A violin plot allows you to visualize the distribution of a numeric variable for one or several groups. Seaborn is particularly adapted to build it thanks to its violin() function. Violin plots deserve more attention than boxplots, which can sometimes hide features of the data.
Seaborn is definitely the best library to quickly build a violin plot. It offers a dedicated violinplot() function that roughly works as follows:🔥
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.
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.
Description
Inputs for plotting long-form data. This parameter specify the variable for the x axes.
Possible values → string, vector, or pd.Series
Can be vector names in the data DataFrame, or external vectors passed directly.
Code Example
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:
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!
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!