As we know, the median is the middle of a dataset: it is the number for which 50% of the samples are below, and 50% of the samples are above. But what if we wanted to find a point at which 40% of the samples are below, and 60% of the samples are above?

This type of point is called a *percentile*. The *Nth percentile* is defined as the point N% of samples lie below it. So the point where 40% of samples are below is called the 40th percentile. Percentiles are useful measurements because they can tell us where a particular value is situated within the greater dataset.

Let’s look at the following array:

d = [1, 2, 3, 4, 4, 4, 6, 6, 7, 8, 8]

There are 11 numbers in the dataset. The 40th percentile will have 40% of the 10 remaining numbers below it (40% of 10 is 4) and 60% of the numbers above it (60% of 10 is 6). So in this example, the 40th percentile is 4.

In NumPy, we can calculate percentiles using the function `np.percentile`

, which takes two arguments: the **array** and the **percentile** to calculate.

Here’s how we would use NumPy to calculate the 40th percentile of array `d`

:

>>> d = np.array([1, 2, 3, 4, 4, 4, 6, 6, 7, 8, 8]) >>> np.percentile(d, 40) 4.00

### Instructions

**1.**

The local public library wants to study how many hours a week their patrons use the computers. At the top of the **script.py**, we have included sample data from 11 users in a NumPy array.

Use NumPy to find the **30th** percentile of the sorted array and save it to a variable named `thirtieth_percentile`

.

**2.**

Next, use NumPy to find the **70th** percentile and save it to the variable `seventieth_percentile`

.

**3.**

Print the 30th and 70th variables to the terminal.