Fancy indexing with integer arrays in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select elements at positions 1, 3, and 4 from the array.

NumPy

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

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

A[2, 3, 4]

B[0, 2, 4]

C[1, 3, 4]

D[1, 2, 3]

Attempts:

3 left

💡 Hint

Common Mistakes

Using indices that are out of range.

Using a tuple instead of a list for indexing.

The code uses fancy indexing with the integer array [1, 3, 4] to select elements at these positions.

2fill in blank

medium

Complete the code to select elements at indices stored in the variable 'indices'.

NumPy

import numpy as np
arr = np.array([5, 10, 15, 20, 25])
indices = np.array([0, 2, 4])
result = arr[[1]]
print(result)

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

Aindices

Bindices.tolist()

C[indices]

D(indices)

Attempts:

3 left

💡 Hint

Common Mistakes

Wrapping the indices array in another list or tuple.

Converting indices to list unnecessarily.

Fancy indexing works directly with a numpy array of indices, so 'arr[indices]' selects the elements at those positions.

3fill in blank

hard

Fix the error in the code to correctly select elements at indices 2, 0, and 3.

NumPy

import numpy as np
arr = np.array([7, 14, 21, 28, 35])
indices = [2, 0, 3]
selected = arr[[1]]
print(selected)

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

Aindices

B(indices)

C[indices]

Dnp.array(indices)

Attempts:

3 left

💡 Hint

Common Mistakes

Using a tuple or list inside another list for indexing.

Not converting the list to a numpy array when needed.

Fancy indexing works with a list of integers as well, so using 'indices' directly is correct.

4fill in blank

hard

Fill both blanks to create a new array selecting elements at indices 1 and 4, then multiply them by 10.

NumPy

import numpy as np
arr = np.array([3, 6, 9, 12, 15])
selected = arr[[1]]
result = selected [2] 10
print(result)

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

A[1, 4]

B*

C+

D[0, 3]

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong indices for selection.

Using '+' instead of '*' for multiplication.

The code selects elements at indices 1 and 4, then multiplies them by 10 using the '*' operator.

5fill in blank

hard

Fill all three blanks to create a dictionary mapping selected elements to their squares for indices 0, 2, and 3.

NumPy

import numpy as np
arr = np.array([2, 4, 6, 8, 10])
squares = { [1]: [2]**2 for [3] in arr[ [0, 2, 3] ] }
print(squares)

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

Ax

Di

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently.

Not using a dictionary comprehension syntax.

The dictionary comprehension uses 'x' as the variable to map each selected element to its square.

Practice

1. What does fancy indexing with integer arrays in NumPy allow you to do?

easy

A. Create a new array filled with zeros

B. Sort an array in ascending order

C. Select multiple elements from an array using a list of positions

D. Change the data type of an array

Solution

1. Step 1: Understand fancy indexing

   Fancy indexing uses a list or array of integers to pick elements by their positions.

2. Step 2: Identify the correct operation

   It selects multiple elements from the original array based on given indices.

3. Final Answer:

   Select multiple elements from an array using a list of positions -> Option C

4. Quick Check:

   Fancy indexing = select elements by positions [OK]

Hint: Fancy indexing picks elements by position list [OK]

Common Mistakes:

• Confusing fancy indexing with sorting
• Thinking it changes data type
• Assuming it creates arrays of zeros

2. Which of the following is the correct syntax to select elements at positions 0, 2, and 4 from a NumPy array arr using fancy indexing?

easy

A. arr{0, 2, 4}

B. arr(0, 2, 4)

C. arr[0:2:4]

D. arr[[0, 2, 4]]

Solution

1. Step 1: Recall fancy indexing syntax

   Fancy indexing uses double square brackets with a list or array of indices inside.

2. Step 2: Match syntax to options

   Only arr[[0, 2, 4]] uses correct double brackets and list format.

3. Final Answer:

   arr[[0, 2, 4]] -> Option D

4. Quick Check:

   Double brackets with list = correct fancy indexing [OK]

Hint: Use double brackets with list for fancy indexing [OK]

Common Mistakes:

• Using parentheses instead of brackets
• Using curly braces which are invalid
• Using slicing syntax incorrectly

3. What is the output of the following code?

import numpy as np
arr = np.array([10, 20, 30, 40, 50])
indices = np.array([1, 3, 4])
result = arr[indices]
print(result)

medium

A. [20 40 50]

B. [10 30 40]

C. [20 30 40]

D. [50 40 20]

Solution

1. Step 1: Understand the array and indices

   Original array is [10, 20, 30, 40, 50]. Indices are [1, 3, 4].

2. Step 2: Select elements at given indices

   Elements at positions 1, 3, 4 are 20, 40, 50 respectively.

3. Final Answer:

   [20 40 50] -> Option A

4. Quick Check:

   arr[1,3,4] = [20 40 50] [OK]

Hint: Match indices to array positions to get output [OK]

Common Mistakes:

• Mixing up indices and values
• Using wrong positions from array
• Confusing slicing with fancy indexing

4. Identify the error in the following code snippet:

import numpy as np
arr = np.array([5, 10, 15, 20])
indices = [1, 4]
print(arr[indices])

medium

A. IndexError because index 4 is out of bounds

B. SyntaxError due to wrong brackets

C. TypeError because indices is a list

D. No error, prints [10 20]

Solution

1. Step 1: Check array size and indices

   Array length is 4, valid indices are 0 to 3. Index 4 is invalid.

2. Step 2: Understand error type

   Accessing index 4 causes IndexError: index out of bounds.

3. Final Answer:

   IndexError because index 4 is out of bounds -> Option A

4. Quick Check:

   Index 4 invalid for length 4 array [OK]

Hint: Check if all indices are within array length [OK]

Common Mistakes:

• Assuming list indices are always valid
• Confusing syntax errors with index errors
• Thinking list indices cause type errors

5. Given a 2D NumPy array arr = np.array([[1,2,3],[4,5,6],[7,8,9]]), how can you use fancy indexing to select elements at positions (0,2), (1,0), and (2,1) and create a new 1D array with these values?

hard

A. arr[[0,2,1], [2,1,0]]

B. arr[[0,1,2], [2,0,1]]

C. arr[[2,1,0], [0,1,2]]

D. arr[[0,1,2]][[2,0,1]]

Solution

1. Step 1: Understand 2D fancy indexing

   To select elements at (row, col) positions, use two index arrays: one for rows, one for columns.

2. Step 2: Match given positions to index arrays

   Positions are (0,2), (1,0), (2,1), so rows = [0,1,2], cols = [2,0,1].

3. Step 3: Use fancy indexing syntax

   Use arr[[0,1,2], [2,0,1]] to get elements [3,4,8].

4. Final Answer:

   arr[[0,1,2], [2,0,1]] -> Option B

5. Quick Check:

   Rows and cols arrays match positions [OK]

Hint: Use two index arrays: rows and columns [OK]

Common Mistakes:

• Mixing row and column indices order
• Using chained indexing instead of fancy indexing
• Using wrong index arrays for positions