The struggle for Data Scientists to balance speed and code quality in Jupyter notebooks is a persistent issue, driven by the need for rapid prototyping and testing in data analysis. Traditional developer methodologies and tools have shown promise in resolving this tension, empowering developers to own their code quality and catch errors during creation. Sonar envisions a solution that works alongside Data Scientists to offer a coding companion, flagging issues and providing educational guidance to enable easy correction without interrupting the flow. By leveraging this approach, Clean Code can be achieved for Jupyter notebooks, benefiting users in terms of understanding, collaboration, and personal growth, ultimately enhancing their reputation and confidence in their work.