The timer Decorator in Python 2026

The timer Decorator in Python 2026 – Best Practices

The @timer decorator is one of the most useful and frequently used decorators in Python. It measures and displays the execution time of any function while keeping your code clean and readable.

TL;DR — The Modern timer Decorator (2026)

• Uses time.perf_counter() for high-precision timing
• Always includes @wraps to preserve function metadata
• Works with both regular and async functions
• Provides clean, consistent output

1. Complete Implementation

from functools import wraps
import time
from typing import Callable, Any

def timer(func: Callable) -> Callable:
    """Decorator that prints the execution time of a function."""
    
    @wraps(func)
    def wrapper(*args: Any, **kwargs: Any) -> Any:
        start = time.perf_counter()
        
        result = func(*args, **kwargs)
        
        end = time.perf_counter()
        elapsed = end - start
        
        print(f"⏱️  {func.__name__}() took {elapsed:.6f} seconds")
        return result
    
    return wrapper

2. Usage Examples

@timer
def heavy_calculation(n: int):
    """Simulate CPU-intensive work."""
    return sum(i ** 2 for i in range(n))

@timer
def fetch_api_data(url: str):
    """Simulate network call."""
    import time
    time.sleep(0.6)
    return {"status": "success"}

# Using the decorator
result1 = heavy_calculation(50000)
result2 = fetch_api_data("https://api.example.com/data")

3. Best Practices for the timer Decorator in 2026

• Use it during development and performance tuning
• Remove or disable it in production (or replace with structured logging)
• Keep the output clean and consistent across your project
• Create a separate @async_timer for async functions
• Combine with other decorators when needed (order matters!)

Conclusion

The @timer decorator is a simple yet powerful tool for measuring function performance. In 2026, using a well-written timer decorator with perf_counter() and wraps is considered standard practice for any developer who cares about performance and clean code.

Next steps:

• Add @timer to your slowest or most critical functions today
• Related articles: Decorators in Python 2026 • Time a Function in Python 2026 • Writing Functions in Python 2026