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.
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.
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
Use this command to install Bokeh:
pip install bokeh
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:
You should see, among other things, a line with information on the installed version of Bokeh.
Installing for development#
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
Installing optional dependencies#
In addition to the required dependencies, some additional packages are necessary for certain optional features:
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.
Necessary for Extending Bokeh or for defining
CustomJSimplementations in TypeScript.
Necessary to use the
from_networkx()function to generate Bokeh graph renderers directly from NetworkX data.
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.
Necessary to enable detailed memory logging in the Bokeh server.
- Selenium, GeckoDriver, Firefox
Necessary for Exporting plots to PNG and SVG images.
Necessary to make use of the
bokeh.sphinxextSphinx 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.
After installing Bokeh, you can automatically download and install the sample data with this command:
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
delivery network (CDN) at
cdn.bokeh.org. The CDN uses the following naming
There are additional components to BokehJS that are necessary only for specific use cases:
"-widgets"files are only necessary if you are using any of the widgets built into Bokeh in
"-tables"files are only necessary if you are using Bokeh data tables.
"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
You should always set
crossorigin="anonymous" on script tags that load
BokehJS from CDN.