Negative indexing in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of negative indexing on 1D numpy array

What is the output of this code snippet using negative indexing on a numpy array?

NumPy

import numpy as np
arr = np.array([10, 20, 30, 40, 50])
result = arr[-3]
print(result)

A 30

B 20

C 40

D 50

💡 Hint

Negative index -1 refers to the last element, -2 the second last, and so on.

Negative indexing counts from the end of the array. Here, -3 means the third element from the end, which is 30.

❓ data_output

intermediate

Slice output using negative indices on 2D numpy array

What is the output of slicing this 2D numpy array using negative indices?

NumPy

import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
slice_result = arr[-2:, -2:]
print(slice_result)

A

[[2 3]
 [5 6]]

B

[[4 5]
 [7 8]]

C

[[6]
 [9]]

D

[[5 6]
 [8 9]]

💡 Hint

Negative slicing starts counting from the end of each dimension.

The slice arr[-2:, -2:] selects the last two rows and last two columns, resulting in [[5,6],[8,9]].

🔧 Debug

advanced

Identify the error in negative indexing with numpy array

What error will this code raise when using negative indexing?

NumPy

import numpy as np
arr = np.array([1, 2, 3])
print(arr[-4])

A TypeError: negative index not supported

B IndexError: index -4 is out of bounds for axis 0 with size 3

C ValueError: invalid negative index

D No error, output is 1

💡 Hint

Negative indices must be within the array length when counted from the end.

Index -4 is out of bounds because the array length is 3, so valid negative indices are -1, -2, -3 only.

🚀 Application

advanced

Using negative indexing to reverse a numpy array

Which code snippet correctly reverses a numpy array using negative indexing?

A arr[::-1]

B arr[-1::-1]

C arr[-1:-len(arr):-1]

D arr[-1:0:-1]

💡 Hint

Slicing with step -1 reverses the array.

arr[::-1] returns the array reversed. Other options either miss elements or cause empty slices.

🧠 Conceptual

expert

Effect of negative indexing on multidimensional numpy array shape

Given a 3D numpy array with shape (4, 5, 6), what is the shape of the result after slicing with arr[:, -3:, :-2]?

A (4, 3, 5)

B (4, 2, 4)

C (4, 3, 4)

D (4, 2, 5)

💡 Hint

Negative slicing counts from the end, and the slice excludes the last 2 elements in the last dimension.

arr[:, -3:, :-2] selects all in first dim (4), last 3 in second dim (5-3=2 to 4), and all except last 2 in third dim (6-2=4). So shape is (4,3,4).

Practice

1. What does negative indexing in a numpy array allow you to do?

easy

A. Sort the array in descending order

B. Access elements starting from the end of the array

C. Change the size of the array

D. Access elements only at the beginning of the array

Solution

1. Step 1: Understand negative indexing concept

   Negative indexing means counting elements from the end of the array, starting at -1 for the last element.

2. Step 2: Compare options with concept

   Only Access elements starting from the end of the array correctly describes accessing elements from the end using negative indexes.

3. Final Answer:

   Access elements starting from the end of the array -> Option B

4. Quick Check:

   Negative indexing = Access from end [OK]

Hint: Negative index -1 means last element [OK]

Common Mistakes:

• Thinking negative index changes array size
• Confusing negative indexing with sorting
• Assuming negative index accesses start only at beginning

2. Which of the following is the correct syntax to get the last element of a numpy array arr using negative indexing?

easy

A. arr[0]

B. arr[1]

C. arr[-0]

D. arr[-1]

Solution

1. Step 1: Recall negative index for last element

   In numpy, -1 is used to access the last element of an array.

2. Step 2: Check each option

   arr[-1] uses arr[-1] which correctly accesses the last element. Others do not.

3. Final Answer:

   arr[-1] -> Option D

4. Quick Check:

   Last element = arr[-1] [OK]

Hint: Use arr[-1] for last element [OK]

Common Mistakes:

• Using arr[-0] which is same as arr[0]
• Using positive index 1 which is second element
• Confusing zero index with negative zero

3. Given the numpy array arr = np.array([10, 20, 30, 40, 50]), what is the output of arr[-3]?

medium

A. 20

B. 40

C. 30

D. 50

Solution

1. Step 1: Understand negative index -3

   Negative index -3 means counting from the end: last is -1 (50), second last -2 (40), third last -3 (30).

2. Step 2: Identify element at index -3

   The element at arr[-3] is 30.

3. Final Answer:

   30 -> Option C

4. Quick Check:

   arr[-3] = 30 [OK]

Hint: Count backward: -1 last, -2 second last, -3 third last [OK]

Common Mistakes:

• Counting negative index from start instead of end
• Mixing up index -3 with positive index 3
• Off-by-one errors in counting

4. Consider the code snippet:

import numpy as np
arr = np.array([5, 10, 15, 20])
print(arr[-5])

What is the error in this code?

medium

A. IndexError: index -5 is out of bounds for axis 0 with size 4

B. SyntaxError due to negative index

C. Prints 5

D. Prints 20

Solution

1. Step 1: Check array size and index used

   Array has 4 elements, valid negative indexes are -1 to -4.

2. Step 2: Understand index -5 usage

   Index -5 is outside valid range, causing IndexError.

3. Final Answer:

   IndexError: index -5 is out of bounds for axis 0 with size 4 -> Option A

4. Quick Check:

   Negative index out of range = IndexError [OK]

Hint: Negative index must be >= -array length [OK]

Common Mistakes:

• Assuming negative index wraps around infinitely
• Thinking negative index causes syntax error
• Expecting last element instead of error

5. You have a 2D numpy array arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]). What does arr[-1, -2] return?

hard

A. 8

B. 7

C. 6

D. 9

Solution

1. Step 1: Understand negative indexing in 2D arrays

   For rows, -1 means last row: [7, 8, 9]. For columns, -2 means second last column.

2. Step 2: Identify element at arr[-1, -2]

   Last row is [7, 8, 9], second last column is element 8.

3. Final Answer:

   8 -> Option A

4. Quick Check:

   arr[-1, -2] = 8 [OK]

Hint: Negative indexes count from end in each dimension [OK]

Common Mistakes:

• Mixing row and column indexes
• Using positive indexes instead of negative
• Confusing which axis negative index applies to