An attendance handoff · Data Playground

The question

Produce a chronological attendance table with identifiable learners and valid dates.

df Attendance handoff

Your inputs

`df`

Only student and date are required. Preserve all columns and other values. An impossible date is not a valid attendance date.

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`
S007	Learner 7	A	8	94	true	2026-04-04
S001	Learner 1	A	2	55	false	2026-04-01
S004	Learner 4	B	7	90	true	2026-04-02
S002	Learner 2	B	5	78	true	2026-04-01
S003	Learner 3	A	3	68	true	2026-02-30

View all 16 rows

`df` · complete input
`submission_id`	`student`	`group`	`hours`	`score`	`attended`	`date`
S007	Learner 7	A	8	94	true	2026-04-04
S001	Learner 1	A	2	55	false	2026-04-01
S004	Learner 4	B	7	90	true	2026-04-02
S002	Learner 2	B	5	78	true	2026-04-01
S003	Learner 3	A	3	68	true	2026-02-30
S005	Learner 5	A	4	None	false	2026-04-03
S006	Learner 6	B	6	85	true	2026-04-03
S008	Learner 8	B	1	49	true	None
S009	Learner 9	A	2	56	false	2026-04-05
S010	None	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 · An attendance handoff

Produce a chronological attendance table with identifiable learners and valid dates.

cleanDataFrameexcluded_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
index: the labels attached to rows or to Series values

Attendance handoffOutputcleanDataFrame
1. Create clean from a copy.
2. Parse date using YYYY-MM-DD, treating invalid dates as missing.
3. Keep rows with known student and date.
4. Sort by date then submission_id and reset to a consecutive index.
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

Which fields make an attendance record identifiable and dated?

Hint 2 — Tools

to_datetime(), dropna(), sort_values() and reset_index()

Revisit: Parse dates · Drop missing rows · Sort and reset

Hint 3 — Approach

Parse dates before testing required fields, then order eligible records and report exclusions.

Explained solution

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

clean = df.copy()
clean['date'] = pd.to_datetime(clean.date, format='%Y-%m-%d', errors='coerce')
clean = (
    clean.dropna(subset=['student', 'date'])
    .sort_values(['date', 'submission_id'])
    .reset_index(drop=True)
)
excluded_count = len(df) - len(clean)
clean

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(
    [
        ('S007', 'Learner 7', 'A', 8, 94, True, '2026-04-04'),
        ('S001', 'Learner 1', 'A', 2, 55, False, '2026-04-01'),
        ('S004', 'Learner 4', 'B', 7, 90, True, '2026-04-02'),
        ('S002', 'Learner 2', 'B', 5, 78, True, '2026-04-01'),
        ('S003', 'Learner 3', 'A', 3, 68, True, '2026-02-30'),
        ('S005', 'Learner 5', 'A', 4, None, False, '2026-04-03'),
        ('S006', 'Learner 6', 'B', 6, 85, True, '2026-04-03'),
        ('S008', 'Learner 8', 'B', 1, 49, True, None),
        ('S009', 'Learner 9', 'A', 2, 56, False, '2026-04-05'),
        ('S010', None, '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

Parsing first exposes impossible dates. Only student and date are required, so missing scores remain acceptable.
Sorting makes the handoff chronological; resetting labels removes gaps left by excluded rows.

Another valid approach

Boolean eligibility masks are equivalent to removing rows missing the required fields after parsing.