<jupyter_start><jupyter_text>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.<jupyter_code># libraries & dataset
import seaborn as sns
import matplotlib.pyplot as plt
# set a grey background (use sns.set_theme() if seaborn version 0.11.0 or above) 
sns.set(style="darkgrid")
df = sns.load_dataset('iris')
 
# Use a color palette
sns.violinplot(x=df["species"], y=df["sepal_length"], palette="Blues")
plt.show()<jupyter_output><empty_output><jupyter_text>Uniform color Setting a unique value for the 'color' parameter will fill all distribution areas with the same color.<jupyter_code># libraries & dataset
import seaborn as sns
import matplotlib.pyplot as plt
# set a grey background (use sns.set_theme() if seaborn version 0.11.0 or above) 
sns.set(style="darkgrid")
df = sns.load_dataset('iris')
 
# plot
sns.violinplot(x=df["species"], y=df["sepal_length"], color="skyblue")
plt.show()<jupyter_output><empty_output><jupyter_text>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.<jupyter_code># libraries & dataset
import seaborn as sns
import matplotlib.pyplot as plt
# set a grey background (use sns.set_theme() if seaborn version 0.11.0 or above) 
sns.set(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()<jupyter_output><empty_output><jupyter_text>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.<jupyter_code># libraries & dataset
import seaborn as sns
import matplotlib.pyplot as plt
# set a grey background (use sns.set_theme() if seaborn version 0.11.0 or above) 
sns.set(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()<jupyter_output><empty_output>
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