Array protocol and __array__ in NumPy - Step-by-Step Execution

Concept Flow - Array protocol and __array__

Object with __array__ method

↓

Call np.array(obj)

↓

Check if obj has __array__

↓

Call obj.__array__()

↓

Get numpy array

↓

Use numpy array for computations

When numpy.array() is called on an object, it checks if the object has a __array__ method. If yes, it calls this method to get a numpy array representation. Otherwise, it converts the object by default.

Execution Sample

NumPy

import numpy as np
class MyData:
    def __array__(self):
        return np.array([1, 2, 3])
obj = MyData()
arr = np.array(obj)

This code creates a class with __array__ method that returns a numpy array. When np.array() is called on an instance, it uses __array__ to get the array.

Execution Table

Step	Action	Check __array__	Call __array__()	Resulting Array	Notes
1	Create instance obj of MyData	N/A	N/A	N/A	obj is a custom object
2	Call np.array(obj)	Yes, obj has __array__	Called obj.__array__()	array([1, 2, 3])	np.array uses __array__ method
3	np.array returns the array	N/A	N/A	array([1, 2, 3])	Array ready for use
4	Use arr in computations	N/A	N/A	array([1, 2, 3])	Standard numpy array behavior
5	End	N/A	N/A	N/A	Process complete

💡 np.array() finishes after calling __array__ and obtaining the numpy array

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
obj	None	MyData instance	MyData instance	MyData instance	MyData instance
arr	None	None	array([1, 2, 3])	array([1, 2, 3])	array([1, 2, 3])

Key Moments - 3 Insights

Why does np.array(obj) call obj.__array__() instead of converting obj directly?

Because numpy checks if the object has a __array__ method to get a direct numpy array representation, which is more efficient and accurate. See execution_table step 2.

What happens if the object does not have a __array__ method?

Numpy tries to convert the object by default methods like iterating or using buffer protocols. This is shown in the concept_flow where the 'No' branch leads to default conversion.

Can __array__ return any type of array?

Yes, __array__ should return a numpy ndarray or something convertible to it. Returning other types may cause errors or unexpected behavior.

Visual Quiz - 3 Questions

Look at the execution_table at step 2, what does np.array(obj) do?

A Converts obj by default without calling __array__

B Calls obj.__array__() to get a numpy array

C Raises an error because obj is not an array

D Returns obj unchanged

💡 Hint

Check the 'Check __array__' and 'Call __array__()' columns at step 2 in execution_table

According to variable_tracker, what is the value of arr after step 3?

A array([1, 2, 3])

B None

C MyData instance

D An error

💡 Hint

Look at the 'arr' row and 'After Step 3' column in variable_tracker

If MyData.__array__ returned array([4,5,6]) instead, what would arr be after step 3?

A MyData instance

B array([1, 2, 3])

C array([4, 5, 6])

D None

💡 Hint

The __array__ method defines the array returned by np.array(obj), see execution_table step 2

Concept Snapshot

Array protocol (__array__) lets objects define how to convert themselves to numpy arrays.
When np.array(obj) is called, numpy checks for __array__ method.
If found, it calls obj.__array__() to get the array.
This allows custom objects to integrate smoothly with numpy.
If no __array__, numpy uses default conversion methods.

Full Transcript

This visual execution trace shows how numpy's array protocol works with the __array__ method. When np.array() is called on an object, numpy checks if the object has a __array__ method. If yes, it calls this method to get a numpy array representation. Otherwise, it converts the object by default. The example code defines a class MyData with __array__ returning array([1, 2, 3]). When np.array(obj) is called, it calls obj.__array__() and returns the numpy array. The execution table traces each step: creating the object, calling np.array, checking for __array__, calling it, and returning the array. The variable tracker shows how variables obj and arr change during execution. Key moments clarify why __array__ is used and what happens if it is missing. The quiz questions test understanding of these steps. The concept snapshot summarizes the array protocol as a way for objects to define their numpy array conversion. This helps beginners see the step-by-step process and understand how numpy integrates with custom objects.