jupyter-notebooks

name	jupyter-notebooks
description	Work effectively with Jupyter notebooks for data exploration and analysis. Use when creating notebooks, converting formats, or establishing notebook best practices.

Jupyter Notebooks

Activate this skill when working with Jupyter notebooks in data science workflows.

When to Use

• Creating exploratory data analysis notebooks
• Converting notebooks to scripts or reports
• Establishing notebook conventions for teams
• Debugging notebook execution issues
• Setting up JupyterLab environments

Best Practices

• Keep notebooks focused on one analysis question
• Use markdown cells for documentation between code
• Clear outputs before committing to version control
• Extract reusable code into .py modules
• Number sections for narrative flow

Structure

notebooks/
  01-data-exploration.ipynb
  02-feature-engineering.ipynb
  03-model-training.ipynb
  04-evaluation.ipynb
  utils/
    __init__.py
    plotting.py
    preprocessing.py

Tools

# Convert to script
jupyter nbconvert --to script notebook.ipynb

# Convert to HTML report
jupyter nbconvert --to html --no-input notebook.ipynb

# Run notebook headless
papermill input.ipynb output.ipynb -p param_name value

Rules

• Never store secrets in notebooks
• Use parameterized notebooks for reproducibility
• Pin library versions in notebook headers
• Keep cell execution order linear (no jumping)
• Use nbstripout to clean outputs before commits