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Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.
How to Format a Float as Currency in Python
This code defines a function that converts a float to a string formatted as US currency with two decimal places and comma separators.
def format_currency(amount):
return f"${amount:,.2f}"
if __name__ == "__main__":
test_amounts = [1234.5, 0, 9999999.999, -42.867]
for amount in test_amounts:
print(f"{amount} -> {format_currency(amount)}")How to define an exception hierarchy for domain errors in Python
Create a custom exception hierarchy with a base DomainError class and specific subclasses to handle validation, not-found, permission, and concurrency errors cleanly in Python apps.
class DomainError(Exception):
"""Base class for all domain errors."""
pass
class ValidationError(DomainError):
"""Raised when input data fails validation rules."""
pass
class NotFoundError(DomainError):
"""Raised when a requested entity does not exist."""
pass
class PermissionDeniedError(Dom…How to Use fcntl for Exclusive File Locking in Python
This code demonstrates how to acquire an exclusive advisory lock on a file using fcntl.flock with a non-blocking flag, simulate work, then release the lock.
import fcntl
import os
import tempfile
import time
def acquire_exclusive_lock(filepath):
fd = os.open(filepath, os.O_RDWR | os.O_CREAT)
try:
fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
print(f"Exclusive lock acquired on {filepath}")
time.sleep(1) # Simulate work while holding the l…How to Create an Immutable Money Class in Python with dataclasses
Define a frozen dataclass Money that holds an amount and currency, enforces non-negative amounts, and supports safe addition across matching currencies.
from dataclasses import dataclass
@dataclass(frozen=True)
class Money:
amount: float
currency: str = "USD"
def __post_init__(self) -> None:
if self.amount < 0:
raise ValueError("amount must be non-negative")
def add(self, other: "Money") -> "Money":
if self.currency != o…How to parallel map embeddings with a thread pool in Python
Run embedding computations in parallel using ThreadPoolExecutor, collect results into a dict keyed by the original item.
import threading
from concurrent.futures import ThreadPoolExecutor
import time
def compute_embedding(item: int) -> tuple[int, int]:
time.sleep(0.05) # Simulate embedding work
return item, item * 10
def parallel_map_embed(items, max_workers=3):
results = {}
with ThreadPoolExecutor(max_workers=max_w…Find Broken Image References Across a Website in Python
Crawl internal pages of a website, collect all image source URLs, then check each with HEAD requests to report any that return HTTP 4xx or connection errors.
import requests
from urllib.parse import urljoin, urlparse
from bs4 import BeautifulSoup
from concurrent.futures import ThreadPoolExecutor, as_completed
def find_all_links(base_url, max_pages=50):
visited, to_visit = set(), {base_url}
while to_visit and len(visited) < max_pages:
url = to_visit.pop()
…How to Build a Cryptocurrency Price Tracker in Python
A continuous Python script that fetches real-time cryptocurrency prices from the CoinGecko API and displays them on a loop.
import requests
import time
def get_crypto_prices(coin_ids=["bitcoin", "ethereum", "solana"]):
url = "https://api.coingecko.com/api/v3/simple/price"
params = {
"ids": ",".join(coin_ids),
"vs_currencies": "usd"
}
try:
response = requests.get(url, params=params, timeout=10)
…How to Check Website Status Codes in Python
This script checks the HTTP status codes of multiple URLs concurrently using a thread pool and prints the results.
import requests
from concurrent.futures import ThreadPoolExecutor
URLS = [
"https://www.google.com",
"https://www.python.org",
"https://www.nonexistent-site-12345.com",
"https://www.github.com",
]
def check_status(url):
try:
response = requests.get(url, timeout=5)
return url, resp…Parallel Extract Multiple Sources with Threads in Python
Extract data from multiple sources in parallel using ThreadPoolExecutor and verify results match sequential processing.
import threading
from concurrent.futures import ThreadPoolExecutor
def extract_from_source(source):
"""Simulate extracting data from a source."""
return f"Data from {source}"
def main():
sources = ["source_a", "source_b", "source_c", "source_d"]
# Sequential extraction for comparison
sequent…Graceful Shutdown Executor Context Manager in Python
A context manager that starts a background thread and ensures it stops gracefully on exit, handling timeouts and exceptions.
import signal
import threading
import time
from contextlib import contextmanager
@contextmanager
def graceful_shutdown_executor(timeout=5.0):
"""Context manager that runs a task and gracefully stops it on timeout or exception."""
stop_event = threading.Event()
def task():
print("Task started")
…How to Build a Producer-Consumer Pattern with asyncio.Queue in Python
This code implements a classic producer-consumer pattern using asyncio.Queue to coordinate one producer task that generates items and two consumer tasks that process them concurrently, with a sentinel value to signal completion.
import asyncio
import random
async def producer(queue, item_count):
for i in range(item_count):
item = random.randint(1, 100)
await queue.put(item)
print(f"Produced: {item}")
await asyncio.sleep(0.1)
await queue.put(None) # Sentinel to signal end
async def consumer(queue, n…How to Cancel an asyncio Task with Graceful Cleanup in Python
Cancel a running asyncio task, handle the cancellation signal inside a worker coroutine to perform cleanup, then re-raise so the cancellation propagates correctly.
import asyncio
async def worker(name: str, sleep: float) -> None:
try:
print(f"{name}: starting")
await asyncio.sleep(sleep)
print(f"{name}: completed")
except asyncio.CancelledError:
print(f"{name}: cancelled, cleaning up...")
