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Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.
Build a Python Performance Profiler That Generates Readable Reports
Use cProfile and pstats to profile Python functions and print a sorted performance report showing the top time-consuming calls.
import cProfile
import pstats
import io
from pathlib import Path
def slow_function():
total = 0
for i in range(500_000):
total += i ** 2
return total
def fast_function():
total = sum(i * i for i in range(500_000))
return total
def profile_functions():
profiler = cProfile.Profile()
…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 Demonstrate the GIL with Python Threads vs Processes
Measure and compare wall-clock time for CPU-bound work using Python threads (limited by the GIL) versus multiprocessing (which bypasses the GIL).
import threading
import multiprocessing
import time
import os
def cpu_heavy(n):
return sum(i * i for i in range(n))
def run_threads(n):
threads = [threading.Thread(target=cpu_heavy, args=(n,)) for _ in range(2)]
start = time.perf_counter()
for t in threads:
t.start()
for t in threads:
…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 Profile CPU Hot Path in Python with cProfile and sort_stats cumtime
Profile a Python function's CPU usage by running cProfile, sorting stats by cumulative time, and printing a readable report to stdout.
import cProfile
import pstats
import io
def slow_function():
total = 0
for i in range(100_000):
total += i * i
return total
def fast_function():
return sum(i for i in range(100))
def main():
slow_function()
fast_function()
if __name__ == "__main__":
profiler = cProfile.Profi…How to Time Code Performance with timeit in Python
Benchmark two implementations of the same logic using Python's timeit module and compare their execution speeds.
import timeit
# Implementation 1: Using a list comprehension
def list_comprehension_squares(n):
return [i ** 2 for i in range(n)]
# Implementation 2: Using a for loop with append
def loop_squares(n):
result = []
for i in range(n):
result.append(i ** 2)
return result
if __name__ == "__main__"…How to Use ThreadPoolExecutor and ProcessPoolExecutor in Python
Compares ThreadPoolExecutor and ProcessPoolExecutor by running CPU-bound and I/O-tolerant tasks over a large list, printing elapsed times and first results.
import time
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
import math
numbers = list(range(1, 1000001))
def compute_square(n):
return n * n
def compute_sqrt(n):
return math.sqrt(n)
def run_executor(executor, func, data):
start = time.perf_counter()
results = list(executo…How to Use bisect.insort in Python to Maintain a Sorted List
Insert items into an already sorted list using Python's bisect.insort to keep it sorted efficiently in O(n) time.
import bisect
def maintain_sorted_list():
data = [3, 1, 4, 1, 5, 9, 2, 6]
sorted_list = []
for num in data:
bisect.insort(sorted_list, num)
print("Original data:", data)
print("Sorted list maintained with insort:", sorted_list)
# Insert new values to maintain sorted orde…How to Use threading.RLock in Python
Demonstrates threading.RLock, a reentrant lock that allows the same thread to acquire it multiple times without deadlocking — essential for recursive functions sharing state across threads.
import threading
import time
lock = threading.RLock()
shared_counter = 0
def recursive_increment(value, depth):
global shared_counter
with lock:
shared_counter += 1
print(f"Depth {depth}: counter = {shared_counter}")
if depth > 1:
recursive_increment(value, depth - 1)
def…How to set a timeout with asyncio.wait_for in Python
Use asyncio.wait_for to bound an async function with a timeout, catching TimeoutError when it exceeds the limit.
import asyncio
async def slow_task():
await asyncio.sleep(3)
return "finished"
async def main():
try:
result = await asyncio.wait_for(slow_task(), timeout=1)
print(result)
except asyncio.TimeoutError:
print("Task timed out")
if __name__ == "__main__":
asyncio.run(main())How to use ThreadPoolExecutor for concurrent tasks in Python
Run blocking functions in parallel with ThreadPoolExecutor and as_completed, cutting total runtime from 5 sequential sleeps to about 1 second.
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
def fetch_data(item):
"""Simulate a slow operation with a fixed delay."""
time.sleep(0.2)
return item * 2
def main():
items = [1, 2, 3, 4, 5]
start = time.perf_counter()
with ThreadPoolExecutor(max_workers=3) as ex…Merge K Sorted Lists in Python with heapq
Merge k sorted lists into one sorted list in O(N log k) time using a min-heap of current elements.
import heapq
def merge_k_sorted_lists(lists):
heap = []
for i, lst in enumerate(lists):
if lst: # only push non-empty lists
heapq.heappush(heap, (lst[0], i, 0))
result = []
while heap:
val, list_idx, elem_idx = heapq.heappop(heap)
result.append(val)
if elem…Thread Pool Map for IO Bound Tasks in Python
Run IO-bound mock tasks concurrently with ThreadPoolExecutor.map and measure total elapsed time in Python.
