Installation details

This section provides more detailed information about installing Bokeh. This includes details about Bokeh’s prerequisites as well as Bokeh’s required and optional dependencies.

Supported platforms

Bokeh is officially supported (and continuously tested) on CPython versions 3.7 and later. It’s possible that Bokeh does work on other versions of Python, but no guarantees or support are provided.

Installing with conda or pip

The easiest way to install Bokeh is to use conda. Conda is part of the Anaconda Python Distribution, which is designed with scientific and data analysis applications like Bokeh in mind.

If you use Anaconda on your system, installing with conda is the recommended method. Otherwise, use pip.

Installing with conda

Make sure you have either Anaconda or Miniconda installed. Use this command to install Bokeh:

conda install bokeh

Alternatively, if you want to make sure you always have the most recent version of Bokeh after each new release, install from the Bokeh channel directly:

conda install -c bokeh bokeh

Installing with pip

Use this command to install Bokeh:

pip install bokeh

Note

On some systems, pip displays an error message about the wheel package when installing tornado. This is a known issue, you can usually ignore the error.

Checking your installation

To verify whether the installation was successful, use this command:

bokeh info

You should see, among other things, a line with information on the installed version of Bokeh.

If you receive an error instead, try searching for more information by using the “bokeh” tag on Stack Overflow or asking a question in the Bokeh Discourse community.

Tip

Once you have Bokeh installed, build your first visualization by following the first steps guides.

Check the user guide for a comprehensive overview of all the things you can do with Bokeh.

Installing for development

If you want to install a development version of Bokeh to contribute to the project, please see the Setting up a development environment instructions in the contributor guide.

Installing required dependencies

For basic usage, Bokeh requires the following libraries:

  • Jinja2 >=2.9
  • numpy >=1.11.3
  • packaging >=16.8
  • pillow >=7.1.0
  • PyYAML >=3.10
  • tornado >=5.1
  • typing_extensions >=3.10.0

All those packages are automatically installed if you use conda or pip.

Installing optional dependencies

In addition to the required dependencies, some additional packages are necessary for certain optional features:

Jupyter

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

NodeJS

Necessary for Extending Bokeh or for defining CustomJS implementations in TypeScript.

NetworkX

Necessary to use the from_networkx() function to generate Bokeh graph renderers directly from NetworkX data.

pandas

Necessary for the hexbin() function. Additionally, having pandas installed makes some aspects of Bokeh simpler to use. For example, glyph functions are able to automatically convert pandas DataFrames to Bokeh data sources.

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 for including Bokeh plots in Sphinx documentation.

Installing sample data

Optionally, Bokeh can download and install a collection of sample data. This includes a variety of freely available data tables and databases that you can use with Bokeh. Because this sample data is rather large, it is not included in Bokeh’s installation packages.

In Bokeh’s GitHub repository, you can find a number of examples. Those examples also use this sample data. See bokeh.sampledata for more information on the data sets included in Bokeh’s sample data.

After installing Bokeh, you can automatically download and install the sample data with this command:

bokeh sampledata

Alternatively, you can download and install the sample data from within your Python code:

import bokeh.sampledata
bokeh.sampledata.download()

If you want to change the location where Bokeh stores the sample data, check the bokeh.sampledata reference for details.

Installing standalone BokehJS

BokehJS is Bokeh’s client-side runtime library. You can also use BokehJS as a standalone JavaScript library. To do so, download the code from Bokeh’s content delivery network (CDN) at cdn.bokeh.org. The CDN uses the following naming scheme:

# Javascript files
https://cdn.bokeh.org/bokeh/release/bokeh-x.y.z.min.js
https://cdn.bokeh.org/bokeh/release/bokeh-widgets-x.y.z.min.js
https://cdn.bokeh.org/bokeh/release/bokeh-tables-x.y.z.min.js
https://cdn.bokeh.org/bokeh/release/bokeh-api-x.y.z.min.js
https://cdn.bokeh.org/bokeh/release/bokeh-gl-x.y.z.min.js
https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-x.y.z.min.js

There are additional components to BokehJS that are necessary only for specific use cases:

  • The "-widgets" files are only necessary if you are using any of the widgets built into Bokeh in bokeh.models.widgets.

  • The "-tables" files are only necessary if you are using Bokeh data tables.

  • The "bokeh-api" files are required to use the BokehJS API and must be loaded after the core BokehJS library.

For example, the links for version 2.0.1 are:

Note

You should always set crossorigin="anonymous" on script tags that load BokehJS from CDN.

First steps First steps 1: Creating a line chart