What Does Immutable Mean Which Data Type in Python Are Immutable?


Heather Bennett

Have you ever come across the term “immutable” while working with Python? If you are new to programming or Python, this concept might seem a bit confusing.

But fear not! In this article, we will explore what “immutable” means and delve into which data types in Python are immutable.

Understanding Immutability

Immutability refers to the inability of an object to be modified after it is created. In simpler terms, once an immutable object is assigned a value, that value cannot be changed. Any attempt to modify an immutable object will result in the creation of a new object with the updated value.

So why would we want objects that cannot be modified? Well, immutability provides several benefits:

  • Security: Immutable objects are useful when dealing with sensitive data where you want to ensure its integrity. Since the values of these objects cannot be changed, they remain secure and unaltered.
  • Caching and Hashing: Immutable objects can be efficiently stored in caches or used as keys in dictionaries because their values never change.

    This allows for faster lookup and retrieval.

  • Simplicity: By making objects immutable, you eliminate the need for complex logic to handle changes in their state. This leads to cleaner and more maintainable code.

Immutable Data Types in Python

In Python, there are several data types that are immutable by nature. Let’s explore some of the commonly used ones:

Numeric Types (int, float, complex)

The numeric types in Python (integers, floating-point numbers, and complex numbers) are all immutable. Once assigned a value, they cannot be changed without creating a new object.


Strings, which represent sequences of characters, are also immutable in Python. Once a string is created, its value cannot be modified. However, you can perform operations on strings that return new string objects.


Tuples are ordered collections of items enclosed in parentheses. They are also immutable, meaning once a tuple is created, its values cannot be modified or appended. However, you can create a new tuple by concatenating or slicing existing tuples.

Mutable vs. Immutable

Now that we understand immutability, it’s worth mentioning the concept of mutability as well. Mutable objects are those that can be modified after creation. Examples of mutable objects in Python include lists, dictionaries, and sets.

The main difference between mutable and immutable objects is how they handle changes to their values. While immutable objects create new instances when modified, mutable objects directly modify their internal state without creating new instances.


In this article, we explored the concept of immutability in Python and discussed which data types in Python are immutable. Understanding immutability is crucial for writing efficient and secure code while avoiding unexpected side effects caused by unintentional modifications to objects.

By using immutable data types appropriately in your programs, you can ensure data integrity and simplify your codebase. Remember to use immutable types like strings, tuples, and numeric types when you need the benefits they provide.

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