Apply the rules
Inspect a small .ipynb example with a suitable tool. Explain its structure and one failure mode before opening untrusted files.
File Formats / Knowledge library
AI model files: understand the .ipynb format, structure and safe handling.
Recognize Jupyter notebook files and avoid incorrect parsing, unsafe loading or format assumptions.
Helps select and validate Jupyter notebook data instead of trusting a filename extension alone.
What is the underlying representation of an .ipynb file? (.ipynb) JSON. Which content can notebook code cells retain? (.ipynb) Source and execution outputs. Why can notebook execution counts be out of order? (.ipynb) Cells can be executed nonsequentially. Why review notebook outputs before sharing? (.ipynb) They may contain sensitive data. What helps check notebook reproducibility? (.ipynb) Restart the kernel and run cells in order.
Work through these exercises in order. Try the task before opening a reference.
Inspect a small .ipynb example with a suitable tool. Explain its structure and one failure mode before opening untrusted files.
Data science teams sharing research notebooks across internal departments.
Unintended exposure of sensitive data or credentials embedded within notebook outputs or metadata.
Programmatic validation of the .ipynb JSON structure to strip unauthorized metadata and sensitive output fields before distribution.
The flow shows how raw notebook files are processed into sanitized versions.
Contains code, outputs, and metadata
Loads structure into memory
Removes sensitive outputs and metadata
Safe for distribution
This code demonstrates the programmatic approach to clearing sensitive outputs from a notebook by manipulating the underlying JSON structure.
import json
# Load the notebook structure
with open('research.ipynb', 'r') as f:
nb = json.load(f)
# Clear outputs from all code cells to prevent data leakage
for cell in nb.get('cells', []):
if cell.get('cell_type') == 'code':
cell['outputs'] = [] # Reset outputs list
# Save the sanitized notebook
with open('clean_research.ipynb', 'w') as f:
json.dump(nb, f)A standardized, clean notebook format that minimizes the risk of accidental data leakage while maintaining code reproducibility.
The format often stores multi-line strings as lists of strings on disk, which can complicate manual regex-based sanitization; always use the official nbformat API to ensure proper string reconstruction.