Smarter Python Visuals in Power BI: 5 UX Tips for Better Insights

🎁PBIX included at the end of this article!

I get really excited when I get to develop Python visuals in Power BI. A whole world of advanced data analysis suddenly opens up to us!

There are a few different ways to use Python in Power BI:

• You can run scripts in Power Query to wrangle or transform your data

• Or you can use the Python visual to build a custom chart directly in your report

While the environment has its quirks and limitations (you can’t just throw in every Python package out there), it still gives us access to powerful plotting libraries like matplotlib, seaborn, and plotly. And let’s be real — we’re not using Python in Power BI to make bar charts. Power BI already does that just fine 😅

Instead, Python visuals give us the tools to build statistical plots like violin charts, correlation matrices, and more — the kinds of visuals that help users take one step deeper into understanding their data.

In this article, I’ll share a few tips I picked up while designing a Python-based violin chart to analyze salary distributions. The goal: show something powerful and sophisticated without overwhelming people who aren’t statisticians by trade.

Read the full article here: https://medium.com/the-bi-corner/smarter-python-visuals-in-power-bi-5-ux-tips-for-better-insights-a73c6b358e70

If you don't have Medium, here is a friend link: https://medium.com/the-bi-corner/smarter-python-visuals-in-power-bi-5-ux-tips-for-better-insights-a73c6b358e70?sk=96b38722db561c0e12aef025b646f541