Space Complexity of List Operations in Python
Last Updated :
19 Mar, 2025
In Python, lists are one of the most commonly used data structures. Lists are versatile, flexible, and can store items of different data types, making them suitable for a wide range of applications. However, just like any other data structure, the space complexity of list operations plays a crucial role in optimizing algorithms and improving performance.
What is Space Complexity?
Space complexity refers to the amount of memory used by an algorithm or data structure as a function of the input size. For lists, it involves considering the memory required to store the list elements and any additional memory used during operations like insertion, deletion, or iteration.
Here are some of the main methods for working with lists in Python with some of their complexities.
Python append()
This method adds an element to the end of a list. The append() method will not result in significant additional memory overhead, as it simply adds the new element to the end of the existing list.
Python
a = [1, 2, 3]
a.append(4)
print(a)
Space Complexity: O(1), as it adds a single element to the list, but the list may occasionally resize, which would involve more memory temporarily.
Python extend()
extend() method appends all elements of an iterable (like another list) to the end of the list.
Python
a = [1, 2, 3]
a.extend([4, 5, 6])
print(a)
Space Complexity: If you are extending a list with another list using extend(), the space complexity is O(n), where n is the length of the list being appended. This is because extend() iterates over the elements of the list being appended and adds them one by one to the end of the original list, resulting in a linear increase in memory usage with the size of the list being appended.
Python insert()
insert() method inserts an element at a specific position in the list. To accommodate the new element, the elements after the insertion point must be shifted.
Python
a = [1, 2, 3]
a.insert(1, 0)
print(a)
Space Complexity: O(n), where n is the number of elements in the list. This is because shifting the elements to the right to make space for the new element requires memory for all affected elements.
Python remove()
remove() removes the first occurrence of an element from the list. This operation involves searching for the element and then shifting the remaining elements to fill the gap left by the removed item.
Python
a = [1, 2, 3]
a.remove(2)
print(a)
Space Complexity: O(n), where n is the number of elements in the list. The operation requires shifting the remaining elements after the target is removed.
Python pop()
pop() method removes and returns the element at the specified position. If no index is specified, the last element is removed.
Python
a = [1, 2, 3]
a.pop(1)
print(a)
Space Complexity: O(1), since it only removes the element without shifting other elements (if removing from the end). Removing from the beginning or middle would require shifting, making the complexity O(n).
Python index()
index() method is used to find the index of the first occurrence of a specified value in the list. If the value is not found, it raises a ValueError.
Python
a = [1, 2, 3]
index = a.index(2)
print(a)
Space Complexity: O(1), because it only returns the index and does not modify the list or create new structures.
Python sort()
sort() method sorts a list in place. This means the list is rearranged in memory.
Python
a = [3, 2, 1]
a.sort()
print(a)
Space Complexity: O(1) for in-place sorting. If a new list is created for sorting (e.g., with sorted()), the space complexity would be O(n), since a new sorted list is returned.
Python reverse()
reverse() method is used to reverse the elements of a list in place. This means that the original list is modified, and no new list is created.
Python
a = [1, 2, 3]
a.reverse()
print(a)
Space Complexity: O(1), because it reverses the list in place without creating a new list.
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