Streamlit is an open-source Python library that makes it easy to create and share beautiful, custom web apps for machine learning and data science. Build an app in a few lines of code with magically simple API. Adding a widget is the same as declaring a variable. No need to write a backend, define routes etc. Effortlessly share, manage, and collaborate on your apps directly from Streamlit. Apps are deployed directly from Github repo.

Streamlit - The fastest way to build data apps in Python

The facets project contains two visualizations for understanding and analyzing machine learning datasets: Facets Overview and Facets Dive. The visualizations are implemented as Polymer web components, backed by Typescript code and can be easily embedded into Jupyter notebooks or webpages.
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It produces a visual feature-by-feature statistical analysis, and can also be used to compare statistics across two or more data sets. The tool can process both numeric and string features, including multiple instances of a number or string per feature.

The facets project contains two visualizations for understanding and analyzing machine learning datasets: Facets Overview and Facets Dive. The visualizations are implemented as Polymer web components, backed by Typescript code and can be easily embedded into Jupyter notebooks or webpages.

Facets - Visualizations for machine learning datasets

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Streamlit - The fastest way to build data apps in Python

Facets - Visualizations for machine learning datasets

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Streamlit - The fastest way to build data apps in Python

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