Some percentiles have specific names:

- The
**25th percentile**is called the*first quartile* - The
**50th percentile**is called the*median* - The
**75th percentile**is called the*third quartile*

The minimum, first quartile, median, third quartile, and maximum of a dataset are called a *five-number summary*. This set of numbers is a great thing to compute when we get a new dataset.

The difference between the first and third quartile is a value called the *interquartile range*. For example, say we have the following array:

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

We can calculate the 25th and 75th percentiles using `np.percentile`

:

np.percentile(d, 25) >>> 3.5 np.percentile(d, 75) >>> 6.5

Then to find the interquartile range, we subtract the value of the 25th percentile from the value of the 75th:

6.5 - 3.5 = 3

**50% of the dataset** will lie within the interquartile range. The interquartile range gives us an idea of how spread out our data is. The smaller the interquartile range value, the less variance in our dataset. The greater the value, the larger the variance.

### Instructions

**1.**

An online movie streaming company wants to know how many movies users watch in one week. At the top of the **script.py**, we have included sample data from 15 users in an array.

Find the **25th** and **75th** percentiles, and save them to the corresponding variables: `first_quarter`

and `third_quarter`

.

**2.**

Create a variable named `interquartile_range`

. Calculate the interquartile range and save it to this variable.

**3.**

Print the results of the 25th percentile, 75th percentile, and interquartile range to the terminal.