Streamlit Tips, Tricks, and Hacks for Data Scientists

The data science team at SSENSE usually builds very complex tools and dashboards. On the other hand, their maintenance was a challenge for the team. It has been more than a year since the SSENSE data science team has been using Streamlit actively. Before employing Streamlit, we were using Dash, Flask, R Shiny, etc. to build our tools and make them available to stakeholders within the company. In October 2019, we started to evaluate the potential power of Streamlit for our projects by understanding its benefits and how to integrate it into our data science infrastructure. At the end of 2019, we began some pilot projects on Streamlit instead of Flask and Dash.

After the evaluation period of Streamlit, we quickly realized that it had a lot of potentials and that it could increase development pace, and decrease the maintenance effort significantly. Besides all the cool features and being easy to work with, Streamlit does not provide the customized behaviors, events, and UI designs that you could get from other web development libraries such as Flask. And eventually, because of the same limitations, it has been much easier to develop clean apps and maintain them easily in the long term. Its uniform UI was also a positive point from my point of view. Firstly, it is clear, clean, and responsive. Secondly, all team members can build tools with uniform designs…