---
id: fingerprints
version: "1.3.1"
license: The MIT License (MIT) Copyright (c) 2025 OpenSanctions Datenbanken GmbH Copyright (c) 2018 Journalism Development Network, Inc. Copyright (c) 2016 Friedrich Lindenberg Permission is hereby granted,… (full text in the JSON record)
license_treatment: permissive
maintenance: aging
---

# fingerprints — A library to generate entity fingerprints.

License: permissive · Maintenance: aging · Downloads: 227.3K/mo

## What it is and what it does

Fingerprints is a library for generating normalized entity identifiers from names and addresses, designed to help match the same entity across different data sources. It takes messy input like 'Mr. Sherlock Holmes' or 'Siemens Aktiengesellschaft' and produces a canonical simplified form ('holmes sherlock', 'ag siemens') suitable for deduplication and cross-referencing. The library includes a database of company legal forms drawn from OCCRP, ISO 20275, and other sources, allowing it to recognize and standardize abbreviations like 'AG' for Aktiengesellschaft or 'ООО' for Russian limited liability companies.

The package depends on normality for text processing and installs as a pure Python wheel with low friction. However, as of May 2025, fingerprints is unmaintained—the codebase has been superseded by rigour, which incorporates the next generation of this functionality. The repository remains available and the latest release is from August 2025, but no further development is planned.

Use it for:

- Deduplicating entity records in compliance or sanctions databases by generating fingerprints for matching.
- Normalizing company names across multiple data sources to identify the same legal entity.
- Extracting and standardizing company legal forms from unstructured text for entity classification.
- Building entity resolution pipelines where fingerprints serve as a first-pass matching key.
- Preprocessing name data for downstream clustering or similarity matching algorithms.

## Worth the install?

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

Generates simplified entity identifiers (fingerprints) from names and addresses for matching entities across different datasets, with built-in recognition of company legal forms.

Yes, but with conditions. Install if you need entity fingerprinting now and can tolerate an unmaintained library; the code is stable and has no known vulnerabilities. However, for new projects, evaluate rigour first—it is the maintained successor and likely has improvements. The low install friction and permissive MIT license make this a low-risk addition, but plan a migration path if long-term support matters.

## Install

pip install fingerprints
uv add fingerprints
poetry add fingerprints

## Installing fingerprints

Before you install: Low friction: pure Python wheel with a single runtime dependency (normality). However, the package is now unmaintained as of 2025-05; active development has moved to rigour. The repository is not archived, but no new releases are planned.

License in practice: MIT license (permissive): you may use, modify, and distribute the package freely with minimal restrictions, provided you retain the copyright notice and license text.

Quickstart:

pip install fingerprints

import fingerprints

fp = fingerprints.generate('Mr. Sherlock Holmes')
print(fp)  # 'holmes sherlock'

Requires Python 3.10 or later.

Verify before relying:

- Whether fingerprints will be maintained or if users should migrate to rigour immediately.
- Performance characteristics on large entity datasets or real-world matching tasks.
- How well the company legal form database covers non-English or emerging markets.

## Package facts

- License: The MIT License (MIT) Copyright (c) 2025 OpenSanctions Datenbanken GmbH Copyright (c) 2018 Journalism Development Network, Inc. Copyright (c) 2016 Friedrich Lindenberg Permission is hereby granted,… (full text in the JSON record) (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: aging
- Downloads: 227.3K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

entity fingerprinting, name normalization, entity matching, company legal form recognition, cross-dataset deduplication, entity identifier generation, name standardization, entity-matching, data-deduplication, unmaintained

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