Understand the category mix · Data Playground

The question

The morning café team wants to understand its drink mix.

df Category profile

Your inputs

`df`

One row is one order. Drink labels are already standardised. Proportions are fractions, not percentages.

Field meanings and units

order_id: Order or order-line identifier, as defined by this brief.
drink: Recorded drink label.
size: Recorded serving size.
price: Price in dollars; unparseable text needs the stated policy.
tip: Tip in dollars.
shift: AM or PM service period.
date: Calendar date supplied as YYYY-MM-DD text unless the task states it is invalid.

`df` · first 5 of 16 rows
`order_id`	`drink`	`size`	`price`	`tip`	`shift`	`date`
C001	Latte	Small	4.5	0	AM	2026-04-01
C002	Tea	Large	2.8	1	PM	2026-04-01
C003	Latte	Small	4.5	0.5	AM	2026-04-02
C004	Mocha	Large	5.2	None	AM	2026-04-02
C005	Latte	Small	4.5	0	PM	2026-04-03

View all 16 rows

`df` · complete input
`order_id`	`drink`	`size`	`price`	`tip`	`shift`	`date`
C001	Latte	Small	4.5	0	AM	2026-04-01
C002	Tea	Large	2.8	1	PM	2026-04-01
C003	Latte	Small	4.5	0.5	AM	2026-04-02
C004	Mocha	Large	5.2	None	AM	2026-04-02
C005	Latte	Small	4.5	0	PM	2026-04-03
C006	Tea	Large	2.8	1	AM	2026-04-03
C007	Latte	Small	4.5	0.5	AM	2026-04-04
C008	Mocha	Large	5.2	None	PM	2026-04-04
C009	Latte	Small	4.5	0	AM	2026-04-05
C010	Tea	Large	2.8	1	AM	2026-04-05
C011	Latte	Small	4.5	0.5	PM	2026-04-06
C012	Mocha	Large	5.2	None	AM	2026-04-06
C013	Latte	Small	4.5	0	AM	2026-04-07
C014	Tea	Large	2.8	1	PM	2026-04-07
C015	Latte	Small	4.5	0.5	AM	2026-04-08
C016	Mocha	Large	5.2	None	AM	2026-04-08

The editable setup in Your Python creates these inputs when you run it.

Your task · Understand the category mix

The morning café team wants to understand its drink mix.

countsSeriesproportionsSeries

Deliverables

Make the outputs listed below. Use each name exactly as shown. Then choose Check answer.

Python name key

Teal outlines name table columns. Violet left rules name Python objects. Dotted rules mark syntax, functions and parameters. Use each name exactly as shown.

Morning countsOutputcountsSeries
1. Store the number of AM orders for each drink in counts.
Morning proportionsOutputproportionsSeries
1. Store each drink’s share of AM orders in proportions, using the same drink labels as counts.
2. The shares should sum to 1.

Data policy Keep all supplied source tables unchanged. Work on separate outputs.

Help Hints & explained solution

Choose the amount of help you need. Opening one hint does not reveal the others.

Hint 1 — Think

Which orders belong in the denominator?

Hint 2 — Tools

Boolean selection, groupby(), agg(), value_counts() and arithmetic

Revisit: Filter rows · Count categories · Combine conditions · Answer one numerical question · Summarise groups

Hint 3 — Approach

Select AM orders and calculate counts and fractions using that same population.

Explained solution

Add these lines below # Your work in the editor. The supplied input setup is already there.

morning = df[df['shift'] == 'AM']
counts = morning['drink'].value_counts()
proportions = counts / counts.sum()
counts

View supplied setup

This code is already at the top of the editor.

import pandas as pd

# Supplied input: df (16 rows)
df = pd.DataFrame(
    [
        ('C001', 'Latte', 'Small', 4.5, 0, 'AM', '2026-04-01'),
        ('C002', 'Tea', 'Large', 2.8, 1, 'PM', '2026-04-01'),
        ('C003', 'Latte', 'Small', 4.5, 0.5, 'AM', '2026-04-02'),
        ('C004', 'Mocha', 'Large', 5.2, None, 'AM', '2026-04-02'),
        ('C005', 'Latte', 'Small', 4.5, 0, 'PM', '2026-04-03'),
        ('C006', 'Tea', 'Large', 2.8, 1, 'AM', '2026-04-03'),
        ('C007', 'Latte', 'Small', 4.5, 0.5, 'AM', '2026-04-04'),
        ('C008', 'Mocha', 'Large', 5.2, None, 'PM', '2026-04-04'),
        ('C009', 'Latte', 'Small', 4.5, 0, 'AM', '2026-04-05'),
        ('C010', 'Tea', 'Large', 2.8, 1, 'AM', '2026-04-05'),
        ('C011', 'Latte', 'Small', 4.5, 0.5, 'PM', '2026-04-06'),
        ('C012', 'Mocha', 'Large', 5.2, None, 'AM', '2026-04-06'),
        ('C013', 'Latte', 'Small', 4.5, 0, 'AM', '2026-04-07'),
        ('C014', 'Tea', 'Large', 2.8, 1, 'PM', '2026-04-07'),
        ('C015', 'Latte', 'Small', 4.5, 0.5, 'AM', '2026-04-08'),
        ('C016', 'Mocha', 'Large', 5.2, None, 'AM', '2026-04-08'),
    ],
    columns=['order_id', 'drink', 'size', 'price', 'tip', 'shift', 'date'],
)

Why these steps exist

Counts and proportions use AM orders only; using all orders as the denominator would understate the morning mix.
Each share divides that drink’s count by the total number of AM orders.

Another valid approach

Group counts and normalised value counts are equivalent when they use the same AM orders.