This week we are building a nullity matrix, a tabular visual that helps us to easily see which rows and columns have null data. There is a library for this in Python, but you need to "roll your own" in Power BI.
I found the Python library and liked the idea of visualizing null or missing data, but I disliked the arrangement of data in the visual. Where the missingno library puts column names along the top and row numbers on the rows axis, I transposed this in my version. There is a slight mental adjustment required since source rows have become columns in the visualization. But I think it makes the visual easier to read as column names don't have a weird orientation to make them fit, and you can fit more source columns. You'll also notice I added an "explainer" to help consumers understand what they are seeing.
So now it's your turn to make a nullity matrix. You can use any visual you'd like to make it. I chose to use Deneb because it was pretty quick and did not require a lot of DAX measures. You could likely do this in a core visual, perhaps with the use of conditional formatting or SVGs. Let me know which orientation you prefer: columns names on the x-axis or y-axis.
For more info, see https://workout-wednesday.com/pbi-2025-w14/ .
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