Member-only story
Top Python Data Structures You Aren’t Using But Should Be!
This article is open to everyone, non-members can view via this link
Python is known for its simplicity and powerful standard library, but many developers stick to basic data structures like lists and dictionaries. While these are great, Python offers a rich set of data structures that can make your code more efficient and readable for specific use cases. Whether you’re handling huge datasets or need to optimize specific operations, understanding and using Python’s advanced data structures can be a game-changer.
In this article, we’ll explore five lesser-known Python data structures that you might not be using, but should definitely consider.
deque: The Double-Ended Queue
When we need efficient appends and pops from both ends of a list, we tend to reach for a list, but Python’s deque (double-ended queue) from the collections module can be much faster. Unlike Python lists, which are optimized for quick appends at the end but slow when you add elements to the front, deque is optimized for both.
Example:
from collections import deque
dq = deque([1, 2, 3])
dq.append(4) # Add to the right
dq.appendleft(0) # Add to the left
print(dq) # deque([0, 1, 2, 3, 4])
