Do longer sessions relate to better scores?

The question

Explore the relationship between study hours and score without treating it as evidence of causation.

df Relationship view

Your inputs

`df`

One row is one learner’s study session and assessment. Hours and scores must stay paired; attendance is not an eligibility condition here.

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 20 rows
`submission_id`	`student`	`group`	`hours`	`score`	`attended`	`date`
S001	Learner 1	A	2	61	false	2026-04-01
S002	Learner 2	B	5	74	true	2026-04-01
S003	Learner 3	A	3	79	true	2026-04-02
S004	Learner 4	B	7	None	true	2026-04-02
S005	Learner 5	A	4	68	false	2026-04-03

View all 20 rows

`df` · complete input
`submission_id`	`student`	`group`	`hours`	`score`	`attended`	`date`
S001	Learner 1	A	2	61	false	2026-04-01
S002	Learner 2	B	5	74	true	2026-04-01
S003	Learner 3	A	3	79	true	2026-04-02
S004	Learner 4	B	7	None	true	2026-04-02
S005	Learner 5	A	4	68	false	2026-04-03
S006	Learner 6	B	6	82	true	2026-04-03
S007	Learner 7	A	None	88	true	2026-04-04
S008	Learner 8	B	1	57	true	2026-04-04
S009	Learner 9	A	2	64	false	2026-04-05
S010	Learner 10	B	5	86	true	2026-04-05
S011	Learner 11	A	3	58	true	2026-04-06
S012	Learner 12	B	7	84	true	2026-04-06
S013	Learner 13	A	4	77	false	2026-04-07
S014	Learner 14	B	6	73	true	2026-04-07
S015	Learner 15	A	8	92	true	2026-04-08
S016	Learner 16	B	1	52	true	2026-04-08
S017	Learner 17	A	2	69	false	2026-04-09
S018	Learner 18	B	5	76	true	2026-04-09
S019	Learner 19	A	3	81	true	2026-04-10
S020	Learner 20	B	7	87	true	2026-04-10

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

Your task · Do longer sessions relate to better scores?

Explore the relationship between study hours and score without treating it as evidence of causation.

pairsDataFramefigFigure

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.

Complete pairsOutputpairsDataFrame
1. Create pairs with hours and score only, keeping rows where both are known in their original row order.
FigureOutputfigFigure
1. Create fig showing one point for each complete hours/score pair, with hours on the horizontal axis and score on the vertical axis.
2. Add a meaningful title and measure labels
3. Display the figure.
Identify Hours 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

Must both coordinates come from the same learner?

Hint 2 — Tools

dropna(), Axes.scatter() or sns.scatterplot()

Revisit: Drop missing rows · Scatter plot · Finish a chart properly

Hint 3 — Approach

Keep complete pairs and represent each learner’s two measurements together.

Explained solution

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

pairs = df[['hours', 'score']].dropna()
fig, ax = plt.subplots()
ax.scatter(pairs.hours, pairs.score)
ax.set(title='Study hours and score', xlabel='Hours', 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 (20 rows)
df = pd.DataFrame(
    [
        ('S001', 'Learner 1', 'A', 2, 61, False, '2026-04-01'),
        ('S002', 'Learner 2', 'B', 5, 74, True, '2026-04-01'),
        ('S003', 'Learner 3', 'A', 3, 79, True, '2026-04-02'),
        ('S004', 'Learner 4', 'B', 7, None, True, '2026-04-02'),
        ('S005', 'Learner 5', 'A', 4, 68, False, '2026-04-03'),
        ('S006', 'Learner 6', 'B', 6, 82, True, '2026-04-03'),
        ('S007', 'Learner 7', 'A', None, 88, True, '2026-04-04'),
        ('S008', 'Learner 8', 'B', 1, 57, True, '2026-04-04'),
        ('S009', 'Learner 9', 'A', 2, 64, False, '2026-04-05'),
        ('S010', 'Learner 10', 'B', 5, 86, True, '2026-04-05'),
        ('S011', 'Learner 11', 'A', 3, 58, True, '2026-04-06'),
        ('S012', 'Learner 12', 'B', 7, 84, True, '2026-04-06'),
        ('S013', 'Learner 13', 'A', 4, 77, False, '2026-04-07'),
        ('S014', 'Learner 14', 'B', 6, 73, True, '2026-04-07'),
        ('S015', 'Learner 15', 'A', 8, 92, True, '2026-04-08'),
        ('S016', 'Learner 16', 'B', 1, 52, True, '2026-04-08'),
        ('S017', 'Learner 17', 'A', 2, 69, False, '2026-04-09'),
        ('S018', 'Learner 18', 'B', 5, 76, True, '2026-04-09'),
        ('S019', 'Learner 19', 'A', 3, 81, True, '2026-04-10'),
        ('S020', 'Learner 20', 'B', 7, 87, True, '2026-04-10'),
    ],
    columns=['submission_id', 'student', 'group', 'hours', 'score', 'attended', 'date'],
)

Why these steps exist

Dropping incomplete pairs keeps each horizontal coordinate attached to its own vertical coordinate.
The scatter shows association in these records. It does not establish that more study causes a particular score.

Another valid approach

Matplotlib and Seaborn point plots are accepted when each complete hours/score pair is represented once.