# Violin ¶

- Title
- Violin Element
- Dependencies
- Bokeh
- Backends
- Bokeh
- Matplotlib
- Plotly

```
import numpy as np
import holoviews as hv
hv.extension('matplotlib')
```

A
```
Violin
```

element is used to visualise the distribution of a dataset by displaying its probability density. It is very similar to the
```
BoxWhisker
```

element but provides a more faithful representation even for bi- or multimodal data. The probability density is shown by the area akin to a vertical and mirrored
```
Distribution
```

element. The thick black bar in the centre represents the interquartile range, the thin black line extended from it represents the 95% confidence intervals, and the white line is the median.

The data of a
```
Violin
```

Element may have any number of key dimensions representing the grouping of the value dimension and a single value dimensions representing the distribution of values within each group. See the
Tabular Datasets
user guide for supported data formats, which include arrays, pandas dataframes and dictionaries of arrays.

In the simplest case a
```
Violin
```

can be used to display a single distribution of values, such as a NumPy array of normally distributed values:

```
np.random.seed(37)
violin = hv.Violin(np.random.randn(1000), vdims='Value')
violin
```

The Violin element supports multiple options for indicating the distribution values in addition to the default
```
inner
```

value of 'box'. The 'medians' option indicates the median as a single line instead. Additionally the
```
bandwidth
```

option may be used to control the kernel density estimate:

```
violin.relabel(group='Medians').opts(inner='medians', bandwidth=0.1)
```

The
```
Violin
```

element is particularly useful to compare multiple distribution across different categories. As a simple example we can create a dataset of values with randomly assigned Group and Category values and compare the distributions.

```
groups = [chr(65+g) for g in np.random.randint(0, 3, 200)]
violin = hv.Violin((groups, np.random.randint(0, 5, 200), np.random.randn(200)),
['Group', 'Category'], 'Value')
violin.opts(aspect=2.5, fig_size=300)
```

For full documentation and the available style and plot options, use
```
hv.help(hv.Violin).
```

Download this notebook from GitHub (right-click to download).