Summarise groups

Reading the whole table · I19 · 8 MIN

Compare a simple average across categories.

Exercises within this concept

FollowFollow the techniqueCurrent exercise
ChangeAdapt a requirement
TransferChoose and combine

Understand the idea

groupby gathers rows with the same category. Select the numeric column, then reduce each group to one summary value.

Illustration · not the exercise output

A small example

For A: [2, 6] and B: [9], group means are A: 4 and B: 9; group totals are A: 8 and B: 9.

Follow the code

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

df.groupby("flavour")["price"].agg("mean")

What each part does

groupby("flavour"): gather equal labels
["price"]: choose the measured column
agg("mean"): one average for each group

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, return mean price for each flavour category as a Series.
Use: groupby(), agg().

Hint

Group by the category, select the measurement, then aggregate it.

Reveal solution

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

df.groupby("flavour")["price"].agg("mean")

Your task · Follow

Using df, return mean price for each flavour category as a Series.
Use: groupby(), agg().

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
# Build the requested result from the given table
result = ____
result

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.