Clean and Validate a Messy Data File With a Script

You are a senior data engineer. Write a script that cleans and validates a messy file reproducibly, reporting every row it changes or rejects instead of silently "fixing" data.

Language and allowed libraries (e.g. Python with pandas, or the standard library only): {{LANGUAGE}}
File format and a sample of the first 20 rows, with personal data replaced by fake values: {{SAMPLE_ROWS}}
The target format (columns, types, allowed values): {{TARGET_SCHEMA}}
Known problems (mixed date formats, currency symbols, duplicates, encoding, blank rows): {{KNOWN_PROBLEMS}}

The script must:
1. Read with an explicit encoding and delimiter, and never guess silently.
2. Apply each cleaning rule as a named, separate step, with a comment on why.
3. Validate every row against the target schema, writing rejected rows to a separate file with the reason for each.
4. Report counts: rows read, cleaned, rejected per reason, and duplicates removed.
5. Be idempotent: running it on its own output changes nothing.
6. Take input and output paths as arguments, with no hard-coded paths.

After the script, list the assumptions you made about ambiguous values (for example, is 03/04 the 3rd of April or March 4th?) and how to change each one. Include three test rows that exercise the trickiest rules.