Consistent product names · Data Playground

The question

Prepare consistent product labels for a catalogue handoff.

df Clean labels

Your inputs

`df`

The only equivalent spellings are note-book and notebook. Other labels must retain their meaning.

Field meanings and units

order_id: Order or order-line identifier, as defined by this brief.
product: Recorded product label.
category: Product category.
region: Sales region.
quantity: Number of units.
unit_price: Dollars per unit.
discount: Discount in dollars for the whole order line.
in_stock: Whether the order line is marked available.

`df` · first 5 of 16 rows
`order_id`	`product`	`category`	`region`	`quantity`	`unit_price`	`discount`	`in_stock`
P001	" Notebook "	Paper	East	2	3.5	0	false
P002	PEN	Writing	West	5	1.2	None	true
P003	" pencil"	Writing	East	3	0.8	0.5	true
P004	Note-book	Paper	West	4	3.5	1	true
P005	" Notebook "	Paper	East	1	3.5	0	true

View all 16 rows

`df` · complete input
`order_id`	`product`	`category`	`region`	`quantity`	`unit_price`	`discount`	`in_stock`
P001	" Notebook "	Paper	East	2	3.5	0	false
P002	PEN	Writing	West	5	1.2	None	true
P003	" pencil"	Writing	East	3	0.8	0.5	true
P004	Note-book	Paper	West	4	3.5	1	true
P005	" Notebook "	Paper	East	1	3.5	0	true
P006	PEN	Writing	West	6	1.2	None	false
P007	" pencil"	Writing	East	2	0.8	0.5	true
P008	Note-book	Paper	West	8	3.5	1	true
P009	" Notebook "	Paper	East	2	3.5	0	true
P010	PEN	Writing	West	5	1.2	None	true
P011	" pencil"	Writing	East	3	0.8	0.5	false
P012	Note-book	Paper	West	4	3.5	1	true
P013	" Notebook "	Paper	East	1	3.5	0	true
P014	PEN	Writing	West	6	1.2	None	true
P015	" pencil"	Writing	East	2	0.8	0.5	true
P016	Note-book	Paper	West	8	3.5	1	false

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

Your task · Consistent product names

Prepare consistent product labels for a catalogue handoff.

cleanDataFramelabelslist

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.

Standardised catalogueOutputcleanDataFrame
1. Create clean as a separate copy.
2. Trim product, lowercase it, and recode note-book to notebook.
3. Keep all other values and row order.
Final labelsOutputlabelslist
1. Store each distinct cleaned product label once in a list named labels.

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 differences are accidental formatting, and which labels represent different products?

Hint 2 — Tools

copy(), str.strip(), str.lower(), replace() and unique()

Revisit: Protect the original · Recode categories · Clean text · What values exist?

Hint 3 — Approach

Work on a copy, normalise formatting, apply the stated spelling equivalence, then inspect the remaining labels.

Explained solution

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

clean = df.copy()
clean['product'] = (
    clean['product'].str.strip().str.lower().replace({'note-book': 'notebook'})
)
labels = list(clean['product'].unique())
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(
    [
        ('P001', ' Notebook ', 'Paper', 'East', 2, 3.5, 0, False),
        ('P002', 'PEN', 'Writing', 'West', 5, 1.2, None, True),
        ('P003', ' pencil', 'Writing', 'East', 3, 0.8, 0.5, True),
        ('P004', 'Note-book', 'Paper', 'West', 4, 3.5, 1, True),
        ('P005', ' Notebook ', 'Paper', 'East', 1, 3.5, 0, True),
        ('P006', 'PEN', 'Writing', 'West', 6, 1.2, None, False),
        ('P007', ' pencil', 'Writing', 'East', 2, 0.8, 0.5, True),
        ('P008', 'Note-book', 'Paper', 'West', 8, 3.5, 1, True),
        ('P009', ' Notebook ', 'Paper', 'East', 2, 3.5, 0, True),
        ('P010', 'PEN', 'Writing', 'West', 5, 1.2, None, True),
        ('P011', ' pencil', 'Writing', 'East', 3, 0.8, 0.5, False),
        ('P012', 'Note-book', 'Paper', 'West', 4, 3.5, 1, True),
        ('P013', ' Notebook ', 'Paper', 'East', 1, 3.5, 0, True),
        ('P014', 'PEN', 'Writing', 'West', 6, 1.2, None, True),
        ('P015', ' pencil', 'Writing', 'East', 2, 0.8, 0.5, True),
        ('P016', 'Note-book', 'Paper', 'West', 8, 3.5, 1, False),
    ],
    columns=['order_id', 'product', 'category', 'region', 'quantity', 'unit_price', 'discount', 'in_stock'],
)

Why these steps exist

Whitespace and letter case are formatting differences; the explicit recode handles only the authorised spelling equivalence.
The separate clean table preserves the original labels for comparison and investigation.

Another valid approach

Equivalent literal replacements are valid, but do not remove punctuation from unrelated labels.