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Powering Energy Storage Beyond Excel with Calvin Hendryx-Parker
"Discover how Jupyter Notebooks, Django, and Python can empower your energy storage projects with real-time prototyping, scalability, and reliability, outgrowing Excel's limitations."
- Use Jupyter Notebooks to prototype and test energy storage projects, leveraging its real-time aspect and ease of use.
- Django provides a reliable and secure platform for building energy storage applications, with features like authentication, rich UI, and support for scalable and long-running tasks.
- Excel’s limitations and lack of scalability make it unsuitable for large-scale energy storage projects, which require complex calculations, version control, and testing.
- Python and Django provide a more robust and maintainable solution for energy storage applications, with built-in support for data manipulation, testing, and deployment.
- Use Codium to import Excel files into pandas and perform operations, and then use Jupyter Notebooks to validate and test formulas.
- Implement version control and testing to ensure consistency and accuracy across different versions of the application.
- Use Ant Design Pro to build a rich UI for the front-end, and Django REST framework to handle API requests.
- Document formulas and cell references to facilitate collaboration and maintainability.
- Leverage the Django ecosystem, including Postgres, Celery, and Codium, to build a scalable and reliable application.
- Prioritize testing and version control to ensure the application’s integrity and accuracy.