Zaoqu-Liu/jupyter

Jupyter Notebook Skill

Description

Create and execute Jupyter notebooks for interactive data analysis and visualization.

Tools Used

• jupyter_execute - Execute Python code in Jupyter kernel (auto-switches to Jupyter)
• jupyter_notebook - Create, read, update, delete, and list notebooks
• update_notebook - Add or update cells in the notebook without executing
• update_gallery - Display generated plots and visualizations in gallery view
• update_data_grid - Display structured tabular data (DataFrames, query results) in AG Grid
• update_code - Show code examples and scripts in the Code Playground
• save_artifact - Save generated artifacts (plots, data files) to workspace collection

Capabilities

• Create new notebooks with proper structure
• Add and execute code cells
• Add markdown documentation cells
• Display inline visualizations
• Display tabular data in interactive grid view
• Show code examples with syntax highlighting
• Export to various formats (HTML, PDF)

Usage Patterns

Create Analysis Notebook

When user says: "Create a notebook for [analysis]"

1. Create notebook with title and imports
2. Add data loading cell
3. Add exploration cells
4. Structure with markdown headers
5. Execute cells sequentially

Execute and Debug

When user says: "Run this code"

1. Execute cell
2. Capture output and errors
3. If error, diagnose and fix
4. Show results or visualizations

Document Workflow

When user says: "Add explanation for this step"

1. Add markdown cell before code
2. Explain methodology
3. Note assumptions and limitations

Best Practices

1. Cell Independence: Each cell should run independently when possible
2. Import First: All imports at notebook start
3. Clear Outputs: Clean outputs before sharing
4. Markdown Structure: Use headers for navigation
5. Save Often: Checkpoint regularly