Boxplots are descriptive diagrams that help to compare the distribution of different series of data. They are *descriptive* because they show measures (e.g. the *median*) which do not assume an underlying probability distribution.

The most basic example of a boxplot in matplotlib can be achieved by just passing the data as a list of lists:

```
import matplotlib as plt
dataline1 = [43,76,34,63,56,82,87,55,64,87,95,23,14,65,67,25,23,85]
dataline2 = [34,45,34,23,43,76,26,18,24,74,23,56,23,23,34,56,32,23]
data = [ dataline1, dataline2 ]
plt.boxplot( data )
```

However, it is a common practice to use `numpy`

arrays as parameters to the plots, since they are often the result of previous calculations. This can be done as follows:

```
import numpy as np
import matplotlib as plt
np.random.seed(123)
dataline1 = np.random.normal( loc=50, scale=20, size=18 )
dataline2 = np.random.normal( loc=30, scale=10, size=18 )
data = np.stack( [ dataline1, dataline2 ], axis=1 )
plt.boxplot( data )
```