NumPy allows us to select elements from an array using their indices. Consider the one-dimensional array

a = np.array([5, 2, 7, 0, 11])

If we wanted to select the first element in this array, we would call:

>>> a[0] 5

In typical Python fashion, the indices for an array start at `0`

. This is known as *zero-indexed numbering*. In the array above, 5 is known as the *zeroth* element, `a[0]`

. It follows that 2 is the first element, `a[1]`

.

We can also select negative indices, which count from opposite end of the array and start at `-1`

. This is particularly useful when you want to access the last element or two of an array:

>>> a[-1] 11 >>> a[-2] 0

If we wanted to select multiple elements in the array, we can define a range, such as `a[1:3]`

, which will select all the elements from `a[1]`

to `a[3]`

, *including* `a[1]`

but *excluding* `a[3]`

.

>>> a[1:3] array([2, 7])

Similarly, if we wanted to select all elements before `a[3]`

we would use:

>>> a[:3] array([5, 2, 7])

We can also use negative indices to select multiple elements. Let’s say we want to select the last 3 elements in an array:

>>> a[-3:] array([7, 0, 11])

Notice that when we select multiple elements, we get an array.

### Instructions

**1.**

Let’s return to our student’s test scores. The following table shows all three test arrays aligned to the names of the students.

Tanya | Manual | Adwoa | Jeremy | Cody | |
---|---|---|---|---|---|

test_1 | 92 | 94 | 88 | 91 | 87 |

test_2 | 79 | 100 | 86 | 93 | 91 |

test_3 | 87 | 85 | 72 | 90 | 92 |

Jeremy wants to know what he scored on the second test.

Select the score from the `test_2`

array and save it to the variable `jeremy_test_2`

.

**2.**

You want to compare how Manual and Adwoa did on the first test.

Select both of their scores and save them in an array named `manual_adwoa_test_1`

.

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