Registry / data / fingerprints

fingerprints

library1.3.1pypypi✓ verified 90d ago

The `fingerprints` library is a utility for generating stable and deterministic hashes (fingerprints) for entities based on their identifying attributes like names, addresses, and identifiers. It's commonly used in data matching and deduplication scenarios, particularly within the 'opensanctions' ecosystem. The current version is 1.3.1, and its release cadence is irregular, typically corresponding to bug fixes or minor feature enhancements.

data serialization

pip install fingerprints

github ↗package ↗docs ↗

INSTALL

IMPORT

SIG · FINGERPRINTS

fingerprints

datapythonv1.3.1

Install

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Import

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Disk

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Pass rate

0/ 10

Env Coverage0 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v? · pip install

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.910 runs

build_error

glibc

py 3.10–3.910 runs

build_error

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

generate

✓ from fingerprints import generate

This quickstart demonstrates how to import and use the `generate` function to create fingerprints for different types of entities. It highlights that the library internally normalizes inputs, leading to identical fingerprints for semantically equivalent data.

from fingerprints import generate # Generate a fingerprint for a person person_fp = generate(name="Angela Merkel", country="de", birth_date="1954-07-17") print(f"Person Fingerprint: {person_fp}") # Generate a fingerprint for an organization with an address org_fp = generate( name="Global Corp Inc.", address="123 Main Street, Cityville, Countryland", country="us", url="http://globalcorp.com" ) print(f"Organization Fingerprint: {org_fp}") # Fingerprints are deterministic for identical, normalized inputs same_person_fp = generate(name="angela merkel", country="germany", birth_date="1954-07-17") print(f"Same Person Fingerprint: {same_person_fp}") assert person_fp == same_person_fp

Debug

Known issues

gotchaInput keys for `generate` are specific and others are ignored. The `generate` function processes a predefined set of keys (e.g., `name`, `address`, `country`, `id_number`, `email`). Providing other, unrecognized keys will not cause an error but will be silently ignored, potentially leading to less distinct or identical fingerprints for entities that differ only by ignored attributes.

fix

Refer to the library's source code or documentation to understand the full list of supported input keys. Ensure you are passing only relevant and recognized identifying attributes as keyword arguments to `generate`.

affects: All versions

gotchaFingerprints are deterministic hashes, not fuzzy matches. This library generates exact cryptographic hashes (SHA1) based on *normalized* input. It is not designed for fuzzy matching (e.g., matching 'John Doe' to 'Jon Doh'). Even minor differences in input (like typos or extra spaces not handled by normalization) will result in completely different fingerprints.

fix

Understand that `fingerprints` provides a strong, deterministic identifier for *exact* matches after normalization. For fuzzy matching or similarity detection, other libraries or algorithms (e.g., Levenshtein distance, Jaccard similarity) are required.

affects: All versions

gotchaDependency on `normality` version and its behavior. The `fingerprints` library relies heavily on `normality` for data cleaning and standardization. Changes, bug fixes, or new normalization rules in newer versions of `normality` can subtly alter how data is processed, potentially leading to different fingerprints for the same input across different `normality` versions or environments.

fix

For production environments requiring stable fingerprint generation, it is advisable to pin the exact version of the `normality` library used in your project. Regularly test `fingerprints` behavior when updating `normality`.

affects: All versions

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'fingerprints'

The `fingerprints` library is not installed in your Python environment or is not accessible from the current path.

fix

Install the library using pip: `pip install fingerprints`

TypeError: generate() missing 1 required positional argument: 'data'

The `generate` function was called without any keyword arguments. It expects identifying attributes to be passed as `key=value` pairs (e.g., `generate(name="Alice", country="US")`).

fix

Provide identifying attributes as keyword arguments, for example: `from fingerprints import generate; generate(name="Example Entity", country="ZZ")`.

ValueError: Could not normalize country: <INVALID_CODE>

The `country` argument provided to `generate` could not be normalized by the underlying `normality` library. This typically happens when an invalid or unresolvable country code or name is passed.

fix

Ensure that country values are valid ISO 3166-1 alpha-2 codes (e.g., 'US', 'DE', 'GB') or common country names that `normality` can resolve. Consult `normality`'s documentation for supported country formats.

Upgrade

Version history

1.3.1latest on PyPI · released Aug 1, 2025

Audit

Dependencies

normalityrequiredUsed for data cleaning and standardization before fingerprint generation, ensuring consistency across inputs.

Agent activity

4 hits · last 30 days

node

Resources

githubhttps://github.com/opensanctions/fingerprints ↗

packagehttps://pypi.org/project/fingerprints/ ↗

docshttps://github.com/opensanctions/fingerprints/ ↗