Negative indexing in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select the last element of the numpy array.

NumPy

import numpy as np
arr = np.array([10, 20, 30, 40, 50])
last_element = arr[1]

Drag options to blanks, or click blank then click option'

A[0]

B[1]

C[-1]

D[-5]

Attempts:

3 left

💡 Hint

Common Mistakes

Using positive index 1 instead of -1.

Using 0 which selects the first element.

Using -5 which selects the first element but is less clear.

Using arr[-1] selects the last element in the array.

2fill in blank

medium

Complete the code to select the second last element of the numpy array.

NumPy

import numpy as np
arr = np.array([5, 15, 25, 35, 45])
second_last = arr[1]

Drag options to blanks, or click blank then click option'

A[-2]

B[1]

C[-1]

D[2]

Attempts:

3 left

💡 Hint

Common Mistakes

Using positive index 2 which selects the third element.

Using -1 which selects the last element.

Using positive index 1 which selects the second element.

arr[-2] selects the second last element in the array.

3fill in blank

hard

Fix the error in the code to correctly select the last three elements using negative indexing.

NumPy

import numpy as np
arr = np.array([2, 4, 6, 8, 10, 12])
last_three = arr[1]

Drag options to blanks, or click blank then click option'

A[-3:]

B[:3]

C[3:]

D[-3]

Attempts:

3 left

💡 Hint

Common Mistakes

Using [:3] which selects first three elements.

Using [3:] which selects elements from index 3 to end but not negative indexing.

Using [-3] which selects only one element.

arr[-3:] selects the last three elements of the array.

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their last two characters as values.

NumPy

words = ['apple', 'banana', 'cherry']
last_two_chars = {word: word[1] for word in words if len(word) [2] 2}

Drag options to blanks, or click blank then click option'

A[-2:]

B>

C<

D[:2]

Attempts:

3 left

💡 Hint

Common Mistakes

Using [:2] which gets first two characters.

Using length less than 2 which excludes longer words.

Using negative index without colon which selects single character.

The slice word[-2:] gets the last two characters. The condition len(word) > 2 ensures words are long enough.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase words as keys and their last character as values, only for words longer than 3 letters.

NumPy

words = ['dog', 'elephant', 'cat', 'giraffe']
result = {word[1]: word[2] for word in words if len(word) [3] 3}

Drag options to blanks, or click blank then click option'

A.upper()

B[-1]

C>

D[:1]

Attempts:

3 left

💡 Hint

Common Mistakes

Using [:1] which gets first character instead of last.

Using length less than or equal to 3 which includes short words.

Not converting keys to uppercase.

word.upper() converts to uppercase keys, word[-1] gets last character, and len(word) > 3 filters words longer than 3 letters.

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