**Quartiles** are the values that break up the dataset into quarters:

- The first quartile is the point below which a quarter of the data lie. It is sometimes called the lower quartile.
- The second quartile is the point below which half of the data lie. We already know this point by the name of
**median**. It is also called the middle quartile.
- The third quartile is the point below which three-quarters of the data lie. It is called the upper quartile.

More generally, **quantiles** are cut points that divide a sample dataset into subgroups. For instance, if the 0.20 quantile equals 5, that means that **20%** of all observations are **less than or equal to** **5**, while the other **80%** of observations are **greater than 5**.

The `describe()`

function computes the **1st**, **2nd** and **3rd** quartiles, but you can use the `quantile()`

function to compute any kind of quantile you want. For instance:

players['aces'].quantile([0.2, 0.4, 0.6, 0.8])

The code above will compute the 5-quantiles (quintiles) of the "aces" column of the "players" DataFrame. Note that we provided a list of **comma-separated** values inside **square brackets**. In the code above, 0.2 stands for the 20th percentile, 0.4 stands for the 40th percentile, and so on.