Python Data Cleaning & Reporting

The raw dataset is ready for you the moment we start. Your main mission is to take that data through a thorough cleaning and preprocessing workflow—detecting and fixing inconsistencies, handling missing values, standardising formats, and preparing tidy tables I can feed straight into later analyses.

I expect you to stay inside the pandas / NumPy tool-set for most of the wrangling, dipping into SQL whenever a relational query is faster or clearer. Please keep each transformation reproducible in a well-commented notebook or script so I can trace every decision.

Deliverables
• A clean, fully documented dataset (CSV or database table)
• The Python notebook / script with step-by-step explanations and inline comments
• A brief markdown or PDF summary highlighting key issues discovered and how they were resolved

Acceptance criteria: the notebook must run end-to-end without manual intervention, and all code blocks should execute in under five minutes on a standard laptop.

When you reply, give a short overview of your relevant experience performing similar data cleaning projects with pandas, NumPy and SQL—links or concise descriptions are fine.