{"categories":[{"label":"Text Processing","url":"https://skillfed.io/packages/category/text-processing"}],"enrichment":{"capability":"Cleans and normalizes US addresses to USPS and RESO standards, converting them to a consistent dictionary format with uppercase fields and standardized abbreviations.","skillfed_tags":["address-normalization","usps-standards","data-cleaning"],"use_cases":["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"],"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' \u2192 'SW') and street types (e.g., 'street' \u2192 '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.\n\nThe package is designed for data cleaning workflows where address consistency matters\u2014real 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.","worth_installing":"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."},"id":"usaddress-scourgify","links":{"html":"https://skillfed.io/packages/usaddress-scourgify","md":"https://skillfed.io/packages/usaddress-scourgify.md","pypi":"https://pypi.org/project/usaddress-scourgify/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-07","license_spdx":null,"license_treatment":"unclear","name":"usaddress-scourgify","python_support":"unspecified","summary":"Clean US addresses following USPS pub 28 and RESO guidelines"},"popularity":{"monthly_downloads":2149915,"position":3251,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.7.1"}