⏱ Quick start

Seaborn is definitely the best library to quickly build a violin plot. It offers a dedicated violinplot() function that roughly works as follows:🔥

# library & dataset
import seaborn as sns
df = sns.load_dataset('iris')

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

Seaborn logoViolin charts with Seaborn

Seaborn is a python library that enables you to make better visualizations. It is well adapted to build density charts thanks to its violin function. The following charts will guide you through its usage, going from a very basic violin plot to something much more customized.

🔎 violinplot() function parameters→ see full doc

→ Description

The violinplot() function of seaborn creates violin plots which show the distribution of quantitative data across several levels of one (or more) categorical variables. It combines a box plot with a kernel density estimation.

→ Arguments

Matplotlib logoViolin charts with Matplotlib

Matplotlib, as usual, is another great otion to build violin charts with python. It comes with a violinplot() function that does all the hard work for us.

Here are a couple of examples:

Matplotlib logoBest python violin chart examples

The web is full of astonishing charts made by awesome bloggers, (often using R). The Python graph gallery tries to display (or translate from R) some of the best creations and explain how their source code works. If you want to display your work here, please drop me a word or even better, submit a Pull Request!

🚨 Grab the Data To Viz poster!


Do you know all the chart types? Do you know which one you should pick? I made a decision tree that answers those questions. You can download it for free!

    dataviz decision tree poster