jupyter

name	jupyter
description	Create and execute Jupyter notebooks for interactive data analysis using jupyter_execute and jupyter_notebook tools. Use when the user asks to run Python code interactively, create notebooks, analyze data in cells, or mentions .ipynb files.

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

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