Write a Python Script That Cleans a Messy CSV Export Before Year End Reporting

You are a careful data engineer who writes small, readable Python scripts that non experts can run safely. I have a messy CSV export that I need to clean before year end reporting. Write a script based only on the columns and problems I describe. Do not invent columns, business rules, or data. If something is missing, mark it [NEEDS INPUT].

Where the export comes from (CRM, store platform, accounting tool, spreadsheet): [SOURCE]
Column names exactly as they appear in the header row, plus a few sample rows with sensitive values replaced: [COLUMNS AND SAMPLE]
Problems I already see (duplicate rows, mixed date formats, currency symbols in numbers, extra spaces, blank rows, inconsistent country or status names): [PROBLEMS]
What the clean file needs to look like for my report (columns to keep, date format, number format, sort order): [OUTPUT SPEC]
My Python setup (version, whether pandas is installed, Windows or Mac): [SETUP]
File size, roughly how many rows: [SIZE]

Requirements:
1. Restate the cleaning rules you will apply, one per line, based on [PROBLEMS] and [OUTPUT SPEC], so I can confirm them before running anything.
2. Write a single script that reads the input path and writes to a new output file. Never overwrite the original file.
3. Handle each problem in [PROBLEMS] with a clearly commented step: trim whitespace, normalize dates to the format in [OUTPUT SPEC], strip currency symbols and thousands separators before converting to numbers, standardize category values with a mapping I can edit, and drop fully blank rows.
4. For duplicates, explain which columns define a duplicate and keep a separate file of removed rows so nothing disappears silently.
5. Write rows that fail validation (unparseable dates, non numeric amounts, missing required fields) to an errors file with a reason column instead of guessing a fix.
6. Print a short summary at the end: rows read, rows written, duplicates removed, rows sent to errors, and totals of key amount columns before and after cleaning.
7. If [SIZE] is large, read the file in chunks and explain why. If pandas is not available in [SETUP], use the standard csv module instead.
8. Give step by step run instructions for [SETUP] in plain language, and a quick way to spot check the output against the original in a spreadsheet.

Deliverables:
1. Cleaning rules list to confirm
2. Complete commented Python script
3. Duplicate and error file explanation
4. Run instructions and spot check steps
5. A list of [NEEDS INPUT] questions