COLUMN | CATALOG CLEANUP SAMPLE

This is a fictional demonstration using five invented product records.
It is not client work, a testimonial, or a claim about customer results.

WHAT IS INCLUDED
before.csv: original fictional catalog with inconsistent names and categories.
after.csv: the same five products after the three agreed rules below.
change-log.csv: one record for each actual changed cell.
decision-list.csv: one unresolved question for the customer.

THREE AGREED RULES FOR THIS DEMONSTRATION
R1: Product names. Normalize the existing names to title case, collapse
repeated spaces, and replace the hyphen in side-table with a space.
No new product characteristics or descriptions are added.

R2: Category mapping. Use this supplied mapping:
home / textile -> Home textiles
KITCHEN -> Kitchenware
Kitchen -> Kitchenware
furniture -> Furniture

R3: Flag missing category. Keep the missing CT-005 category blank in the
cleaned catalog. Ask the customer to supply it in decision-list.csv.
Do not guess the category from the product name.

RESULTS
5 product rows preserved in the original order.
9 changed cells: 5 product names and 4 categories.
All 5 SKUs unchanged.
1 unresolved category: CT-005, Cotton Throw.
No products added or deleted.

HOW TO READ THE FILES
Files are UTF-8 CSV with a header row and comma-separated fields.
The row field in the log and decision list numbers product rows from 1.
The header is not counted. Product row 5 is CSV line 6.
An empty category field means the value remains missing.
Website labels such as Review needed are visual status labels, not category
values written into after.csv.

The customer_response field in decision-list.csv is blank for the customer
to complete. The status remains Open until the question is resolved.
The missing category is not counted as a changed cell.

These are generic catalog samples, not a platform-specific import file.
For client work, category mapping and all cleanup rules are confirmed in
writing before changes are made.