Decorators and Closures Flashcards

What does @staticmethod mean when applied to a method inside a class?

Answer: The method receives no implicit first argument — no self or cls

@staticmethod indicates the method does not receive an implicit first argument, making it essentially a regular function that belongs to the class's namespace.

What is the key difference between @classmethod and @staticmethod?

Answer: @classmethod receives the class as its first argument (cls), @staticmethod receives no implicit argument

@classmethod receives the class itself as its first argument (conventionally called cls), while @staticmethod receives no implicit first argument at all.

What is the output of the following code? def make_multiplier(n): def multiplier(x): return x * n return multiplier double = make_multiplier(2) triple = make_multiplier(3) print(double(5), triple(5))

Answer: 10 15

double closes over n=2 and triple closes over n=3; each closure independently remembers its own captured value of n, so double(5) returns 10 and triple(5) returns 15.

What is a common use of decorators in Python web frameworks like Flask?

Answer: Route registration — mapping URLs to handler functions with @app.route

In Flask, decorators like @app.route('/path') register URL routes to handler functions, which is one of the most widespread real-world applications of decorators.

What happens to a decorated function if you do not use functools.wraps inside the wrapper?

Answer: The wrapped function loses its original metadata such as __name__ and __doc__

Without functools.wraps, the wrapper function's metadata (like __name__ and __doc__) replaces the original function's metadata, which causes problems with debugging and introspection tools.

Which description best defines memoization as implemented with a decorator?

Answer: A decorator that caches function results keyed by arguments to avoid redundant computation

Memoization is a technique where a decorator caches the results of function calls keyed by their arguments, so repeated calls with the same arguments return cached results without recomputing.

Which built-in Python module provides the lru_cache decorator for automatic memoization?

Answer: functools

functools.lru_cache is a built-in decorator that implements memoization with a Least Recently Used eviction strategy to limit cache size.