Skip to content
Related Articles

Related Articles

Seaborn – Color Palette
  • Last Updated : 20 Jan, 2021

In this article, We are going to see seaborn color_palette(), which can be used for coloring the plot. Using the palette we can generate the point with different colors. In this below example we can see the palette can be responsible for generating the different colormap values.

Syntax: seaborn.color_palette(palette=None, n_colors=None, desat=None)

Parameters:

  • palette: Name of palette or None to return current palette. 
  • n_colors: Number of colors in the palette.
  • desat: Proportion to desaturate each color.

Returns: list of RGB tuples or matplotlib.colors.Colormap

We will classify the different ways for using color_palette() types −

  • Qualitative
  • Sequential
  • Diverging

Qualitative

A qualitative palette is used when the variable is categorical in nature, the color assigned to each group need to be distinct. Each possible value of the variable is assigned one color from a qualitative palette within a plot as shown in figure.



Example:

Python3




from matplotlib import pyplot as plt
import seaborn as sns
current_palette = sns.color_palette()
sns.palplot(current_palette)
plt.show()

Output:

Sequential

In sequential palettes color moved sequentially from a lighter to a darker. When the variable assigned to be colored is numeric or has inherently ordered values, then it can be depicted with a sequential palette as shown in figure.

Example:



Python3




from matplotlib import pyplot as plt
import seaborn as sns
current_palette = sns.color_palette()
sns.palplot(sns.color_palette("Greys"))
plt.show()

Output:

Diverging

When we work on mixed value like +ve and -ve(low and high values) then diverging palette is the best suit for visualization.

Example:

Python3




from matplotlib import pyplot as plt
import seaborn as sns
current_palette = sns.color_palette()
sns.palplot(sns.color_palette("terrain_r", 7))
plt.show()

Output:

Let’s understand this with some examples:

Example 1:

In this example, we have used sns.color_palette() to construct a colormap and sns.palplot() to display the colors present in the colormap with “deep” attributes.

Python3




# import module
import pandas as pd
import seaborn as sns
  
sns.palplot(sns.color_palette("deep", 10))

Output:

The possible value of the palette are:

