A simple Python (3.2+ & 2.6+) module for parsing human names into their individual components.
- hn.title
- hn.first
- hn.middle
- hn.last
- hn.suffix
- hn.nickname
Supports 3 different comma placement variations in the input string.
- Title Firstname "Nickname" Middle Middle Lastname Suffix
- Lastname [Suffix], Title Firstname (Nickname) Middle Middle[,] Suffix [, Suffix]
- Title Firstname M Lastname [Suffix], Suffix [Suffix] [, Suffix]
Instantiating the HumanName class with a string splits on commas and then spaces, classifying name parts based on placement in the string and matches against known name pieces like titles and suffixes.
It correctly handles some common conjunctions and special prefixes to last names like "del". Titles and conjunctions can be chained together to handle complex titles like "Asst Secretary of State". It can also try to correct capitalization of names that are all upper- or lowercase names.
It attempts the best guess that can be made with a simple, rule-based approach. Its main use case is English and it is not likely to be useful for languages that do not share the same structure as English names. It's not perfect, but it gets you pretty far.
pip install nameparser
If you want to try out the latest code from GitHub you can install with pip using the command below.
pip install -e git+git://github.com/derek73/python-nameparser.git#egg=nameparser
If you're looking for a web service, check out eyeseast's nameparse service, a simple Heroku-friendly Flask wrapper for this module.
>>> from nameparser import HumanName
>>> name = HumanName("Dr. Juan Q. Xavier de la Vega III (Doc Vega)")
>>> name
<HumanName : [
title: 'Dr.'
first: 'Juan'
middle: 'Q. Xavier'
last: 'de la Vega'
suffix: 'III'
nickname: 'Doc Vega'
]>
>>> name.last
'de la Vega'
>>> name.as_dict()
{'last': 'de la Vega', 'suffix': 'III', 'title': 'Dr.', 'middle': 'Q. Xavier', 'nickname': 'Doc Vega', 'first': 'Juan'}
>>> name.string_format = "{first} {last}"
>>> str(name)
'Juan de la Vega'
The parser does not attempt to correct mistakes in the input. It mostly just splits on white space and puts things in buckets based on their position in the string. This also means the difference between 'title' and 'suffix' is positional, not semantic. "Dr" is a title when it comes before the name and a suffix when it comes after. ("Pre-nominal" and "post-nominal" would probably be better names.)
>>> name = HumanName("1 & 2, 3 4 5, Mr.")
>>> name
<HumanName : [
title: ''
first: '3'
middle: '4 5'
last: '1 & 2'
suffix: 'Mr.'
nickname: ''
]>
Your project may need some adjustment for your dataset. You can do this in your own pre- or post-processing, by customizing the configured pre-defined sets of titles, prefixes, etc., or by subclassing the HumanName class. See the full documentation for more information.
If you come across name piece that you think should be in the default config, you're probably right. Start a New Issue and we can get them added.
Please let me know if there are ways this library could be structured to make it easier for you to use in your projects. Read CONTRIBUTING.md for more info on running the tests and contributing to the project.
GitHub Project