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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 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 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 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 Cache Function Results with Redis in Python
A RedisCache helper class caches function results using a decorator, with JSON serialization and TTL-based expiry.
import redis
import json
from functools import wraps
class RedisCache:
def __init__(self, host='localhost', port=6379, db=0, ttl=60):
self.client = redis.Redis(host=host, port=port, db=db, decode_responses=True)
self.ttl = ttl
def cached(self, key_prefix):
def decorator(func):
…Build a Rate Limiter Decorator in Python
This code defines a reusable rate limiter decorator that caps function calls within a sliding time window using a deque and monotonic time.
import time
from collections import deque
def rate_limiter(max_calls: int, period: float):
calls = deque()
def decorator(func):
def wrapper(*args, **kwargs):
now = time.monotonic()
while calls and now - calls[0] >= period:
calls.popleft()
if len(ca…How to Inject Random Latency for Chaos Testing in Python
Mock unreliable services by wrapping functions with a decorator that adds random network-like delays before execution.
import random
import time
from functools import wraps
def inject_latency(func):
@wraps(func)
def wrapper(*args, **kwargs):
latency = random.uniform(0.1, 0.5)
print(f"Injecting {latency:.3f}s latency...")
time.sleep(latency)
return func(*args, **kwargs)
return wrapper
@inje…How to Retry on Specific Exception Tuples in Python
A decorator-based retry pattern that retries a function only when it raises exceptions specified in a tuple, with configurable retries and delay.
import time
import random
from unittest.mock import patch
def retry_on_exceptions(retries=3, exceptions=(ValueError,), delay=0.1):
def decorator(func):
def wrapper(*args, **kwargs):
for attempt in range(retries):
try:
return func(*args, **kwargs)
…How to hide incomplete mock features with a Python feature toggle
A simple decorator-based feature toggle that returns a placeholder when a mock feature is disabled, so incomplete code can ship safely.
import functools
class FeatureToggle:
def __init__(self, enabled=False):
self.enabled = enabled
def feature(self, func=None):
"""Decorator to conditionally enable a feature."""
if func is None:
return self.feature
@functools.wraps(func)
def wrapper(*args,…Browse by section
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