Why Streamlit is my favorite tool for data science app development

Summarising the useful Python libraries for data dashboarding and my recommended resources to learn about Streamlit for data science app development

The role of data scientist is clear: To analyse the data, plot visualisation graphs and consolidate the findings into a report or publication. However, with greater interest in deeper understanding of big data and urgent need for more novel tools to gain insights from biological datasets, there is a growing interest in employing data engineers. Their roles and responsibilities can include app development, constructing pipelines, data testing and maintaining architectures such as databases and large-scale processing systems. This is unlike data scientists, who are mostly involved in data cleaning, data visualisation and big data organisation.

To build data dashboards, Javascript is often used. However, recently, there are packages that can be implemented in Python (which means that you don’t have to learn another language). These packages include Voilà, Panel, Dash and Streamlit. On my end, I have tried Voilà and Streamlit as they are both easier to implement as compared to Panel and Dash. This blog post will hence compare my experience with Voilà and Streamlit.