What are the best color practices for Python visualizations?

1 Why color matters

Color is not just a cosmetic feature of your visualizations. It can also convey meaning, emotion, and contrast. Color can help you highlight important data points, group related categories, or show relationships and trends. However, color can also mislead, confuse, or distract your audience if used poorly. Therefore, you need to be intentional and consistent with your color choices and follow some basic principles of color theory and design.

2 How to choose colors

One of the first steps in creating a Python visualization is to decide on a color palette, or a set of colors that work well together. There are different types of color palettes, such as monochromatic, complementary, analogous, or diverging, depending on the purpose and mood of your visualization. You can use online tools, such as ColorBrewer or Coolors, to generate and test color palettes for your data. Alternatively, you can use predefined color palettes from Python libraries, such as Matplotlib, Seaborn, or Plotly, which offer a variety of options for different data types and formats.

3 How to apply colors

When applying a color palette to data elements such as bars, lines, points, or areas, it is important to use color sparingly and strategically. Focus on the most important or relevant data and use color to emphasize or differentiate them. Additionally, use color consistently and logically; assign the same color to the same data element across different visualizations or charts. Additionally, it is important to use color appropriately and accurately; match the color to the data type and format. For instance, use sequential colors for ordinal or quantitative data, diverging colors for bipolar or contrasting data, and categorical colors for nominal or qualitative data. Lastly, avoid using colors that imply a false or misleading association or order, such as red for good and green for bad, or rainbow colors for continuous data.

4 How to ensure accessibility

Accessibility is an important aspect of color usage, particularly for those with color vision deficiencies or impairments. To ensure your audience can easily perceive and understand your visualizations, you should consider using contrast and brightness, shapes and patterns, and labels and legends. For example, you can use online tools such as Contrast Checker or WebAIM to measure and adjust the contrast and brightness of your colors. Additionally, supplementing your colors with shapes and patterns can help distinguish data elements. Finally, providing clear and descriptive labels and legends for data elements and colors can be beneficial. Annotations or tooltips can explain the meaning or value of each color, while a colorbar or legend can show the range or scale of your colors.

5 How to code colors

The final step in creating a Python visualization is to code your colors using the syntax and parameters of your chosen library. You can specify your colors in different ways, such as using color names or abbreviations, hexadecimal codes or RGB values, colormaps or palettes. Most Python libraries have a set of predefined color names or abbreviations that you can use to refer to common colors, while hexadecimal codes are six-digit codes that start with a hash sign. RGB values are three numbers between 0 and 255 that represent the red, green, and blue components of a color. Colormaps or palettes can also be used to apply a range or a sequence of colors to your data elements. Furthermore, you can create your own custom ones. Here are some examples of how to code colors in Python using different libraries and methods.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?