What's the Difference Between `==` and `is` in Python?

Understanding the Difference Between "=='' and "is'' in Python

When comparing values in Python, there are two common operators: "==" and "is." Often, it may seem that these operators perform the same equality check. However, there is a subtle distinction between the two in terms of what they evaluate.

The "==" and "is'' Operators

• "==": Tests for value equality. This means that it evaluates if two variables refer to objects that contain the same value.
• "is": Tests for object identity. This checks if two variables refer to the same object in memory, regardless of their values.

Value Equality vs. Object Identity

Value Equality:

• When using "==", Python compares the values of the objects being referenced by the variables.
• If the values are equal, the result is True, regardless of whether the objects are different instances in memory.

Example:

a = 10
b = 10

if a == b:
    print("Yay!")  # Will print "Yay!" as 10 == 10

Object Identity:

• When using "is," Python compares the object identifiers of the variables.
• If the variables refer to the same object in memory, the result is True, even if their values are different.

Example:

a = [1, 2, 3]
b = a

if a is b:
    print("Yay!")  # Will print "Yay!" as a and b point to the same list

Exceptions to the Rule

• Integers: Python caches small integer objects (up to 256) for performance reasons. This means that "is" may return True even for different integer variables within this range:

a = 100
b = 100

if a is b:
    print("Yay!")  # Will print "Yay!" due to integer caching

• Strings: Similarly, Python caches common string literals. However, note that this does not apply to strings created from variables:

a = "a"
b = "a"

if a is b:
    print("Yay!")  # Will print "Yay!" as string literals are cached

b = "aa"

if a is b:
    print("Nay!")  # Will not print as b is a different object

In summary, "==" tests for value equality, while "is" tests for object identity. Understanding this distinction is essential for writing correct and efficient Python code.

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