Data pipelines & processing
ETL-style flows, batch transforms, validation, and moving data between formats.
How to create a dated snapshot path for a dataset in Python
Generate a versioned directory path combining a base directory, dataset name, and today's date, ready for creating snapshots in data pipelines.
import datetime
import os
from pathlib import Path
def snapshot_path(base_dir: str, dataset_name: str) -> Path:
"""Return a dated snapshot path for a dataset under a base directory."""
today = datetime.date.today().isoformat()
return Path(base_dir) / dataset_name / today
if __name__ == "__main__":
…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.
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…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.