---
name: jupyter-351f8766f011
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


---
Source attribution: Prismer-AI
Original project: https://github.com/Prismer-AI/Prismer
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Dave AI.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Dave AI resource metadata

Creator / publisher: Prismer-AI
Original project: https://github.com/Prismer-AI/Prismer
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Development & Engineering, Education & Research, Product Management
Roles: Data Analyst, Data Scientist, ML / AI Engineer, Product Manager, Researcher
Specialties: API, Backend, Cross Platform
Tools: Jupyter
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: a46521572c95c741d260c4a7fc7bbdaf597039c064a25c9fa1e12549c42aa83c