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Showing content from https://www.geeksforgeeks.org/python/integrating-bokeh-visualizations-in-django-application/ below:

Integrating Bokeh Visualizations in Django Application

Last Updated : 23 Jul, 2025

Bokeh is an interactive visualization library that helps us to create a visual representation of data set and interact with it. You can create various types of visualizations such as bar charts, horizontal plots,time-series, etc. There are various methods to include Bokeh apps and widgets into web apps and pages.

In this tutorial, we are going to create a basic bokeh graph and embed it into our Django web app. For that, we will be importing components from bokeh.embed which returns the individual components. The function bokeh.embed.components() returns a script that contains that data for your plot with a <div> tag in which the plot view is loaded. We will look in detail at the step-by-step procedure.

Step 1: Setting up a basic Django project

For this project, we are using PyCharm IDE. PyCharm is one of the most popular IDE used for the python Scripting language.

Open PyCharm and create a new project and save it as BokehProject.
Go to the terminal and install Django using the command:

pip install django

In the same way, we will install bokeh in our project as:

pip install bokeh

Step 2: Create the Django project

Create a Django project using the following command:

django-admin startproject BokehDjango

Change the project folder using the below command:

cd BokehDjango

Run manage.py to initially migrate data changes to our project by using migrate as below

python manage.py migrate

Create a superuser using the following command to create a superuser account

python manage.py createsuperuser

Add the name, email, and password.
At this stage, the directory structure is as shown below:

Now let us run the command below command to check if Django is installed successfully.

python manage.py runserver

Navigate to the address http://127.0.0.1:8000/ and you will see something like this.

Now we create a Django application using the following command

python manage.py startapp BokehApp

The directory structure at this stage will be as shown below:

Since we have created an app we need to add it to settings. Open settings.py and add the following in installed apps:

INSTALLED_APPS = [
   'django.contrib.admin',
   'django.contrib.auth',
   'django.contrib.contenttypes',
   'django.contrib.sessions',
   'django.contrib.messages',
   'django.contrib.staticfiles',
   'BokehApp',
]

Update urls.py file and add URL patterns. Open urls.py from our project folder i.e.,BokehDjango, and add the include function in the import statement. Also, add the path to include the URLs of our new application as shown below:

Python


 from django.contrib import admin
from django.urls import path, include


urlpatterns = [
    path("admin/", admin.site.urls),
    path("", include("BokehApp.urls")),
]

Now create a new file in our app folder i.e., BokehApp, and save it as urls.py.
Open the file and add the path to route for your home page as below and also don’t forget to import path and views.

Python


 from django.urls import path
from . import views

urlpatterns = [path("", views.home, name="home")]

Next, we create the view for our home page that will render our first Bokeh Graph. Open views.py and create a new method called home() and before that we import HttpResponse..HttpResponse is most frequently used as a return object from a Django view.
As of now, we are simply displaying a welcome message as below:

Python


 from django.shortcuts import render
from django.http import HttpResponse

# Create your views here.
def home(request):
    return HttpResponse("Welcome to home page")

Let us run the server using python manage.py runserver and look at the result:

Great! So this was all about setting our Django website.

Step 3:Complete Bokeh Setup into our project:

Go to your python shell and check the version of Bokeh as:

bokeh.__version__

As shown in the image below:

Now let's create a template folder in our BokehApp directory and save it as templates. Create a new file in the templates directory and save it as base.html.
Add the following links of CSS in your base.html file in the head tag and replace the version of your bokeh at the place bokeh-x.y.z.min (underlined place x.y.z.)

HTML


 <link href=”https://cdn.pydata.org/bokeh/release/bokeh-2.3.2.min.css
   " rel=”stylesheet” type=”text/css”>
<link href=”https://cdn.pydata.org/bokeh/release/bokeh-widgets-2.3.2.min.css" 
   rel=”stylesheet” type=”text/css”>

And the JavaScript links below the ending body tag i.e., after </body> and similarly replace your bokeh version at x.y.z

HTML


 <script src="https://cdn.bokeh.org/bokeh/release/bokeh-2.3.2.min.js"></script>
<script src="https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.3.2.min.js"></script>
<script src="https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.3.2.min.js"></script>
<script src="https://cdn.bokeh.org/bokeh/release/bokeh-api-2.3.2.min.js"></script>

The base.html file looks like

HTML


 <html>
   <head>
      <link href=”https://cdn.pydata.org/bokeh/release/bokeh-2.3.2.min.css
         " rel=”stylesheet” type=”text/css”>
      <link href=”https://cdn.pydata.org/bokeh/release/bokeh-widgets-2.3.2.min.css" 
         rel=”stylesheet” type=”text/css”>
   </head>
   <body>
      <h1>Our first Bokeh Graph</h1>
      {{div| safe}}
   </body>
   <script src="https://cdn.bokeh.org/bokeh/release/bokeh-2.3.2.min.js"></script>
   <script src="https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.3.2.min.js"></script>
   <script src="https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.3.2.min.js"></script>
   <script src="https://cdn.bokeh.org/bokeh/release/bokeh-api-2.3.2.min.js"></script>
   {{script| safe}}
</html>

Let us now replace the view function home so that it renders our first graph. Add the below code which creates basic circle scatter marks on our plot:

