Multi-dimensional fancy indexing in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select elements from a 2D array using fancy indexing.

NumPy

import numpy as np
arr = np.array([[10, 20, 30], [40, 50, 60], [70, 80, 90]])
result = arr[[1], [0, 2]]

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A[1, 2]

B1

C2

D[0, 2]

Attempts:

3 left

💡 Hint

Common Mistakes

Using a single integer instead of a list for row indices.

Confusing row and column indices.

The code uses fancy indexing to select rows 1 and 2 and columns 0 and 2, resulting in elements 40, 60, 70, and 90.

2fill in blank

medium

Complete the code to select elements from a 3x3 array using fancy indexing with two arrays.

NumPy

import numpy as np
arr = np.arange(9).reshape(3, 3)
result = arr[[1], [0, 2, 1]]

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A[1, 2, 0]

B[0, 1, 2]

C[2, 1, 0]

D[0, 0, 0]

Attempts:

3 left

💡 Hint

Common Mistakes

Using row indices of different length than column indices.

Using a single integer instead of a list for row indices.

Using row indices [0, 1, 2] and column indices [0, 2, 1] selects elements (0,0), (1,2), and (2,1) from the array.

3fill in blank

hard

Fix the error in the code to correctly select elements using multi-dimensional fancy indexing.

NumPy

import numpy as np
arr = np.array([[1, 2], [3, 4], [5, 6]])
result = arr[[0, 2], [1]]

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A[0, 1]

B[1, 0]

C[1, 1]

D[2, 1]

Attempts:

3 left

💡 Hint

Common Mistakes

Using column indices that do not match the length of row indices.

Using invalid column indices.

The code selects elements at positions (0,1) and (2,1), which are 2 and 6 respectively.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

NumPy

words = ['data', 'science', 'ai', 'ml']
lengths = { [1] : len([2]) for word in words if len(word) > 3 }

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Aword

Bwords

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Using the list 'words' instead of the variable 'word'.

Using a string literal instead of a variable.

The dictionary comprehension uses each word as the key and its length as the value, filtering words longer than 3 characters.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their lengths if length is greater than 3.

NumPy

words = ['data', 'science', 'ai', 'ml']
lengths = { [1] : [2] for word in words if len(word) [3] 3 }

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Aword.upper()

Blen(word)

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word as key without converting to uppercase.

Using incorrect comparison operators.

The comprehension maps each word in uppercase to its length, filtering words longer than 3 characters.