Array protocol and __array__ in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert a custom object to a NumPy array using the array protocol.

NumPy

import numpy as np

class MyArray:
    def __array__(self):
        return np.array([1])

obj = MyArray()
result = np.array(obj)
print(result)

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

A[1, 2, 3]

B(1, 2, 3)

C{1, 2, 3}

D1, 2, 3

Attempts:

3 left

💡 Hint

Common Mistakes

Returning a tuple or set may work but is less common and can cause unexpected behavior.

Returning multiple values without a container causes errors.

The __array__ method should return a NumPy array or something that can be converted to one. Returning a list like [1, 2, 3] works correctly.

2fill in blank

medium

Complete the code to check if an object supports the array protocol by using np.asarray.

NumPy

import numpy as np

class Custom:
    def __array__(self):
        return np.array([10, 20, 30])

obj = Custom()
arr = np.asarray([1])
print(arr)

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

Aobj.__array__()

Bobj

C[10, 20, 30]

Dnp.array(obj)

Attempts:

3 left

💡 Hint

Common Mistakes

Calling __array__ manually inside np.asarray is redundant.

Passing a list instead of the object ignores the custom method.

np.asarray calls the __array__ method automatically if it exists. So passing the object directly is correct.

3fill in blank

hard

Fix the error in the code by completing the __array__ method to return a NumPy array.

NumPy

import numpy as np

class Data:
    def __array__(self):
        [1]

obj = Data()
print(np.array(obj))

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

Areturn np.array([1, 2, 3])

Breturn [1, 2, 3]

Creturn (1, 2, 3)

Dreturn '123'

Attempts:

3 left

💡 Hint

Common Mistakes

Returning a list or tuple directly can cause unexpected behavior.

Returning a string is invalid and causes errors.

The __array__ method must return a NumPy array. Returning np.array([1, 2, 3]) ensures this.

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

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Alen(word)

Bword

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

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

Comparing the word string directly to a number causes errors.

The dictionary comprehension maps each word to its length using len(word). The condition filters words with length greater than 3 using len(word) > 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their lengths only if length is less than 5.

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
result = [1]: [2] for w in words if [3]
print(result)

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

A{w.upper()}

Blen(w)

Clen(w) < 5

D{w}

Attempts:

3 left

💡 Hint

Common Mistakes

Using curly braces incorrectly for keys.

Not converting words to uppercase.

Using wrong comparison operators.

The dictionary comprehension creates keys as uppercase words using w.upper(), values as their lengths with len(w), and filters words with length less than 5 using len(w) < 5.