This article was originally published in January 2021. It has been updated to reflect recent Streamlit company news, new features, and a new demo app

Streamlit review and demo: best of the Python data app tools

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2021-06-17 17:30:08

This article was originally published in January 2021. It has been updated to reflect recent Streamlit company news, new features, and a new demo app of our own. One thing that has not changed is that The Crosstab Kite does not accept compensation of any kind from vendors, so our reviews are as unbiased as possible.

Streamlit launched in 2018 to help data scientists build data apps, emphasizing fast development with a pure Python approach. Our hands-on testing showed that it’s the real deal; if you want to build a good front-end for your Python data science project as quickly as possible, Streamlit is the clear choice. If you want complete control of layout, aesthetics, user interactions, and compute architecture, Streamlit can feel frustratingly confined.

Streamlit is a data app framework, sometimes called a data dashboarding tool. Data apps are a fantastic way for data scientists to present their results. They give the audience interactive control of visualizations, engaging the user more intuitively with the narrative. Data app frameworks are code-based, which allows for more complexity than BI dashboarding tools. In my demo, for example, I simulate data and use a Bayesian model to analyze an experiment. This kind of model-based visualization is much easier to do with code than with the SQL-plus-GUI combination used by most BI dashboarding tools.