The Viridis Color Palette: A Good Choice in Data Visualization

Among the many tools available to optimize the clarity and impact of visualizations, the Viridis color palette has emerged as a go-to choice for data analysts in recent years. It’s important to remember that poor visualization can hamper data understanding and even steer a decision in the wrong direction.

This article explores why and how the Viridis palette can be used in BI projects.

Initially developed for the Python package Matplotlib , the Viridis color palette was later integrated into R and other visualization tools. It was designed to meet specific needs in terms of data accessibility and readability.

It thus replaces the Parula color palette, created for MATLAB, an intellectual property of Mathworks.

One of the most remarkable features of Viridis is its compatibility with various forms of color blindness. Unlike many traditional palettes, Viridis remains uniform in a perceptible way and is easily distinguished by people with visual impairments.

Viridis maintains perceptual uniformity, meaning that color variations are consistent and not misleading, ensuring proper data interpretation. This is crucial to avoid visual bias in data analysis. Viridis colors also allow for printing in black and white while maintaining good distinction between different hues.

The Viridis palette includes several variants such as:

• Magma,
• Plasma
• Inferno
• Cividis
• Mako
• Rocket,
• and Turbo

Each one is adapted to different visualization needs. This diversity allows you to choose the most appropriate palette depending on the type of data and the message to be conveyed.

For business intelligence analysts, integrating the Viridis palette into visualizations can transform the way data is perceived and interpreted. Here are some tips for optimizing its use :

Select the Viridis variant that best suits your data. For example, Plasma may be better suited for visualizations requiring higher contrasts, while Cividis is ideal for users with color blindness.

Viridis can be used for data visualizations where the data is continuous. Its ability to represent a wide range of values consistently makes it an excellent choice for heat maps and density plots.

The Viridis palette is easily integrated into popular visualization tools like ggplot2 in R, where it can be applied via functions, or in Power BI, where it may be possible to fetch the palette from the web.

The Viridis color palette is a significant advancement in data visualization. Designed to be accessible, consistent, and flexible, it solves many of the problems associated with traditional color palettes. Its open and royalty-free code is also a very welcome feature.

AI tools may have supported the creation of this content.

Sources:

1. https://cran.r-project.org/web/packages/viridis/vignettes/intro-to-viridis.html
2. https://jmsallan.netlify.app/blog/the-viridis-palettes/
3. https://sjmgarnier.github.io/viridis/
4. https://waldyrious.net/viridis-palette-generator/
5. https://www.thinkingondata.com/something-about-viridis-library/
6. A Better Default Colormap for Matplotlib | SciPy 2015 | Nathaniel Smith and Stéfan van der Walt (youtube.com)