Interactive dashboards are revolutionizing the way data is explored, analyzed, and presented. In the world of data-driven decision-making, these powerful tools provide an intuitive and engaging interface for users to interact with complex data sets and gain valuable insights. Python, being one of the most popular programming languages for data analysis and visualization, offers a range of libraries and frameworks to create interactive dashboards. In this post, we will explore some of the top Python libraries for building interactive dashboards. Whether you are a data scientist, a business analyst, or a developer, these Python libraries will empower you to create compelling visualizations and drive data-driven decision-making.
Streamlit
Streamlit has garnered a lot of attention since they were purchased by Snowflake a little over a year ago, and rightly so. It occupies the space right above Jupyter Notebooks in going from code to plots, forms, and widgets relatively easily with minimal code. Compared to the other libraries on this list, the API is extremely simple and intuitive. Knowledge of React is needed to create custom components since it’s used under the hood. If you don’t want to go that route, just pick one from many built from a very active community. They also have Community Cloud where you can deploy, manage, and share your apps for free.
