Show a small sample honestly · Data Playground

The question

Show every attending learner’s score so a small sample is not hidden behind a summary.

df Individual observations

Your inputs

`df`

One row is one learner’s submission. Use attendance and score availability; missing study hours do not disqualify a score.

Field meanings and units

submission_id: Attendance-submission identifier.
student: Learner label.
group: Comparison group.
hours: Study duration in hours.
score: Assessment score in points.
attended: Whether the learner attended.
date: Calendar date supplied as YYYY-MM-DD text unless the task states it is invalid.

`df` · first 5 of 12 rows
`submission_id`	`student`	`group`	`hours`	`score`	`attended`	`date`
S001	Learner 1	A	2	55	false	2026-04-01
S002	Learner 2	B	5	78	true	2026-04-01
S003	Learner 3	A	3	68	true	2026-04-02
S004	Learner 4	B	7	None	true	2026-04-02
S005	Learner 5	A	4	72	false	2026-04-03

View all 12 rows

`df` · complete input
`submission_id`	`student`	`group`	`hours`	`score`	`attended`	`date`
S001	Learner 1	A	2	55	false	2026-04-01
S002	Learner 2	B	5	78	true	2026-04-01
S003	Learner 3	A	3	68	true	2026-04-02
S004	Learner 4	B	7	None	true	2026-04-02
S005	Learner 5	A	4	72	false	2026-04-03
S006	Learner 6	B	6	85	true	2026-04-03
S007	Learner 7	A	8	94	true	2026-04-04
S008	Learner 8	B	1	49	true	2026-04-04
S009	Learner 9	A	2	56	false	2026-04-05
S010	Learner 10	B	5	79	true	2026-04-05
S011	Learner 11	A	3	69	true	2026-04-06
S012	Learner 12	B	7	91	true	2026-04-06

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

Your task · Show a small sample honestly

Show every attending learner’s score so a small sample is not hidden behind a summary.

observationsDataFramefigFigure

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.

Words in this task

eligible: rows that meet this task’s stated conditions

Attending learnersOutputobservationsDataFrame
1. Create observations with group and score for attended=True and known score, keeping source order.
FigureOutputfigFigure
1. Create fig showing every eligible score as a point within its group.
2. Put groups on the horizontal axis and scores on the vertical axis; modest sideways jitter is optional.
3. Add a meaningful title and labels
4. Display the figure.
Identify Group and Score 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

Would a single average make the individual observations visible?

Hint 2 — Tools

Boolean selection and sns.stripplot() or Axes.scatter()

Revisit: Combine conditions · Raw points · Finish a chart properly

Hint 3 — Approach

Select the requested learners and show each score at its group position without summarising away observations.

Explained solution

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

observations = df.loc[df.attended & df.score.notna(), ['group', 'score']]
fig, ax = plt.subplots()
sns.stripplot(
    data=observations, x='group', y='score', jitter=False, ax=ax
)
ax.set(title='Attending learner scores', xlabel='Group', ylabel='Score')
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 (12 rows)
df = pd.DataFrame(
    [
        ('S001', 'Learner 1', 'A', 2, 55, False, '2026-04-01'),
        ('S002', 'Learner 2', 'B', 5, 78, True, '2026-04-01'),
        ('S003', 'Learner 3', 'A', 3, 68, True, '2026-04-02'),
        ('S004', 'Learner 4', 'B', 7, None, True, '2026-04-02'),
        ('S005', 'Learner 5', 'A', 4, 72, False, '2026-04-03'),
        ('S006', 'Learner 6', 'B', 6, 85, True, '2026-04-03'),
        ('S007', 'Learner 7', 'A', 8, 94, True, '2026-04-04'),
        ('S008', 'Learner 8', 'B', 1, 49, True, '2026-04-04'),
        ('S009', 'Learner 9', 'A', 2, 56, False, '2026-04-05'),
        ('S010', 'Learner 10', 'B', 5, 79, True, '2026-04-05'),
        ('S011', 'Learner 11', 'A', 3, 69, True, '2026-04-06'),
        ('S012', 'Learner 12', 'B', 7, 91, True, '2026-04-06'),
    ],
    columns=['submission_id', 'student', 'group', 'hours', 'score', 'attended', 'date'],
)

Why these steps exist

Attendance and a known score define the population. Other missing fields are irrelevant.
Each mark is one score. Sideways jitter can reduce overlap without changing any score or group.

Another valid approach

Unjittered or modestly jittered points are accepted; the measured score and group membership must not change.