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The is not operator is a negated identity operator in Python that evaluates whether two operands reference different objects in memory. It returns True if the variables point to distinct memory addresses, regardless of whether their underlying values are equivalent. 
operand_a is not operand_b
Under the hood, the is not operator compares the unique integer identities of the objects, which are exposed via Python’s built-in id() function. The expression a is not b is functionally equivalent to evaluating id(a) != id(b).

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Identity vs. Equality

It is critical to distinguish between the is not operator and the != (not equal) operator:
  • is not evaluates object identity. It strictly checks memory allocation.
  • != evaluates object value. It delegates to the object’s __ne__() dunder method to determine if the contents of the objects differ.
Two variables can have identical values but exist as separate objects in memory. In such scenarios, != evaluates to False, while is not evaluates to True. 

# Two distinct list objects with identical values
list_a = [1, 2, 3]
list_b = [1, 2, 3]


# Value comparison
print(list_a != list_b)      # False: The contents are the same


# Identity comparison
print(list_a is not list_b)  # True: They occupy different memory addresses


# Reference assignment
list_c = list_a
print(list_a is not list_c)  # False: Both reference the exact same object

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CPython Interning and Constant Folding

The behavior of is not is heavily influenced by CPython’s memory optimization techniques, specifically object interning and constant folding. CPython globally caches and reuses specific immutable objects, such as small integers (typically -5 to 256) and certain string literals. When two variables are assigned the same interned value, the interpreter points them to the same cached object. In these specific cases, is not will evaluate to False even if the variables were declared independently. For immutable literals outside the intern range, CPython’s AST optimizer performs constant folding. If identical literals are defined within the same code block (such as a standard Python script), the compiler co-allocates them to the same memory address. To reliably demonstrate distinct memory allocation for identical values outside the intern range, the values must be computed at runtime. 

# Small integers are globally interned
x = 256
y = 256
print(x is not y)  # False: CPython reuses the memory address for 256


# Constant folding in the same code block reuses memory
a = 257
b = 257
print(a is not b)  # False: The compiler optimizes identical literals in the same block


# Runtime computation forces separate allocation outside the intern range
c = 257
d = int('257')
print(c is not d)  # True: Distinct memory addresses are allocated at runtime
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