Bokeh vs Streamlit: What are the differences?

Introduction

Bokeh and Streamlit are two popular tools used for creating interactive data visualizations and web applications. While both have their own strengths and use cases, there are several key differences between Bokeh and Streamlit that set them apart from each other.

1. Programming Model: The programming model of Bokeh revolves around creating plots and visualizations using a declarative API. It focuses on building complex visualizations with interactivity and embedding them into a web page or a standalone HTML file. On the other hand, Streamlit follows an imperative programming model where the data app is built as a script using Python functions. It aims to simplify the process of building simple data apps and dashboards without much complexity.

2. Flexibility and Customization: Bokeh provides a high level of flexibility and customization options for creating visually appealing and interactive plots. It offers a wide range of tools, glyphs, and styling options to control every aspect of the visualization. In contrast, Streamlit focuses on simplicity and ease of use, providing limited customization options. It offers a set of pre-built UI components that can be easily integrated into the data app.

3. Deployment and Scalability: Bokeh is designed to be deployed as a server-based application where the plots and visualizations are rendered on the server and served to the client's web browser. It supports multiple deployment options like standalone HTML files, Bokeh server, or embedding into Flask/Django applications. Streamlit, on the other hand, allows for single-click deployment directly from the script. It simplifies the deployment process by automatically converting the data app to a web app and handles the underlying infrastructure. However, Streamlit may face scalability issues when handling a large number of concurrent users compared to Bokeh's server architecture.

4. Data Interactivity and Real-time updates: Bokeh provides extensive support for data interactivity and real-time updates in the visualizations. It offers various tools like hover tooltips, zooming, panning, and brushing to explore and interact with the data. Bokeh server allows for streaming and updating data in real-time, enabling dynamic visualizations. In contrast, Streamlit lacks the extensive interactivity features of Bokeh and is more focused on building static data apps with limited real-time capabilities.

5. Backend Integration and Ecosystem: Bokeh integrates well with other Python libraries and frameworks like NumPy, Pandas, and Scikit-learn. It has a mature ecosystem with a wide range of community-contributed extensions and plugins. Streamlit, on the other hand, offers a simpler and more lightweight framework and does not have the extensive ecosystem of Bokeh. It is primarily built for quick data exploration and prototyping, rather than integration with complex backend systems.

6. Learning Curve and Documentation: Bokeh has a steeper learning curve due to its declarative API and vast customization options. It requires a good understanding of JavaScript and web development concepts. On the other hand, Streamlit has a shallower learning curve as it follows a Python-first approach, making it easier for Python developers to get started quickly. Streamlit also provides comprehensive documentation and examples to help users quickly build data apps.

In summary, Bokeh is a powerful tool for building highly interactive and customizable data visualizations, while Streamlit focuses on simplicity and ease of use for building quick data apps and dashboards with less customization and interactivity.