I have a raw set of customer records that must move from messy spreadsheets to a structured, analysis-ready format. The job starts in Excel—think Power Query, advanced formulas, or VBA for quick wins—then shifts to Python (Pandas, NumPy, openpyxl) so the process can run automatically whenever fresh data arrives. Here’s what I need from you: first, clean and normalise the fields (remove duplicates, unify date and address formats, fill or flag missing values). Next, reshape the information into tidy tables that feed our reporting model. Finally, package everything into a repeatable Python script that reads the latest file, runs the full transformation pipeline, and exports polished XLSX/CSV outputs along with a simple log of what changed. Deliverables • One fully documented Excel workbook showing the steps applied • A well-commented .py script that performs the same preparation end-to-end • Output files generated by the script to prove it works on my sample data I will supply the current spreadsheet and a small subset for testing. Just keep your code readable, modular, and easy for me to tweak later, and we’ll be in great shape.