np.argsort() for sort indices in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to get the indices that would sort the array.

NumPy

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

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

Asort

Bargsort

Csorted

Dargmax

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.sort() which returns sorted values, not indices.

Using Python's sorted() which does not work on numpy arrays.

Using np.argmax() which returns index of max value only.

The np.argsort() function returns the indices that would sort the array.

2fill in blank

medium

Complete the code to get the indices that would sort the array in descending order.

NumPy

import numpy as np
arr = np.array([5, 2, 9, 1])
sorted_indices = np.argsort(arr)[[1]]
print(sorted_indices)

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

A:

B[1:]

C[::-2]

D::-1

Attempts:

3 left

💡 Hint

Common Mistakes

Using [:] which keeps the order ascending.

Using [::-2] which skips elements and reverses partially.

Using [1:] which removes the first element.

Using [::-1] reverses the array of indices to get descending order.

3fill in blank

hard

Fix the error in the code to correctly get sorted indices of a 2D array along axis 1.

NumPy

import numpy as np
arr = np.array([[3, 1, 2], [9, 7, 8]])
sorted_indices = np.argsort(arr, axis=[1])
print(sorted_indices)

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

A1

B0

C2

D-1

Attempts:

3 left

💡 Hint

Common Mistakes

Using axis=0 which sorts columns instead of rows.

Using axis=2 which is invalid for 2D arrays.

Using axis=-1 which is same as axis=1 but might confuse beginners.

Axis 1 means sorting each row. So axis=1 is correct.

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their sorted index of length as values, only for words longer than 3 letters.

NumPy

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

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

A>

B<

C==

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' which filters shorter words.

Using '==' which filters words exactly length 3.

Using '!=' which filters words not length 3.

We filter words with length greater than 3 using > 3. The loop variable is i but here we reuse {{BLANK_2}} for the index in range.

5fill in blank

hard

Fill all three blanks to create a dictionary mapping each word to its sorted index based on length, only for words longer than 3 letters.

NumPy

words = ['pear', 'kiwi', 'banana', 'fig']
lengths = {word: [1] for word in words if [2](word) > 3}
sorted_indices = np.argsort([lengths[word] for word in lengths])
result = {word: sorted_indices[[3]] for [3] in range(len(sorted_indices))}
print(result)

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

Alen(word)

Bword

Ci

Dlen

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'word' instead of len(word) for length.

Using 'len' without calling it as a function.

Using 'word' as index variable instead of 'i'.

We use len(word) to get length, loop over word, and use i as index in the final dictionary comprehension.