Short Description Built a Python workflow

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.