Product Catalog CSV Cleaning & Standardization by Brandon Canales AlvaradoProduct Catalog CSV Cleaning & Standardization by Brandon Canales Alvarado
Product Catalog CSV Cleaning & StandardizationBrandon Canales Alvarado
Cover image for Product Catalog CSV Cleaning & Standardization
I clean and standardize product catalog CSV files using a structured Python workflow designed to reduce repetitive manual work while keeping every change traceable.
This service is for catalogs of up to 500 rows and 10 columns.
You’ll receive:
A cleaned and standardized CSV
A transformation/change report
A QA and issues report
A separate review queue for ambiguous values that should not be guessed
Typical cleanup can include:
Whitespace and formatting inconsistencies
Duplicate detection
Standardization of agreed text fields
Basic validation of structured values
Identification of missing or ambiguous data
Before starting, we’ll agree on the cleanup rules that apply to your file. Values that cannot be safely corrected are flagged for review instead of being invented.
Not included: manual research to reconstruct missing product information, external system integrations, database work, or large-scale semantic rewriting.
FAQs

Starting at$75
Duration2 days
Tags
CSV
Data Cleaning
Python
Automation Engineer
Data Analyst
Data Entry Specialist
Software Engineer
E-Commerce
Product Data
Service provided by
Brandon Canales Alvarado San José Province, Costa Rica
Product Catalog CSV Cleaning & StandardizationBrandon Canales Alvarado
Starting at$75
Duration2 days
Tags
CSV
Data Cleaning
Python
Automation Engineer
Data Analyst
Data Entry Specialist
Software Engineer
E-Commerce
Product Data
Cover image for Product Catalog CSV Cleaning & Standardization
I clean and standardize product catalog CSV files using a structured Python workflow designed to reduce repetitive manual work while keeping every change traceable.
This service is for catalogs of up to 500 rows and 10 columns.
You’ll receive:
A cleaned and standardized CSV
A transformation/change report
A QA and issues report
A separate review queue for ambiguous values that should not be guessed
Typical cleanup can include:
Whitespace and formatting inconsistencies
Duplicate detection
Standardization of agreed text fields
Basic validation of structured values
Identification of missing or ambiguous data
Before starting, we’ll agree on the cleanup rules that apply to your file. Values that cannot be safely corrected are flagged for review instead of being invented.
Not included: manual research to reconstruct missing product information, external system integrations, database work, or large-scale semantic rewriting.
FAQs

$75