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Category: Visualization

Visualizing ML Model Outcomes with Matplotlib

Published 2025-08-19 by Kevin Feasel

Matthew Mayo shares a few tips:

Visualizing model performance is an essential piece of the machine learning workflow puzzle. While many practitioners can create basic plots, elevating these from simple charts to insightful, elevated visualizations that can help easily tell the story of your machine leanring model’s interpretations and predictions is a skill that sets great professionals apart. The Matplotlib library, the foundational plotting tool in the scientific and computational Python ecosystem, is packed with features that can help you achieve this.

This tutorial provides 7 practical Matplotlib tricks that will help you better understand, evaluate, and present your machine learning models. We’ll move beyond the default settings to create visualizations that are not only aesthetically pleasing but also rich in information. These techniques are designed to integrate smoothly into your workflow with libraries like NumPy and Scikit-learn.

Click through for those tips.

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Storytelling with Time Series Scatter Charts in Power BI

Published 2025-08-04 by Kevin Feasel

Reza Rad takes us through data changes:

Column or Bar chart can be easily used for showing a single measure’s insight across a category. Mixed charts such as Line and Column chart can be used for showing two measure and comparing their values across a set of categories. However there are some charts that can be used to show values of three measures, such as Scatter Chart. Scatter chart not only shows values of three measure across different categories, it also has a special Play axis that helps you to tell the story behind the data. In this post you’ll learn how easy is to visualize something with Scatter chart and tell a story with that. If you like to learn more about Power BI, read Power BI online book; from Rookie to Rock Star.

Read on for the blog post as well as a video version.

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Animated Maps in R with gganimate

Published 2025-08-01 by Kevin Feasel

Osheen MacOscar looks at a new version of an old package:

In this blog post, we are going to use data from the {gapminder} R package, along with global spatial boundaries from ‘opendatasoft’. We are going to plot the life expectancy of each country in the Americas and animate it to see the changes from 1957 to 2007.

The {gapminder} package we are using is from the Gapminder foundation, an independent educational non-profit fighting global misconceptions. The cover issues like global warming, plastic in the oceans and life satisfaction.

There are several common gotchas that Osheen takes us through before building an animated map of the western hemisphere.

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Building a Snowflake Dashboard that Uses Filters

Published 2025-07-30 by Kevin Feasel

Kevin Wilkie does a bit of filtering:

Snowflake Dashboards can do a lot more than just show pretty numbers. Today, let’s focus on something that every data pro eventually has to deal with—filters that make navigating your dashboards less painful, especially when it comes to everyone’s favorite task: AUDITING.

Ah yes, auditing—because nothing says “data dream job” like tracing permissions. Whether it’s quarterly compliance checks or a sudden request from an overly curious auditor, somebody, at some point, will ask, “Who has access to what in Snowflake?” So let’s make that answer easy to deliver.

Click through for the process, using the development of a permissions auditing dashboard as the example.

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Building a Pareto Chart in Power BI

Published 2025-07-10 by Kevin Feasel

Boniface Muchendu creates a Pareto chart:

Creating a Pareto chart in Power BI is a powerful way to visualize the 80/20 rule in action. This type of chart helps you quickly identify the top contributors to your business metrics—whether you’re analyzing sales, categories, or customer segments. In this guide, you’ll learn how to build a dynamic Pareto chart using DAX, customize it, and apply it across different data dimensions.

Read on for the instructions.

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Bioconductor in the Wake of ggplot2 4.0.0

Published 2025-07-07 by Kevin Feasel

Maria Doyle lays it out:

A major update to ggplot2 (version 4.0.0) is expected around mid-to-late July 2025. It brings a significant internal change, replacing most of the S3 backend with the newer S7 object system. While this improves long-term maintainability and extensibility, it may break Bioconductor packages that depend on ggplot2, especially those that customise how plots are built or styled. Packages that use ggplot2 for typical plotting tasks, such as creating plots with ggplot() and geom_*(), are unlikely to be affected.

Click through for notes, tips on what to do, and whether the code you’re using will break with ggplot2 4.0.0. H/T R-Bloggers.

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Drop Shadows and Power BI

Published 2025-07-07 by Kevin Feasel

Elena Drakulevska has some thoughts on drop shadows:

I get why people add them. Shadows might feel like a design upgrade. A quick way to make your visuals pop or feel more “finished.”

But here’s the thing: just like rounded corners, drop shadows are easy to overdo—and they’re not actually helping. Not with clarity. Not with accessibility. Definitely not with UX.

Click through for Elena’s full thoughts. I’m generally against drop shadows. They draw visual attention without providing the report viewer any value. That’s chartjunk.

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Creating a Power BI Date Picker without Custom Visuals

Published 2025-06-23 by Kevin Feasel

Boniface Muchendu lets users pick the date:

Many users need the ability to select a single date not a range to filter their entire report. While Power BI’s default slicer shows a long list of dates or uses relative filters like “Today” or “Yesterday,” these options can be limiting.

Additionally, relying on the filter pane often isn’t ideal for dashboards meant for end users, especially when the pane is hidden or locked. An on-screen date picker provides a more intuitive and controlled experience.

Read on to see how.

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The Role of Padding in Power BI Reports

Published 2025-06-12 by Kevin Feasel

Elena Drakulevska explains why padding is so important between visuals in Power BI reports:

Now that we’ve all learned to love rounded corners, let’s talk about another quiet champion of good design: padding.

You know, that tiny bit of space inside your visuals that keeps content from being awkwardly pressed right up against the border, with no room to breathe. Yeah. That.

The ideal here is to have densely informative visuals that have sufficient padding to make it easy for a viewer to move between them.

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Self-Intersecting Quadrilaterals in R

Published 2025-05-27 by Kevin Feasel

Jerry Tuttle talks shapes:

A quadrilateral is a polygon having four sides, four angles, and four vertices. A polygon means that the figure is a closed shape, meaning the last line segment connects back to the first one, effectively enclosing an area.

We usually think of quadrilaterals as squares, rectangles, parallelograms, trapezoids, rhombuses, or kites. (I was impressed that my four year-old granddaughter knew the last one, although she called it a diamond!) It could also be irregularly shaped with no name.

However, a polygon may intersect itself. 

Click through for a demonstration of a self-intersecting quadrilateral, including the R code you can use to try it out yourself.

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