iso-review

ISO Review

Audit a dataset for ISO-standardisable fields and optionally refactor to the standard.

When to invoke

• User asks "is this using ISO standards?", "what could be standardised here?", "audit against ISO".
• Preparing a dataset for external distribution, where standards-compliance aids interoperability.

Standards covered

Standard	Scope	Detection heuristics
ISO 3166-1 (countries)	alpha-2 (US), alpha-3 (USA), numeric (840)	Column names country, nation, country_code; values matching country names or 2/3-letter codes
ISO 3166-2 (subdivisions)	e.g. US-CA, GB-ENG	State / province / region columns
ISO 3166-3 (formerly-used)	Historical country codes	USSR, DDR, YUG values
ISO 4217 (currencies)	USD, EUR, JPY	Currency columns, symbols in amounts
ISO 639 (languages)	639-1 (en), 639-2/3 (eng)	Language columns, free-text language labels
ISO 8601 (dates/times)	YYYY-MM-DD, YYYY-MM-DDTHH:MM:SSZ, durations	Date-like columns in non-ISO formats
ISO 80000 / SI units	SI units and prefixes	Unit columns, mixed unit systems
ISO 3166-1 alpha-2 in emails / URLs (TLDs)	—	Domain / email columns with ccTLDs
IANA time zones (not ISO but standard)	Europe/London	Timezone columns
IANA media types / MIME	application/json	Content-type / format columns
ISO 10962 (CFI)	Financial instrument classification	Instrument columns
ISO 6166 (ISIN)	Securities identifiers	Security ID columns; checksum validation
ISO 17442 (LEI)	Legal Entity Identifier	Entity ID columns; 20-char format

Procedure

1. Profile the dataset — column names, types, samples, unique-value sets for low-cardinality columns.
2. Pattern-match to standards — use header heuristics (column-name keywords) plus value heuristics (regex, known-value sets from pycountry, iso4217, langcodes, etc.).
3. Classify each candidate column:
   • Compliant — already matches the standard cleanly.
   • Partially compliant — most values match; some don't (report exceptions).
   • Non-compliant, easily refactorable — values can be deterministically mapped (e.g. "United States" → "US").
   • Non-compliant, ambiguous — requires user input (e.g. short codes that could be 3166-1 or 3166-2, date formats 01/02/2024 that could be UK or US ordering).
4. Produce a review report iso_review.md:
   • Per column: applicable standard, compliance status, evidence, recommended canonical form, refactor feasibility.
   • Priority list — which columns to standardise first, based on impact + feasibility.
5. Offer refactor mode (require explicit user confirmation):
   • For each "easily refactorable" column, apply the mapping and add the standardised column (or overwrite, per user choice).
   • For dates, enforce ISO 8601 (with user-confirmed source-format assumption).
   • For currencies, promote symbols / free-text names to ISO 4217 codes.
   • Route through existing skills where they apply: standardise-country-names, add-iso3166, enrich-with-currency.
6. Validate — re-scan after refactor and report residual non-compliance.
7. Update the data dictionary — note the standard each column now follows.

Dependencies

pip install pandas pycountry langcodes iso4217 python-stdnum python-dateutil babel

Edge cases

• Mixed standards in one column — rare but real (e.g. a country column mixing alpha-2 and full names). Report and pick a target form with the user.
• Date format ambiguity (01/02/2024) — never guess. Require user confirmation; prefer source metadata or data dictionary for the answer.
• Historical values — ISO 3166-3 covers formerly-used codes; offer this rather than forcing a successor.
• Checksum-validated identifiers (ISIN, LEI) — verify the checksum; report invalid entries, do not silently accept.
• Non-ISO regional standards (e.g. FIPS codes) that look like ISO — note them, don't conflate.
• Performance — for very large datasets, sample for detection, then apply refactor in full-file pass.