@Harsh Kalra​ :

there are a few ways to manage package dependencies in Databricks notebooks:

1. Use Databricks' built-in package management: Databricks allows you to install packages using a built-in package manager. You can do this through the UI by going to the "Libraries" tab of your cluster and adding the packages you need. Alternatively, you can use the dbutils.library.install command to install packages programmatically.
2. Use a virtual environment: You can create a virtual environment using conda or pipenv and install all the packages you need there. Then, you can activate the environment in each cell where you need to use those packages. However, note that this approach can be tricky to set up, as you'll need to make sure that all the packages you need are installed in the virtual environment and that you activate the environment correctly in each cell.
3. Install packages in each cell: You can install the necessary packages in each cell where you need them using pip
4. However, this can be time-consuming and can clutter your notebook code.

The recommended approach may depend on your specific use case and the complexity of your package dependencies. If you're having issues with package dependency conflicts, it may be worth trying the built-in package management approach first, as this will automatically handle dependency resolution.