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Short Description
Built a Python workflow that cleans messy customer exports, normalizes fields, detects duplicates, validates rows, and exports clean data, review files, quality reports, and processing logs.
Problem
Business data exports often contain inconsistent emails, phone numbers, countries, dates, revenue fields, duplicate records, and missing values. Manual cleanup is repetitive and creates reporting risk.
Solution
I built a Python cleanup workflow that loads Excel/CSV customer data, maps raw columns to a target schema, normalizes fields, detects duplicates, validates rows, and exports clean and review-ready outputs.
Tools
Python, Pandas, OpenPyXL, YAML configuration, validation rules, CSV/XLSX input, automated reports.
Deliverables
Excel/CSV cleanup pipeline
Clean customer CSV
Duplicate/review output
Data quality report
Processing log
Reusable configuration files
Result
The workflow processed a messy customer export, removed duplicate records, separated clean and review rows, and produced a repeatable data-cleanup process for similar business data tasks.
The network for creativity
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