## Libraries

First, we need to load a few libraries:

- matplotlib: for displaying the chart
- seaborn: for creating the chart

```
import seaborn as sns
import matplotlib.pyplot as plt
```

## Dataset

Since boxplot is used to display the **distribution of a numerical variable**, we need a dataset that contains at least one numerical variable.

In this example, we will use the `iris`

dataset that we can easily **load**:

`df = sns.load_dataset('iris')`

## One single numerical variable

The simplest form of boxplot: analysis of the **overall distribution** of a single numerical variable with the `boxplot()`

function.

```
sns.set_theme(style="darkgrid")
sns.boxplot(y=df["sepal_length"])
plt.show()
```

## One numerical variable and several groups

Depending on your data, you may want to have a better understanding of the distribution of a given variable between **two or more groups**.

You can do so by specifying the `x`

parameter in the `boxplot()`

function.

```
sns.set_theme(style="darkgrid")
sns.boxplot(x=df["species"], y=df["sepal_length"])
plt.show()
```

## Going further

This post explains how to create and customize a boxplot with the seaborn library.

You might be interested in how adding individual data points in boxplot and how to create a raincloud plot with the `ptitprince`

library.