Jupyter vs Streamlit: What are the differences?

Introduction

Markdown is a lightweight markup language used to format plain text into easily readable and shareable content on websites. It allows for simple formatting, such as headers, lists, and links, and is commonly used in documentation or online forums.

Streamlit and Jupyter are both tools used by data scientists and developers to create interactive and shareable data applications. However, there are some key differences between the two.

1. Deployment: Streamlit is designed for easy deployment of data applications in production. It provides a framework for creating and sharing interactive web apps quickly, making it suitable for real-time visualization and reporting. On the other hand, Jupyter is primarily used for prototyping and developing code. While it does support interactive widgets and dashboards, it requires additional configurations and dependencies to deploy applications outside of the Jupyter notebook environment.

2. Frontend Framework: Streamlit uses a declarative syntax, where the user defines the components and streamlit takes care of the rest. It automatically handles the rendering and updating of components, making it easy to create reactive applications without having to write additional code. In contrast, Jupyter uses a combination of markdown cells and code cells, making it a more flexible but less streamlined process for creating user interfaces.

3. Collaboration: Jupyter notebooks are designed for collaborative work and allow multiple users to edit and run code in the same notebook simultaneously. It provides features like version control and the ability to leave comments on specific cells, making it suitable for team collaboration. Streamlit, on the other hand, is focused on individual development and deployment of applications. While it does have sharing capabilities, it lacks the collaborative features and version control provided by Jupyter.

4. Code Execution: Jupyter allows for the execution of code cells in any order, making it easy to prototype and experiment with code. It also provides interactive widgets and the ability to visualize data within the notebook itself. Streamlit, on the other hand, executes the entire script from top to bottom, making it more suitable for linear or step-by-step workflows. It does not currently support code execution outside of the script.

5. Learning Curve: Streamlit has a minimalistic and streamlined API, making it easy to learn and use for developers with basic Python knowledge. It provides a simplified way to create interactive apps without the need for extensive web development skills. Jupyter, on the other hand, has a steeper learning curve due to its flexibility and the need to understand the notebook interface, markdown syntax, and interactive widgets.

6. Community and Ecosystem: Jupyter has a large and active community, with a wide range of extensions, libraries, and plugins available. It is widely used in academia and industry, and has strong integration with popular data science libraries such as Pandas, NumPy, and Matplotlib. Streamlit, being a newer tool, is still growing its community and ecosystem. While it does have some integrations with popular libraries, it may not have the same level of support or maturity as Jupyter.

In summary, Streamlit is a tool focused on easy deployment and creation of interactive web applications, while Jupyter is more versatile, enabling prototyping, collaboration, and a wider range of application types. Streamlit has a simpler API and execution model, but Jupyter offers more flexibility, a larger ecosystem, and better collaboration features.