Build a pivot table

Groups & reshaping · W21 · 8 MIN

Summarise a measurement across two category axes.

Exercises within this concept

FollowFollow the techniqueCurrent exercise
ChangeAdapt a requirement
TransferChoose and combine

Understand the idea

pivot_table groups by two categories and summarizes a numeric value for each combination.

Illustration · not the exercise output

A small example

If Cat + room A has weights 2 and 6, its cell is 4 with aggfunc="mean" or 8 with aggfunc="sum".

Follow the code

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

df.pivot_table(index="flavour", columns="shelf", values="price", aggfunc="mean")

What each part does

index=: output row labels
columns=: output column labels
values=: numbers to summarise
aggfunc="mean": average repeated combinations

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 a pivot table of mean price, with flavour on rows and shelf on columns.
Leave absent combinations missing.
Use: pivot_table().

Hint

Choose the row categories, column categories, measured field and aggregation separately.

Reveal solution

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

df.pivot_table(index="flavour", columns="shelf", values="price", aggfunc="mean")

Your task · Follow

Using df, return a pivot table of mean price, with flavour on rows and shelf on columns.
Leave absent combinations missing.
Use: pivot_table().

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.