Reference library

Functions & basics

Reusable building blocks — parameters, returns, scope, and clear function design.

9 matches
Functions & basics easy

Build a Context Manager in Python with contextlib.contextmanager

Create a reusable context manager that safely opens and closes files using the contextlib contextmanager decorator.

context manager contextlib file handling
Python
from contextlib import contextmanager

@contextmanager
def managed_file(filename, mode='r'):
    """Context manager that opens and closes a file safely."""
    file = open(filename, mode)
    yield file
    file.close()

if __name__ == "__main__":
    # Write a sample file
    with managed_file("sample.txt", "w") as f…
14 0 Open
Functions & basics easy

Cache expensive function with lru_cache in Python

Use functools.lru_cache to memoize an expensive recursive function and show the dramatic speedup on repeated calls.

lru_cache caching decorators
Python
from functools import lru_cache
import time


@lru_cache(maxsize=128)
def expensive_operation(n):
    """Simulate an expensive Fibonacci-like calculation."""
    if n < 2:
        return n
    return expensive_operation(n - 1) + expensive_operation(n - 2)


if __name__ == "__main__":
    # First call (uncached) - take…
15 0 Open
Functions & basics easy

Create a retry decorator with max attempts in Python

A decorator that retries a function up to a specified number of times when it raises an exception, with an optional delay between attempts.

decorator retry error-handling
Python
import functools
import time


def retry(max_attempts, delay=0.1):
    """Retry a function up to max_attempts times on exception."""
    def decorator(func):
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            for attempt in range(1, max_attempts + 1):
                try:
                …
12 0 Open
Functions & basics easy

How to Build a Simple Decorator That Logs Function Calls in Python

This code shows how to create a reusable decorator that logs each function call, including arguments, return value, and execution time.

decorator logging functools
Python
import functools
import time

def log_calls(func):
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        print(f"Calling {func.__name__} with args={args}, kwargs={kwargs}")
        start = time.time()
        result = func(*args, **kwargs)
        end = time.time()
        print(f"{func.__name__} return…
11 0 Open
Functions & basics easy

How to Create a Timing Decorator in Python

A Python decorator that measures and prints the execution time of any function using time.perf_counter.

decorator timing perf_counter
Python
import time
from functools import wraps


def timing_decorator(func):
    @wraps(func)
    def wrapper(*args, **kwargs):
        start = time.perf_counter()
        result = func(*args, **kwargs)
        end = time.perf_counter()
        elapsed = end - start
        print(f"{func.__name__} took {elapsed:.6f} seconds"…
11 0 Open
Functions & basics medium

How to Implement a Trampoline for Tail Recursion in Python

This code implements a trampoline decorator that converts tail-recursive functions into iterative loops, allowing deep recursion without hitting Python's recursion limit.

trampoline tail-recursion decorator
Python
def trampoline(fn):
    """Convert a tail-recursive function into an iterative loop."""
    def wrapper(*args, **kwargs):
        result = fn(*args, **kwargs)
        while callable(result):
            result = result()
        return result
    return wrapper

@trampoline
def factorial(n, acc=1):
    """Tail-recursi…
11 0 Open
Functions & basics medium

How to Invalidate Cache When Arguments Change in Python

A memoization decorator that caches function results keyed by arguments, automatically invalidating when inputs change.

decorators caching memoization
Python
from functools import wraps

def memoize(func):
    cache = {}
    
    @wraps(func)
    def wrapper(*args, **kwargs):
        key = (args, tuple(sorted(kwargs.items())))
        if key not in cache:
            cache[key] = func(*args, **kwargs)
        return cache[key]
    
    return wrapper

@memoize
def expensiv…
14 0 Open
Functions & basics easy

How to Use singledispatch for Type-Based Overloading in Python

This code demonstrates Python's functools.singledispatch decorator to create functions that behave differently based on the type of their first argument.

singledispatch overloading functools
Python
from functools import singledispatch

@singledispatch
def process(value):
    return f"Unknown type: {type(value).__name__}"

@process.register(int)
def _(value):
    return f"Integer: {value * 2}"

@process.register(str)
def _(value):
    return f"String: {value.upper()}"

@process.register(list)
def _(value):
    re…
12 0 Open
Functions & basics easy

How to Write a Python Decorator with functools.wraps

Create a decorator that wraps a function while preserving its metadata using functools.wraps.

decorator functools wraps
Python
from functools import wraps


def logger(func):
    @wraps(func)
    def wrapper(*args, **kwargs):
        print(f"Calling {func.__name__}")
        return func(*args, **kwargs)
    return wrapper


@logger
def greet(name):
    """Return a friendly greeting."""
    return f"Hello, {name}!"


if __name__ == "__main__":…
12 0 Open

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