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Python Code Samples

Easy snippets you can copy, study, and run in the browser editor.

7 matches
Data pipelines & processing easy

Group Python Events into Sessions with a Gap Timeout

Groups timestamped events into sessions, starting a new session when the time gap exceeds a specified timeout.

sessions grouping datetime
Python
from itertools import groupby
from datetime import datetime, timedelta

def session_window_group(events, gap_seconds=300):
    """Group events into sessions where gap > gap_seconds starts a new session."""
    if not events:
        return []
    
    events = sorted(events, key=lambda x: x[0])
    sessions = []
    c…
14 0 Open
Data pipelines & processing easy

Trigger a Pipeline When a New File Appears in a Directory

Poll a directory every 0.5 seconds and return the name of the first new file that appears, or None after a timeout.

polling filesystem file-watcher
Python
import time
from pathlib import Path


def watch_for_file(directory: str, interval: float = 0.5, timeout: float = 10.0) -> str | None:
    """Poll a directory and trigger when a new file appears."""
    watch_dir = Path(directory)
    watch_dir.mkdir(exist_ok=True)
    
    known_files = set(watch_dir.iterdir())
    s…
13 0 Open
Concurrency & performance easy

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.

asyncio timeout concurrency
Python
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())
13 0 Open
API design & gRPC easy

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.

polling api async
Python
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 …
13 0 Open
Reliability & rate limiting easy

How to Implement Message Visibility Timeout Renewal in Python

Simulate queue message visibility control with timeout renewal using a simple Python class that tracks received time and visibility state.

visibility-timeout queue sqs
Python
import time
import uuid

class Message:
    def __init__(self, body, visibility_timeout=30):
        self.body = body
        self.visibility_timeout = visibility_timeout
        self.receipt_handle = str(uuid.uuid4())
        self.received_at = time.time()
        self.deleted = False

    def is_visible(self):
     …
13 0 Open
Reliability & rate limiting easy

How to Mock a Timeout per HTTP Request in Python

Simulate a per-request HTTP timeout using unittest.mock to test timeout handling without network access.

mocking timeout testing
Python
import time
from unittest.mock import Mock, patch

# Simulate an HTTP client that might time out
def fetch_data(url, timeout=5):
    time.sleep(0.5)  # Simulate network delay
    return f"Response from {url}"

# Mock to test timeout behavior without real network
def test_timeout():
    mock_response = Mock(side_effect…
13 0 Open
Observability & SRE easy

How to Mock HTTP Client Latency in Python

Simulate outbound HTTP request latency with configurable ranges to test timeouts, retries, and SLO monitoring without external services.

latency mocking http-client
Python
import time
import random

def mock_latency(host: str, min_ms: int = 100, max_ms: int = 500) -> dict:
    """Simulate an outbound HTTP request with mock latency."""
    latency_ms = random.randint(min_ms, max_ms)
    start = time.perf_counter()
    time.sleep(latency_ms / 1000)
    elapsed_ms = (time.perf_counter() - …
14 0 Open

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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.