Embedding Plots and Apps

Bokeh provides a variety of ways to embed plots and data into HTML documents.

Standalone HTML

Bokeh can generate standalone HTML documents using the file_html() function. This function can emit HTML from its own generic template, or a template you provide. These files contain the data for the plot inline and are completely transportable, while still providing interactive tools (pan, zoom, etc.) for your plot. Here is an example:

from bokeh.plotting import figure
from bokeh.resources import CDN
from bokeh.embed import file_html

plot = figure()
plot.circle([1,2], [3,4])

html = file_html(plot, CDN, "my plot")

The returned HTML text can be saved to a file using standard python file operations.


This is a fairly low-level, explicit way to generate an HTML file. When using the bokeh.plotting or bokeh.charts interfaces, users will typically call the function output_file() in conjunction with show() or save() instead.

You can also provide your own template and pass in custom, or additional, template variables. See the file_html() function for more details. You can see an example of this in the gapminder example plot.


It is also possible to ask Bokeh to return the individual components for a inline embedding using the components() function. This function returns a <script> that contains the data for your plot, together with an accompanying <div> tag that the plot view is loaded into. These tags can be used in HTML documents however you like:

from bokeh.plotting import figure
from bokeh.embed import components

plot = figure()
plot.circle([1,2], [3,4])

script, div = components(plot)

The returned <script> will look something like:

<script type="text/javascript">
    $(function() {
        var modelid = "fba97329-a355-499e-9252-0adc64b19d2e";
        var modeltype = "Plot";
        var elementid = "8ed68feb-d258-4953-9dfb-fb1c13326509";
        Bokeh.logger.info("Realizing plot:")
        Bokeh.logger.info(" - modeltype: Plot");
        Bokeh.logger.info(" - modelid: fba97329-a355-499e-9252-0adc64b19d2e");
        Bokeh.logger.info(" - elementid: 8ed68feb-d258-4953-9dfb-fb1c13326509");

        var all_models = [ JSON PLOT MODELS AND DATA ARE HERE ];

        var model = Bokeh.Collections(modeltype).get(modelid);
        var view = new model.default_view({
            model: model, el: '#8ed68feb-d258-4953-9dfb-fb1c13326509'
        Bokeh.index[modelid] = view

All of the data and plot objects are contained in the all_models variable (contents omitted here for brevity). The resulting <div> will look something like:

<div class="plotdiv" id="8ed68feb-d258-4953-9dfb-fb1c13326509"></div>

These two elements can be inserted or templated into your HTML text, and the script, when executed, will replace the div with the plot.

Using these components assumes that BokehJS has already been loaded, for instance either inline in the document text, or from CDN. To load BokehJS from CDN, add the following lines in your HTML text or template with the appropriate version replacing x.y.z:

    rel="stylesheet" type="text/css">
<script src="https://cdn.bokeh.org/bokeh/release/bokeh-x.y.z.min.js"></script>

For example, to use version 0.10.0:

    rel="stylesheet" type="text/css">
<script src="https://cdn.bokeh.org/bokeh/release/bokeh-0.10.0.min.js"></script>


You must provide the closing </script> tag. This is required by all browsers and the page will typically not render without it.

The components() function takes either a single Bokeh Model a list/tuple of Models, or a dictionary of keys and Models. Each returns a corresponding data structure of script and div pairs.

The following illustrates how different input types correlate to outputs:

#=> (script, plot_div)

components((plot_1, plot_2))
#=> (script, (plot_1_div, plot_2_div))

components({"Plot 1": plot_1, "Plot 2": plot_2})
#=> (script, {"Plot 1": plot_1_div, "Plot 2": plot_2_div})

Here’s an example of how you would use the multiple plot generator:

# scatter.py

from bokeh.plotting import figure
from bokeh.models import Range1d
from bokeh.embed import components

# create some data
x1 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
y1 = [0, 8, 2, 4, 6, 9, 5, 6, 25, 28, 4, 7]
x2 = [2, 5, 7, 15, 18, 19, 25, 28, 9, 10, 4]
y2 = [2, 4, 6, 9, 15, 18, 0, 8, 2, 25, 28]
x3 = [0, 1, 0, 8, 2, 4, 6, 9, 7, 8, 9]
y3 = [0, 8, 4, 6, 9, 15, 18, 19, 19, 25, 28]

# select the tools we want

# the red and blue graphs will share this data range
xr1 = Range1d(start=0, end=30)
yr1 = Range1d(start=0, end=30)

# only the green will use this data range
xr2 = Range1d(start=0, end=30)
yr2 = Range1d(start=0, end=30)

# build our figures
p1 = figure(x_range=xr1, y_range=yr1, tools=TOOLS, plot_width=300, plot_height=300)
p1.scatter(x1, y1, size=12, color="red", alpha=0.5)

p2 = figure(x_range=xr1, y_range=yr1, tools=TOOLS, plot_width=300, plot_height=300)
p2.scatter(x2, y2, size=12, color="blue", alpha=0.5)

p3 = figure(x_range=xr2, y_range=yr2, tools=TOOLS, plot_width=300, plot_height=300)
p3.scatter(x3, y3, size=12, color="green", alpha=0.5)

