📝 Choices for Figures 🎨

📝 Choices for Figures 🎨#

  • Many choices are necessary when visualizing data. Here, we’ll discuss visualization which uses color scale to represent an additional dimension of data.

Types of Graphs#

  • Data to Viz provides a helpful decision tree for selecting the appropriate visualization, along with examples of each kind of plot, and tips on how to use them appropriately.

Choosing Colors#

Why do colormaps matter?#

  • The audiences interpretation of the colormap represents a dimension of the data.

  • Use of an inappropriate colormap is like having a non-linear y-axis!

Perceptually uniform colormaps [Viridis]#

  • Python has packages, such as palettable for finding excellent color maps for color scales.

  • Colorbrewer has an interactive interface for choosing the appropriate color map, which can then be imported using the colorbrewer module in palettable. It provides guidance with colorblindness and gray scale printing in mind.

    • If the data are all positive or negative on a number line (e.g., linear, log), a sequential color map is appropriate.

    • If the data range from negative to positive on a number line (e.g., linear, log), a diverging color map is appropriate.

    • If the data are qualitative, only then is a color scheme with unrelated colors adjacent to one another appropriate.