# datamade/usaddress

:us: a python library for parsing unstructured United States address strings into address components

Repository: https://github.com/datamade/usaddress
Canonical: https://ross.abutalabs.com/products/usaddress
Homepage: https://parserator.us/usaddress
Language: Python
License: MIT
License Family: permissive
Topics: python-library, address, nlp, parserator, python, address-parser, natural-language-processing, machine-learning, conditional-random-fields, crf
Last push: 2025-08-07T14:51:41+00:00

## Health v2 (maintenance only)
Score: 48/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 35, release rhythm 35, longevity 100
- inputs: {"age_days": 4430, "days_push": 391, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1636, forks 308 (observed 2026-08-28T04:05:14.764441+00:00)

## What it is
usaddress is a Python library that parses unstructured United States address strings into labeled address components using a conditional random fields model. It provides parse and tag methods that split messy addresses into fields like AddressNumber, StreetName, and PlaceName, without validating or normalizing addresses.

## Use cases
- parse unstructured US address strings into components
- split addresses into street, city, state, and zip columns
- extract address fields from messy data for cleaning
- tag address parts in a dataset before geocoding
- handle tricky addresses that rule-based parsers fail on

## When to choose
- you need to parse free-form US addresses in Python
- your addresses are messy and rule-based parsers break down
- you want an MIT-licensed, pip-installable address parser
- you need labeled address components as input to geocoding or deduplication

## When to avoid
- you need address validation or deliverability checks
- you need address normalization or standardization out of the box
- you need to parse non-US addresses
- you require perfect accuracy - the model is probabilistic

## Facets
- artifact type: library
- maturity: active
- function: nlp, parser, machine-learning
- domain: developer-tools, data-science
- platform: python, cross-platform
- tags: address-parsing, conditional-random-fields, us-addresses, geocoding-preprocessing, probabilistic-parsing, natural-language-processing

## Member repositories
- datamade/usaddress (main) score 48

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:14.764441+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:46:58.070702+00:00, confidence not recorded.
  - readme: https://github.com/datamade/usaddress (fetched 2026-08-28T04:05:14.764441+00:00, sha a57f63c5b8d2)
  - homepage: https://parserator.us/usaddress (fetched 2026-08-29T11:20:09.434289+00:00, sha b251cbe3b465)
  - site_page: https://parserator.us/api-docs (fetched 2026-08-29T11:20:09.445007+00:00, sha f153ed3f1e16)
  - site_page: https://parserator.us/about (fetched 2026-08-29T11:20:09.447168+00:00, sha 29d65184e564)
  - registry_pypi: https://pypi.org/pypi/usaddress/json (fetched 2026-08-29T11:20:09.448790+00:00, sha aad1ae2ef2ab)
  - site_page: https://parserator.us/pricing (fetched 2026-08-29T11:20:09.443204+00:00, sha 4e71b07cd726)
- Data as of 2026-08-30T08:39:29.467469+00:00.