address-parser
Parse unstructured addresses into structured components - street, city, state, zip, country with validation.
When & Why to Use This Skill
This Claude skill provides a robust solution for transforming messy, unstructured address strings into clean, standardized, and validated components such as street, city, state, postal code, and country. By automating address normalization and verification, it ensures high data integrity for databases, improves geocoding accuracy, and streamlines logistics workflows.
Use Cases
- E-commerce Logistics: Standardize customer-entered shipping addresses in real-time to reduce delivery errors and optimize last-mile routing.
- CRM Data Migration: Cleanse legacy databases by parsing concatenated address strings into structured fields for better searchability and reporting.
- Geocoding Preparation: Convert raw location data into standardized formats required by mapping APIs like Google Maps or Mapbox for spatial analysis.
- International Data Management: Handle diverse global address formats automatically, ensuring consistency across multi-national customer datasets.
- Batch Lead Processing: Use the CLI tool to process large CSV files of marketing leads, extracting precise geographic components for targeted campaigns.
| 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