Array protocol and __array__ in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this code using __array__?

Consider the following Python class that implements the __array__ method. What will be the output when converting an instance to a NumPy array?

NumPy

import numpy as np

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

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

A [10 20 30]

B [[10 20 30]]

C

[[10]
 [20]
 [30]]

D TypeError: Cannot convert object to array

💡 Hint

The __array__ method returns a NumPy array representation of the object. NumPy uses this to create the array.

The __array__ method returns a 1D NumPy array [10, 20, 30]. When np.array(obj) is called, it uses this method to get the array data. So the output is a 1D array printed as [10 20 30].

❓ data_output

intermediate

What is the shape of the array returned by __array__?

Given this class with a __array__ method, what is the shape of the resulting NumPy array?

NumPy

import numpy as np

class Custom:
    def __array__(self):
        return np.array([[1, 2], [3, 4], [5, 6]])

obj = Custom()
arr = np.array(obj)
print(arr.shape)

A (1, 6)

B (2, 3)

C (6,)

D (3, 2)

💡 Hint

Look at the shape of the array returned inside __array__.

The __array__ method returns a 2D array with 3 rows and 2 columns, so the shape is (3, 2).

🔧 Debug

advanced

Why does this code raise an error when using __array__?

Examine the code below. Why does calling np.array(obj) raise an error?

NumPy

import numpy as np

class Broken:
    def __array__(self):
        return [1, 2, 'three']

obj = Broken()
np.array(obj)

A No error, returns array(['1', '2', 'three'], dtype='<U5')

B TypeError because the list contains mixed types that cannot be converted to a numeric array

C AttributeError because __array__ must return a NumPy array, not a list

D ValueError because the list length is inconsistent

💡 Hint

NumPy can create arrays from lists with mixed types by upcasting to strings.

The __array__ method returns a list with mixed types. NumPy converts this to an array of strings automatically, so no error occurs. The output is array(['1', '2', 'three'], dtype='.

🧠 Conceptual

advanced

What is the purpose of the __array__ method in NumPy?

Choose the best description of what the __array__ method does in a Python object used with NumPy.

A It automatically converts all object attributes to arrays

B It defines how the object converts itself to a NumPy array when passed to np.array()

C It is used to check if an object is already a NumPy array

D It prevents the object from being converted to a NumPy array

💡 Hint

Think about how NumPy uses this method internally.

The __array__ method tells NumPy how to get an array representation of the object. When np.array() is called on the object, NumPy calls this method to get the data.

🚀 Application

expert

Using __array__ to customize array conversion behavior

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

NumPy

import numpy as np

class Wrapper:
    def __init__(self, data):
        self.data = data
    def __array__(self, dtype=None):
        arr = np.array(self.data)
        if dtype:
            return arr.astype(dtype)
        return arr * 2

obj = Wrapper([1, 2, 3])
result = np.array(obj, dtype=np.float64)
print(result)

A [2 4 6]

B [2. 4. 6.]

C [1. 2. 3.]

D TypeError because __array__ does not accept dtype argument

💡 Hint

The __array__ method can accept a dtype argument to customize conversion.

The __array__ method receives the dtype=np.float64 argument from np.array(). It converts self.data to an array [1 2 3], then since dtype is truthy, returns arr.astype(np.float64), resulting in [1. 2. 3.]. The multiplication by 2 only occurs if no dtype is specified.

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