh Python session. Display a value by leaving it on the final line.

Python starts when you open a lesson.

Output

Run your code to see what Python returns.