Negative indexing in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is negative indexing in numpy arrays?

Negative indexing means counting elements from the end of the array instead of the start. For example, -1 refers to the last element, -2 to the second last, and so on.

beginner

How do you access the last element of a numpy array using negative indexing?

Use array[-1] to get the last element of the numpy array.

beginner

What will array[-3] return in a numpy array?

It returns the third element from the end of the array.

intermediate

Can negative indexing be used with slicing in numpy? Give an example.

Yes. For example, array[-3:-1] returns a slice starting from the third last element up to (but not including) the second last element.

intermediate

Why is negative indexing useful in data science with numpy?

It allows easy access to elements from the end without needing to know the array length. This is helpful for tasks like getting recent data points or last values.

What does array[-1] return in a numpy array?

A The first element

B The last element

C The middle element

D An error

Negative index -1 always points to the last element in a numpy array.

If array = np.array([10, 20, 30, 40]), what is array[-2]?

A 30

B 20

C 40

D 10

array[-2] accesses the second last element, which is 30.

Which slice uses negative indexing to get the last three elements of array?

A array[3:]

B array[:3]

C array[-3:]

D array[:-3]

array[-3:] slices from the third last element to the end.

What happens if you use array[-len(array)-1] in numpy?

A IndexError

B Returns the first element

C Returns the last element

D Returns None

Negative indices less than -len(array) cause an IndexError.

Why might negative indexing be preferred over positive indexing?

A It is faster

B It avoids errors

C It uses less memory

D It allows easy access from the end without knowing length

Negative indexing lets you access elements from the end without calculating array length.

Explain how negative indexing works in numpy arrays and give an example.

Think about how you count backwards from the last element.

Describe a real-life situation where negative indexing in numpy would be helpful.

Imagine you want the last few days of sales data.