Similar to how a painter uses a color palette to make a painting, a Bokeh palette is a collection of colors for color mapping in Python. Bokeh provides 5 different Bokeh Palettes for plotting in Python where each color palette has its own set of colors:
- Matplotlib Bokeh Palette
- D3 Bokeh Palette
- Brewer Bokeh Palette
- Color-Deficient Usability Bokeh Palette
- Large Bokeh Palette
Let us go over some of these color palettes one-by-one in this article and learn how to use the palettes.
1. Matplotlib Bokeh Palette
Bokeh includes the Matplotlib palettes called Magma, Inferno, Plasma, Viridis, and Cividis. Here is a visual aid to understand which palette provides which color map:
Using the Matplotlib Bokeh Palette is extremely simple and you only have to specify the color
parameter in your plots as shown below:
# Importing Bokeh plotting and palettes modules from bokeh.plotting import figure, output_file, show from bokeh.palettes import Magma, Inferno, Plasma, Viridis, Cividis # File to save the model output_file("output.html") # Instantiating the figure object graph = figure(title = "Bokeh Palettes") # Demonstrating the Magma palette graph.vbar(x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], top = [9] * 11, bottom = [8] * 11, width = 1, color = Magma[11]) # Demonstrating the Inferno palette graph.vbar(x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], top = [7] * 11, bottom = [6] * 11, width = 1, color = Inferno[11]) # Demonstrating the Plasma palette graph.vbar(x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], top = [5] * 11, bottom = [4] * 11, width = 1, color = Plasma[11]) # Demonstrating the Viridis palette graph.vbar(x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], top = [3] * 11, bottom = [2] * 11, width = 1, color = Viridis[11]) # Demonstrating the Cividis palette graph.vbar(x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], top = [1] * 11, width = 1, color = Cividis[11]) # Showing the model show(graph)
D3 Bokeh Palette
Bokeh includes the categorical palettes from D3, which are shown below:
Using the D3 Bokeh Palette is extremely simple and you only have to specify the color
parameter in your plots as shown below:
# Importing Bokeh plotting and palettes modules from bokeh.plotting import figure, output_file, show from bokeh.palettes import Category10, Category20, Category20b, Category20c # File to save the model output_file("output.html") # Instantiating the figure object graph = figure(title = "Bokeh Palettes") # Demonstrating the Category10 palette graph.vbar(x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], top = [9] * 10, bottom = [8] * 10, width = 1, color = Category10[10]) # Demonstrating the Category20 palette graph.vbar(x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], top = [7] * 10, bottom = [6] * 10, width = 1, color = Category20[10]) # Demonstrating the Category20b palette graph.vbar(x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], top = [5] * 10, bottom = [4] * 10, width = 1, color = Category20b[10]) # Demonstrating the Category20c palette graph.vbar(x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], top = [3] * 10, bottom = [2] * 10, width = 1, color = Category20c[10]) # Showing the model show(graph)
To learn about the rest of the color palette, you can refer to the official Bokeh.Palette documentation. The implementation method is same as displayed for the two palettes above.
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