Combining fancy and slice indexing in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select rows 1 and 3, and columns 2 to 4 from the array.

NumPy

import numpy as np
arr = np.arange(20).reshape(5,4)
result = arr[[1], 2:4]
print(result)

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

A[0, 2]

B[2, 4]

C[1, 2]

D[1, 3]

Attempts:

3 left

💡 Hint

Common Mistakes

Using a slice instead of a list for row indices.

Selecting columns outside the array shape.

Using fancy indexing with [1, 3] selects rows 1 and 3. The slice 2:4 selects columns 2 to 3.

2fill in blank

medium

Complete the code to select rows 0, 2, 4 and columns 1 and 3 from the array.

NumPy

import numpy as np
arr = np.arange(20).reshape(5,4)
result = arr[[1], [1, 3]]
print(result)

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

A[0, 1, 2]

B[0, 2, 4]

C[1, 3, 5]

D[1, 2, 3]

Attempts:

3 left

💡 Hint

Common Mistakes

Using a range instead of a list for rows.

Selecting columns with a slice instead of a list.

Fancy indexing with [0, 2, 4] selects rows 0, 2, and 4. Columns 1 and 3 are selected by the list [1, 3].

3fill in blank

hard

Fix the error in the code to select rows 1 and 2, and columns 0 to 2.

NumPy

import numpy as np
arr = np.arange(15).reshape(3,5)
result = arr[[1], 0:3]
print(result)

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

A[1, 2]

B1, 2

Cslice(1,3)

Dnp.array([1, 2])

Attempts:

3 left

💡 Hint

Common Mistakes

Using comma-separated indices instead of a list.

Using slice object where a list is expected.

Fancy indexing requires a list or array of indices. Using [1, 2] correctly selects rows 1 and 2.

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their lengths if length > 3.

NumPy

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

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

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

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

Using less than (<) instead of greater than (>) in the condition.

The dictionary comprehension uses len(word) as the value and filters words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys and values greater than 0.

NumPy

data = {'a': 1, 'b': -2, 'c': 3}
result = [1]: [2] for k, v in data.items() if v [3] 0}
print(result)

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

Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using original keys instead of uppercase keys.

Using less than (<) instead of greater than (>) in the condition.

Using keys as values instead of values.

The dictionary comprehension uses uppercase keys with k.upper(), values v, and filters values greater than 0 using >.