Can this report use the data?

The question

A study report requires known hours and scores. Establish what can be used without changing the incoming table.

df Eligibility report

Your inputs

`df`

One row is one recorded observation. IDs identify records; dates use YYYY-MM-DD.

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 16 rows
`submission_id`	`student`	`group`	`hours`	`score`	`attended`	`date`
S001	Learner 1	A	2	55	false	2026-04-01
S002	None	B	5	78	true	2026-04-01
S003	Learner 3	A	None	68	true	2026-04-02
S004	Learner 4	B	7	90	true	2026-04-02
S005	Learner 5	A	4	72	false	2026-04-03

View all 16 rows

`df` · complete input
`submission_id`	`student`	`group`	`hours`	`score`	`attended`	`date`
S001	Learner 1	A	2	55	false	2026-04-01
S002	None	B	5	78	true	2026-04-01
S003	Learner 3	A	None	68	true	2026-04-02
S004	Learner 4	B	7	90	true	2026-04-02
S005	Learner 5	A	4	72	false	2026-04-03
S006	Learner 6	B	6	None	true	2026-04-03
S007	Learner 7	A	8	94	true	2026-04-04
S008	Learner 8	B	1	None	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
S013	Learner 13	A	4	73	false	2026-04-07
S014	Learner 14	B	6	86	true	2026-04-07
S015	Learner 15	A	8	95	true	2026-04-08
S016	Learner 16	B	1	50	true	2026-04-08

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

Your task · Can this report use the data?

A study report requires known hours and scores. Establish what can be used without changing the incoming table.

missingSerieseligibleDataFrameexcluded_countnumber

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

missing: no value was supplied
eligible: rows that meet this task’s stated conditions

Required-field gapsOutputmissingSeries
1. Store the missing-value count for each of hours and score in missing.
Eligible recordsOutputeligibleDataFrame
1. Create eligible containing all columns for records with both hours and score known, in original order.
Excluded recordsOutputexcluded_countnumber
1. Store the number excluded in excluded_count.

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 gaps in unrelated columns matter for this report?

Hint 2 — Tools

isna(), notna(), sum() and boolean selection

Revisit: Find missing values · Combine conditions

Hint 3 — Approach

Count gaps in the required fields, retain records complete in both, then compare eligible and original row counts.

Explained solution

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

missing = df[['hours', 'score']].isna().sum()
eligible = df[df.hours.notna() & df.score.notna()]
excluded_count = len(df) - len(eligible)
eligible

View supplied setup

This code is already at the top of the editor.

import pandas as pd

# Supplied input: df (16 rows)
df = pd.DataFrame(
    [
        ('S001', 'Learner 1', 'A', 2, 55, False, '2026-04-01'),
        ('S002', None, 'B', 5, 78, True, '2026-04-01'),
        ('S003', 'Learner 3', 'A', None, 68, True, '2026-04-02'),
        ('S004', 'Learner 4', 'B', 7, 90, True, '2026-04-02'),
        ('S005', 'Learner 5', 'A', 4, 72, False, '2026-04-03'),
        ('S006', 'Learner 6', 'B', 6, None, True, '2026-04-03'),
        ('S007', 'Learner 7', 'A', 8, 94, True, '2026-04-04'),
        ('S008', 'Learner 8', 'B', 1, None, 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'),
        ('S013', 'Learner 13', 'A', 4, 73, False, '2026-04-07'),
        ('S014', 'Learner 14', 'B', 6, 86, True, '2026-04-07'),
        ('S015', 'Learner 15', 'A', 8, 95, True, '2026-04-08'),
        ('S016', 'Learner 16', 'B', 1, 50, True, '2026-04-08'),
    ],
    columns=['submission_id', 'student', 'group', 'hours', 'score', 'attended', 'date'],
)

Why these steps exist

Only hours and score determine eligibility. Dropping rows for unrelated gaps would exclude usable learners.
The difference in row counts documents how much evidence the report loses.

Another valid approach

Two not-missing masks or a required-column missingness mask can identify the same complete records.