How to use Color Palettes in Python-Bokeh?

Bokeh is a Python interactive data visualization. It renders its plots using HTML and JavaScript. It targets modern web browsers for presentation providing elegant, concise construction of novel graphics with high-performance interactivity. Bokeh provides us with multiple color palettes in the bokeh.palettes module. Let us see how to use these color palettes in Bokeh.

A palette is a simple plain Python list of (hex) RGB color strings. For example, the blues8 palette has the colors : ('#084594', '#2171b5', '#4292c6', '#6baed6', '#9ecae1', '#c6dbef', '#deebf7', '#f7fbff').

There are 5 types of built-in color palettes in Bokeh :

1. Matplotlib Palettes
2. D3 Palettes
3. Brewer Palettes
4. Color-Deficient Usability Palette
5. Large Palettes

Matplotlib Palettes

Bokeh provides us with Matplotlib color palettes. There are 5 types of Matplotlib color palettes :

• Magma
• Inferno
• Plasma
• Viridis
• Cividis

Each type of color palette has 10 different versions of the palette with varying number of colors, which are 3, 4, 5, 6, 7, 8, 9, 10, 11 and 256.

Example : We will be demonstrating the Matplotlib palettes by plotting multiple vertical bars using the vbar() function.

# importing the 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("gfg.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])

# displaying the model
show(graph)

Output :

D3 Palettes

Bokeh provides us with D3 categorical color palettes. There are 4 types of D3 color palettes available :

• Category10
• Category20
• Category20b
• Category20c

Example : We will be demonstrating the D3 palettes by plotting multiple vertical bars using the vbar() function.

# importing the modules
from bokeh.plotting import figure, output_file, show
from bokeh.palettes import Category10, Category20, Category20b, Category20c

# file to save the model
output_file("gfg.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])

# displaying the model
show(graph)

Output :

Brewer Palettes

Bokeh provides us with ColorBrewer palettes. There are 35 types of ColorBrewer palettes available :

• Accent
• Blues
• BrBG
• BuGn
• BuPu
• Dark2
• GnBu
• Greens
• Greys
• OrRd
• Oranges
• PRGn
• Paired
• Pastel1
• Pastel2
• PiYG
• PuBu
• PuBuGn
• PuOr
• PuRd
• Purples
• RdBu
• RdGy
• RdPu
• RdYlBu
• RdYlGn
• Reds
• Set1
• Set2
• Set3
• Spectral
• YlGn
• YlGnBu
• YlOrBr
• YlOrRd

Example : We will be demonstrating the ColorBrewer palettes by plotting multiple vertical bars using the vbar() function.

# importing the modules
from bokeh.plotting import figure, output_file, show
from bokeh.palettes import BrBG, PiYG, RdGy, RdYlGn, YlGnBu

# file to save the model
output_file("gfg.html")

# instantiating the figure object
graph = figure(title = "Bokeh Palettes")

# demonstrating the BrBG palette
graph.vbar(x = [1, 2, 3, 4, 5, 6, 7, 8, 9],
           top = [9] * 9,
           bottom = [8] * 9,
           width = 1,
           color = BrBG[9])

# demonstrating the PiYG palette
graph.vbar(x = [1, 2, 3, 4, 5, 6, 7, 8, 9],
           top = [7] * 9,
           bottom = [6] * 9,
           width = 1,
           color = PiYG[9])

# demonstrating the RdGy palette
graph.vbar(x = [1, 2, 3, 4, 5, 6, 7, 8, 9],
           top = [5] * 9,
           bottom = [4] * 9,
           width = 1,
           color = RdGy[9])

# demonstrating the RdYlGn palette
graph.vbar(x = [1, 2, 3, 4, 5, 6, 7, 8, 9],
           top = [3] * 9,
           bottom = [2] * 9,
           width = 1,
           color = RdYlGn[9])

# demonstrating the YlGnBu palette
graph.vbar(x = [1, 2, 3, 4, 5, 6, 7, 8, 9],
           top = [1] * 9,
           width = 1,
           color = YlGnBu[9])

# displaying the model
show(graph)

Output :

Usability Palettes

Bokeh provides us with a palette that is useful for people with color deficiency or color blindness.

Example : We will be demonstrating the usability palette by plotting multiple vertical bars using the vbar() function.

# importing the modules
from bokeh.plotting import figure, output_file, show
from bokeh.palettes import Colorblind

# file to save the model
output_file("gfg.html")

# instantiating the figure object
graph = figure(title = "Bokeh Palettes")

# demonstrating the Colorblind palette
graph.vbar(x = [1, 2, 3, 4, 5, 6, 7, 8],
           top = [1] * 8,
           width = 1,
           color = Colorblind[8])

# displaying the model
show(graph)

Output :

Large Palettes

The color palettes discussed above might be small for some applications. Bokeh provides us with large palettes that have 256 colors each. There are 7 large palettes :

• Greys256
• Inferno256
• Magma256
• Plasma256
• Viridis256
• Cividis256
• Turbo256

Example : We will be demonstrating the large palettes by plotting multiple vertical bars using the vbar() function.

# importing the modules
from bokeh.plotting import figure, output_file, show
from bokeh.palettes import Greys256, Inferno256, Magma256, Plasma256
from bokeh.palettes import Viridis256, Cividis256, Turbo256

# file to save the model
output_file("gfg.html")

# instantiating the figure object
graph = figure(title = "Bokeh Palettes")

# demonstrating the Greys256 palette
graph.vbar(x = [i for i in range(256)],
           top = [20] * 256,
           bottom = [18] * 256,
           width = 1,
           color = Greys256)

# demonstrating the Inferno256 palette
graph.vbar(x = [i for i in range(256)],
           top = [17] * 256,
           bottom = [15] * 256,
           width = 1,
           color = Inferno256)

# demonstrating the Magma256 palette
graph.vbar(x = [i for i in range(256)],
           top = [14] * 256,
           bottom = [12] * 256,
           width = 1,
           color = Magma256)

# demonstrating the Plasma256 palette
graph.vbar(x = [i for i in range(256)],
           top = [11] * 256,
           bottom = [9] * 256,
           width = 1,
           color = Plasma256)

# demonstrating the Viridis256 palette
graph.vbar(x = [i for i in range(256)],
           top = [8] * 256,
           bottom = [6] * 256,
           width = 1,
           color = Viridis256)

# demonstrating the Cividis256 palette
graph.vbar(x = [i for i in range(256)],
           top = [5] * 256,
           bottom = [3] * 256,
           width = 1,
           color = Cividis256)

# demonstrating the Turbo256 palette
graph.vbar(x = [i for i in range(256)],
           top = [2] * 256,
           bottom = [0] * 256,
           width = 1,
           color = Turbo256)

# displaying the model
show(graph)

Output :