Is the Decorators lesson free?

Yes. The first 5 lessons on PyRun are free forever — no signup, no card. Decorators runs real Python in your browser via Pyodide.

How long does the Decorators lesson take?

About 10 minutes end-to-end. It's a hands-on lesson — you read a paragraph, edit the code, and run it in the same tab.

Do I need to install Python to try the Decorators lesson?

No install required. PyRun runs real CPython in your browser via Pyodide (Python compiled to WebAssembly). Works on Chrome, Firefox, Safari, and Edge — desktop or mobile.

What level is the Decorators lesson?

Advanced level. Assumes comfort with classes, functions, and standard library. Not for first-time Python learners.

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Advanced·10 min·advanced · functions

Decorators

You’ll be able to

Write decorators that wrap functions with new behavior
Preserve function metadata with functools.wraps
Understand decorator syntax as sugar for higher-order functions
Chain multiple decorators on a single function

Why this matters

Flask's @app.route, pytest's @pytest.fixture, and functools.lru_cache are all decorators — the pattern powers every routing, DI, and caching layer in modern Python. Senior engineers write parameterized decorators with functools.wraps to preserve introspection for logging and OpenAPI generation.

Common pitfalls

Skipping @functools.wraps(func) — breaks __name__, __doc__, and framework introspection like FastAPI's schema builder.
Confusing @deco and @deco(...) — a parameterized decorator needs three nested functions, not two.
Decorating methods without handling self — bound-method semantics differ; test on classes, not just free functions.

A decorator is just a function that takes a function and returns a (usually wrapped) function.

The anatomy

@timed          # syntactic sugar for: fib = timed(fib)
def fib(n): ...

@wraps(fn) from functools preserves the original function's name and docstring on the wrapper.

Common uses

Logging / timing
Caching (@functools.lru_cache)
Authentication checks
Retry logic

Try it

Add an @functools.lru_cache(maxsize=None) above @timed and watch fib(30) get instant.
Write a @retry(times=3) decorator factory that retries on exceptions.

Practice

3 graded exercises

Write the code, click Check. We’ll tell you exactly what to fix.

Progress0 / 3

Exercise 1
Write a decorator shouting that wraps any function returning a string and returns the same string uppercased. Apply it to a function greet(name) that returns "hello, " + name. Print greet("Ada") — expected: HELLO, ADA.
Exercise 2
Write a decorator count_calls that adds a .calls counter to the function. Apply it to def hit(): pass. Call hit() three times, then print hit.calls — expected: 3.
Exercise 3
Use functools.lru_cache to memoize a recursive Fibonacci function fib(n). Print fib(30) — expected: 832040. It should compute in well under a second (unlike the naive recursive version).

← PreviousMagic / Dunder Methods Next →Closures & nonlocal

Decorators

You’ll be able to

Write decorators that wrap functions with new behavior
Preserve function metadata with functools.wraps
Understand decorator syntax as sugar for higher-order functions
Chain multiple decorators on a single function

Why this matters

Common pitfalls

Skipping @functools.wraps(func) — breaks __name__, __doc__, and framework introspection like FastAPI's schema builder.
Confusing @deco and @deco(...) — a parameterized decorator needs three nested functions, not two.
Decorating methods without handling self — bound-method semantics differ; test on classes, not just free functions.

A decorator is just a function that takes a function and returns a (usually wrapped) function.

The anatomy

@timed          # syntactic sugar for: fib = timed(fib)
def fib(n): ...

@wraps(fn) from functools preserves the original function's name and docstring on the wrapper.

Common uses

Logging / timing
Caching (@functools.lru_cache)
Authentication checks
Retry logic

Try it

Add an @functools.lru_cache(maxsize=None) above @timed and watch fib(30) get instant.
Write a @retry(times=3) decorator factory that retries on exceptions.

Practice

3 graded exercises

Write the code, click Check. We’ll tell you exactly what to fix.

Progress0 / 3

Exercise 1
Write a decorator shouting that wraps any function returning a string and returns the same string uppercased. Apply it to a function greet(name) that returns "hello, " + name. Print greet("Ada") — expected: HELLO, ADA.
Exercise 2
Write a decorator count_calls that adds a .calls counter to the function. Apply it to def hit(): pass. Call hit() three times, then print hit.calls — expected: 3.
Exercise 3
Use functools.lru_cache to memoize a recursive Fibonacci function fib(n). Print fib(30) — expected: 832040. It should compute in well under a second (unlike the naive recursive version).