What Does a Decorator Look Like? – Visual Guide & Examples (Python 2026)
A decorator in Python is a function that wraps another function to add extra behavior. Visually, decorators are easy to spot because they are placed directly above the function definition using the @ symbol.
TL;DR — How a Decorator Looks
- Starts with
@followed by the decorator name - Placed immediately above the
defline - Can be stacked (multiple decorators)
- Can accept parameters (decorator factory)
1. Basic Decorator – Simple Look
@timer # ← This is how a decorator looks
def slow_task():
import time
time.sleep(1)
return "Done"
# The code above is equivalent to:
def slow_task():
...
slow_task = timer(slow_task) # Manual wrapping
2. Decorator with Parameters – Factory Look
@repeat(5) # ← Decorator with arguments
@log_execution # ← Multiple decorators (stacked)
def process_data(data):
return [x * 2 for x in data]
# Execution order (bottom to top):
# 1. process_data is first wrapped by log_execution
# 2. Then the result is wrapped by repeat(5)
3. Real-World Decorator Examples (How They Look)
# 1. Timing decorator
@timer
def heavy_computation(n: int):
...
# 2. Authentication decorator
@require_login
def get_user_profile(user_id: int):
...
# 3. Caching decorator
@cache(ttl=300)
def fetch_from_api(endpoint: str):
...
# 4. Multiple decorators
@log_execution
@validate_input
@retry(max_attempts=3)
def call_external_service(payload):
...
4. Visual Structure Summary
@decorator_name→ Simple decorator@decorator_name(arg1, arg2)→ Decorator with parameters- Multiple
@lines one after another → Stacked decorators - Decorators are applied from bottom to top
Conclusion
In Python 2026, decorators are instantly recognizable by the @ symbol placed directly above a function definition. They provide a clean, readable way to add behavior such as logging, timing, authentication, caching, and validation without modifying the original function code.
Next steps:
- Look at your functions and identify places where you can replace repeated code with clean decorators