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## [Bokeh](https://github.com/bokeh)
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Interactive Data Visualization.
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- Bokeh intro Notebooks can be [run in Binder](https://mybinder.org/v2/gh/bokeh/bokeh-notebooks/master?filepath=tutorial%2F00%20-%20Introduction%20and%20Setup.ipynb)
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- Or found on GitHub: https://github.com/bokeh/bokeh-notebooks
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- [bokeh/bokeh](https://github.com/bokeh/bokeh) - Interactive Data Visualization in the browser, from Python
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#### User Guide
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- [Quickstart](https://docs.bokeh.org/en/latest/docs/user_guide/quickstart.html#userguide-quickstart)
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- [Using with Jupyter](https://docs.bokeh.org/en/latest/docs/user_guide/jupyter.html#userguide-jupyter)
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#### Example Use (cuxfilter)
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[rapidsai/cuxfilter](https://github.com/rapidsai/cuxfilter)
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![cuxfilter architecture](https://github.com/rapidsai/cuxfilter/raw/branch-0.18/docs/_images/RAPIDS_cuxfilter.png)
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## [HoloViz](https://github.com/holoviz)
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High-level tools to simplify visualization in Python.
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- [holoviz/datashader](https://github.com/holoviz/datashader) - Quickly and accurately render even the largest data.
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- [holoviz/holoviews](https://github.com/holoviz/holoviews) - Python library designed to make data analysis and visualization seamless and simple.
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## [Jupyter Widgets](https://github.com/jupyter-widgets)
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Interactive Widgets for the Jupyter Notebook.
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- [jupyter-widgets/ipywidgets](https://github.com/jupyter-widgets/ipywidgets) - interactive HTML widgets for Jupyter notebooks, JupyterLab and the IPython kernel.
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- Intro Notebooks can be [run in Binder](https://mybinder.org/v2/gh/jupyter-widgets/ipywidgets/master?filepath=docs%2Fsource%2Fexamples)

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