Jupythunder
Jupythunder is a middleware that connects LLM chat interfaces (such as Claude) with Jupyter Notebook, supporting code conversion, execution, and data visualization analysis through natural language interaction.
rating : 2 points
downloads : 9
What is Jupythunder?
Jupythunder is an intelligent bridge that allows you to directly operate Jupyter Notebook in natural language through a chat interface (such as Claude). It automatically converts your text instructions into executable Python code and runs it in Jupyter, enabling code - free data analysis.How to use Jupythunder?
Just three steps: 1) Run the Jupyter Notebook server. 2) Start the Jupythunder middleware. 3) Enter natural language instructions in chat interfaces such as Claude. The system will automatically connect and execute your requests.Use cases
It is particularly suitable for scenarios that require frequent interactive programming, such as rapid prototype development, teaching demonstrations, data analysis exploration, and automated report generation.Main features
Natural language to codeAutomatically convert your natural language descriptions into executable Python code
Remote control of JupyterSecurely connect to a remote Jupyter Notebook server via URL and token
Intelligent queryUse the /question command to query information such as variables and functions in the current environment
Notebook managementCreate, open, and save .ipynb files, and manage cell contents
Data visualizationGenerate various statistical charts through natural language instructions
Cell controlView the content of specific cells and execute single or all cells
Advantages and limitations
Advantages
Perform data analysis without writing code
Retain a complete code history and reproducibility
Support local and remote Jupyter servers
Automatically save natural language explanations as Markdown documents
Limitations
Require basic Python environment configuration
Complex logic may require multiple iterations of adjustment
Manually configure the URL and token for the first connection
How to use
Prepare the Jupyter environment
Ensure that Python 3.10+ and Jupyter Notebook are installed
Start the Jupyter server
Run Jupyter Notebook in a new terminal
Configure the Claude client
Add the MCP server configuration to claude_desktop_config.json
Connect to Jupyter
Enter the Jupyter URL containing the token in the chat interface
Usage examples
Data exploration and analysisQuickly understand the characteristics of the dataset and visualize it
Machine learning prototypeQuickly build and evaluate simple models
Automated reportGenerate data analysis reports regularly
Frequently Asked Questions
What should I do if I get the error 'could not connect to MCP server' when connecting?
How can I view all variables in the current notebook?
What should I do if the execution result is not displayed?
What types of data visualization are supported?
Related resources
Official GitHub repository
Project source code and latest updates
Jupyter official documentation
Jupyter Notebook usage guide
Python Data Science Handbook
Data analysis and visualization tutorials
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