Python provides multiple data structures to handle collections of data, with list and deque (double-ended queue) being two commonly used structures, each offering unique advantages depending on the operation at hand.
- List is a dynamic array that supports indexing and slicing.
- Deque is a doubly linked list optimized for fast insertions and deletions at both ends.
List in Python
In Python, a list is a built-in dynamic sized array (automatically grows and shrinks). We can store all types of items (including another list) in a list. A list may contain mixed type of items, this is possible because a list mainly stores references at contiguous locations and actual items maybe stored at different locations.
- List can contain duplicate items.
- List in Python are Mutable. Hence, we can modify, replace or delete the items.
- List are ordered. It maintain the order of elements based on how they are added.
- Accessing items in List can be done directly using their position (index), starting from 0.
read more about - Python Lists
Deque in Python
A deque stands for Double-Ended Queue. It is a data structure that allows adding and removing elements from both ends efficiently. Unlike regular queues, which are typically operated on using FIFO (First In, First Out) principles, a deque supports both FIFO and LIFO (Last In, First Out) operations.
read more about - Deque in Python
Performance Comparison: List vs Deque
The performance of list and deque varies significantly based on the type of operation being performed:
| Operation | List (O Complexity) | Deque (O Complexity) |
|---|
| Append at end | O(1) | O(1) |
|---|
| Insert at beginning | O(n) | O(1) |
|---|
| Delete from end | O(1) | O(1) |
|---|
| Delete from beginning | O(n) | O(1) |
|---|
| Random access | O(1) | O(n) |
|---|
- Appending Elements: Both lists and deques provide O(1) performance for appending elements at the end. However, if elements need to be inserted at the beginning frequently, deques are significantly faster since lists require shifting elements (O(n) complexity).
- Deleting Elements: Removing elements from the end is efficient (O(1)) for both lists and deques. However, deleting from the beginning of a list is slow (O(n)) due to the need for shifting elements, whereas deques can do it in O(1) time.
- Random Access: Lists are optimized for direct indexing, allowing O(1) access to any element. Deques do not support efficient random access (O(n)), making them less suitable for applications that require frequent indexed lookups.
- Memory Overhead: Lists in Python dynamically allocate memory in chunks to allow efficient appending, whereas deques use a linked list internally, which can be more memory-efficient in scenarios requiring frequent insertions/deletions.
Use Cases
When to Use Lists
- When frequent random access to elements is needed.
Python a = [10, 20, 30, 40] print(a[2])
- When appending elements at the end without frequent insertions at the beginning.
- When needing support for slicing operations.
When to Use Deques
- When frequent insertions and deletions occur at both ends of the sequence.
Python from collections import deque dq = deque([10, 20, 30]) dq.appendleft(5) # Inserts at the beginning dq.pop() # Removes from the end print(dq)
- When implementing queue-like or stack-like structures.
- When working with large datasets requiring fast insertions/removals at the beginning.
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