Reference library

Data pipelines & processing

ETL-style flows, batch transforms, validation, and moving data between formats.

4 matches
Data pipelines & processing medium

Build a Python Utility That Detects Duplicate Records Across Multiple Excel Sheets

A Python utility that uses pandas to find overlapping records across different Excel sheets based on specified key columns.

pandas excel data cleaning
Python
import pandas as pd
from pathlib import Path

def find_duplicate_records_across_sheets(file_path: str, key_columns: list, sheet_names: list) -> dict:
    """
    Detect duplicate records across multiple Excel sheets based on specified key columns.
    
    Args:
        file_path: Path to the Excel file
        key_co…
46 0 Open
Data pipelines & processing medium

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.

deduplication events heapq
Python
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…
14 0 Open
Data pipelines & processing medium

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".

web-scraping structured-data schema-org
Python
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…
50 0 Open
Data pipelines & processing medium

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.

datetime grouping time-window
Python
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,
…
12 0 Open

Browse by section

Each section groups closely related Python snippets.

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.