In this lesson, you'll learn how to calculate quantiles using NumPy

Quantiles

One of the most common quantiles is the 2-quantile. This value splits the data into two groups of equal size. Half the data will be above this value, and half the data will be below it. This is also known as the <a href="https://www.codecademy.com/courses/learn-statistics-with-python/lessons/median/exercises/introduction">median</a>!

<img src="https://content.codecademy.com/courses/statistics/quantiles/median.svg" alt="Ten points are below the median and ten points are above the median.">

The 4-quantiles, or the <a href="https://www.codecademy.com/courses/quartiles-quantiles-and-interquartile-range/lessons/quartiles/exercises/quartiles">quartiles</a>, split the data into four groups of equal size. We found the quartiles in the previous exercise. 
Options

<img src="https://content.codecademy.com/courses/statistics/quantiles/quartiles.svg" alt="Quartiles split a dataset of 20 points into 4 groups with 5 points each">

Finally, the percentiles, or the values that split the data into 100 groups, are commonly used to compare new data points to the dataset. You might hear statements like "You are above the 80th percentile in height". This means that your height is above whatever value splits the first 80% of the data from the remaining 20%.


Common Quantiles

The NumPy library has a function named `quantile()` that will quickly calculate the quantiles of a dataset for you.

`quantile()` takes two parameters. The first is the dataset that you are using. The second parameter is a single number or a list of numbers between `0` and `1`. These numbers represent the places in the data where you want to split.

For example, if you only wanted the value that split the first 10% of the data apart from the remaining 90%, you could use this code:

```py
import numpy as np

dataset = [5, 10, -20, 42, -9, 10]
ten_percent = np.quantile(dataset, 0.10)
```

`ten_percent` now holds the value `-14.5`. This result _technically_ isn't a quantile, because it isn't splitting the dataset into groups of equal sizes &mdash; this value splits the data into one group with 10% of the data and another with 90%. 

However, it would still be useful if you were curious about whether a data point was in the bottom 10% of the dataset.


Quantiles in NumPy

Quantiles are points that split a dataset into groups of equal size. For example, let's say you just took a test and wanted to know whether you're in the top 10% of the class. One way to determine this would be to split the data into ten groups with an equal number of datapoints in each group and see which group you fall into.

<img src="https://content.codecademy.com/courses/statistics/quantiles/deciles.svg" alt="Thirty students grades split into ten groups of three."> 

There are nine values that split the dataset into ten groups of equal size &mdash; each group has 3 different test scores in it. 

Those nine values that split the data are quantiles! Specifically, they are the 10-quantiles, or deciles.

You can find any number of quantiles. For example, if you split the dataset into 100 groups of equal size, the 99 values that split the data are the 100-quantiles, or percentiles.

The <a href="https://www.codecademy.com/courses/quartiles-quantiles-and-interquartile-range/lessons/quartiles/exercises/quartiles">quartiles</a> are some of the most commonly used quantiles. The quartiles split the data into four groups of equal size.

In this lesson, we'll show you how to calculate quantiles using NumPy and discuss some of the most commonly used quantiles.

Nice work! Here are some of the major takeaways about quantiles:

* Quantiles are values that split a dataset into groups of equal size.
* If you have `n` quantiles, the dataset will be split into `n+1` groups of equal size.
* The median is a quantile. It is the only 2-quantile. Half the data falls below the median and half falls above the median.
* Quartiles and percentiles are other common quantiles. Quartiles split the data into 4 groups while percentiles split the data into 100 groups.

Quantiles Review

In the last exercise, we found a single "quantile" &mdash; we split the first 23% of the data away from the remaining 77%.

However, quantiles are usually a set of values that split the data into groups of equal size. For example, you wanted to get the 5-quantiles, or the four values that split the data into five groups of equal size, you could use this code:

```py
import numpy as np

dataset = [5, 10, -20, 42, -9, 10]
ten_percent = np.quantile(dataset, [0.2, 0.4, 0.6, 0.8])
```
Note that we had to do a little math in our head to make sure that the values `[0.2, 0.4, 0.6, 0.8]` split the data into groups of equal size. Each group has 20% of the data.

<img src="https://content.codecademy.com/courses/statistics/quantiles/even.svg" alt="The data is split into 5 groups where each group has 4 datapoints.">

If we used the values `[0.2, 0.4, 0.7, 0.8]`, the function would return the four values at those split points. However, those values wouldn't split the data into five equally sized groups. One group would only have 10% of the data and another group would have 30% of the data!

<img src="https://content.codecademy.com/courses/statistics/quantiles/uneven.svg" alt="The data is split into groups of uneven size. One group has 6 data points and one group only has 2.">