jupyter vs jupyterlab: What are the differences?

# Introduction
Jupyter and JupyterLab are both popular tools for interactive computing and data science workflows, but they have some key differences that set them apart.

1. **Interface**: JupyterLab offers a more robust and flexible user interface with features like draggable tabs, multiple viewing panes, and integrated text editors, making it more versatile for complex workflows compared to Jupyter.

2. **Extension Ecosystem**: JupyterLab has a built-in extension system that allows users to customize and extend its functionality, enabling a larger variety of tools and integrations compared to Jupyter, which has a more limited extension ecosystem.

3. **File Management**: JupyterLab provides a file browser sidebar for managing files and directories within the interface, allowing for easier navigation and organization of projects compared to Jupyter's more basic file handling capabilities.

4. **Code Console**: JupyterLab includes an integrated code console that supports multiple languages and kernel connections, offering a more seamless experience for interactive coding compared to Jupyter, which lacks this feature.

5. **Notebook Editing**: JupyterLab provides improved notebook editing features such as cell tagging, advanced navigation, and a table of contents view, enhancing the editing and organization options available compared to Jupyter's more straightforward notebook interface.

6. **Compatibility**: While Jupyter is a standalone application, JupyterLab is the next-generation interface for Jupyter notebooks, designed to eventually replace the traditional Jupyter interface while remaining compatible with Jupyter notebooks, ensuring a smooth transition for users.

In Summary, Jupyter and JupyterLab have notable differences in their interface, extension ecosystem, file management, code console, notebook editing features, and compatibility, which cater to different user needs and preferences in the realm of interactive computing and data science workflows.