A delivery just arrived · Data Playground

The question

A delivery file has arrived. Establish what it contains before anyone uses it.

delivery.csv CSV intake

Your inputs

`delivery.csv`

One row is one delivery record. delivery.csv has a header row; dates are YYYY-MM-DD text.

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.

`delivery.csv` · first 5 of 16 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 16 rows

`delivery.csv` · 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

The file is available in the Python working folder. Load it yourself.

Your task · A delivery just arrived

A delivery file has arrived. Establish what it contains before anyone uses it.

previewDataFramedimensionstupletypesSeries

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.

First lookOutputpreviewDataFrame
1. Load delivery.csv into df and keep its first five records in preview.
Table shapeOutputdimensionstuple
1. Store the shape of the full df in dimensions: its number of rows and columns (df.shape).
Column typesOutputtypesSeries
1. Store the data type of each imported column in types (df.dtypes).

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

How can you inspect a sample without shortening the imported table?

Hint 2 — Tools

read_csv(), head(), shape and dtypes

Revisit: Load a CSV · Take the first look · How big is it? · What types are these?

Hint 3 — Approach

Load the entire file, inspect a small preview, and collect metadata from the entire imported table.

Explained solution

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

df = pd.read_csv('delivery.csv')
preview = df.head()
dimensions = df.shape
types = df.dtypes
preview

View supplied setup

This code is already at the top of the editor.

import pandas as pd

Why these steps exist

The preview contains five rows; df.shape and df.dtypes describe the entire imported file.
Keeping df intact allows a later analyst to inspect records beyond the first five.

Another valid approach

You can inspect the complete file and keep a separate preview variable; slicing the imported df itself would lose records.