The re Module

Introduction

Python’s re module provides comprehensive regular expression support with functions for searching, matching, replacing, and splitting strings.

Core Functions

re.search() — Find First Match

import re

text = "Contact: support@example.com or admin@test.org"
match = re.search(r"[\w.+-]+@[\w-]+\.[\w.-]+", text)
if match:
    print(match.group())        # support@example.com
    print(match.start())        # 9 (start index)
    print(match.end())          # 29 (end index)
    print(match.span())         # (9, 29)

re.findall() — Find All Matches

emails = re.findall(r"[\w.+-]+@[\w-]+\.[\w.-]+", text)
print(emails)  # ['support@example.com', 'admin@test.org']

re.match() — Match at Start

text = "Python is great"
print(re.match(r"Python", text))    # Match object
print(re.match(r"great", text))      # None (must match at start!)

re.fullmatch() — Match Entire String

print(re.fullmatch(r"\d{3}-\d{4}", "555-1234"))  # Match
print(re.fullmatch(r"\d{3}-\d{4}", "ext 555-1234"))  # None

re.sub() — Search and Replace

text = "My number is 555-1234. Call 555-5678 too."
result = re.sub(r"\d{3}-\d{4}", "[REDACTED]", text)
print(result)  # My number is [REDACTED]. Call [REDACTED] too.

# With callback
def mask_number(match):
    return "***-****"
result = re.sub(r"\d{3}-\d{4}", mask_number, text)

re.split() — Split by Pattern

text = "apple,banana;cherry|date"
parts = re.split(r"[,;|]", text)
print(parts)  # ['apple', 'banana', 'cherry', 'date']

Compiled Patterns

# Compile for reuse (faster for repeated use)
pattern = re.compile(r"\d{3}-\d{4}")

# Same methods on compiled pattern
pattern.search(text)
pattern.findall(text)
pattern.sub("[REDACTED]", text)

# Flags during compilation
pattern = re.compile(r"^hello", re.IGNORECASE)
print(pattern.search("HELLO World"))  # Match!

Flags

# Common flags (can be combined with |)
re.IGNORECASE     # Case-insensitive matching
re.MULTILINE      # ^ and $ match line boundaries
re.DOTALL         # . matches newlines too
re.VERBOSE        # Allow comments in patterns

# Examples
re.findall(r"^hello", "HELLO\nhello", re.IGNORECASE | re.MULTILINE)

# VERBOSE allows readable patterns
pattern = re.compile(r"""
    \b          # Word boundary
    [A-Z][a-z]+ # First name
    \s+         # Whitespace
    [A-Z][a-z]+ # Last name
    \b          # Word boundary
""", re.VERBOSE)

Greedy vs Non-greedy

text = "<div>Hello</div><span>World</span>"

# Greedy (default) — matches as much as possible
print(re.findall(r"<.+>", text))   # ['<div>Hello</div><span>World</span>']

# Non-greedy — matches as little as possible
print(re.findall(r"<.+?>", text))  # ['<div>', '</div>', '<span>', '</span>']

Best Practices

Always use raw strings r"pattern" to avoid escaping issues
Compile patterns when using the same regex multiple times
Use re.VERBOSE for complex patterns to add comments
Test your regex with various inputs — edge cases matter!
Avoid overly complex regex — sometimes string methods are simpler

Practice Exercises

Exercise 1: Write a function that validates email addresses using regex.

Exercise 2: Create a function that extracts all hashtags from a tweet.

Exercise 3: Implement a simple template engine that replaces {{variable}} placeholders with values from a dictionary.