Prepare a focused extract · Data Playground

The question

The East depot needs a list of completed deliveries, longest first.

df Focused extract

Your inputs

`df`

One row is one delivery. The handoff is for completed East-depot records only; durations are minutes.

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	45	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	45	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	3.3	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 · Prepare a focused extract

The East depot needs a list of completed deliveries, longest first.

extractDataFramerecord_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.

Depot handoffOutputextractDataFrame
1. Create extract for depot East and status Delivered.
2. Keep delivery_id, minutes and weight_kg, ordered by minutes descending then delivery_id ascending.
Record countOutputrecord_countnumber
1. Store the number of selected records in record_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 conditions must a record satisfy at the same time?

Hint 2 — Tools

loc, boolean conditions and sort_values()

Revisit: Pick several columns · Combine conditions · Sort the table

Hint 3 — Approach

Select the specified depot and completed status, retain the requested fields, then order the handoff and count it.

Explained solution

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

extract = df.loc[
    (df.depot == 'East') & (df.status == 'Delivered'),
    ['delivery_id', 'minutes', 'weight_kg'],
].sort_values(['minutes', 'delivery_id'], ascending=[False, True])
record_count = len(extract)
extract

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', 45, 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, 3.3, '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

Both conditions must hold on the same record; accepting either would include unrelated deliveries.
The secondary ID order makes ties reproducible without changing the longest-first requirement.

Another valid approach

A boolean mask with column selection or an equivalent query can select the same records. The tie-break and final order still matter.