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Benchmark list append vs comprehension in Python
This micro-benchmark compares the speed of building a list with a for loop and append versus a list comprehension, using the timeit module to get precise timings.
import timeit
# Build a list of the first 1,000,000 integers using append in a loop
def append_loop(n=1_000_000):
result = []
for i in range(n):
result.append(i)
return result
# Build the same list using a list comprehension
def comprehension(n=1_000_000):
return [i for i in range(n)]
if __n…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 Build a Simple Debug Timer in Python
Create a context manager class to time the execution of a code block with a one-line printout.
import time
class DebugTimer:
"""Context manager that times the execution of a code block."""
def __init__(self, label="Operation"):
self.label = label
self.start_time = None
def __enter__(self):
self.start_time = time.perf_counter()
return self
def __exit__(self, e…Benchmark list.append vs deque.append in Python
Measures and compares the performance of appending to a Python list versus a collections.deque using timeit.repeat, showing best and average timings.
"""Benchmark list.append vs collections.deque.append."""
import timeit
def bench(stmt, setup, repeat=5, number=1_000_000):
times = timeit.repeat(stmt, setup=setup, repeat=repeat, number=number)
return min(times), sum(times) / len(times)
if __name__ == "__main__":
number = 1_000_000
list_best, list_a…How to Use ThreadPoolExecutor for Concurrent Tasks in Python
Compare sequential execution with ThreadPoolExecutor for I/O-bound tasks, measuring speedup and timing with perf_counter.
import time
import threading
from concurrent.futures import ThreadPoolExecutor
def fetch_data(index):
"""Simulate a synchronous data fetch."""
time.sleep(0.1)
return f"data-{index}"
def run_sequential(total=10):
"""Run tasks one after another."""
start = time.perf_counter()
results = [fetch…How to Write a Fast Smoke Test for a Critical Path in Python
A quick smoke test that validates the /health critical path executes fast enough, raising errors on wrong paths or slow responses.
import time
def smoke_test(path):
if path != "/health":
raise ValueError("Critical path expected /health")
start = time.perf_counter()
# Simulate the critical health check work
time.sleep(0.01)
elapsed = time.perf_counter() - start
if elapsed > 0.05:
raise RuntimeError("Health …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")
…Database indexing and query timing optimization in Python
Create SQLite indexes and time query performance to measure speedup for large table lookups in Python.
import sqlite3
import time
def time_query(db_path, query, params=()):
conn = sqlite3.connect(db_path)
conn.execute("PRAGMA journal_mode = WAL")
start = time.perf_counter()
result = conn.execute(query, params).fetchall()
elapsed = time.perf_counter() - start
conn.close()
return result, ela…How to Salt Passwords per User in Python
Hash each user's password with a unique random salt using hashlib, and verify logins with timing-safe comparison.
import hashlib
import secrets
def hash_password(password: str, salt: str | None = None) -> tuple[str, str]:
"""Hash a password with a random salt (or provided salt).
Returns:
(salt_hex, password_hash_hex)
"""
if salt is None:
salt = secrets.token_hex(16)
salted = (salt + password)…How to Verify Passwords in Constant Time in Python
Use hmac.compare_digest to verify passwords in constant time, preventing timing attacks that could reveal password length or character positions.
import hmac
import time
# Mock of a constant-time password comparison (prevents timing attacks)
def verify_password(stored_password: str, supplied_password: str) -> bool:
# hmac.compare_digest runs in constant time (for a given length)
return hmac.compare_digest(stored_password.encode(), supplied_password.enc…How to Implement a Manual Approval Gate Mock in Python
Simulates a manual approval workflow with threshold-based rules, random decisions for medium amounts, and logs each result with timing.
import random
import time
def approve_request(amount: float) -> bool:
if amount <= 1000:
return True
if amount <= 5000:
return random.random() < 0.7
return False
def main():
requests = [500, 1200, 7500, 3000, 50]
for amount in requests:
start = time.perf_counter()
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Guide: free Python code samples library
Copy-ready Python snippets for learners and developers
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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.