---
id: rigour
version: "2.3.1"
license: unclear
license_treatment: permissive
maintenance: active
---

# rigour — Financial crime domain data validation and normalization library.

License: permissive · Maintenance: active · Downloads: 135.0K/mo

## What it is and what it does

Rigour is a data validation and normalization library designed for the financial crime and business intelligence domain. It consolidates production-tested implementations for handling human names, company names, language codes, country and territory identifiers, corporate and tax identifiers, and address formatting. The package addresses the gap between simple text handling and the complex edge cases that emerge in production systems—handling name variations, corporate entity formats, multilingual inputs, and standardized codes across jurisdictions.

The library depends on pyyaml, normality, prefixdate, orjson, python-stdnum, and jinja2 to provide its validation and normalization capabilities. It's actively maintained, supports Python 3.10 through 3.14, and includes pre-compiled wheels for macOS, Linux, and Windows architectures. The package is part of the OpenSanctions ecosystem and consolidates several older libraries (languagecodes, pantomime, fingerprints) into a single codebase.

Use it for:

- Normalize and validate company and individual names in financial crime compliance systems or sanctions screening workflows.
- Standardize country, language, and territory codes in international business data pipelines.
- Format addresses according to local customs for a given country in mail or document generation systems.
- Validate corporate and tax identifiers (e.g., VAT numbers, registration IDs) in accounting or regulatory reporting systems.
- Clean and deduplicate business entity records from multiple sources with varying name formats and conventions.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Rigour provides data cleaning and validation functions for business-world text: human and company names, language and territory codes, corporate identifiers, and addresses, with production-ready handling of edge cases.

Yes. Rigour is actively maintained, has no known vulnerabilities, supports current Python versions, and provides production-tested implementations for a specific but important domain (business data validation). The medium install friction is offset by comprehensive platform coverage and the consolidation of multiple specialized libraries into one package. Install if you work with business entity data, compliance systems, or international business records.

## Install

pip install rigour
uv add rigour
poetry add rigour

## Installing rigour

Before you install: Medium install friction due to compiled wheels for multiple Python versions and platforms (3.10–3.14, including Rust components). Active maintenance with a release 15 days ago and no known vulnerabilities.

License in practice: MIT license permits unrestricted use, modification, and distribution in both open and proprietary projects with minimal restrictions.

Quickstart:

pip install rigour

from rigour.names import normalize_name
from rigour.countries import country_name

normalized = normalize_name('John Doe')
country = country_name('US')

Requires Python 3.10 or later; wheels include Rust-compiled components.

Verify before relying:

- Whether the package handles all major business identifier standards (tax IDs, corporate registration numbers) or a subset.
- Performance characteristics when processing large datasets or real-time validation scenarios.
- Completeness of address formatting coverage across all countries and territories.

## Package facts

- License: not declared (permissive)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 135.0K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

business data validation, company name normalization, country code validation, text data cleaning, financial crime compliance, address formatting, corporate identifier validation, data-validation, business-intelligence, compliance

[View on SkillFed](https://skillfed.io/packages/rigour) · [View on PyPI](https://pypi.org/project/rigour/)