List vs Tuple: Difference Between List and Tuple

Summary:

In this Article, you will learn the difference between List and Tuple.

List	Tuple
It is mutable	It is immutable
The implication of iterations is time-consuming in the list.	Implications of iterations are much faster in tuples.
Operations like insertion and deletion are better performed.	Elements can be accessed better.
Consumes more memory.	Consumes less memory.
Many built-in methods are available.	Does not have many built-in methods.
Unexpected errors and changes can easily occur in lists.	Unexpected errors and changes rarely occur in tuples.

Read more to know each in detail.

In Python, list and tuple are a class of data structures that can store one or more objects or values. A list is used to store multiple items in one variable and can be created using square brackets. Similarly, tuples also can store multiple items in a single variable and can be declared using parentheses.

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In the list vs tuple conversation there is one more thing, that is their modifying nature. The tuples cannot be modified whereas the lists can be modified. One of the reasons why the tuples are known to have a good memory is because of this non-modifying nature.  The number of methods available in these two also differ such as tuples have 33 methods whereas the list has 46 available methods. 

There is a difference in python tuple vs list and the syntax for both tuple and the list also differs such as, the items in tuples are surrounded by parentheses( ) and the items in lists are surrounded by square brackets [ ]. The list consumes more storage space than the tuples. Also, the creation and accessing of the lists is a slower process than the tuples. 

The list and tples are not the same and should not be considered same at all. There is a significant difference between these two. Apart from the mutability difference, their variable sizes are also different, the lists have a variable size whereas the tuples hav e affixed size.

Although there are many differences between list and tuple, there are some similarities too, as follows:

• The two data structures are both sequence data types that store collections of items.
• Items of any data type can be stored in them.
• Items can be accessed by their index.

The table below includes the basic difference between list and tuple in Python.

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Before jumping to Python tuple vs. list differentiation, let’s find out what exactly these two are. 

Lists

A list is one of the major Python data structures used to contain a setlist of things called items. Like arrays, list to tuple Python helps keep similar types of data values together, condensing their code together. This helps perform several detailed operations on multiple values at the same time. For example, a folder of songs on your desktop has different other subfolders adjusted following different genres for a hassle-free collection. List to tuple Python is used to improve efficiency and help the system manage all the values together. 

Tuples

Just like lists, tuples also contain a set of objects in an ordered manner. These objects are kept separated by commas. Tuples are immutable and do not allow the entrance of additional objects once a tuple is created. Tuples are incapable of expanding or making modifications; therefore, they differ from lists. Removing elements is also not possible from tuples which restrict its collection. The immutability often serves as an advantage in delivering faster, efficient results. 

While the major purpose and foundation of tuple vs. list Python are the same, various methods differentiate the two. Here’s what you will find in this Python tuple vs. list blog!

List And Tuples In Python: Syntax

List Syntax

The [ ] symbol initiates a list. Here is a syntax representing how to declare a list in Python. 

num_list = [1,2,3,4,5]
print(num_list)
alphabets_list = [‘a’,‘b’,‘c’,‘d’,‘e’]
print(alphabets_list)

Finding different types of data within a list is something common. However, to initiate it, do as follows:

mixed_list = [‘a’, 1,‘b’,2,‘c’,3,‘4’]
print(mixed_list)

Furthermore, one can create a nested list (a list within a list) in Python as well. Here’s how: 

nested_list = [1,2,3,[4,5,6],7,8]
print(nested_list)

Tuple Syntax

The () symbol initiates a tuple. Here is a syntax representing how to declare a tuple in Python

num_tuple = (1,2,3,4,5)
print(num_tuple)
alphabets_tuple = (‘a’,‘b’,‘c’,‘d’,‘e’)
print(alphabets_tuple)

A Tuple consists of data of different types. However, follow this syntax to initiate it:

mixed_tuple = (‘a’, 1,‘b,’ 2,‘c,’ 3, ‘4’).
print(mixed_tuple)