# plots can be a single Bokeh Model, a list/tuple, or even a dictionary
plots = {'Red': p1, 'Blue': p2, 'Green': p3}

script, div = components(plots)

Running python scatter.py will print out:

<script type="text/javascript">
    Bokeh.$(function() {
        var all_models = [ JSON PLOT MODELS AND DATA ARE HERE ]
        for (idx in plots) {
            var plot = plots[idx]
            var model = Bokeh.Collections(plot.modeltype).get(plot.modelid);
            Bokeh.logger.info('Realizing plot:')
            Bokeh.logger.info(' - modeltype: ' + plot.modeltype);
            Bokeh.logger.info(' - modelid: ' + plot.modelid);
            Bokeh.logger.info(' - elementid: ' + plot.elementid);
            var view = new model.default_view({
                model: model,
                el: plot.elementid
            Bokeh.index[plot.modelid] = view;

{'Blue': '<div class="plotdiv" id="5fb494f2-e2cb-4eb8-8ec7-11b38143ea30"></div>', 'Green': '<div class="plotdiv" id="f37808b2-e1cc-494e-a126-32f979e2a60d"></div>', 'Red': '<div class="plotdiv" id="a30e4c01-290a-4a19-9b36-c242c53cba8b"></div>'}

Then inserting the script and div elements into this boilerplate:

<!DOCTYPE html>
<html lang="en">
        <meta charset="utf-8">
        <title>Bokeh Scatter Plots</title>

        <link rel="stylesheet" href="https://cdn.bokeh.org/bokeh/release/bokeh-0.9.0.min.css" type="text/css" />
        <script type="text/javascript" src="https://cdn.bokeh.org/bokeh/release/bokeh-0.9.0.min.js"></script>

        <!-- COPY/PASTE SCRIPT HERE -->

        <!-- INSERT DIVS HERE -->

You can see an example by running:

python /bokeh/examples/embed/embed_multiple.py

IPython Notebook

Bokeh can also generate <div> tags suitable for inline display in the IPython notebook using the notebook_div() function:

from bokeh.plotting import figure
from bokeh.embed import notebook_div

plot = figure()
plot.circle([1,2], [3,4])

div = notebook_div(plot)

The returned div contains the same sort of <script> and <div> that the components() function above returns.


This is a fairly low-level, explicit way to generate an IPython notebook div. When using the bokeh.plotting or bokeh.charts interfaces, users will typically call the function output_notebook() in conjunction with show() instead.


Finally it is possible to ask Bokeh to return a <script> tag that will replace itself with a Bokeh plot, wherever happens to be located. The script will also check for BokehJS and load it, if necessary, so it is possible to embed a plot by placing this script tag alone in your document.

There are two cases:

server data

The simplest case is to use the Bokeh server to persist your plot and data. Additionally, the Bokeh server affords the opportunity of animated plots or updating plots with streaming data. The autoload_server() function accepts a plot object and a Bokeh server Session object. It returns a <script> tag that will load both your plot and data from the Bokeh server.

As a concrete example, here is some simple code using autoload_server() with a default session:

from bokeh.plotting import figure, push
from bokeh.embed import autoload_server
from bokeh.session import Session
from bokeh.document import Document

# alternative to these lines, bokeh.io.output_server(...)
document = Document()
session = Session()

plot = figure()
plot.circle([1,2], [3,4])

push(session, document)

script = autoload_server(plot, session)

The resulting <script> tag that you can use to embed the plot inside a document looks like:



To execute the code above, a Bokeh server must be running.

static data

If you do not need or want to use the Bokeh server, then the you can use the autoload_static() function. This function takes the plot object you want to display together with a resources specification and path to load a script from. It will return a self-contained <script> tag, together with some JavaScript code that contains the data for your plot. This code should be saved to the script path you provided. The <script> tag will load this separate script to realize your plot.

Here is how you might use autoload_static() with a simple plot:

from bokeh.resources import CDN
from bokeh.plotting import figure
from bokeh.embed import autoload_static

plot = figure()
plot.circle([1,2], [3,4])

js, tag = autoload_static(plot, CDN, "some/path")

The resulting <script> tag looks like:


The resulting JavaScript code should be saved to a file that can be reached on the server at “some/path”, from the document that has the plot embedded.


In both cases the <script> tag loads a <div> in place, so it must be placed under <head>.