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Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.
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.
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…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.
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…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.
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:
…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.
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…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.
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"…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.
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…How to Invalidate Cache When Arguments Change in Python
A memoization decorator that caches function results keyed by arguments, automatically invalidating when inputs change.
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…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.
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…How to Write a Python Decorator with functools.wraps
Create a decorator that wraps a function while preserving its metadata using functools.wraps.
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__":…Composition over Inheritance: How to Build a Wallet Account in Python
Demonstrates composition by wrapping a WalletAccount class in an AuditedWallet decorator-like class to add behavior without changing the original class.
class WalletAccount:
def __init__(self, owner, balance=0.0):
self.owner = owner
self.balance = balance
def deposit(self, amount):
if amount <= 0:
raise ValueError("Deposit must be positive")
self.balance += amount
return self.balance
def withdraw(self, …How to Create Static Methods in a Python Class
Shows how to define and call static methods inside a class using @staticmethod, with utility functions that don't need instance or class state.
class MathUtils:
"""Utility class demonstrating static methods."""
@staticmethod
def add(a, b):
"""Return the sum of two numbers."""
return a + b
@staticmethod
def multiply(a, b):
"""Return the product of two numbers."""
return a * b
@staticmethod
…How to Implement the Decorator Pattern in Python to Add Behavior
This Python code demonstrates the decorator pattern by wrapping a function to add logging behavior without modifying the original function.
import functools
def logger(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
print(f"Calling {func.__name__} with {args} {kwargs}")
result = func(*args, **kwargs)
print(f"{func.__name__} returned {result}")
return result
return wrapper
@logger
def add(a, b):
…How to Create a Generator Context Manager in Python with contextlib
Create a custom context manager with the @contextlib.contextmanager decorator to manage resources using a generator function.
import contextlib
@contextlib.contextmanager
def temporary_directory():
"""Yield a string and clean up after the block exits."""
print("Creating temp directory...")
dir_name = "/tmp/example"
try:
yield dir_name
finally:
print(f"Removing {dir_name}...")
if __name__ == "__main__":
…How to implement exponential backoff for LLM API calls in Python
A decorator that retries flaky LLM API calls with exponential delay, using a mock client to demonstrate the pattern.
import time
import random
class MockLLM:
def call(self, prompt):
if random.random() < 0.7: # 70% chance of transient failure
raise ConnectionError("API unavailable")
return f"LLM response for: {prompt}"
def with_exponential_backoff(max_retries=5, base_delay=0.1):
def decorator(fu…How to Parametrize Tests in Python with pytest
This code demonstrates how to use pytest's @pytest.mark.parametrize decorator to run a single test function against multiple input sets, ensuring comprehensive coverage with minimal code duplication.
import pytest
def multiply(a, b):
return a * b
@pytest.mark.parametrize("x, y, expected", [
(2, 3, 6),
(4, 5, 20),
(0, 10, 0),
(7, 1, 7),
])
def test_multiply(x, y, expected):
result = multiply(x, y)
assert result == expected, f"multiply({x}, {y}) = {result}, expected {expected}"
if _…How to Use functools.cache for Unbounded Memoization in Python
Speed up repeated recursive calls by memoizing function results with Python's built-in functools.cache decorator.
```python
import functools
import time
@functools.cache
def fib(n):
if n < 2:
return n
return fib(n - 1) + fib(n - 2)
if __name__ == "__main__":
start = time.perf_counter()
result = fib(30)
elapsed = time.perf_counter() - start
print(f"fib(30) = {result}")
print(f"computed in {…How to Vectorize a Function with a Pure Python Fallback
Create a decorator that calls a scalar function directly for a single value and routes list inputs to a pure-Python fallback for vectorized processing without NumPy.
import math
def fallback_vectorize(func, fallback=None):
"""Vectorize a scalar function with a pure-Python fallback for lists."""
if fallback is None:
fallback = lambda x: [func(i) for i in x]
def wrapped(*args):
if len(args) == 1 and isinstance(args[0], (list, tuple)):
retur…How to Parametrize pytest Tests with Multiple Input Cases in Python
This code shows how to use pytest's @pytest.mark.parametrize decorator to run the same test function across multiple input-output combinations, checking that an add function behaves correctly for each case.
import pytest
def add(a, b):
return a + b
@pytest.mark.parametrize("a,b,expected", [
(1, 2, 3),
(5, 5, 10),
(-1, 1, 0),
(0, 0, 0),
(10, -3, 7),
])
def test_add(a, b, expected):
assert add(a, b) == expected
if __name__ == "__main__":
pytest.main([__file__, "-v"])How to freeze time in Python tests with freezegun
Use the freezegun decorator to freeze datetime.now() at a fixed timestamp so tests that depend on current time run deterministically.
from datetime import datetime
from freezegun import freeze_time
@freeze_time("2024-01-15 12:30:00")
def test_frozen_time():
now = datetime.now()
return now
if __name__ == "__main__":
result = test_frozen_time()
print(result)How to Build a Sidecar Logging Proxy in Python
Wrap any object with a proxy that transparently logs every method call, arguments, return value, and execution time to a file — mimicking a sidecar pattern.
import logging
import time
from datetime import datetime
class LoggingProxy:
"""Sidecar-style proxy that logs all calls to a wrapped object."""
def __init__(self, target, log_file="proxy.log"):
self._target = target
logging.basicConfig(
filename=log_file,
level=loggin…How to Mock a Metrics Decorator in Python with unittest.mock
This code demonstrates a timing decorator that wraps a function to measure execution time and prints the duration, with a unit test using unittest.mock to patch the print function and assert it was called.
import time
from functools import wraps
from unittest.mock import patch
def add_metrics(func):
@wraps(func)
def wrapper(*args, **kwargs):
start = time.perf_counter()
result = func(*args, **kwargs)
elapsed = time.perf_counter() - start
print(f"{func.__name__} took {elapsed:.6f}s…How to Implement RBAC Permission Checks with a Route Decorator in Python
Build a reusable Python decorator that checks a user's role against allowed roles and raises a custom PermissionError when access is denied.
from functools import wraps
from enum import Enum
class Role(Enum):
ADMIN = "admin"
MODERATOR = "moderator"
USER = "user"
class PermissionError(Exception):
pass
def require_role(*allowed_roles):
def decorator(func):
@wraps(func)
def wrapper(user_role, *args, **kwargs):
…How to Add TTL Jitter to Cache Expiration in Python
A Python decorator that adds random jitter to cache TTLs, staggering expiration times to prevent cache avalanche.
import random
import time
from functools import wraps
def add_jitter(ttl: float, jitter_range: float = 0.1) -> float:
"""Add random jitter (as % of TTL) to stagger cache expiration and prevent avalanche."""
jitter = random.uniform(-jitter_range, jitter_range)
return ttl * (1 + jitter)
def cache_with_jitt…How to Cache Function Results in Redis with Python
A Python decorator that caches function results in Redis using TTL, with optional fakeredis for testing without a server.
import redis
import json
import time
try:
import fakeredis
except ImportError:
fakeredis = None
from functools import wraps
def cache_redis(cache_key_prefix="cache", ttl=60):
"""Decorator to cache function results in Redis."""
if fakeredis:
r = fakeredis.FakeStrictRedis()
else:
r…Browse by section
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