Make repeated measurements usable

The question

Prepare an activity extract that keeps each value connected to its measurement and unit.

df Long measurements

Your inputs

`df`

The measure label carries the unit: steps is a count, minutes is elapsed time. Never combine their numeric values into one total.

Field meanings and units

record_id: Activity-observation identifier.
group: Comparison group.
date: Calendar date supplied as YYYY-MM-DD text unless the task states it is invalid.
steps: Recorded step count, additive across observations.
minutes: Elapsed duration in minutes; unknown is not zero.
week: Current or Previous reporting week.

`df` · first 5 of 12 rows
`record_id`	`group`	`date`	`steps`	`minutes`	`week`
A001	Morning	2026-04-01	2500	20	Previous
A002	Evening	2026-04-01	3070	27	Previous
A003	Morning	2026-04-02	3640	34	Previous
A004	Evening	2026-04-02	4210	41	Previous
A005	Morning	2026-04-03	4780	48	Previous

View all 12 rows

`df` · complete input
`record_id`	`group`	`date`	`steps`	`minutes`	`week`
A001	Morning	2026-04-01	2500	20	Previous
A002	Evening	2026-04-01	3070	27	Previous
A003	Morning	2026-04-02	3640	34	Previous
A004	Evening	2026-04-02	4210	41	Previous
A005	Morning	2026-04-03	4780	48	Previous
A006	Evening	2026-04-03	5350	55	Previous
A007	Morning	2026-04-04	5920	21	Previous
A008	Evening	2026-04-04	2640	28	Previous
A009	Morning	2026-04-05	3210	35	Previous
A010	Evening	2026-04-05	3780	42	Previous
A011	Morning	2026-04-06	4350	49	Previous
A012	Evening	2026-04-06	4920	56	Previous

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

Your task · Make repeated measurements usable

Prepare an activity extract that keeps each value connected to its measurement and unit.

longDataFrameduration_recordsDataFrame

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.

One row per measurementOutputlongDataFrame
1. Create long with record_id, measure and value, using steps and minutes only.
2. Include both measurements for every source record, including unknown values.
Duration extractOutputduration_recordsDataFrame
1. Create duration_records from long for measure minutes and value at least 35.

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

How will a reader know whether a value counts steps or measures minutes?

Hint 2 — Tools

melt() and boolean selection

Revisit: Wide → long · Combine conditions

Hint 3 — Approach

Retain the record identifier while stacking the two measures, then select qualifying duration observations.

Explained solution

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

long = df.melt(
    id_vars=['record_id'],
    value_vars=['steps', 'minutes'],
    var_name='measure',
    value_name='value',
)
duration_records = long[(long.measure == 'minutes') & (long.value >= 35)]
long

View supplied setup

This code is already at the top of the editor.

import pandas as pd

# Supplied input: df (12 rows)
df = pd.DataFrame(
    [
        ('A001', 'Morning', '2026-04-01', 2500, 20, 'Previous'),
        ('A002', 'Evening', '2026-04-01', 3070, 27, 'Previous'),
        ('A003', 'Morning', '2026-04-02', 3640, 34, 'Previous'),
        ('A004', 'Evening', '2026-04-02', 4210, 41, 'Previous'),
        ('A005', 'Morning', '2026-04-03', 4780, 48, 'Previous'),
        ('A006', 'Evening', '2026-04-03', 5350, 55, 'Previous'),
        ('A007', 'Morning', '2026-04-04', 5920, 21, 'Previous'),
        ('A008', 'Evening', '2026-04-04', 2640, 28, 'Previous'),
        ('A009', 'Morning', '2026-04-05', 3210, 35, 'Previous'),
        ('A010', 'Evening', '2026-04-05', 3780, 42, 'Previous'),
        ('A011', 'Morning', '2026-04-06', 4350, 49, 'Previous'),
        ('A012', 'Evening', '2026-04-06', 4920, 56, 'Previous'),
    ],
    columns=['record_id', 'group', 'date', 'steps', 'minutes', 'week'],
)

Why these steps exist

The identifier is repeated beside each measurement so values retain their source record.
Filtering on both measure and value prevents a large step count from being mistaken for a long duration.

Another valid approach

Equivalent reshaping is accepted if every requested record and measurement pair appears once with the correct value.