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This section provides complete details about Bokeh’s required andoptional dependencies as well as information about how to installBokeh in different situations. To get up and running as fast as possible,see the Installation section of theQuickstart.

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Supported Platforms¶

Bokeh is officially supported (and continuously tested) on CPython versions3.6+ only. Other Python versions or implementations may function, possiblylimited capacity, but no guarantees or support is provided.

Required Dependencies¶

For basic usage, have the following libraries installed:

Optional Dependencies¶

In addition to the required dependencies above, some additional packages arenecessary for certain optional features:

Jupyter

Bokeh can display content in classic Jupyter notebooks as well as inJupyterLab. Depending on your setup, there may be additional packages orJupyter extensions to install. See Using with Jupyter for fulldetails.

NodeJS

Necessary for Extending Bokeh or for definingCustomJS implementations in TypeScript.

NetworkX

Necessary to use the from_networkx function to generate Bokeh graphrenderers directly from NetworkX data.

Pandas

Necessary for the hexbin function. Additionally, some usage issimplified by using Pandas e.g. Pandas DataFrames will be convertedautomatically to Bokeh data sources by glyph functions.

psutil

Necessary to enable detailed memory logging in the Bokeh server.

Selenium, GeckoDriver, Firefox

Necessary for Exporting Plots to PNG and SVG images.

Sphinx

Necessary to make use of the bokeh.sphinxext Sphinx extension forincluding Bokeh plots in Sphinx documentation.

Standard Releases¶

These Bokeh dependencies are best obtained via theAnaconda Python Distribution, which was designed to include robustversions of popular libraries for the Python scientific and data analysisstacks.

If you are already an Anaconda user, you can simply run the command:

This will install the most recent published Bokeh release from theAnaconda, Inc. package repository, along with all dependencies.

Alternatively, it is possible to install from PyPI using pip:

Sample Data¶

Some of the Bokeh examples rely on sample data that is not included in theBokeh GitHub repository or released packages, due to their size. Once Bokehis installed, the sample data can be obtained by executing the followingcommand at a Bash or Windows prompt:

Alternatively, the following statements can be executed in a Pythoninterpreter:

Finally, the location that the sample data is stored can be configured.See the bokeh.sampledata reference for details.

Advanced Cases¶

In addition to the standard installation methods above, Bokeh can alsobe installed in some specialized ways for advanced usage or development.

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Source Code¶

Installing Bokeh from source requires rebuilding the BokehJS libraryfrom its TypeScript sources. Some additional toolchain support is required.Please consult the Getting Set Up section of the Developers fordetailed instructions.

BokehJS¶

If you would like to use BokehJS as a standalone JavaScript library, releasedversions of BokehJS are available for download from CDN at cdn.bokeh.org,under the following naming scheme:

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The '-widgets' files are only necessary if you are using any of the widgetsbuilt into Bokeh in bokeh.models.widgets in your documents. Similarly, the'-tables' files are only necessary if you are using Bokeh data tables inyour document. The 'bokeh-api' files are required to use the BokehJS API,and must be loaded after the core BokehJS library.

As a concrete example, the links for version 2.0.1 are:

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Note

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You should always set crossorigin=”anonymous” on script tags that loadBokehJS from CDN.