Understand What Is Mutable And Immutable In Python

Most of us have heard the terms “mutable” and “immutable” when discussing variables. You may even know that by assigning a variable with a mutable or immutable type, you can change its value over time instead of creating a new copy every time you want to update it. However, do you know how and why this is beneficial? They’re fairly nuanced terms that are frequently misunderstood, so if you’re still unsure about their meanings, continue reading for more details.

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Mutable Objects

An object is mutable if it is open to change after it’s been created. In practice, this means you can change an existing object’s value at any point. Most types in Python are mutable, including most standard data types, such as integers, strings, and lists. You can also create your own custom types that are mutable. In fact, most types are designed to be mutable by default. However, there are some exceptions. The special kinds of Boolean and None are designed to be immutable, meaning that you cannot alter them.

Immutable Objects

Immutable objects are not open to change after they’ve been created, meaning you cannot assign new values, remove them, or replace them. Immutable objects are a particular type with specific rules that dictate what can and cannot be done with them. There are only a few types of built-in immutable objects in Python. These include integers, which are used to represent numbers without a decimal point, like the number 3. Strings are also immutable, but they are also a different data type, which we’ll discuss. Immutable objects are useful when you need to create a unique value that is not open to alteration at any point. Some examples of where immutable objects’ implementation include password hashes, security encryption keys, hashes for integrity verification, and IDs for identifying users or items.

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Understanding Why Immutability is Important

Immutable objects are designed to be confident that the value remains the same, even when the code has been written and run on a different computer. This is important to ensure that the code runs correctly, without errors, and that the results are accurate. A good example is using an ID for a user on a website. If that ID is created as an immutable integer, it’s guaranteed to remain the same throughout the website’s lifetime. This means that when you have a user with an ID of 123456, you know that the ID will never change. This is particularly important in online banking and eCommerce, where the ID can be used as a unique identifier for each user, allowing them to log in and use the service. If the ID was a mutable object, like a string, the value could change, meaning that the user would no longer be identified correctly. This would also mean that any data they’ve entered or sent would be incorrect, which is clearly not a situation you want to be in.

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Examples and Syntax of Mutable and Immutable Python Types

Here are some key examples of both mutable and immutable object types in Python: 

– Mutable Objects: Lists, Dictionaries, and Strings are all examples of mutable types. 

– Immutable Objects: Integers, Floats, and Symbols are all examples of immutable types.

While there are many different types within each category, these examples should help you understand the distinctions between mutable and immutable types and how they behave differently in Python. The best way to fully understand these distinctions is to experiment with them. Try creating variables of different types and then changing them over time to see how they behave. You can also see the complete documentation on Python’s data types to learn more.


As you can see, mutable and immutable data types are important factors when developing code. They can affect how your code runs and how accurate your results are, so it’s essential to understand the difference between them and spot when they’re being used. When you know how and when to use these types, you’ll be well creating more efficient and accurate code. You’ll also be able to avoid some common pitfalls that are found when you misuse these types, such as building code that is susceptible to hacking and data breaches. In the end, the best way to master the use of mutable and immutable data types is to practice writing code. Once you encounter situations where you need to use them, you’ll start to understand better how they work and how to use them.

It is also even more critical for you to get a working knowledge of such concepts in Python because Python does form the foundation of any data science endeavor. So, if you wish to start your data science journey, mastering Python fundamentals should be your first step towards it. All you require is proper guidance and a network of people who have worked in the industries. At upGrad, our Professional Certificate in Data Science and Business Analytics aims to provide such techniques. This 20-month course is designed for both freshers as well as experienced professionals, extending a chance to choose from 3 specializations, work with industry experts, and learn all the relevant tools and technologies required for a bright career in data science.  

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Is it important to know mutable and immutable types in Python?

Definitely. You must know which types in Python are mutable and which aren’t mutable. So that when you are programming for any of your projects, you know exactly which kind of variables you would want.

How are mutable data types in Python used?

Mutable data types are used just like any other data type in Python. The only difference is that one can modify mutable data types in terms of the value they hold.

Are mutable data types better than immutable data types?

There is no better or worse when it comes to data types in Python. In the end, it depends on what you want to do with the variables. If you envision your variables requiring lots of modifications with time, you might want to use a mutable data type. On the other hand, if you are sure that your variables will hold fixed values, you can also go for immutable data types.

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