Deploying Bokeh Dashboard with Python on VPS: A Step-by-Step Guide

Bokeh

Bokeh is an open-source Python library for creating interactive, web-based visualizations and dashboards. It provides high-performance statistical and scientific computing capabilities, making it particularly useful for data analysis and exploration.

• Interactive Plots: Bokeh allows you to create highly customizable and interactive plots, such as line plots, scatter plots, bar charts, and more.

• Dashboard Applications: With Bokeh, you can build complex dashboard applications that combine multiple plots, widgets, and layouts in a single web application.

• Data Visualization: Bokeh is widely used for data visualization tasks, enabling you to explore and present data in an interactive and visually appealing manner.

• Scientific Computing: Bokeh integrates well with other Python libraries like NumPy, Pandas, and SciPy, making it a powerful tool for scientific computing and data analysis.

• Web Applications: Bokeh’s ability to create web-based visualizations makes it suitable for building web applications that require interactive data exploration and presentation.

• Code: A python code was developed for a dashboard with bokeh, using the panda and bokeh libraries.

• Here we have the compiled code and the generated graphics.

Creating a dashboard with BOKEH in python

Requirements:

• Python
• Visual Studio Code
  ### First steps
• Open visual studio code

• We create a working environment and add a .py file

• Important, to make this dashboard it is necessary to install the dependencies, for this we open a console in administrator mode and we execute

pip install bokeh

• Once installed, run the sample code to generate graphics.

As we can see we have the panda and bokeh packages imported.

Last step

• Run the project.
  To run the project we will open the console and paste the following code:

python -m bokeh serve --show dashboard.py

this will show the dashboard on a local page.

Deploy the project in the cloud

To deploy the project it is necessary to have a cloud service provider, in this case I used a debian VPS.

to install python in linux

apt update

apt install python3

install the environment

sudo apt install python3-venv
mkdir my_project
cd my_project
python3 -m venv my_env
pip install bokeh

Make my_env permanent:

nano ~/.bashrc

copy and past at the end:

source /ruta/a/my_env/bin/activate

in my case it was:

source /opt/dashboardpy/my_env/bin/activate

ctrl+o ENTER ctrl+x

with “source” you’re activating bashrc

source ~/.bashrc

Permanent configuration of the project

finally we make the created websocket permanent, that is to say, it does not close when closing putty, now we create a nohup that will always be executed:

nohup python -m bokeh serve --show dashboard.py &