Also, one can create a nested tuple in Python as well. Here’s how: 

nested_tuple = (1,2,3,(4,5,6),7,8)
print(nested_tuple)

Tuples and List in Python: Syntax Differences

Objects are stored in containers called lists and tuples. However, there are differences in both its use cases and syntax. Tuples are enclosed by round brackets () and lists by square brackets []. Here’s how to create a list and tuple in python.

list_numbers  = [1,2,3,4,5]
tuple_numbers  = (1,2,3,4,5)
print(list_numbers)
print(tuple_numbers)

To check any object’s data type, use the type function.

type(list_numbers)
type(tuple_numbers)

List vs Tuple

Python’s most widely used build-in data types Python list vs. tuple are hard to differentiate between considering many similarities they both emit, confusing Python beginners in using the most appropriate one. Here’s how you can differentiate between Python tuple vs. list. 

The table below includes the basic difference between list and tuple in Python.

List	Tuple
It is mutable	It is immutable
The implication of iterations is time-consuming in the list.	Implications of iterations are much faster in tuples.
Operations like insertion and deletion are better performed.	Elements can be accessed better.
Consumes more memory.	Consumes less memory.
Many built-in methods are available.	Does not have many built-in methods.
Unexpected errors and changes can easily occur in lists.	Unexpected errors and changes rarely occur in tuples.

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The following sections provide a detailed version of the list vs tuple for better understanding.

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Difference in syntax

Tuple vs. list Python syntax has a very slight difference between the two but is essential for the right implementation. One major obvious difference between Python list vs. tuple is list syntax uses a square bracket, while the tuple syntax is surrounded using parentheses. As mentioned in the introduction, the syntax for list and tuple are different. For example:

list_num = [10, 20, 30, 40]

tup_num = (10, 20, 30, 40)

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Mutability

One of the most important differences between a list and a tuple is that list is mutable, whereas a tuple is immutable. This means that in list vs. tuple Python lists can be changed after it is created to comply with requirements, while tuples cannot be changed or altered after their creation following your required edits, causing tuples to have a fixed size. 

So, some operations can work on lists, but not on tuples. For example, in data science, if a list already exists, particular elements of it can be reassigned. Along with this, the entire list can be reassigned. Elements and slices of elements can be deleted from the list.

On the other hand, particular elements on the tuple cannot be reassigned or deleted, but it is possible to slice it, and even reassign and delete the whole tuple. Because tuples are immutable, they cannot be copied.

An item in the list can be changed, it can be accessed directly. The items in the list can be changed by using the indexing operator [ ]. The individual values can also be changed in the lists.

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Operations

Although there are many operations similar to both lists and tuples, lists have additional functionalities that are not available with tuples. These are insert and pop operations, and sorting and removing elements in the list.

There are many operators such as

Operator	Purpose
The + operator	returns a tuple that contains the first and the second tuple object.
The [ ] operator	This returns the item at the given index.
The * operator	This concatenates many copies of a tuple.
The [: ] operator	Returns the item in a specific range, the two index operands are separated by the :: symbol.

Functions

Some of the Python functions can be applied to both data structures, such as len, max, min, any, sum, all, and sorted.

Description of some the functions are mentioned below-

1. max(tuple) → Returns item from the tuple with the max value.
2. min(tuple) → Returns item from the tuple with the min value.
3. tuple(seq) → Converts a list into tuple.
4. cmp(tuple1, tuple2) → Compares the elements of both the mentioned tuples.

 

Python Expression	Description
len	Length
(1,2) + (3,4)	Concatenation
2 in (1,2,3)	Membership
(‘Morning’,) * 2	Repetition

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Size

In Python, tuples are allocated large blocks of memory with lower overhead, since they are immutable; whereas for lists, small memory blocks are allocated. Between the two, tuples have smaller memory. This helps in making tuples faster than lists when there are a large number of elements.

In simple terms, the size would mean the amount of memory a tuple is storing if it is small or large memory. The size can be calculated using the built-in len() function.

