Python Regular Expressions: Complete Guide to the 're' Module

A Regular Expression (Regex) is a powerful tool for pattern matching and text parsing. With regex, you can validate email addresses, verify telephone numbers, scrape specific text patterns from web pages, and sanitize user input with surgical precision using Python's built-in re module.

1. Common Regex Metacharacters Reference

2. Primary re Functions: search(), findall(), sub()

import re

text = "Contact support at info@veewom.com or sales@veewom.com by 2026-11-04."

# 1. re.search(): Find first match
phone_pattern = r"d{4}-d{2}-d{2}"
match = re.search(phone_pattern, text)
if match:
    print("Found Date:", match.group())  # 2026-11-04

# 2. re.findall(): Extract all matching instances
email_pattern = r"[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+.[a-zA-Z0-9-.]+"
emails = re.findall(email_pattern, text)
print("Extracted Emails:", emails)
# ['info@veewom.com', 'sales@veewom.com']

# 3. re.sub(): Find and replace text
censored = re.sub(email_pattern, "[EMAIL HIDDEN]", text)
print("Sanitized Text:", censored)

3. Practical Validation: Indian Phone Number Validator

def is_valid_indian_mobile(phone: str) -> bool:
    # 10-digit mobile number starting with 6, 7, 8, or 9:
    pattern = r"^[6-9]d{9}$"
    return bool(re.match(pattern, phone))

print(is_valid_indian_mobile("9876543210"))  # True
print(is_valid_indian_mobile("1234567890"))  # False (Starts with 1)
print(is_valid_indian_mobile("987654"))       # False (Too short)

Frequently Asked Questions (FAQ)