# fingerprints
This library helps with the generation of fingerprints for entity data. A fingerprint
in this context is understood as a simplified entity identifier, derived from it's
name or address and used for cross-referencing of entity across different datasets.
## Usage
```python
import fingerprints
fp = fingerprints.generate('Mr. Sherlock Holmes')
assert fp == 'holmes sherlock'
fp = fingerprints.generate('Siemens Aktiengesellschaft')
assert fp == 'ag siemens'
fp = fingerprints.generate('New York, New York')
assert fp == 'new york'
```
## Company type names
A significant part of what `fingerprints` does it to recognize company legal form
names. For example, `fingerprints` will be able to simplify `Общество с ограниченной ответственностью` to `ООО`, or `Aktiengesellschaft` to `AG`. The required database
is based on two different sources:
* A [Google Spreadsheet](https://docs.google.com/spreadsheets/d/1Cw2xQ3hcZOAgnnzejlY5Sv3OeMxKePTqcRhXQU8rCAw/edit?ts=5e7754cf#gid=0) created by OCCRP.
* The ISO 20275: [Entity Legal Forms Code List](https://www.gleif.org/en/about-lei/code-lists/iso-20275-entity-legal-forms-code-list)
Wikipedia also maintains an index of [types of business entity](https://en.wikipedia.org/wiki/Types_of_business_entity).
## See also
* [Clustering in Depth](https://github.com/OpenRefine/OpenRefine/wiki/Clustering-In-Depth), part of the OpenRefine documentation discussing how to create collisions in data clustering.
* [probablepeople](https://github.com/datamade/probablepeople), parser for western names made by the brilliant folks at datamade.us.
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