# Spread ¶

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

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

```
Spread
```

elements have the same data format as the
```
ErrorBars
```

element, namely x- and y-values with associated symmetric or asymmetric errors, but are interpreted as samples from a continuous distribution (just as
```
Curve
```

is the continuous version of
```
Scatter
```

). These are often paired with an overlaid
```
Curve
```

to show an average trend along with a corresponding spread of values; see the
Tabular Datasets
user guide for examples.

Note that as the
```
Spread
```

element is used to add information to a plot (typically a
```
Curve
```

) the default alpha value is less that one, making it partially transparent.

##### Symmetric ¶

Given two value dimensions corresponding to the position on the y-axis and the error,
```
Spread
```

will visualize itself assuming symmetric errors:

```
np.random.seed(42)
xs = np.linspace(0, np.pi*2, 20)
err = 0.2+np.random.rand(len(xs))
hv.Spread((xs, np.sin(xs), err))
```

##### Asymmetric ¶

Given three value dimensions corresponding to the position on the y-axis, the negative error and the positive error,
```
Spread
```

can be used to visualize assymmetric errors:

```
xs = np.linspace(0, np.pi*2, 20)
spread = hv.Spread((xs, np.sin(xs), 0.1+np.random.rand(len(xs)), 0.1+np.random.rand(len(xs))),
vdims=['y', 'yerrneg', 'yerrpos'])
spread.opts(alpha=1, facecolor='indianred')
```

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

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