Python


 from django.shortcuts import render
from django.http import HttpResponse
from bokeh.plotting import figure
from bokeh.embed import components

# Create your views here.

def home(request):

   #create a plot
    plot = figure(plot_width=400, plot_height=400)

   # add a circle renderer with a size, color, and alpha

   plot.circle([1, 2, 3, 4, 5], [6, 7, 2, 4, 5], size=20, color="navy", alpha=0.5)

   script, div = components(plot)

   return render(request, 'base.html', {'script': script, 'div': div})

The components method returns a script that contains the data for your plot and provides a <div>tag to display the plot view. These two elements can be inserted into the HTML text and the <script> when executed will replace the div with the plot.
The circle method is a glyph method which is a method of the figure object. Glyphs are the basic visual building blocks of Bokeh plots. This includes elements such as lines, rectangles, squares, wedges, or the circles of a scatter plot
The plot variable enables us to create a plot that holds all the various objects such as glyphs, annotations, etc. of a visualization.

So let us refresh our page after saving all the files and the output will be as shown below.

To enhancing the look of the page we are adding bootstrap to our base.html file. We have added a few of the components and the final HTML will be as shown below:

HTML


 <html>
   <head>
      <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.0.1/dist/css/bootstrap.min.css" 
         rel="stylesheet"
         integrity="sha384-+0n0xVW2eSR5OomGNYDnhzAbDsOXxcvSN1TPprVMTNDbiYZCxYbOOl7+AMvyTG2x"
         crossorigin="anonymous">
      <link href=”https://cdn.pydata.org/bokeh/release/bokeh-2.3.2.min.css
         " rel=”stylesheet” type=”text/css”>
      <link href=”https://cdn.pydata.org/bokeh/release/bokeh-widgets-2.3.2.min.css"
         rel=”stylesheet” type=”text/css”>
   </head>
   <body>
      <ul class="nav nav-tabs">
         <li class="nav-item">
            <a class="nav-link active" aria-current="page" href="#">Active</a>
         </li>
         <li class="nav-item">
            <a class="nav-link" href="#">Link</a>
         </li>
         <li class="nav-item">
            <a class="nav-link" href="#">Link</a>
         </li>
         <li class="nav-item">
            <a class="nav-link disabled" href="#" tabindex="-1" 
               aria-disabled="true">Disabled</a>
         </li>
      </ul>
      <h1 align="center">Data Visualization using Bokeh and Django</h1>
      <div class="container overflow-hidden">
         <div class="row gx-5">
            <div class="col">
               <div class="p-3 border bg-light">Bokeh is a data 
                  visualization library for Python. Unlike Matplotlib and
                  Seaborn, they are also Python packages for data visualization,
                  Bokeh renders its plots using HTML and
                  JavaScript. Hence, it proves to be extremely useful
                  for developing web based dashboards.
                  The Bokeh project is sponsored by NumFocus
                  https://numfocus.org/ NumFocus also supports PyData, an
                  educational program, involved in development of 
                  important tools such as NumPy, Pandas and more.
                  Bokeh can easily connect with these tools and
                  produce interactive plots, dashboards and data applications.
                  Features
                  Bokeh primarily converts the data source into a JSON file
                  which is used as input for BokehJS, a JavaScript library,
                  which in turn is written in TypeScript and renders the 
                  visualizations in modern browsers.
                  Some of the important features of Bokeh are as follows −
                  Flexibility
                  Bokeh is useful for common plotting requirements as
                  well as custom and complex use-cases.
                  Productivity
                  Bokeh can easily interact with other popular Pydata
                  tools such as Pandas and Jupyter notebook.
                  Interactivity
                  This is an important advantage of Bokeh over Matplotlib and 
                  Seaborn, both produce static plots. Bokeh
                  creates interactive plots that change when the user
                  interacts with them. You can give your audience a
                  wide range of options and tools for inferring and
                  looking at data from various angles so that user can
                  perform “what if” analysis.
                  Powerful
                  By adding custom JavaScript, it is possible to generate 
                  visualizations for specialised use-cases.
                  Sharable
                  Plots can be embedded in output of Flask or Django 
                  enabled web applications. They can also be rendered in
                  Jupyter notebooks.
                  Open source
                  Bokeh is an open source project. It is distributed under 
                  Berkeley Source Distribution (BSD) license. Its
                  source code is available on https://github.com/bokeh/bokeh
               </div>
            </div>
            <div class="col">
               <div class="p-3 border bg-light">
                  <h1>Simple Bokeh Graph</h1>
                  {{ div| safe}}
               </div>
            </div>
         </div>
      </div>
      <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.0.1/dist/js/bootstrap.bundle.min.js"
         integrity="sha384-gtEjrD/SeCtmISkJkNUaaKMoLD0//ElJ19smozuHV6z3Iehds+3Ulb9Bn9Plx0x4"
         crossorigin="anonymous"></script>
   </body>
   <script src="https://cdn.bokeh.org/bokeh/release/bokeh-2.3.2.min.js"></script>
   <script src="https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.3.2.min.js"></script>
   <script src="https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.3.2.min.js"></script>
   <script src="https://cdn.bokeh.org/bokeh/release/bokeh-api-2.3.2.min.js"></script>
   {{script| safe}}
</html>

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