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The == operator is Python’s value equality operator. It evaluates whether the internal data or state of two objects is equivalent, regardless of whether they occupy the same memory address. 
operand_a == operand_b

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Internal Mechanics

When the == operator is invoked, Python translates the expression into a method call to the __eq__() dunder (magic) method. In Python’s data model, dunder methods are always looked up on the class, not the instance, to bypass instance dictionaries. Therefore, the internal lookup is conceptually type(operand_a).__eq__(operand_a, operand_b). The evaluation follows a strict dispatch mechanism that accounts for inheritance:
  1. Subclass Priority Rule: If operand_b is an instance of a strict subclass of operand_a’s type, Python reverses the standard dispatch order. It will call type(operand_b).__eq__(operand_b, operand_a) first. This ensures that subclasses can properly override and dictate the equality behavior of their parent classes.
  2. Primary Dispatch: If the subclass rule does not apply, Python attempts to call the __eq__ method of the left operand: type(operand_a).__eq__(operand_a, operand_b).
  3. Fallback Dispatch: If the first method called returns the built-in NotImplemented constant (indicating it does not know how to compare itself to the other type), Python falls back to the other operand’s __eq__ method.
  4. Identity Fallback: If both operands return NotImplemented, Python falls back to comparing object identity, effectively evaluating operand_a is operand_b (comparing their memory addresses).

# The conceptual equivalent of the == operator's internal logic
def evaluate_equality(a, b):
    type_a = type(a)
    type_b = type(b)
    
    # 1. Subclass Priority Rule
    if type_a is not type_b and issubclass(type_b, type_a):
        result = type_b.__eq__(b, a)
        if result is not NotImplemented:
            return result
        result = type_a.__eq__(a, b)
        
    # 2. Standard Dispatch
    else:
        result = type_a.__eq__(a, b)
        if result is NotImplemented:
            # 3. Fallback Dispatch
            result = type_b.__eq__(b, a)
            
    # 4. Identity Fallback
    if result is NotImplemented:
        return a is b
        
    return result

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Type Handling and Coercion

Python is strongly typed, meaning == does not perform implicit type coercion across disparate data types (e.g., a string "1" will never equal an integer 1). However, the operator implements specific behaviors for certain built-in types:
  • Numeric Types: Python’s numeric types (int, float, complex, decimal, fractions) implement cross-type equality. The == operator will evaluate 1 == 1.0 as True because their mathematical values are equivalent, despite differing memory representations.
  • Collections: For built-in container types (like list, tuple, dict, and set), the == operator performs a deep, recursive equality check. It iterates through the collections, invoking the == operator on each corresponding pair of elements. Both the structure and the values must match.
  • Custom Objects: By default, user-defined classes inherit their __eq__ method from the base object class. The default implementation strictly compares memory addresses. To achieve value equality in custom objects, the developer must explicitly override the __eq__ method to define the specific attributes that constitute equality.

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Hashing Implications

In Python’s data model, the == operator is tightly coupled with the __hash__() method. If two objects evaluate as equal via the == operator (a == b is True), they must yield the exact same integer value when passed to the hash() function, provided they are hashable. If a custom class overrides __eq__ to provide value equality, Python automatically sets its __hash__ to None, making the object unhashable unless __hash__ is also explicitly overridden.
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