Regular Expressions in Python (`re` Module)

🟡 Intermediate

📖 Definition

Regular Expressions (Regex) are special character search patterns used to match, extract, validate, and replace complex text sequences inside strings using Python’s built-in re module.

🇮🇳 Hindi

Text mein complex patterns (jaise Emails, Phone numbers, ZIP codes) search, validate, aur replace karne ke liye Regular Expressions (re module) ka use hota hai. Escape issues se bachne ke liye hamesha Raw Strings (r"pattern") ka use karein.

🚩 Marathi

Text patterns (Emails, Phone numbers) validate aani replace karnyasathi re module cha wapar kela jato. Pattern sathi Raw Strings (r"pattern") vapara.

📝 1. Core Regex Metacharacters

Symbol	What It Matches	Example
`\d`	Any digit (`0-9`)	`r"\d{3}"` matches 3 digits
`\w`	Word character (`a-z`, `A-Z`, `0-9`, `_`)	`r"\w+"` matches word
`\s`	Whitespace character (space, tab, newline)	`r"\s+"` matches spaces
`.`	Any single character except newline	`r"a.c"` matches `"abc"`
`^` / `$`	Start / End of string	`r"^Hello$"`
`+`	1 or more occurrences	`r"\d+"`
`*`	0 or more occurrences	`r"\d*"`
`?`	0 or 1 occurrence (optional)	`r"colou?r"`
`[a-z]`	Character set range	`r"[A-Za-z]+"`

📝 2. Key `re` Module Functions

re.search(pattern, text): Searches entire string for FIRST match. Returns Match object or None.
re.findall(pattern, text): Searches string and returns a list of all matching substrings.
re.match(pattern, text): Matches pattern starting at the very beginning of string.
re.sub(pattern, replacement, text): Replaces all pattern matches with replacement string.

import re

text = "Call us at 987-654-3210 or 912-345-6789"
pattern = r"\d{3}-\d{3}-\d{4}"

# Extracting all phone numbers
phones = re.findall(pattern, text)
print(phones) # ['987-654-3210', '912-345-6789']

💡 Complete Example: Email & Phone Number Validation Engine

import re

def validate_user_input(email, phone):
    """Validates email and Indian phone number formats using Regex."""
    
    # 1. Email Regex Pattern
    email_pattern = r"^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$"
    
    # 2. Indian Phone Number Pattern (10 digits starting with 6,7,8,9)
    phone_pattern = r"^[6-9]\d{9}$"
    
    is_valid_email = bool(re.match(email_pattern, email))
    is_valid_phone = bool(re.match(phone_pattern, phone))
    
    return is_valid_email, is_valid_phone

def mask_sensitive_emails(text):
    """Replaces email addresses with '[REDACTED]' using re.sub()."""
    email_pattern = r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}"
    return re.sub(email_pattern, "[REDACTED_EMAIL]", text)

# Testing Regex Engine
print("=== REGEX VALIDATION & SANITIZATION ===")

email_test = "aarav.verma@example.com"
phone_test = "9876543210"

v_email, v_phone = validate_user_input(email_test, phone_test)

print(f"Testing Email: '{email_test}' -> Valid? {v_email}")
print(f"Testing Phone: '{phone_test}' -> Valid? {v_phone}")

sample_log = "User Rahul (rahul@company.org) raised ticket #501."
masked_log = mask_sensitive_emails(sample_log)
print("\nOriginal Log :", sample_log)
print("Masked Log   :", masked_log)

👀 Output

=== REGEX VALIDATION & SANITIZATION ===
Testing Email: 'aarav.verma@example.com' -> Valid? True
Testing Phone: '9876543210' -> Valid? True

Original Log : User Rahul (rahul@company.org) raised ticket #501.
Masked Log   : User Rahul ([REDACTED_EMAIL]) raised ticket #501.

⚠️ Common Mistakes

Forgetting to use Raw Strings r"pattern" (writing "\\d+" instead of r"\d+"), leading to backslash escape syntax conflicts.
Confusing re.match() (matches ONLY at start of string) with re.search() (searches anywhere across entire string!).

🛡️ Safety / Important Notes

Avoid overly complex nested regex patterns on untrusted user inputs to prevent Catastrophic Backtracking (ReDoS - Regular Expression Denial of Service attacks).

🌍 Real-World Usage

Form validation (Email, Password strength, Phone), web scraping data extraction, log file parsing, and data masking/redaction.

🧪 Try It Yourself

Write a regex pattern r"\d+" and use re.findall() to extract all numbers from "Order #501 with 3 items costing $450".
Replace all spaces in "Hello World Python" with a single hyphen - using re.sub().

🎯 Mini Challenge

Write a function extract_hashtags(text) using re.findall() that extracts all Twitter-style hashtags starting with # (e.g. "#python #coding").

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Regular Expressions in Python (re Module)