usaddress-scourgify
Clean US addresses following USPS pub 28 and RESO guidelines
Decision gist · record as of 2026-08-14
Yes, with a license caveat. The package is actively maintained, has low install friction, no known vulnerabilities, and solves a real problem for US address normalization. However, verify the license status before production use, as it is currently marked unclear in the metadata. For projects that need USPS-compliant address standardization and can tolerate the multi-word street name parsing limitation, this is a practical choice.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.5 or later.
- For geocoder-based normalization, set GOOGLE_API_KEY environment variable.
- Custom constants require ADDRESS_CONFIG_DIR environment variable.
License · maintenance · safety
(unclear) — License status is unclear—no SPDX identifier or raw license text is available in the package metadata. Verify the actual license before use in commercial or restricted contexts.
last release 2026-08-07 (7 days) · last repo commit 2026-08-07 · 242 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,149,915 downloads/mo, #3,251 on PyPI
Alternatives
Verify before relying
pip install usaddress-scourgify
from scourgify import normalize_address_record
result = normalize_address_record('123 southwest Main street, Boring, or, 97203')
print(result)- Whether the package handles international addresses or only US addresses despite the name
- Performance characteristics with large address batches or streaming workflows
- Exact Python version support beyond the classifiers (3.5–3.8 listed, but current support unclear)
What it is and what it does
usaddress-scourgify is a Python library that takes messy or informal US addresses and converts them into a standardized format following USPS Publication 28 and RESO guidelines. It accepts addresses as either a single string or a dictionary, parses them using the usaddress library, and returns a normalized dictionary with uppercase values mapped to standard keys: address_line_1, address_line_2, city, state, and postal_code. The library abbreviates directional indicators (e.g., 'southwest' → 'SW') and street types (e.g., 'street' → 'ST') by default, though a long_hand parameter preserves full words if needed. It also normalizes postal codes to standard zip or zip+4 format with zero-padding.
The package is designed for data cleaning workflows where address consistency matters—real estate databases, shipping systems, or address validation pipelines. It does not validate addresses, only normalize them. It depends on usaddress for parsing and yaml-config for loading custom normalization rules. A known limitation exists with multi-word street or city names when parsing single-string addresses, which can be mitigated by providing addresses as dictionaries with pre-identified city fields.
Use it for
- Standardize address fields in real estate or property management databases before geocoding or matching
- Clean bulk address imports from forms or CSV files to ensure consistent formatting for downstream systems
- Normalize addresses in shipping or logistics systems to match USPS standards for mail delivery
- Prepare address data for deduplication or record linkage by converting to a canonical format
- Customize normalization rules via YAML config for domain-specific abbreviations or oddities
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with a license caveat.
The package is actively maintained, has low install friction, no known vulnerabilities, and solves a real problem for US address normalization. However, verify the license status before production use, as it is currently marked unclear in the metadata. For projects that need USPS-compliant address standardization and can tolerate the multi-word street name parsing limitation, this is a practical choice.
Install
usaddress-scourgify on PyPI
Before you install
Low friction install with a pure Python wheel. Actively maintained as of 2026-08-07 with recent activity; 242 repository stars suggest modest but stable adoption.
Requires Python 3.5 or later. For geocoder-based normalization, set GOOGLE_API_KEY environment variable. Custom constants require ADDRESS_CONFIG_DIR environment variable.
License in practice
License status is unclear—no SPDX identifier or raw license text is available in the package metadata. Verify the actual license before use in commercial or restricted contexts.
Quickstart
pip install usaddress-scourgify
from scourgify import normalize_address_record
result = normalize_address_record('123 southwest Main street, Boring, or, 97203')
print(result)Verify before relying
- Whether the package handles international addresses or only US addresses despite the name
- Performance characteristics with large address batches or streaming workflows
- Exact Python version support beyond the classifiers (3.5–3.8 listed, but current support unclear)
Package facts
| License | Not declared unclear |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesusaddressyaml-config |
| Maintenance | Actively maintained 7 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 2,149,915 / month, #3,251 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersOperating System :: OS IndependentProgramming Language :: Python :: 3.5Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8 |
Evidence: usaddress_scourgify-0.7.1-py3-none-any.whl
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See also google-i18n-address · usaddress · pyap2 · random-address · pyap · postal · url-normalize · censusgeocode · geocoder · normality