What changed over time? · Data Playground

The question

Communicate the total recorded steps each day, combining all observations on that date.

df Daily activity

Your inputs

`df`

Each row is a separate activity session. Multiple sessions on a date contribute to that day’s total; include both groups and reporting weeks.

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 20 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 20 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
A017	Morning	2026-04-09	3920	50	Current
A018	Evening	2026-04-09	4490	57	Current
A019	Morning	2026-04-10	5060	23	Current
A020	Evening	2026-04-10	5630	30	Current

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

Your task · What changed over time?

Communicate the total recorded steps each day, combining all observations on that date.

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 totalsOutputdailySeries
1. Create daily as the sum of steps by parsed date, ordered chronologically.
2. Do not average steps.
FigureOutputfigFigure
1. Create fig connecting the daily totals in time order, earliest first.
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

Do repeated dates represent separate days or contributions to the same day?

Hint 2 — Tools

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

Revisit: Parse dates · Summarise groups · Line plot · Finish a chart properly

Hint 3 — Approach

Parse dates, total observations within each date and show the daily values in time order.

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')
ax.set(title='Daily recorded steps', 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 (20 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'),
        ('A017', 'Morning', '2026-04-09', 3920, 50, 'Current'),
        ('A018', 'Evening', '2026-04-09', 4490, 57, 'Current'),
        ('A019', 'Morning', '2026-04-10', 5060, 23, 'Current'),
        ('A020', 'Evening', '2026-04-10', 5630, 30, 'Current'),
    ],
    columns=['record_id', 'group', 'date', 'steps', 'minutes', 'week'],
)

Why these steps exist

Each date can contain several sessions, so daily steps add their contributions.
Chronological connections show change over time; connecting file order could suggest changes that never occurred.

Another valid approach

You can aggregate dates before or after explicitly sorting the summary; the plotted series must be chronological.