Python Code
Samples
Medium snippets you can copy, study, and run in the browser editor.
How to Get Current Git Branch Name in Python with Mock Subprocess
Mocks the subprocess call to reliably test the current git branch name retrieval using GitPython.
import subprocess
from unittest.mock import patch, MagicMock
from git import Repo
import os
def get_current_branch(repo_path="."):
"""Get the current branch name of a git repository."""
repo = Repo(repo_path)
return repo.active_branch.name
if __name__ == "__main__":
# Mock subprocess to control the…How to Mock Git Pre-commit Hooks (black and ruff) in Python
Mock subprocess to test black and ruff pre-commit commands without actually running them, verifying exit codes.
import sys
import subprocess
from unittest.mock import patch
def run_hook(command: list[str]) -> int:
with patch("subprocess.run") as mock_run:
mock_run.return_value.returncode = 0
mock_run.return_value.stdout = f"Mocked: {' '.join(command)}"
result = subprocess.run(command, capture_output…Mock smtplib to Test Patch Email Series in Python
Simulate sending a numbered series of patch emails with smtplib and verify the calls using unittest.mock without a real mail server.
import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
from unittest.mock import patch, Mock
def send_patch_series(subject_prefix, patches, smtp_host="localhost", smtp_port=25):
"""Simulate sending a series of patch emails."""
for i, patch_content in enumerate(patch…Exponential Backoff with Jitter for Cloud API Calls in Python
A Python snippet demonstrating exponential backoff with jitter for retrying transient cloud API failures, using a simulated client that has a configurable success rate.
import random
import time
def exponential_backoff_with_jitter(retries=5, base_delay=0.5, max_delay=4.0, jitter_factor=0.3):
for attempt in range(1, retries + 1):
delay = min(max_delay, base_delay * (2 ** (attempt - 1)))
jitter = delay * random.uniform(-jitter_factor, jitter_factor)
effect…How to Evaluate IAM Policy Allow vs Deny in Python
Evaluate an AWS-style IAM policy dict with explicit deny overriding allow and default deny.
import json
def evaluate_policy(action, resource, policy):
"""Evaluate an IAM-like policy dict.
Explicit deny wins over allow. Default is deny.
"""
for statement in policy.get("Statement", []):
effect = statement.get("Effect")
actions = statement.get("Action", [])
resources = …How to mock boto3 S3 upload file wrapper in Python
Wrap an S3 put_object call in a testable function that returns metadata, and mock boto3 to verify the upload without touching AWS.
import boto3
import io
def upload_file_to_s3(file_obj, bucket, key, object_metadata=None):
"""Upload a file-like object to S3 and return a metadata dict."""
s3 = boto3.client("s3")
content = file_obj.read()
s3.put_object(
Bucket=bucket,
Key=key,
Body=content,
Metadata=…Mock Route53 change_resource_record_sets in Python
This code demonstrates how to mock AWS Route53 change_resource_record_sets API calls using the botocore Stubber, allowing you to test DNS update logic without touching real infrastructure.
import boto3
from botocore.exceptions import ClientError
def mock_change_resource_record_sets():
"""Demonstrates Route53 change_resource_record_sets with a mock client."""
# Create a mock Route53 client
route53 = boto3.client('route53', region_name='us-east-1',
aws_access_key_id…How to Mock a PEP 517 Build Backend in Python
Use unittest.mock.Mock to simulate a PEP 517 backend interface, stub build hooks, and verify calls for package build automation.
import json
from unittest.mock import Mock
# Simulate a PEP 517 backend interface
class Pep517Backend:
def build_wheel(self, wheel_directory, config_settings=None, metadata_directory=None):
return f"{wheel_directory}/mock_package-1.0.0-py3-none-any.whl"
def get_requires_for_build_wheel(self, config_s…How to Mock subprocess.run for Black Formatter in Python
Use unittest.mock to simulate subprocess.run calls in a Python function that runs the Black formatter, allowing isolated testing without executing external commands.
import subprocess
from unittest.mock import Mock, patch
def run_black_formatter(file_path: str, check_only: bool = False) -> dict:
"""Run black formatter on a file via subprocess."""
cmd = ["black", "--check" if check_only else "-", file_path]
result = subprocess.run(cmd, capture_output=True, text=True)
…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()
…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 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 Mock anyio.run Backends (asyncio vs trio) in Python
Demonstrates how to mock anyio.run to verify backend selection (asyncio or trio) without actually running the event loop.
import anyio
from unittest.mock import Mock, patch
async def fetch_data():
await anyio.sleep(0.1)
return {"data": 42}
def run_with_backend(backend: str):
async def main():
result = await fetch_data()
print(f"[{backend}] Result: {result}")
anyio.run(main, backend=backend)
if __nam…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 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 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 Use ProcessPoolExecutor for CPU Parallel Map in Python
Run a function over a sequence of inputs in parallel across multiple CPU cores with ProcessPoolExecutor.map.
from concurrent.futures import ProcessPoolExecutor
import math
def compute_square(num):
return num * num
def is_prime(n):
if n < 2:
return False
for i in range(2, int(math.sqrt(n)) + 1):
if n % i == 0:
return False
return True
if __name__ == "__main__":
numbers = rang…How to Use Thread Pool Executor map for IO-Bound Tasks in Python
Run multiple I/O-bound tasks concurrently with ThreadPoolExecutor map and collect their results in order.
import time
from concurrent.futures import ThreadPoolExecutor
def io_bound_task(task_id: int) -> str:
time.sleep(0.2) # mock I/O wait
return f"Task {task_id} completed"
def main() -> None:
task_ids = [1, 2, 3, 4, 5]
with ThreadPoolExecutor(max_workers=3) as executor:
results = list(executor.…How to Use multiprocessing Pool map and starmap in Python
Parallelize functions over iterables with Pool.map, and unpack multiple arguments via Pool.starmap.
from multiprocessing import Pool
def square(x):
return x * x
def add_and_multiply(a, b, c):
return (a + b) * c
if __name__ == "__main__":
numbers = [1, 2, 3, 4, 5]
with Pool(processes=2) as pool:
squares = pool.map(square, numbers)
print(f"squares: {squares}")
starmap_arg…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…Profile Memory Usage with tracemalloc Snapshot Diff in Python
Use tracemalloc to take two memory snapshots, compute a diff, and print the top changes (size and count) by line number.
import tracemalloc
def profile_memory():
tracemalloc.start()
# Allocate some objects to track
data = [i * 2 for i in range(10000)]
text = "x" * 5000
nested = {"key": [1, 2, 3], "value": (4, 5)}
# Take first snapshot
snapshot1 = tracemalloc.take_snapshot()
# Free some mem…asyncio Condition wait notify pattern in Python
Coordinate coroutines with asyncio.Condition: workers wait for notifications and the main task notifies one or all of them.
import asyncio
async def worker(condition, name):
async with condition:
print(f"{name} waiting...")
await condition.wait()
print(f"{name} notified!")
async def main():
condition = asyncio.Condition()
tasks = [asyncio.create_task(worker(condition, f"worker-{i}")) for i in range(3…How to Mock an Object Method in Python unittest
Mock a method on an instance or class with @patch.object, set its return value, and assert its call arguments in Python unittest.
import unittest
from unittest.mock import patch
class Calculator:
def add(self, a, b):
return a + b
def multiply(self, a, b):
return a * b
class TestCalculator(unittest.TestCase):
def test_add_normal(self):
calc = Calculator()
result = calc.add(2, 3)
self.asse…Browse by section
Each section groups closely related Python snippets.
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.