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)

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

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.

Practice

1. What is the main purpose of the __array__ method in a custom Python class when working with NumPy?

easy

A. To create a plot from the array data

B. To allow the object to be converted automatically into a NumPy array

C. To perform element-wise multiplication on arrays

D. To define a new data type for NumPy arrays

Solution

1. Step 1: Understand the role of __array__

   The __array__ method is designed to let custom objects tell NumPy how to convert them into arrays.

2. Step 2: Identify the main purpose

   This method enables automatic conversion, so NumPy can work with the data efficiently.

3. Final Answer:

   To allow the object to be converted automatically into a NumPy array -> Option B

4. Quick Check:

   Conversion to NumPy array [OK]

Hint: Remember: __array__ means 'convert me to array' [OK]

Common Mistakes:

• Confusing __array__ with data type definition
• Thinking __array__ performs calculations
• Assuming __array__ creates plots

2. Which of the following is the correct way to define the __array__ method in a custom class to return a NumPy array?

easy

A. def __array__(self, dtype): return self.data.astype(dtype)

B. def __array__(self): return list(self.data)

C. def __array__(self): return self.data.tolist()

D. def __array__(self, dtype=None): return np.array(self.data, dtype=dtype)

Solution

1. Step 1: Check method signature

   The __array__ method should accept an optional dtype argument for flexibility.

2. Step 2: Verify return type

   It must return a NumPy array, so using np.array(self.data, dtype=dtype) is correct.

3. Final Answer:

   def __array__(self, dtype=None): return np.array(self.data, dtype=dtype) -> Option D

4. Quick Check:

   Correct signature and return type [OK]

Hint: Include dtype=None and return np.array inside __array__ [OK]

Common Mistakes:

• Omitting dtype parameter
• Returning list instead of np.array
• Using tolist() which returns Python list

3. Given the code below, what will be the output of print(np.array(obj))?

import numpy as np

class MyData:
    def __init__(self):
        self.data = [1, 2, 3]
    def __array__(self, dtype=None):
        return np.array(self.data, dtype=dtype)

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

medium

A. [1 2 3]

B. [[1 2 3]]

C. TypeError: object cannot be converted to array

D. [1, 2, 3]

Solution

1. Step 1: Understand __array__ method behavior

   The method returns a NumPy array created from the list [1, 2, 3].

2. Step 2: Predict np.array conversion

   Calling np.array(obj) triggers obj.__array__(), returning np.array([1, 2, 3]), which prints as [1 2 3].

3. Final Answer:

   [1 2 3] -> Option A

4. Quick Check:

   Custom __array__ returns array [1 2 3] [OK]

Hint: np.array calls __array__, returns array, prints without commas [OK]

Common Mistakes:

• Expecting Python list output with commas
• Thinking output is nested array
• Assuming TypeError due to custom class

4. What is wrong with this __array__ method implementation?

def __array__(self):
    return self.data.astype(float)

medium

A. It does not accept the optional dtype argument

B. astype cannot convert to float

C. Returning self.data directly is required

D. The method should return a list, not an array

Solution

1. Step 1: Check method signature

   The __array__ method should accept an optional dtype argument to allow flexible conversions.

2. Step 2: Identify missing dtype parameter

   This method lacks the dtype=None parameter, so it cannot handle dtype requests from NumPy.

3. Final Answer:

   It does not accept the optional dtype argument -> Option A

4. Quick Check:

   Missing dtype parameter [OK]

Hint: Always include dtype=None in __array__ method [OK]

Common Mistakes:

• Forgetting dtype argument
• Thinking astype cannot convert types
• Returning list instead of array

5. You have a custom class with a __array__ method that returns a NumPy array. You want to ensure that when you call np.array(obj, dtype=np.float64), the returned array respects the requested dtype. How should you modify the __array__ method?

hard

A. def __array__(self, dtype=None): return np.array(self.data)

B. def __array__(self): return np.array(self.data, dtype=np.float64)

C. def __array__(self, dtype=None): return np.array(self.data, dtype=dtype)

D. def __array__(self, dtype): return self.data.astype(dtype)

Solution

1. Step 1: Understand dtype propagation

   NumPy passes the requested dtype to __array__ as an argument; the method must accept it.

2. Step 2: Implement dtype-aware conversion

   Return np.array(self.data, dtype=dtype) so the requested dtype is applied correctly.

3. Final Answer:

   def __array__(self, dtype=None): return np.array(self.data, dtype=dtype) -> Option C

4. Quick Check:

   Accept dtype and pass to np.array [OK]

Hint: Pass dtype argument to np.array inside __array__ method [OK]

Common Mistakes:

• Ignoring dtype argument
• Hardcoding dtype inside method
• Not accepting dtype parameter