Put the benchmark in context · Data Playground

The question

Show daily recorded steps against a supplied benchmark of 9,000 steps per day.

df Benchmark context

Your inputs

`df`

The benchmark is 9,000 total recorded steps per day for this dataset, not a health recommendation. Show the benchmark with a legend label.

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 16 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 16 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
A013	Morning	2026-04-07	5490	22	Previous
A014	Evening	2026-04-07	6060	29	Previous
A015	Morning	2026-04-08	2780	36	Current
A016	Evening	2026-04-08	3350	43	Current

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

Your task · Put the benchmark in context

Show daily recorded steps against a supplied benchmark of 9,000 steps per day.

dailySeriesfigFigure

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.

Daily activityOutputdailySeries
1. Store summed steps by parsed date in daily, in time order, earliest first.
FigureOutputfigFigure
1. Create fig connecting daily totals in time order, earliest first, with a horizontal benchmark at 9,000 steps identified in a legend.
2. Add a meaningful title and measure labels
3. Display the figure.
Identify Date and Steps on the appropriate axes; extra wording is welcome.

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

Does the reference value use the same units and time period as the observations?

Hint 2 — Tools

to_datetime(), groupby(), sum(), Axes.plot() and axhline()

Revisit: Parse dates · Line plot · Reference lines · Legends and palettes

Hint 3 — Approach

Calculate daily totals, show their ordered change and label a horizontal benchmark at the supplied value.

Explained solution

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

work = df.copy()
work['date'] = pd.to_datetime(work.date, format='%Y-%m-%d')
daily = work.groupby('date').steps.sum().sort_index()
fig, ax = plt.subplots()
ax.plot(daily.index, daily.values, marker='o', label='Recorded')
ax.axhline(9000, linestyle='--', label='Benchmark: 9000 steps')
ax.legend()
ax.set(title='Daily steps and benchmark', xlabel='Date', ylabel='Steps')
fig.tight_layout()
plt.show()

View supplied setup

This code is already at the top of the editor.

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Supplied input: df (16 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'),
        ('A013', 'Morning', '2026-04-07', 5490, 22, 'Previous'),
        ('A014', 'Evening', '2026-04-07', 6060, 29, 'Previous'),
        ('A015', 'Morning', '2026-04-08', 2780, 36, 'Current'),
        ('A016', 'Evening', '2026-04-08', 3350, 43, 'Current'),
    ],
    columns=['record_id', 'group', 'date', 'steps', 'minutes', 'week'],
)

Why these steps exist

Daily totals and the supplied benchmark share the same unit and time period.
A labelled reference line lets a reader identify the benchmark without confusing it with another observation series.

Another valid approach

The line may use markers or no markers. The supplied reference still needs an identifiable legend entry.