Jupyter

• 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

Tool Examples

Execute Python code

jupyter_execute code="import pandas as pd\ndf = pd.read_csv('/workspace/data/results.csv')\nprint(df.describe())"

Create a notebook with cells

update_notebook cells=[{"type": "markdown", "source": "# Analysis"}, {"type": "code", "source": "import pandas as pd\nimport matplotlib.pyplot as plt"}] execute=false

Display generated plots

update_gallery images=[{"url": "/workspace/data/plot.png", "title": "Analysis Results"}]

Best Practices

1. Cell Independence: Each cell should run independently when possible
2. Import First: All imports at notebook start
3. Check output before proceeding: Verify execution output is correct before running dependent cells
4. Markdown Structure: Use headers for navigation
5. Save Often: Checkpoint regularly