Building dashboards with drag-and-drop tools like Tableau is a skill set that every data analyst must have, but relying solely on quick-and-easy is a costly mistake.

These enterprise-scale platforms require lots of enterprise-scale infrastructure and support. These systems are expensive. And it's basically impossible to see what's going on "under the hood" because the code isn't in a version control system like GitHub.

That's where Python comes to the rescue.

Most data analysts don't realize that Python is a great way to build BI dashboards.

And all of the code is available for review and version control. Take a look at these 4 Python data libraries for your next dashboard project.

⚡ Plotly Dash

Plotly's Python graphing library makes interactive, high-quality graphs and charts. You can use Plotly to make all kinds of data visualizations in a dashboard and it's 100% free and open source.

Learn more: https://plotly.com/python/

📈 Panel

Panel is a high-level app and dashboarding solution for Python. You can create custom interactive web apps and dashboards by connecting user-defined widgets to plots, images, tables, or text.

Learn more: https://panel.holoviz.org/

🔮 Voila

Voilà allows you to convert a Jupyter Notebook into an interactive dashboard that allows you to share your work with others.

Learn more: https://voila.readthedocs.io/

👑 Streamlit

Streamlit turns data scripts into shareable web apps in minutes. All in pure Python. No front‑end experience is required.

Learn more: https://streamlit.io/

Drag-and-drop might be quick-and-easy, but there are other options.

Consider adding Python to your toolbox for your next dashboard project!