await asyncio.sleep(0.2) # Simulate clea…How to Convert Data in Parallel with ThreadPoolExecutor in Python
This example demonstrates converting a list of items in parallel using ThreadPoolExecutor, showing performance gains over serial processing.
import time
from concurrent.futures import ThreadPoolExecutor
def convert_data(item):
"""Simulate a CPU/IO-bound conversion task."""
time.sleep(0.05) # simulate work
return item.upper()
if __name__ == "__main__":
items = [f"item_{i}" for i in range(20)]
start = time.perf_counter()
serial_…How to Implement a Batch Requests Flush Interval in Python
A simple async batcher that accumulates items and flushes them either when a max batch size is reached or after a time-based flush interval.
import asyncio
from collections import deque
class Batcher:
def __init__(self, flush_interval=0.5, max_batch=5):
self.flush_interval = flush_interval
self.max_batch = max_batch
self.queue = deque()
self.lock = asyncio.Lock()
async def add(self, item):
async with self.l…How to Implement a Token Bucket Rate Limiter with asyncio in Python
This code implements a thread-safe token bucket rate limiter for asyncio, allowing you to limit the rate of async tasks or API calls.
import asyncio
import time
class TokenBucket:
def __init__(self, rate_per_second, capacity):
self.rate = rate_per_second
self.capacity = capacity
self.tokens = capacity
self.last_refill = time.monotonic()
self.lock = asyncio.Lock()
async def acquire(self):
asy…How to Memoize Async Functions with lru_cache in Python
Cache async function results with functools.lru_cache to avoid repeated expensive awaits, cutting total execution from ~0.4s to ~0.2s in this example.
from functools import lru_cache
import asyncio
@lru_cache(maxsize=128)
async def fetch_data(user_id: int) -> str:
# Simulate expensive async operation
await asyncio.sleep(0.1)
return f"Data for user {user_id}"
async def main():
start = asyncio.get_event_loop().time()
# First calls (miss cach…How to Parse JSON Files in Parallel with Python ThreadPoolExecutor
Load and transform JSON records from multiple files concurrently using ThreadPoolExecutor for faster I/O-bound parsing.
import time
from concurrent.futures import ThreadPoolExecutor
import json
def load_json_file(path):
with open(path, 'r') as f:
return json.load(f)
def transform_record(record):
record['full_name'] = f"{record.pop('first_name', '')} {record.pop('last_name', '')}".strip()
record['score'] = int(reco…How to Pause and Resume Threads with threading.Event in Python
Use threading.Event to pause and resume worker threads in Python, controlling execution flow with set and clear methods.
import threading
import time
workers = []
def worker(name, event):
for i in range(10):
event.wait()
print(f"{name} step {i}")
time.sleep(0.1)
def pause_worker(name):
global pause_event
for w in workers:
if w.name == name:
pause_event.clear()
print(…How to Run Blocking Code in an Executor with asyncio in Python
This code runs blocking functions concurrently without stalling the event loop by offloading them to thread pool executors via asyncio.
import asyncio
import time
def blocking_task(name: str, duration: float) -> str:
"""Simulate a blocking operation."""
time.sleep(duration)
return f"Finished {name} after {duration}s"
async def main() -> None:
loop = asyncio.get_running_loop()
results = await asyncio.gather(
loop.run_in_…How to Run Coroutines Concurrently with asyncio.gather in Python
Run multiple async coroutines concurrently and collect their results in the order they were passed.
import asyncio
async def fetch_data(name: str, delay: float) -> str:
"""Simulate an async operation (e.g., API call) with a delay."""
await asyncio.sleep(delay)
return f"{name} data (after {delay}s)"
async def main() -> None:
"""Run multiple coroutines concurrently with asyncio.gather."""
resul…How to Send and Receive Messages Between Processes with multiprocessing.Pipe in Python
Use multiprocessing.Pipe to create a two-way connection between two processes, send a message from parent to child, and receive a reply back.
import multiprocessing
def child_process(conn):
"""Receive from parent and send back a response."""
message = conn.recv()
print(f"Child received: {message}")
conn.send("Hello from child!")
if __name__ == "__main__":
parent_conn, child_conn = multiprocessing.Pipe()
process = multiprocessing…How to Share Memory Between Processes in Python with multiprocessing.Value and Array
Share a numeric value and a list-like array across multiple Python processes using multiprocessing.Value and multiprocessing.Array, with each process modifying the same memory.
import multiprocessing
def worker(shared_value, shared_array, index):
shared_value.value += 10
shared_array[index] = shared_array[index] * 2
if __name__ == "__main__":
shared_value = multiprocessing.Value("i", 5)
shared_array = multiprocessing.Array("i", [1, 2, 3, 4, 5])
processes = []
for i…How to Share a Dict and List Between Processes with multiprocessing Manager in Python
This code demonstrates how to share a dictionary and a list between multiple processes using multiprocessing.Manager, enabling safe concurrent updates.
import multiprocessing as mp
def worker(shared_dict, shared_list, name):
shared_dict[name] = name.upper()
shared_list.append(name)
print(f"{name} added to shared structures")
def main():
with mp.Manager() as manager:
shared_dict = manager.dict()
shared_list = manager.list()
…How to Share a Queue Between Processes in Python
Use multiprocessing.Queue to pass work from a producer process to multiple consumer processes, coordinating with a sentinel stop message.
import multiprocessing
import time
def producer(queue, items):
for item in items:
queue.put(item)
time.sleep(0.1)
queue.put("STOP")
def consumer(queue, name):
while True:
item = queue.get()
if item == "STOP":
break
print(f"{name} processed: {item}")
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