Python Code
Samples
Medium snippets you can copy, study, and run in the browser editor.
How to Monitor Laptop Battery Health Over Time in Python
Log battery percentage, power status, and remaining time every N seconds to a JSON file using psutil for ongoing health monitoring.
import time
import json
from pathlib import Path
from datetime import datetime
try:
import psutil
except ImportError:
print("psutil required: pip install psutil")
exit(1)
LOG_FILE = Path("battery_health_log.json")
def monitor_battery(log_interval=60, duration=300):
"""Log battery percentage and rema…How to Track GitHub Stars, Forks, and Watchers in Python
Automatically fetch and track stars, forks, and watchers for multiple GitHub repositories, saving snapshots locally as JSON files for historical analysis.
import os
import time
import json
import requests
from pathlib import Path
from datetime import datetime
REPOS = [
"psf/requests",
"python/cpython",
"pallets/flask",
]
DATA_DIR = Path("github_metrics")
def fetch_repo_stats(repo):
url = f"https://api.github.com/repos/{repo}"
resp = requests.get(ur…How to apply Kubernetes YAML files from a folder in Python
Uses the Kubernetes Python client to apply all YAML manifests in a directory, with sorted processing and per-file error handling.
import os
import yaml
from kubernetes import client, config
from kubernetes.utils import create_from_yaml
def apply_yaml_folder(folder_path):
"""Apply all YAML files in a folder using the Kubernetes mock client."""
# Load mock configuration
config.load_kube_config()
k8s_client = client.ApiClient()
…Track File Changes with Version History in Python
A Python utility that monitors a file for changes, creating versioned backups with SHA-256 hashing to detect modifications and store a local JSON history.
import hashlib, json, os, shutil, time
from pathlib import Path
class FileTracker:
def __init__(self, history_file="file_history.json"):
self.history_file = Path(history_file)
self.history = self._load_history()
def _load_history(self):
if self.history_file.exists():
retur…Track Internet Connectivity and Downtime Automatically in Python
Monitors internet connectivity by pinging a remote host and logs any downtime events with timestamps and duration.
import time
import subprocess
from datetime import datetime
def check_internet(host="8.8.8.8", timeout=3):
"""Returns True if internet is reachable via ping."""
try:
subprocess.run(
["ping", "-c", "1", "-W", str(timeout), host],
capture_output=True,
timeout=timeout …Deduplicate events by ID within a window in Python
Deduplicate event streams by ID within sliding time windows, keeping the newest occurrence per window using heaps and sets.
import heapq
from collections import defaultdict
def deduplicate_events(events, window_size):
"""Return events deduplicated by id, keeping newest within each sliding window."""
# Index events by (timestamp, id) for deterministic ordering
events_by_id = defaultdict(list)
for ts, eid, *payload in events…How to Count Events by Minute with a Tumbling Window in Python
Group timestamps into fixed 60-second tumbling windows and count events per bucket using a dict.
from collections import defaultdict
from datetime import datetime, timedelta
def tumbling_window_count(events, window_seconds=60):
buckets = defaultdict(int)
for event in events:
ts = datetime.fromisoformat(event["timestamp"])
bucket_start = ts - timedelta(seconds=ts.second % window_seconds,
…Normalize Timestamps to UTC DateTime in Python
Convert timestamps in multiple formats to UTC-aware datetime objects using datetime.strptime and astimezone.
from datetime import datetime, timezone
raw_timestamps = [
"2024-01-15 14:30:00+02:00",
"17/05/2024 09:15:00 -0500",
"2024-03-01T22:45:00Z",
"2024-06-20 08:00:00+09:30"
]
def parse_and_convert(ts: str) -> datetime:
normalized_ts = ts.strip().replace("Z", "+00:00")
formats = [
"%Y-%m-%…How to Mock RDS Snapshot Create and Restore in Python
Mock AWS RDS snapshot creation and restore operations in Python tests using moto and boto3 without hitting real AWS services.
import boto3
from moto import mock_rds
@mock_rds
def create_and_restore_snapshot():
client = boto3.client("rds", region_name="us-east-1")
client.create_db_instance(
DBInstanceIdentifier="my-db",
DBInstanceClass="db.t3.micro",
Engine="postgres",
AllocatedStorage=20,
Mas…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 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 Speed Up Downloads with ThreadPoolExecutor in Python
Compare sequential and thread-pool download loops to measure real speedup when I/O s bound.
import time
import threading
from concurrent.futures import ThreadPoolExecutor
def download_file(file_id):
"""Simulate fetching a file by sleeping briefly."""
time.sleep(0.2) # pretend network latency
return f"file_{file_id}"
def sequential_downloads(num_files):
"""Process files one at a time."""
