The Truth About is vs == in Python — And Why Most Get It Wrong

You might think is and == are interchangeable in Python — but this simple mistake could be silently breaking your code.

The Truth About is vs == in Python — And Why Most Get It Wrong

Learn the critical difference between identity and equality in Python, and how to avoid one of the most misunderstood bugs in Python programming.

If you’ve ever stared at a bug in your Python code wondering why two variables that look the same aren’t equal, you’re not alone. Many developers — even experienced ones — confuse is and ==, often using them interchangeably. But in Python, that tiny difference isn't cosmetic. It’s fundamental.

In this article, we’ll break down what is and == actually mean in Python, when to use which, and why getting it wrong can lead to subtle, hard-to-detect bugs.

First, The Core Difference

Let’s start with the definitions:

== checks value equality — whether two objects have the same content.

is checks identity equality — whether two references point to the same object in memory.

Let’s demonstrate this with an example:

a = [1, 2, 3] 
b = [1, 2, 3] 
 
print(a == b)  # True 
print(a is b)  # False

At first glance, both lists look identical — and they are, in terms of content.

But Python treats them as two separate objects in memory. That’s why == returns True, but is returns False.

Why This Confuses So Many Developers

In certain cases, Python interns or caches objects behind the scenes to optimize performance. This behavior can make is appear to work “correctly” — until it doesn’t.

Consider this:

x = 256 
y = 256 
 
print(x == y)  # True 
print(x is y)  # True

Now try this:

x = 257 
y = 257 
 
print(x == y)  # True 
print(x is y)  # False

What just happened?

Python caches small integers between -5 and 256, so any variable assigned one of those values will point to the same memory reference. But anything outside that range? Separate objects.

So, when x = 257 and y = 257, Python creates two different objects that just happen to have the same value.

A Real-World Bug Waiting to Happen

Imagine you’re building a Django or Flask application and you’re comparing user input with a stored token.

if user_token is valid_token: 
    # grant access

This might pass your unit tests during development if both values are the same string reference. But in production, the valid_token may come from a database, and now you're comparing different objects with the same content. Result? Access denied — and hours wasted debugging.

The fix?

if user_token == valid_token: 
    # grant access

Use == to compare the actual content. Simple.

You should only use is when you care about identity, not value.

Here are valid use cases:

• Checking for None:

if result is None: 
    handle_missing_result()

• Comparing Singleton Objects:

if logger is Logger.get_instance(): 
    print("Same instance")

• Performance-critical identity checks (rare but valid).

Any other time? Reach for ==.

Summary: Quick Rule of Thumb

| Use Case                                                  | Use `==` or `is`? | 
| --------------------------------------------------------- | ----------------- | 
| Checking values (numbers, strings, lists, etc.)           | `==`              | 
| Checking if something is `None`                           | `is`              | 
| Comparing if two variables point to the exact same object | `is`              | 
| Any other situation                                       | Probably `==`     |

Final Thoughts

The difference between is and == is subtle — and that’s exactly why it trips people up. Understanding how Python handles object identity and value comparison will not only help you avoid bugs but also deepen your intuition for how the language works.

So the next time you reach for is, pause and ask: Am I checking identity, or just value?

Knowing the answer could save you hours of debugging — and make you a better Pythonista.

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