Regarding Website Design & Development

[sanjay-kv]

I want all the data to visualizing in website rather than the ipynb files. Can you suggest what all changes might required.

[ankit-webmavens]

@sanjay-kv To move data visualization from .ipynb to the website, convert the logic to Laravel or backend scripts, store data in the database, use chart libraries for frontend, and build dashboards with Blade or Livewire. Automate data updates with jobs or schedulers...

[santoshnaya]

To visualize data on a website instead of Jupyter notebooks, move your analysis code to Python scripts or APIs (using Flask or FastAPI).
Build a frontend with Laravel or a JavaScript framework and use chart libraries like Chart.js or ApexCharts to display data.
Connect the frontend to your backend or database to fetch real-time data and render it directly in the browser.

[A-Hemeda]

To move visualizations from Jupyter notebooks into a website, you can follow this workflow:

Backend

Expose your analysis logic as APIs instead of keeping it in .ipynb.

If you’re already using .NET, you can build REST APIs with ASP.NET Core to serve processed data.

Alternatively, Flask, FastAPI, or Node.js can also handle this.

Data Handling

Store processed data in a database (MySQL, PostgreSQL, or MongoDB depending on the use case).

Schedule regular updates using background jobs (Hangfire in .NET, Celery in Python, or CRON jobs).

Frontend

Use chart libraries such as Chart.js, D3.js, or ApexCharts to render interactive graphs.

Frameworks like React, Vue, or Angular make it easier to build dashboards, but you can also use Laravel with Blade/Livewire if you prefer server-side rendering.

Deployment

Host your backend APIs and frontend on the same server or separately.

Containerization with Docker helps in scaling and deployment.

This setup keeps your workflows automated and makes the data accessible directly in the browser without needing notebooks.