Brief the next analyst · Data Playground

The question

Give the next analyst a compact intake brief and a reliable completed-delivery summary.

df Intake brief

Your inputs

`df`

One row is one delivery record. Some durations are unknown; Pending and Returned records still belong in the intake metadata.

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 18 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	Pending	25	2.6	2026-04-02
D004	East	Returned	61	3.3	2026-04-02
D005	West	Delivered	None	4	2026-04-03

View all 18 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	Pending	25	2.6	2026-04-02
D004	East	Returned	61	3.3	2026-04-02
D005	West	Delivered	None	4	2026-04-03
D006	North	Delivered	37	1.2	2026-04-03
D007	East	Pending	72	1.9	2026-04-04
D008	West	Returned	29	2.6	2026-04-04
D009	North	Delivered	33	None	2026-04-05
D010	East	Delivered	49	4	2026-04-05
D011	West	Pending	26	1.2	2026-04-06
D012	North	Returned	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	Pending	73	4	2026-04-08
D016	East	Returned	30	1.2	2026-04-08
D017	West	Delivered	34	1.9	2026-04-09
D018	North	Delivered	50	2.6	2026-04-09

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

Your task · Brief the next analyst

Give the next analyst a compact intake brief and a reliable completed-delivery summary.

dimensionstuplemissingSeriesmean_minutesSeries

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

Table shapeOutputdimensionstuple
1. Store the shape of the full df in dimensions: its number of rows and columns (df.shape).
Missing values by columnOutputmissingSeries
1. Store the missing-value count for every column in missing.
Completed-delivery meansOutputmean_minutesSeries
1. Store the mean minutes for Delivered records by depot in mean_minutes.
2. Unknown durations are excluded from each mean.

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 outputs describe all records, and which describe completed deliveries only?

Hint 2 — Tools

shape, isna(), sum(), groupby() and mean()

Revisit: How big is it? · Find missing values · Summarise groups · Combine conditions

Hint 3 — Approach

Collect whole-table metadata first; independently restrict the delivery summary to completed records.

Explained solution

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

dimensions = df.shape
missing = df.isna().sum()
mean_minutes = df[df.status == 'Delivered'].groupby('depot').minutes.mean()
mean_minutes

View supplied setup

This code is already at the top of the editor.

import pandas as pd

# Supplied input: df (18 rows)
df = pd.DataFrame(
    [
        ('D001', 'East', 'Delivered', 32, 1.2, '2026-04-01'),
        ('D002', 'West', 'Delivered', 48, 1.9, '2026-04-01'),
        ('D003', 'North', 'Pending', 25, 2.6, '2026-04-02'),
        ('D004', 'East', 'Returned', 61, 3.3, '2026-04-02'),
        ('D005', 'West', 'Delivered', None, 4, '2026-04-03'),
        ('D006', 'North', 'Delivered', 37, 1.2, '2026-04-03'),
        ('D007', 'East', 'Pending', 72, 1.9, '2026-04-04'),
        ('D008', 'West', 'Returned', 29, 2.6, '2026-04-04'),
        ('D009', 'North', 'Delivered', 33, None, '2026-04-05'),
        ('D010', 'East', 'Delivered', 49, 4, '2026-04-05'),
        ('D011', 'West', 'Pending', 26, 1.2, '2026-04-06'),
        ('D012', 'North', 'Returned', 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', 'Pending', 73, 4, '2026-04-08'),
        ('D016', 'East', 'Returned', 30, 1.2, '2026-04-08'),
        ('D017', 'West', 'Delivered', 34, 1.9, '2026-04-09'),
        ('D018', 'North', 'Delivered', 50, 2.6, '2026-04-09'),
    ],
    columns=['delivery_id', 'depot', 'status', 'minutes', 'weight_kg', 'date'],
)

Why these steps exist

The table shape and missing counts describe all incoming records, including incomplete deliveries.
Only completed deliveries belong in the duration summary; unknown durations do not represent zero-minute deliveries.

Another valid approach

Whole-table metadata and the filtered group summary can be calculated independently; do not reuse the smaller population for missingness.