parse_sheet treated every column that was not company/city as a salary category, with no check that the column actually held numeric salary data. This turned free-text columns (e.g. Notes) into bogus string categories and numeric identifier columns (e.g. Id) into mistaken salary indexes.
- Drop identifier headers (ID_PATTERNS = {id, personnummer}) at classification time.
- Skip non-numeric standalone values and fully-null count/index pairs at row-processing time.
- Adds regression tests (skips_free_text_column, skips_numeric_identifier_column, keeps_numeric_salary_column) that fail on master and pass after the fix.
convert_salary_excel.py detected the company column via exact membership
in COMPANY_PATTERNS, so common real-world headers like "Company Name" or
"Employer Name" were never matched. parse_sheet then returned [] for that
sheet, silently dropping it from salary_data.json (or exiting with no
output for a single-sheet file).
Route company-column detection through the existing header_matches()
token matcher (already used for count/index detection). This only adds
detections; inputs that already worked (bare "Company"/"Firma"/...) are
unaffected.
Adds a regression test in tests/test_convert_salary_excel.py that fails
on master (returns []) and passes after the fix.
Alaa-Taieb