…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…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 Flag Unexpected Diff Changes in Python
Compares two snapshot lists, detects unexpected differences, and returns a flag indicating whether the snapshot should be updated.
import difflib
def snapshot_diff(before, after, intentional_changes=None):
"""Compare snapshots and flag only unexpected differences."""
intentional_changes = intentional_changes or set()
diff = list(difflib.unified_diff(before, after, lineterm=""))
has_unexpected = False
for line in diff:
…How to Snapshot Test JSON with Mock in Python
Use pytest-snapshot to capture the exact output of a JSON-loading function, with and without mocking json.loads, so future changes are automatically detected.
import json
from unittest.mock import Mock, patch
import pytest
def load_config(data):
config = json.loads(data)
return {"host": config["host"], "port": config["port"]}
def test_load_config_snapshot(snapshot):
mock_data = json.dumps({"host": "localhost", "port": 8080, "extra": "ignored"})
result = …How to implement saga orchestration with compensating steps in Python
Orchestrate a distributed transaction across services, rolling back completed steps with compensations when a later step fails.
class InventoryService:
def reserve(self, order_id):
print(f"[Inventory] Reserving stock for order {order_id}")
return True
def compensate(self, order_id):
print(f"[Inventory] Releasing stock for order {order_id}")
class PaymentService:
def charge(self, order_id):
print(f…How to implement stale-while-revalidate caching in Python
A Python cache wrapper that returns a stale cached value with a fallback flag when the upstream fetch fails, using TTL-based freshness checks.
import time
from functools import lru_cache
class CachedService:
def __init__(self, fetch_func, ttl=5):
self.fetch_func = fetch_func
self.ttl = ttl
self._cache = {}
self._timestamp = {}
def get(self, key):
now = time.time()
if key in self._cache and now - self…Template Method Workflow Steps Base Class in Python
Define a reusable workflow skeleton in a base class and let subclasses fill in each step with the Template Method design pattern.
from abc import ABC, abstractmethod
class DataPipeline(ABC):
"""Template Method pattern: defines a workflow skeleton."""
def run(self):
"""Template method - defines the algorithm's structure."""
result = {"extracted": False, "transformed": False, "loaded": False}
raw_data = self._ext…How to Build a Mock REST GET Endpoint Handler in Python
Create a lightweight mock REST GET server in Python using the standard library, with a dict-based route registry that maps paths to handler functions and returns JSON responses with proper HTTP status codes.
from http.server import BaseHTTPRequestHandler, HTTPServer
import json
# Mock API handler registry
def handle_users():
return {"status": "ok", "data": [{"id": 1, "name": "Alice"}, {"id": 2, "name": "Bob"}]}
def handle_products():
return {"status": "ok", "data": [{"id": 101, "name": "Laptop", "price": 999.99}…How to Aggregate Periodic Snapshot Data in Python
Generates mock snapshot data and groups values into periods to compute average aggregates with Python's standard library.
import random
from collections import defaultdict
def snapshot_aggregate(n=10, period=3):
data = defaultdict(list)
for i in range(n):
key = f"item_{i % period}"
data[key].append(random.randint(1, 100))
return dict(data)
def aggregate_periodic(snapshots, period=3):
result = {}
for …How to Read Redis Streams with XREADGROUP in Python
Read new messages from a Redis stream using a consumer group with XREADGROUP, handling JSON payloads and group creation.
import redis
import json
def read_group_messages(stream_key, group_name, consumer_name, count=10):
r = redis.Redis(host="localhost", port=6379, decode_responses=True)
try:
r.xgroup_create(stream_key, group_name, id="0", mkstream=True)
except redis.exceptions.ResponseError:
pass
messag…How to Stream Join Windowed Mock Topics in Python
Simulates two message topics and joins their events when timestamps fall within a sliding time window using Python generators and deques.
import itertools
import random
import time
from collections import deque
from dataclasses import dataclass, field
@dataclass
class Event:
key: str
value: int
timestamp: float = field(default_factory=time.time)
def generate_topic(prefix, keys, start_time):
while True:
yield Event(
…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.