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)

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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)

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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)

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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)

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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)

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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.