NumPy includes a powerful data structure known as an *array*. A NumPy array is a special type of list. It’s a data structure that organizes multiple items. Each item can be of any type (strings, numbers, or even other arrays).

Arrays are most powerful when they are used to store numbers. This is because arrays give us special ways of performing mathematical operations that are both simpler to write and more efficient computationally. We’ll get more into this later.

A NumPy array looks a lot like a Python list:

my_array = np.array([1, 2, 3, 4, 5, 6])

We can transform a regular list into a NumPy array by using `np.array()`

and saving the value to a new variable:

my_list = [1, 2, 3, 4, 5, 6] my_array = np.array(my_list)

### Instructions

**1.**

Imagine you’re a teacher and you need to keep track of your student’s test scores. On the first test, the students received the following scores:

`92, 94, 88, 91, 87`

Create a NumPy array with these values and save it with the name `test_1`

.