How are delivery times distributed?

The question

Show how delivery durations are distributed, excluding measurements that cannot be interpreted.

df Duration distribution

Your inputs

`df`

These records describe completed deliveries. Durations are minutes; unknown text and blank values are not measurements.

Field meanings and units

delivery_id: Delivery identifier.
depot: Depot responsible for the delivery.
status: Recorded delivery state.
minutes: Elapsed duration in minutes; unknown is not zero.
weight_kg: Weight in kilograms.
date: Calendar date supplied as YYYY-MM-DD text unless the task states it is invalid.

`df` · first 5 of 20 rows
`delivery_id`	`depot`	`status`	`minutes`	`weight_kg`	`date`
D001	East	Delivered	32	1.2	2026-04-01
D002	West	Delivered	48	1.9	2026-04-01
D003	North	Delivered	25	2.6	2026-04-02
D004	East	Delivered	unknown	3.3	2026-04-02
D005	West	Delivered	45	4	2026-04-03

View all 20 rows

`df` · complete input
`delivery_id`	`depot`	`status`	`minutes`	`weight_kg`	`date`
D001	East	Delivered	32	1.2	2026-04-01
D002	West	Delivered	48	1.9	2026-04-01
D003	North	Delivered	25	2.6	2026-04-02
D004	East	Delivered	unknown	3.3	2026-04-02
D005	West	Delivered	45	4	2026-04-03
D006	North	Delivered	37	1.2	2026-04-03
D007	East	Delivered	72	1.9	2026-04-04
D008	West	Delivered	29	2.6	2026-04-04
D009	North	Delivered	33	3.3	2026-04-05
D010	East	Delivered	None	4	2026-04-05
D011	West	Delivered	26	1.2	2026-04-06
D012	North	Delivered	62	1.9	2026-04-06
D013	East	Delivered	46	2.6	2026-04-07
D014	West	Delivered	38	3.3	2026-04-07
D015	North	Delivered	73	4	2026-04-08
D016	East	Delivered	30	1.2	2026-04-08
D017	West	Delivered	34	1.9	2026-04-09
D018	North	Delivered	50	2.6	2026-04-09
D019	East	Delivered	27	3.3	2026-04-10
D020	West	Delivered	63	4	2026-04-10

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

Your task · How are delivery times distributed?

Show how delivery durations are distributed, excluding measurements that cannot be interpreted.

durationsSeriesfigFigure

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

numeric: a number, rather than text
missing: no value was supplied
eligible: rows that meet this task’s stated conditions

Valid durationsOutputdurationsSeries
1. Store parsed, non-missing minutes in durations, keeping source order.
2. Do not fill unknown durations with zero.
FigureOutputfigFigure
1. Create fig showing delivery frequencies across at least two contiguous numeric duration intervals.
2. Include every eligible duration and start the frequency axis at zero.
3. Add a meaningful title and measure labels
4. Display the figure.
Identify Minutes and Deliveries 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 zero-filled unknown become a misleading instant delivery?

Hint 2 — Tools

to_numeric(), dropna() and Axes.hist()

Revisit: Convert numeric text · Drop missing rows · Histogram · Finish a chart properly

Hint 3 — Approach

Parse durations, retain observed values and show their frequency across contiguous numeric intervals.

Explained solution

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

durations = pd.to_numeric(df.minutes, errors='coerce').dropna()
fig, ax = plt.subplots()
ax.hist(durations, bins=5)
ax.set(title='Delivery duration distribution', xlabel='Minutes', ylabel='Deliveries')
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(
    [
        ('D001', 'East', 'Delivered', '32', 1.2, '2026-04-01'),
        ('D002', 'West', 'Delivered', '48', 1.9, '2026-04-01'),
        ('D003', 'North', 'Delivered', '25', 2.6, '2026-04-02'),
        ('D004', 'East', 'Delivered', 'unknown', 3.3, '2026-04-02'),
        ('D005', 'West', 'Delivered', '45', 4, '2026-04-03'),
        ('D006', 'North', 'Delivered', '37', 1.2, '2026-04-03'),
        ('D007', 'East', 'Delivered', '72', 1.9, '2026-04-04'),
        ('D008', 'West', 'Delivered', '29', 2.6, '2026-04-04'),
        ('D009', 'North', 'Delivered', '33', 3.3, '2026-04-05'),
        ('D010', 'East', 'Delivered', None, 4, '2026-04-05'),
        ('D011', 'West', 'Delivered', '26', 1.2, '2026-04-06'),
        ('D012', 'North', 'Delivered', '62', 1.9, '2026-04-06'),
        ('D013', 'East', 'Delivered', '46', 2.6, '2026-04-07'),
        ('D014', 'West', 'Delivered', '38', 3.3, '2026-04-07'),
        ('D015', 'North', 'Delivered', '73', 4, '2026-04-08'),
        ('D016', 'East', 'Delivered', '30', 1.2, '2026-04-08'),
        ('D017', 'West', 'Delivered', '34', 1.9, '2026-04-09'),
        ('D018', 'North', 'Delivered', '50', 2.6, '2026-04-09'),
        ('D019', 'East', 'Delivered', '27', 3.3, '2026-04-10'),
        ('D020', 'West', 'Delivered', '63', 4, '2026-04-10'),
    ],
    columns=['delivery_id', 'depot', 'status', 'minutes', 'weight_kg', 'date'],
)

Why these steps exist

Invalid durations are unknown observations, not instant deliveries.
Bins collect counts across contiguous intervals; different sensible bin counts show the same underlying observations.

Another valid approach

Different sensible bin counts are accepted. Every eligible duration must be included and bar heights must represent frequencies.