How to Extract Emails, URLs, and Phone Numbers From Text
Extracting structured data from unstructured text saves hours of manual copying. Learn pattern-based extraction for common data types.
Key Takeaways
- The most commonly extracted data types are email addresses, URLs, phone numbers, IP addresses, and dates.
- Email addresses follow the pattern `[email protected]`.
- URLs can be tricky to extract because they contain special characters that might be confused with surrounding punctuation.
- Phone numbers vary dramatically by country.
- Extracted data often needs normalization.
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Common Extraction Targets
The most commonly extracted data types are email addresses, URLs, phone numbers, IP addresses, and dates. Each has recognizable patterns that can be matched programmatically.
Email Extraction
Email addresses follow the pattern [email protected]. While the full RFC 5322 email specification is complex, a practical extraction pattern catches 99.9% of real-world addresses.
URL Extraction
URLs can be tricky to extract because they contain special characters that might be confused with surrounding punctuation. Look for patterns starting with http:// or https:// and handle trailing periods and parentheses carefully.
Phone Number Extraction
Phone numbers vary dramatically by country. US numbers might appear as (555) 123-4567, 555-123-4567, or 5551234567. International numbers add country codes and different grouping conventions.
Post-Extraction Cleanup
Extracted data often needs normalization. Phone numbers should be converted to a standard format. Email addresses should be lowercased. URLs should have trailing punctuation removed. Deduplication removes any repeated values.
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