Control the colors in seaborn violinplots

Using a color palette

Simply set the palette parameter in the violinplot function. Doing so can add information on the groups order for example. Thus, keep in mind that adding a color palette to your graph can orientate the way readers will interpret it.

Control the colors in seaborn violinplots: Using a color palette

# libraries & dataset
import seaborn as sns
import matplotlib.pyplot as plt

sns.set_theme(style="darkgrid")
df = sns.load_dataset('iris')

# Use a color palette
sns.violinplot(x=df["species"], y=df["sepal_length"], palette="Blues")
plt.show()

Uniform color

Setting a unique value for the color parameter will fill all distribution areas with the same color.

Control the colors in seaborn violinplots: Uniform color

# libraries & dataset
import seaborn as sns
import matplotlib.pyplot as plt

sns.set_theme(style="darkgrid")
df = sns.load_dataset('iris')

# plot
sns.violinplot(x=df["species"], y=df["sepal_length"], color="skyblue")
plt.show()

Specify a color for each distribution

Rather than using a generic palette color, you can also instantiate a dictionary (here named my_pal) and set the palette parameter to such dictionary.

Control the colors in seaborn violinplots: Specify a color for each distribution

# libraries & dataset
import seaborn as sns
import matplotlib.pyplot as plt

sns.set_theme(style="darkgrid")
df = sns.load_dataset('iris')

# creating a dictionary with one specific color per group:
my_pal = {"versicolor": "g", "setosa": "b", "virginica": "m"}

# plot it
sns.violinplot(x=df["species"], y=df["sepal_length"], palette=my_pal)
plt.show()

Highlight a group

Whenever one group is to be differentiated from the others, you can build the custom palette dictionary so that this group's color is unique.

Control the colors in seaborn violinplots: Highlight a group

# libraries & dataset
import seaborn as sns
import matplotlib.pyplot as plt

sns.set_theme(style="darkgrid")
df = sns.load_dataset('iris')

# creating a dictionary composed of species as keys and colors as values: red for the interesting group, blue for others
my_pal = {species: "r" if species ==
          "versicolor" else "b" for species in df["species"].unique()}

# make the plot
sns.violinplot(x=df["species"], y=df["sepal_length"], palette=my_pal)
plt.show()

Going further

This post explains how to build a violinplot with seaborn.

You might be interested in how to create a violin plot with boxplots on top of it and how to add a beeswarm plot.