Glasgow Property Data Cleaning

I’m sitting on a CSV/Excel dataset of roughly 200,000 Glasgow real-estate listings. Each record already carries a free-text property description, but nothing else has been standardised or verified yet.

What I need now is a thorough data-clean-up and structuring pass so the file becomes immediately usable for analysis, dashboards, or import into a database later on.

Core tasks
• Load the Excel/CSV, remove obvious duplicates and empty rows
• Parse and normalise key fields you can extract from the descriptions (e.g., bedrooms, property type, postcode, square footage if present) – the richer the capture, the better
• Flag listings with missing critical info so I can review them quickly
• Deliver a final, well-formatted CSV/Excel plus a short summary sheet that outlines row counts before/after cleaning and any notable issues you found

Tools and skills I expect will make the job easier include Python, Pandas, or Power Query, but I’m open to whatever workflow you’re comfortable with as long as the result is rock-solid and reproducible.

Acceptance criteria
• Clean file opens with no errors in Excel and loads into Pandas without manual tweaks
• Duplicate rate and row loss clearly documented
• At least 95 % of listings have the key fields you extracted populated

If you’ve handled large property datasets before (especially UK-based) let me know—otherwise, tell me briefly how you’ll tackle the volume and parsing so I can be confident in the approach.