Why indexing matters in NumPy - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a NumPy array from a list.

NumPy

import numpy as np
arr = np.array([1])
print(arr)

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

A[1, 2, 3, 4]

B(1, 2, 3, 4)

C1, 2, 3, 4

D{1, 2, 3, 4}

Attempts:

3 left

💡 Hint

Common Mistakes

Using a tuple or set instead of a list inside np.array()

Forgetting the brackets around the numbers

NumPy arrays are created from lists using np.array(). Lists are enclosed in square brackets.

2fill in blank

medium

Complete the code to access the third element of the NumPy array.

NumPy

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

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

A1

B0

C3

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Using 3 instead of 2 for the third element

Confusing 1-based indexing with 0-based indexing

Indexing in Python starts at 0, so the third element is at index 2.

3fill in blank

hard

Fix the error in the code to correctly slice the first three elements of the array.

NumPy

import numpy as np
arr = np.array([5, 10, 15, 20, 25])
slice_arr = arr[[1]]
print(slice_arr)

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

A[0:3]

B0,3

C0:3

D(0:3)

Attempts:

3 left

💡 Hint

Common Mistakes

Using commas instead of colon for slicing

Adding extra brackets around the slice

Slicing uses the colon : inside square brackets without extra brackets or commas.

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their lengths as values, only for words longer than 3 characters.

NumPy

words = ['cat', 'elephant', 'dog', 'horse']
lengths = { [1] : [2] for word in words if len(word) > 3 }
print(lengths)

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

Aword

Blen(word)

Cword.upper()

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using the length as the key and the word as the value

Using transformations like upper or lower instead of the original word

The dictionary comprehension uses the word as the key and its length as the value.

5fill in blank

hard

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

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
lengths = { [1] : [2] for word in words if len(word) [3] 3 }
print(lengths)

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

Aword.upper()

Blen(word)

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase or original words as keys

Using wrong comparison operators in the condition

The dictionary keys are uppercase words, values are their lengths, and the condition filters words longer than 3 characters.

Practice

1. What does indexing in NumPy allow you to do?

easy

A. Create new arrays from scratch

B. Select specific elements or slices from an array

C. Automatically sort the array elements

D. Convert arrays into lists

Solution

1. Step 1: Understand the purpose of indexing

   Indexing is used to access specific parts of an array, like single elements or slices.

2. Step 2: Compare with other options

   Creating new arrays, sorting, or converting are different operations, not indexing.

3. Final Answer:

   Select specific elements or slices from an array -> Option B

4. Quick Check:

   Indexing = selecting parts of array [OK]

Hint: Indexing means picking parts of data quickly [OK]

Common Mistakes:

• Confusing indexing with array creation
• Thinking indexing sorts data
• Mixing indexing with data type conversion

2. Which of the following is the correct syntax to get the first element of a NumPy array arr?

easy

A. arr.first()

B. arr(0)

C. arr[0]

D. arr{0}

Solution

1. Step 1: Recall NumPy indexing syntax

   NumPy arrays use square brackets with zero-based indices to access elements.

2. Step 2: Check each option

   Only arr[0] uses correct square brackets and zero index; others are invalid syntax or methods.

3. Final Answer:

   arr[0] -> Option C

4. Quick Check:

   First element = arr[0] [OK]

Hint: Use square brackets and zero-based index [OK]

Common Mistakes:

• Using parentheses instead of brackets
• Using curly braces which are invalid
• Assuming methods like .first() exist

3. What is the output of the following code?

import numpy as np
arr = np.array([10, 20, 30, 40, 50])
print(arr[1:4])

medium

A. [20 30 40]

B. [10 20 30 40]

C. [20 30 40 50]

D. [30 40 50]

Solution

1. Step 1: Understand slicing syntax

   The slice arr[1:4] selects elements from index 1 up to but not including 4.

2. Step 2: Identify elements at these indices

   Indices 1, 2, 3 correspond to values 20, 30, 40.

3. Final Answer:

   [20 30 40] -> Option A

4. Quick Check:

   Slice 1:4 excludes index 4 [OK]

Hint: Slice excludes end index, so 1:4 means 1,2,3 [OK]

Common Mistakes:

• Including the end index element
• Confusing zero-based indexing
• Printing the whole array by mistake

4. What is wrong with this code snippet?

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(arr[5])

medium

A. Index 5 is out of bounds for the array

B. The array is not defined properly

C. The print statement syntax is incorrect

D. The array should be a list, not a NumPy array

Solution

1. Step 1: Check array length and indexing

   The array has 5 elements with indices 0 to 4; index 5 is invalid.

2. Step 2: Identify error cause

   Accessing arr[5] causes an IndexError because it is out of bounds.

3. Final Answer:

   Index 5 is out of bounds for the array -> Option A

4. Quick Check:

   Index must be less than array length [OK]

Hint: Array indices go from 0 to length-1 [OK]

Common Mistakes:

• Trying to access index equal to array length
• Confusing array length with max index
• Assuming Python allows out-of-range indexing

5. Given a 2D NumPy array arr = np.array([[1,2,3],[4,5,6],[7,8,9]]), which indexing expression will select the second column as a 1D array?

hard

A. arr[1, :]

B. arr[:, 2]

C. arr[1]

D. arr[:, 1]

Solution

1. Step 1: Understand 2D array indexing

   In 2D arrays, arr[:, 1] means all rows, column index 1 (second column).

2. Step 2: Check other options

   arr[1, :] selects second row, arr[1] is same as second row, arr[:, 2] selects third column.

3. Final Answer:

   arr[:, 1] -> Option D

4. Quick Check:

   All rows, column 1 = second column [OK]

Hint: Use colon for all rows, index for column [OK]

Common Mistakes:

• Mixing row and column indices
• Using single index for 2D array
• Selecting wrong column index