I have a single source file – input.csv – with the columns date, name, amount and category. Some cells come with stray spaces, inconsistent date formats, blank categories and the occasional non-numeric amount. What I need is a straightforward Python program (main.py) that I can run with python main.py input.csv and that will do the following in one pass: • Strip leading/trailing spaces from every string field. • Normalise every valid date to the YYYY-MM-DD format and drop any row whose date cannot be parsed. • Convert amount to a numeric value; rows with invalid amounts must also be removed. • Fill empty or missing category cells with the literal “Unknown”. • Save the cleaned data to cleaned.csv using the standard comma delimiter. • Produce a second file, summary.csv, that shows, for each category, the total amount and the number of rows contributing to that total. You are free to build it with pandas or stick to the standard library; either approach is fine with me as long as it runs unchanged on both Windows and macOS. If you choose pandas, include a minimal requirements.txt. Deliverables: • main.py (single entry point) • README.txt with exact run instructions • requirements.txt (only if you use pandas) Acceptance criteria: • Script runs with: python main.py input.csv • Generates cleaned.csv and summary.csv • Works on Windows and Mac (Python 3.10+) Turnaround time is two to three days. Let me know if anything is unclear and I’ll respond quickly so you can keep moving.