Why advanced indexing matters in NumPy - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select the first row of the array using basic indexing.

NumPy

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

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A[[0]]

B[[0, 0]]

C[0, 0]

D[0]

Attempts:

3 left

💡 Hint

Common Mistakes

Using double brackets returns a 2D array instead of 1D.

Using two indices selects a single element, not a row.

Using [0] selects the first row as a 1D array.

2fill in blank

medium

Complete the code to select rows 0 and 2 using advanced indexing.

NumPy

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

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A0, 2

B[0, 2]

C[[0, 2]]

Dnp.array([0, 2])

Attempts:

3 left

💡 Hint

Common Mistakes

Using a plain list without np.array may work but is less explicit.

Using comma-separated indices causes syntax errors.

Advanced indexing requires a list or array inside the brackets. Using [0, 2] correctly selects rows 0 and 2.

3fill in blank

hard

Fix the error in the code to select elements at positions (0,1) and (2,0) using advanced indexing.

NumPy

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

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Anp.array([0, 2])

B[0, 2]

C0, 2

Dnp.array([1, 0])

Attempts:

3 left

💡 Hint

Common Mistakes

Using lists instead of arrays causes unexpected behavior.

Using the same index array for both rows and columns without matching positions.

To select elements at (0,1) and (2,0), you need two arrays of indices: one for rows and one for columns. Using np.array([0, 2]) for rows and np.array([1, 0]) for columns fixes the error.

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', 'is', 'fun']
lengths = {word: [1] for word in words if [2]

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Alen(word)

Blen(word) > 3

Cword

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Using the word in the condition instead of its length.

The dictionary comprehension maps each word to its length using len(word). The condition len(word) > 3 filters 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 only if length is greater than 2.

NumPy

words = ['AI', 'ML', 'Data', 'Code']
result = [1]: [2] for w in words if [3]

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Aw.upper()

Blen(w)

Clen(w) > 2

Dw.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase conversion instead of uppercase.

Using the word itself instead of its length as the value.

The dictionary comprehension maps each word in uppercase (w.upper()) to its length (len(w)) only if the length is greater than 2 (len(w) > 2).