Difference Between Append and Extend in Python List Methods

Learn via video course
FREE
View all courses
Python Course for Beginners With Certification: Mastering the Essentials
Python Course for Beginners With Certification: Mastering the Essentials
by Rahul Janghu
1000
4.90
Start Learning
Python Course for Beginners With Certification: Mastering the Essentials
Python Course for Beginners With Certification: Mastering the Essentials
by Rahul Janghu
1000
4.90
Start Learning
Topics Covered

Overview

You've come to the correct place if you'd like to discover how to use append() and extend() and grasp their differences. They are effective list approaches that you will undoubtedly implement in your Python applications. The append() method in the Python programming language adds an item to a list that already exists whereas the extend() method adds each of the iterable elements which is supplied as a parameter to the end of the original list.

Let's know more about the two functions in more detail.

Extend vs Append Python List Methods

Lists in the programming language Python are mutable (The term mutable means that the programmers can update an element in any given list by retrieving it as a component of the allocation statement), meaning they can be extended or shortened per the programmer's choice. In other words, programmers can either add or remove elements from specified indices. Append() and extend() are two built-in list functions generally used to add elements, tuples, etc into any given list.

What is Append in Python?

The append() method in the programming language Python adds an item to a list that already exists. Instead of being returned to a new list, the item will be added toward the end of the existing list. Because lists can include elements of several data types, programmers can add items of any data type.

The method is responsible for adding its parameters to the end of a list as a single element. The list's length grows by one. The original list is updated when the append() method is used. The approach alters the original list in memory rather than creating a copy.

USE OF APPEND

Note: Lists can include items of several data types, you can add items of any data type.

Syntax of Append() Function

Let's discuss the parameters of the syntax:

  • This is the list that's going to be changed. Typically, this is a variable that refers to a list.
  • A dot, preceded by the method's name.
  • append() method
  • The element that needs to be added towards the end of the list is enclosed in parentheses, this is a mandatory parameter.

Keep in Mind: The dot in the syntax of the append() method is quite significant. This is known as "dot notation." The dot simply indicates "call this function on this specific list," so the function's effect will be implemented in the list that comes before the dot.

Let's go through a few examples to understand the concept of the append method in Python.

Example 1) Adding an element to the list using the append method in Python.

Output:

Example 2) Adding a list of elements to the list using the append method in Python.

Output:

You may be wondering why the entire list was added as a single item. This is due to the fact that the append() function appends an element as a whole to the list at the end. If the element is a sequence, such as a list, dictionary, or tuple, then the entire sequence will be added to the existing list as one single item.

Example 3) Adding a tuple as an item to the list using the append method in Python.

In this situation, the element to be added is a tuple to the list as a single item rather than as distinct items.

Output:

What is Extend in Python?

The extend method in Python is responsible for appending each iterable (it can be a tuple, any string, or a list) member to the list's end as well as increasing the length of the original list by the count of iterable elements supplied as an argument.

Note: The original list is modified using the extend method.

Let's go through a few examples.

Example 1) Extending an existing List.

Output of the Code:

Example 2) Extending an existing list by adding several elements one by one to the list.

Output of the code:

Example 3) Extending an existing list by adding strings.

Output:

Strings behave differently when using the .extend() function. Since each character in any given string is treated as a "item," the characters are inserted one by one in the sequence in which they occur in the string.

Difference Between Append and Extend in Python

The differences between the methods append() and extend() are shown in the tables below:

AppendExtend
The element passed in the append() argument is added at the end of the list.Iterable is passed as an argument and added at the end of the list.
It will add an element to the list without any changes.Iterable Object will append each of the elements at the end of the list.
The length of the list will increase by 1. The length of the list using extend() will increase by the length of the iterable object.
The time complexity of the append() method is O(1).The time complexity of the extend() method is O(k), where k is the length of the iterable.
append() is ideal for adding individual elements or a single object to the list.extend() is suitable for combining multiple lists or appending elements from other iterable sources efficiently.
When appending a list using append(), the entire list becomes a single element in the original list.When using extend(), individual elements from the iterable are added, not the iterable itself.
The append() method is simpler and more straightforward, making it easier to add single elements to a list.The extend() method is more versatile as it can concatenate multiple iterables, providing flexibility in combining different data sources.
append() is efficient when you want to add elements one at a time, especially within loops or conditional statements.extend() is efficient when you need to merge lists or add elements from complex iterable structures like tuples, sets, or other lists in one go.

Comparing Extend and Append in Single Program

Let's compare the two methods which we have discussed in the article above through one simple program.

Output of the Code:

The output of the extend is such that all the elements are iterable whereas the output of the append method adds the elements as a singular item.

The above program will make it easy for programmers to understand the difference between the two methods extend and append.

Learn More

If you are new to Python and would like to know what Python is and why is it widely used, check out this article What is Python Programming Language?.

Check out this article Applications of Python to know about the various applications of Python in real life.

To know how to install Python in your Windows system, you can check out this article How to Install Python in Windows?

Do you know what lists are in Python? If not, programmers can go through the given article, List in Python which covers the parameters of list in Python as well as examples.

Conclusion

  • The append() method adds a single element at the end of the list. It increases the length of the list by one and is particularly useful when you want to add individual elements without altering their structure.

  • The extend() method is used to concatenate iterable objects (like lists) to the end of the list. It appends each element of the iterable, effectively increasing the list’s length by the length of the iterable. It’s a powerful way to combine multiple lists or add elements from other iterable sources.

  • The key distinction lies in the type of object they accept. append() takes a single element and adds it as is, while extend() takes an iterable (like a list) and appends its elements individually to the list. Understanding this difference is crucial for precise list manipulation.

  • The append() method has a constant time complexity of O(1) since it adds only one element. In contrast, the time complexity of extend() is O(k), where k is the length of the iterable being added. Being aware of these complexities helps in optimizing code, especially when dealing with large datasets.