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 In list vs. tuple Python has to provide an extra memory block to lists as it is expandable and might require it with potential modifications. 

Type of elements

Elements belonging to different data types, i.e., heterogeneous elements, are usually stored in tuples. While homogeneous elements, elements of the same data types, are usually stored in lists. But this is

not a restriction for the data structures. Similar data type elements can be stored in tuples, and different data type elements can also be stored in lists.

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Length

Lengths differ in the two data structures. Tuples have a fixed length, while lists have variable lengths. Thus, the size of created lists can be changed, but that is not the case for tuples.

Methods

The methods that apply only to lists in Python are, insert(), clear(), sort(), pop(), reverse(), remove(), and append(). While these methods are only applicable to lists, a few more are shared by both lists and tuples. These include count() and index() methods.

Debugging

When it comes to debugging, in lists vs tuples, tuples are easier to debug for large projects due to its immutability. So, if there is a smaller project or a lesser amount of data, it is better to use lists. This is because lists can be changed, while tuples cannot, making tuples easier to track.

Nested lists and tuples

Tuples can be stored in lists, and likewise, lists can be stored inside tuples. In nested tuples, a tuple can hold more tuples and might extend in more than two dimensions. On the other hand,  in nested lists, a list can hold more lists in countless dimensions as you wish.

Uses

It is important to understand that there are different cases where it is better to use one of these data structures, such as; using either one depends on the programmer, i.e., choosing one based on whether they want to change the data later or not.

Tuples can be used as equivalent to a dictionary without keys to store data. When tuples are stored within lists, it is easier to read data. Meanwhile, lists can be used to contain similar resembling elements. Tuples are comparatively more time and memory efficient than lists with restricted usage. However, the lists’ immutability makes it efficient to align with potential changes effectively. 

Read: More types of data structures in Python

List And Tuple In Python: Use Cases

The following scenarios call for data storage that works best for Python’s lists: 

• Lists can hold a variety of data kinds, and you can retrieve them using the index of the list. 
• Lists are useful for performing computations on a collection of elements since Python enables you to do so directly on the list. 
• It is simple to expand or decrease the size of your list as necessary if you are unsure how many elements will be kept in it in advance. 

The following scenarios lend themselves to the use of Python’s tuples for data storage: 

• When you are certain of the precise data that will be entered into an object’s fields, it is ideal to use a tuple. 
• You can use a tuple, for instance, to store login information for your website. 
• tuples can only be used as dictionary keys because they are immutable (unchangeable). 
• However, you must convert a list into a tuple before using it as a key. 

Tuples Over Lists: When To Use It?

Although Lists and Tuple in Python ensures holding sets of data effortlessly, they both are distinct in many ways. However, there are certain circumstances where using Tuples over Lists would be a better decision. These scenarios are as follows:

• Immutable Data: Since tuples cannot have their contents modified after they have been produced, they are immutable. Given this, Tuples emerge as a better choice for storing data, such as setup parameters, constants, or other data, that mustn’t undergo modifications and remain consistent when the application is running. 
• Performance: Due to their immutability, tuples are more compact than lists and may be quicker to create, access, and loop over. If you have a sizable amount of data you must regularly store, access, and use but doesn’t require to be changed, utilizing a tuple may be more efficient than using a list. 
• Data Integrity: Tuples are a useful tool for maintaining data integrity since they guarantee that the data’s structure and contents remain constant. If a function returns a certain number of values, for example, you may intend to return them as a tuple rather than a list so the caller knows how much data to expect. 

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Conclusion

In this article we have discussed about difference between list and tuple and understood about them. This article helps in understanding the differences between lists and tuples. Even though both types are data structures in Python, it is important to be familiar with these differences when making a choice. The most important differences to keep in mind is that lists are mutable, and tuples are not, lists have variable sizes and tuples have fixed sizes. Lastly, operations in tuples can be executed faster.

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