Introduction

The re module is Python's built-in library for working with regular expressions (regex) — a powerful, compact language for describing patterns in text. With re you can search for patterns, validate input (emails, phone numbers), extract specific pieces of data, split strings intelligently, and perform advanced find-and-replace operations that would be tedious or impossible with ordinary string methods.

Real-World Analogy

Prerequisites

Getting Started

Import the module and run your first pattern search.

import re

text = "My email is rumman@example.com"
match = re.search(r"\w+@\w+\.\w+", text)

if match:
    print("Found:", match.group())   # Found: rumman@example.com

Core Functions of re

Essential re Functions

Common Metacharacters & Tokens

Practical Examples

Extract all numbers with findall

import re

text = "Order 42 shipped, 7 pending, 128 total"
numbers = re.findall(r"\d+", text)
print(numbers)   # ['42', '7', '128']

Capturing groups

import re

date = "2026-08-16"
m = re.search(r"(\d{4})-(\d{2})-(\d{2})", date)

print(m.group(0))   # 2026-08-16  (whole match)
print(m.group(1))   # 2026        (year)
print(m.group(2))   # 08          (month)
print(m.group(3))   # 16          (day)

Named groups

import re

m = re.search(r"(?P<year>\d{4})-(?P<month>\d{2})", "2026-08")
print(m.group("year"))    # 2026
print(m.group("month"))   # 08

Replace with re.sub

import re

text = "Call me at 123-456-7890 or 987-654-3210"
masked = re.sub(r"\d", "*", text)
print(masked)   # Call me at ***-***-**** or ***-***-****

Split on multiple delimiters

import re

data = "apple, banana;cherry orange"
parts = re.split(r"[,;\s]+", data)
print(parts)   # ['apple', 'banana', 'cherry', 'orange']

Compiling Patterns for Reuse

If you use a pattern many times, re.compile() pre-compiles it for better performance and cleaner code.

import re

# Compile once
email_pattern = re.compile(r"\w+@\w+\.\w+")

emails = ["a@x.com", "invalid", "b@y.org"]
for e in emails:
    if email_pattern.fullmatch(e):
        print(e, "is valid")

Useful Flags

import re

text = "Hello WORLD"
print(re.findall(r"world", text, re.IGNORECASE))   # ['WORLD']

A Note on Performance

A well-written regex runs in roughly linear time \(O(n)\) over input length \(n\). But catastrophic backtracking from nested quantifiers (like (a+)+) can explode to exponential time:

\[ T_{\text{worst}} = O(2^{n}) \]

Avoid ambiguous nested quantifiers on untrusted input to prevent this "ReDoS" pitfall.

Best Practices

Common Mistakes

Interview Questions

Quick Revision