Regex Data Cleaning Pattern Library

Create a regex pattern library for {{data_type}} cleaning:

1. Common malformats:
   - Phone numbers (extra spaces, country codes, dashes)
   - Email addresses (+ addressing, domains)
   - URLs (trailing slashes, query params)
   - Dates (format variations, timezones)
   - Postal codes (spaces, hyphens)

2. For each pattern, provide:
   - Regex expression (with comments)
   - Python / SQL implementation
   - Test cases (valid & invalid examples)
   - Caveats (locale-specific issues, false positives)

3. Edge cases:
   - Null handling
   - Encoding issues (UTF-8)
   - Special characters

Format as code snippet library (Python function or SQL UDF).