import concurrent.futures
import time
from pathlib import Path
def mock_io_task(filename):
"""Simulate an IO-bound task by creating a small file and measuring its latency."""
path = Path(filename)
path.write_text("data")
time.sleep(0.1) # Simulate slow disk/network
return f"{filename} written in …How to Compare Execution Speed Between Python Functions
Measure and compare the average execution time of multiple Python functions using a reusable benchmark helper with time.perf_counter.
import time
import random
def method_a(values):
"""Sort using built-in sorted."""
return sorted(values)
def method_b(values):
"""Sort using list's sort method."""
values_copy = values[:]
values_copy.sort()
return values_copy
def method_c(values):
"""Sort manually using bubble sort (slow,…How to Validate Data in Python with Typing Hints
Build a runtime validation helper that checks values against Python type hints like Optional, list, and basic types.
from typing import Any, Optional, Union, TypeVar, get_origin, get_args
T = TypeVar("T")
def validate(value: Any, expected_type: type) -> Optional[str]:
"""Returns an error message if value doesn't match expected_type, else None."""
# Handle Optional[...] types
origin = get_origin(expected_type)
if or…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)Mock datetime with time-machine in Python
Use the time-machine library to travel to a fixed datetime when running tests or scripts, mocking datetime.utcnow().
from time_machine import travel
from datetime import datetime
@travel("2020-01-01 10:30:00")
def check_date():
return datetime.utcnow()
if __name__ == "__main__":
print(check_date())Mock datetime.now to freeze time in Python
Use unittest.mock.patch to replace datetime.now with a fixed value so your code always sees the same time during tests.
from datetime import datetime
from unittest.mock import patch
def current_message():
now = datetime.now()
return f"Current time: {now:%Y-%m-%d %H:%M:%S}"
if __name__ == "__main__":
with patch("__main__.datetime") as mock_dt:
mock_dt.now.return_value = datetime(2024, 3, 15, 10, 30, 0)
prin…Circuit Breaker Pattern in Python: Closed, Open, and Half-Open States
Implement a circuit breaker with closed, open, and half-open states to prevent repeated calls to failing services and allow recovery after a timeout.
class CircuitBreaker:
def __init__(self, failure_threshold=3, timeout_seconds=5):
self.failure_threshold = failure_threshold
self.timeout_seconds = timeout_seconds
self.state = "closed"
self.failure_count = 0
self.last_failure_time = None
def record_success(self):
…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 Implement the Strategy Pattern in Python
This Python code demonstrates the Strategy design pattern using interchangeable sorting algorithms (bubble sort and quick sort) that can be swapped at runtime.
class SortingStrategy:
def sort(self, data):
raise NotImplementedError
class BubbleSort(SortingStrategy):
def sort(self, data):
result = data.copy()
n = len(result)
for i in range(n):
for j in range(0, n - i - 1):
if result[j] > result[j + 1]:
…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 Mock a Timeout per Dependency Call in Python
This code demonstrates how to simulate and test per-call timeouts for external dependencies using Python's unittest.mock and a simple timing wrapper.
```python
import time
from unittest.mock import Mock, patch
def call_dependency(dependency, timeout):
start = time.time()
result = dependency.call()
elapsed = time.time() - start
if elapsed > timeout:
raise TimeoutError(f"Dependency call took {elapsed:.2f}s, exceeding timeout {timeout}s")
…How to Poll an Operation Status Endpoint in Python
Mock a polling endpoint in Python that simulates checking an async operation's status until it completes or times out.
import time
import random
def poll_status(url: str, timeout: float = 5.0) -> dict:
"""Mock a polling endpoint that eventually returns a completed status."""
start = time.time()
while time.time() - start < timeout:
# Simulate delayed response
time.sleep(0.2)
# 80% chance to report …Browse by section
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Guide: free Python code samples library
Copy-ready Python snippets for learners and developers
PythonSkillset code samples are short, focused examples organised by topic and difficulty. Every snippet is server-rendered HTML — readable by search engines and easy to copy. Open any sample, read the notes, copy the code, then press Try in editor to run it in the browser with Pyodide.
How to use this library
- Pick a topic section — strings, lists, files, functions, and more
- Open a sample, read How it works, and copy the code block
- Run it in the IDE, tweak values, then take a related quiz or tutorial lesson
Samples vs tutorials and challenges
Samples are quick reference — one concept per page. For step-by-step teaching, use our Python tutorials. To test yourself, try quizzes or coding challenges. Clean up style with the Python formatter.