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📚 Quick Guide to Data Structures
📌 What are Data Structures?
A data structure is a way of organizing, managing, and storing data efficiently so that it can be accessed and modified effectively.
They are the foundation of efficient algorithms and system design.
🧱 Basic Data Structures
1. Array
- Fixed-size collection of elements indexed by position.
- Good for indexed access.
- Poor for insertion/deletion in the middle.
arr = [1, 2, 3]
arr[0] # Access element at index 0
2. Linked List
- A linear collection of nodes where each node points to the next.
- Great for insertions/deletions.
- Poor for indexed access.
class Node:
def __init__(self, val):
self.val = val
self.next = None
3. Stack
- Last-In, First-Out (LIFO)
- Used in undo operations, call stacks, parsing
stack = []
stack.append(1)
stack.pop() # Removes 1
4. Queue
- First-In, First-Out (FIFO)
- Used in scheduling, message passing
from collections import deque
queue = deque()
queue.append(1)
queue.popleft()
5. Hash Table (Dictionary/Map)
- Key-value pairs
- Fast access, insertion, and deletion
map = {"a": 1, "b": 2}
map["a"] # Returns 1
6. Set
- Collection of unique elements
- Great for membership tests
s = {1, 2, 3}
1 in s # True
🌲 Advanced Data Structures
7. Binary Tree
- Each node has at most two children
- Used in hierarchical data
8. Binary Search Tree (BST)
- Ordered binary tree
- Left < Root < Right
9. Heap (Min/Max)
- Complete tree structure
- Fast access to min/max element
10. Trie
- Prefix tree, used for auto-complete and dictionary word storage
11. Graph
- Nodes connected by edges
- Used in networks, social graphs, routing
🧠Choosing the Right Data Structure
| Task | Suggested Structure |
|---|---|
| Fast lookup | Hash Table (Map) |
| Insertion/deletion at ends | Stack/Queue |
| Ordered data | Binary Search Tree, Array |
| Prefix-based search | Trie |
| Hierarchical data | Tree |
| Complex relationships | Graph |
📈 Time Complexities
| Operation | Array | LinkedList | HashMap | BST (Balanced) |
|---|---|---|---|---|
| Access | O(1) | O(n) | O(1) | O(log n) |
| Search | O(n) | O(n) | O(1) | O(log n) |
| Insert/Delete | O(n) | O(1) | O(1) | O(log n) |
Understanding data structures is critical to designing efficient algorithms and systems. Choose the right one based on the problem requirements!
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