Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets. Bokeh can help anyone who would like to quickly and easily make interactive plots, dashboards, and data applications.
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It works in Python close to all the PyData tools you are already familiar with. Plots, dashboards, and apps can be published in web pages or Jupyter notebooks.

Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets. Bokeh can help anyone who would like to quickly and easily make interactive plots, dashboards, and data applications.

Bokeh -  Interactive Data Visualization in the browser, from Python

Altair is a declarative statistical visualization library for Python, based on <a href="http://vega.github.io/vega" target="_blank">Vega</a> and <a href="http://vega.github.io/vega-lite" target="_blank">Vega-Lite</a>. Altair’s API is simple, friendly and consistent and built on top of the powerful Vega-Lite visualization grammar. This elegant simplicity produces beautiful and effective visualizations with a minimal amount of code. It offers a powerful and concise visualization grammar that enables you to build a wide range of statistical visualizations quickly.

Altair is a declarative statistical visualization library for Python, based on Vega and Vega-Lite. Altair’s API is simple, friendly and consistent and built on top of the powerful Vega-Lite visualization grammar. This elegant simplicity produces beautiful and effective visualizations with a minimal amount of code. It offers a powerful and concise visualization grammar that enables you to build a wide range of statistical visualizations quickly.

Altair - Declarative statistical visualization library for Python

Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.Online documentation is available at seaborn.pydata.org. Installation requires numpy, scipy, pandas, and matplotlib. Some functions will optionally use statsmodels if it is installed.

Seaborn - Statistical data visualization using matplotlib

napari is a fast, interactive, multi-dimensional image viewer for Python. It’s designed for browsing, annotating, and analyzing large multi-dimensional images. It’s built on top of Qt (for the GUI), vispy (for performant GPU-based rendering), and the scientific Python stack (numpy, scipy). <BR><BR>
napari supports six main different layer types, Image, Labels, Points, Vectors, Shapes, and Surface, each corresponding to a different data type, visualization, and interactivity. You can add multiple layers of different types into the viewer and then start working with them, adjusting their properties.

napari is a fast, interactive, multi-dimensional image viewer for Python. It’s designed for browsing, annotating, and analyzing large multi-dimensional images. It’s built on top of Qt (for the GUI), vispy (for performant GPU-based rendering), and the scientific Python stack (numpy, scipy).

napari - a fast, interactive, multi-dimensional image viewer for python 

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Bokeh - Interactive Data Visualization in the browser, from Python

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Seaborn - Statistical data visualization using matplotlib

napari - a fast, interactive, multi-dimensional image viewer for python

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Bokeh - Interactive Data Visualization in the browser, from Python

Altair - Declarative statistical visualization library for Python

Seaborn - Statistical data visualization using matplotlib

napari - a fast, interactive, multi-dimensional image viewer for python

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