‘Accent’, ‘Accent_r’, ‘Blues’, ‘Blues_r’, ‘BrBG’, ‘BrBG_r’, ‘BuGn’, ‘BuGn_r’, ‘BuPu’, ‘BuPu_r’, ‘CMRmap’, ‘CMRmap_r’, ‘Dark2’, ‘Dark2_r’, ‘GnBu’, ‘GnBu_r’,  ‘Greens’, ‘Greens_r’, ‘Greys’, ‘Greys_r’, ‘OrRd’, ‘OrRd_r’, ‘Oranges’, ‘Oranges_r’,  ‘PRGn’, ‘PRGn_r’, ‘Paired’, ‘Paired_r’, ‘Pastel1’, ‘Pastel1_r’, ‘Pastel2’,  ‘Pastel2_r’, ‘PiYG’, ‘PiYG_r’, ‘PuBu’, ‘PuBuGn’, ‘PuBuGn_r’, ‘PuBu_r’, ‘PuOr’, ‘PuOr_r’, ‘PuRd’, ‘PuRd_r’, ‘Purples’, ‘Purples_r’, ‘RdBu’, ‘RdBu_r’, ‘RdGy’, ‘RdGy_r’, ‘RdPu’, ‘RdPu_r’, ‘RdYlBu’, ‘RdYlBu_r’, ‘RdYlGn’, ‘RdYlGn_r’, ‘Reds’, ‘Reds_r’, ‘Set1’, ‘Set1_r’, ‘Set2’, ‘Set2_r’, ‘Set3’, ‘Set3_r’, ‘Spectral’, ‘Spectral_r’, ‘Wistia’, ‘Wistia_r’, ‘YlGn’, ‘YlGnBu’, ‘YlGnBu_r’, ‘YlGn_r’, ‘YlOrBr’, ‘YlOrBr_r’, ‘YlOrRd’, ‘YlOrRd_r’, ‘afmhot’, ‘afmhot_r’, ‘autumn’, ‘autumn_r’, ‘binary’, ‘binary_r’,  ‘bone’, ‘bone_r’, ‘brg’, ‘brg_r’, ‘bwr’, ‘bwr_r’, ‘cividis’, ‘cividis_r’, ‘cool’, ‘cool_r’,  ‘coolwarm’, ‘coolwarm_r’, ‘copper’, ‘copper_r’, ‘cubehelix’, ‘cubehelix_r’, ‘flag’, ‘flag_r’,  ‘gist_earth’, ‘gist_earth_r’, ‘gist_gray’, ‘gist_gray_r’, ‘gist_heat’, ‘gist_heat_r’, ‘gist_ncar’,  ‘gist_ncar_r’, ‘gist_rainbow’, ‘gist_rainbow_r’, ‘gist_stern’, ‘gist_stern_r’, ‘gist_yarg’,  ‘gist_yarg_r’, ‘gnuplot’, ‘gnuplot2’, ‘gnuplot2_r’, ‘gnuplot_r’, ‘gray’, ‘gray_r’, ‘hot’, ‘hot_r’,  ‘hsv’, ‘hsv_r’, ‘icefire’, ‘icefire_r’, ‘inferno’, ‘inferno_r’, ‘jet’, ‘jet_r’, ‘magma’, ‘magma_r’,  ‘mako’, ‘mako_r’, ‘nipy_spectral’, ‘nipy_spectral_r’, ‘ocean’, ‘ocean_r’, ‘pink’, ‘pink_r’,  ‘plasma’, ‘plasma_r’, ‘prism’, ‘prism_r’, ‘rainbow’, ‘rainbow_r’, ‘rocket’, ‘rocket_r’,  ‘seismic’, ‘seismic_r’, ‘spring’, ‘spring_r’, ‘summer’, ‘summer_r’, ‘tab10’, ‘tab10_r’,’tab20′, ‘tab20_r’, ‘tab20b’, ‘tab20b_r’, ‘tab20c’, ‘tab20c_r’, ‘terrain’, ‘terrain_r’, ‘turbo’,  ‘turbo_r’, ‘twilight’, ‘twilight_r’, ‘twilight_shifted’, ‘twilight_shifted_r’, ‘viridis’,  ‘viridis_r’, ‘vlag’, ‘vlag_r’, ‘winter’, ‘winter_r’

Example 2:

In this example, we have used sns.color_palette() to construct a colormap and sns.palplot() to display the colors present in the colormap with “muted” attributes.

Python3




import pandas as pd
import seaborn as sns
  
sns.palplot(sns.color_palette("muted", 10))

Output:

Example 3:

In this example, we have used sns.color_palette() to construct a colormap and sns.palplot() to display the colors present in the colormap with “bright” attributes.

Python3




import pandas as pd
import seaborn as sns
  
sns.palplot(sns.color_palette("bright", 10))

Output:

Example 4:

In this example, we have used sns.color_palette() to construct a colormap and sns.palplot() to display the colors present in the colormap with “dark” attributes.

Python3




import pandas as pd
import seaborn as sns
  
sns.palplot(sns.color_palette("dark", 10))

Output:

Example 5:

In this example, we have used sns.color_palette() to construct a colormap and sns.palplot() to display the colors present in the colormap with “BuGn_r” attributes.

Python3




import pandas as pd
import seaborn as sns
  
sns.palplot(sns.color_palette("BuGn_r", 10))

Output:

Example 6:

In this example, creating an own color palette and set it as the current color palette

Python3




import pandas as pd
import seaborn as sns
  
color = ["green", "White", "Red", "Yellow", "Green", "Grey"]
sns.set_palette(color)
sns.palplot(sns.color_palette())

Output:

 Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.  

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning – Basic Level Course

My Personal Notes arrow_drop_up
Recommended Articles
Page :