This content originally appeared on DEV Community and was authored by Gabriela Luzkalid Gutierrez Mamani
Modern data visualization tools like Streamlit, Dash, and Bokeh make it easy to build interactive dashboards and reports. These tools are Python-based, open-source, and can be deployed to cloud services for sharing insights.
Streamlit
Streamlit is a fast way to build and share data apps. It uses simple Python scripts and is ideal for quick dashboards.
Demo Code: See streamlit_demo/app.py for a basic bar chart dashboard.
Cloud Deployment: Streamlit apps can be deployed to Streamlit Cloud or platforms like Heroku.
Dash
Dash (by Plotly) is a powerful framework for analytical web apps. It supports complex layouts and interactive components.
Demo Code: See dash_demo/app.py for a dashboard using Dash and Plotly.
Cloud Deployment: Dash apps can be deployed to Dash Enterprise or Heroku.
Bokeh
Bokeh creates interactive visualizations for web browsers. It is flexible and integrates with other web frameworks.
Demo Code: See bokeh_demo/app.py for a simple Bokeh dashboard.
Cloud Deployment: Bokeh apps can be deployed to Bokeh Server or cloud platforms.
Automation & Version Control
All demo code is organized in folders and can be versioned with Git or other VCS. For CI/CD, use GitHub Actions or similar tools to automate deployment to the cloud.
Example Automation (GitHub Actions)
# .github/workflows/deploy.yml
name: Deploy Dashboards
on: [push]
jobs:
deploy:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: '3.9'
- name: Install dependencies
run: |
pip install streamlit dash bokeh
- name: Run Streamlit app
run: streamlit run streamlit_demo/app.py &
- name: Run Dash app
run: python dash_demo/app.py &
- name: Run Bokeh app
run: python bokeh_demo/app.py &
Github Link: [https://github.com/LuzkalidGM/IN_Art01Other-Visualization-Tools]
This content originally appeared on DEV Community and was authored by Gabriela Luzkalid Gutierrez Mamani