address-parser
Parse unstructured addresses into structured components - street, city, state, zip, country with validation.
When & Why to Use This Skill
The Address Parser skill is a specialized utility designed to transform messy, unstructured address strings into clean, standardized, and validated data components. By automating the extraction of streets, cities, states, and postal codes, it eliminates manual data entry errors and ensures high-quality location data for databases, CRMs, and logistics systems. It features international support and batch processing capabilities, making it an essential tool for maintaining data hygiene and preparing information for geocoding services.
Use Cases
- CRM Data Enrichment: Automatically clean and structure customer address lists imported from various sources to ensure consistent formatting and improved searchability.
- E-commerce Logistics: Validate and format shipping addresses during order processing to reduce delivery failures and optimize shipping routes.
- Geocoding Preparation: Convert raw text addresses into the structured fields required by mapping APIs like Google Maps or Mapbox for accurate coordinate plotting.
- Marketing Automation: Standardize mailing lists for direct mail campaigns to comply with postal service requirements and improve delivery rates.
- Data Migration: Cleanse and reformat legacy address data when migrating between different software platforms or database schemas.
| name | address-parser |
|---|---|
| description | Parse unstructured addresses into structured components - street, city, state, zip, country with validation. |
Address Parser
Parse unstructured addresses into structured fields.
Features
- Component Extraction: Street, city, state, zip, country
- Format Standardization: Normalize address formats
- Validation: Verify address components
- Batch Processing: Parse multiple addresses
- International Support: Multiple country formats
- Geocoding Ready: Output for geocoding APIs
CLI Usage
python address_parser.py --input addresses.csv --column address --output parsed.csv
Dependencies
- pandas>=2.0.0