Data pipelines & processing
ETL-style flows, batch transforms, validation, and moving data between formats.
Extract Schema.org Structured Data from Any Website in Python
A Python tool that fetches a webpage and extracts all JSON-LD structured data (Schema.org) embedded in <script> tags with type="application/ld+json".
import requests
from bs4 import BeautifulSoup
import json
def extract_schema_org(url):
"""Extract structured data (Schema.org) from a website."""
try:
response = requests.get(url, timeout=10)
response.raise_for_status()
except requests.exceptions.RequestException as e:
return {"err…Fan Out Records to Multiple Sinks in Python
Distribute the same records across multiple target sinks (database, API, queue, etc.) using a defaultdict-based fan-out pattern.
import json
from collections import defaultdict
SINKS = ["database", "api", "message_queue", "data_lake", "monitoring"]
def fan_out(records, *sinks):
dist = defaultdict(list)
for record in records:
for sink in sinks:
dist[sink].append(record)
return dict(dist)
if __name__ == "__main_…Browse by section
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Data pipelines & processing — Python code examples
What you will find here
This page collects data pipelines & processing snippets — short, copy-ready Python you can paste into our free online IDE and run without installing anything. Each sample includes a plain-English explanation and the full source code.
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.