Python Projects

Python Data Structures

Data structures in Python are fundamental constructs for organizing and managing data efficiently. This page will be updated with explanations, come back regularly to check for updates. The repo of python data structures can be found here

You can check out these sorting algorithms visually at the great site visualgo

Lists:

Lists are ordered collections of data. They can hold elements of different types. Lists are flexible and allow dynamic resizing.

Example:

Python

my_list = [1, 2, 3, "GFG", 2.3]

Dictionaries:

Dictionaries store key-value pairs. Efficient for quick lookups (time complexity O(1)).

Example:

Python

my_dict = {'Name': 'Geeks', 1: [1, 2, 3, 4]}

Tuples:

Immutable sequences of elements. Useful for constants or fixed data.

Example:

Python

my_tuple = (1, 2, 3)

Sets:

Unordered collections of unique elements. Ideal for set operations (union, intersection, etc.).

Example:

Python

my_set = {1, 2, 3}

Strings:

Immutable sequences of characters. Commonly used for text manipulation.

Example:

Python

my_string = "Hello, World!"

Linked Lists:

Linear data structures with nodes linked together. Useful for dynamic data insertion and deletion.

Example: Implementing a stack or queue.

Graphs:

Nonlinear data structures with nodes and edges. Represent relationships between entities.

Example: Social networks, web pages.

Binary Trees:

Hierarchical data structures with parent-child relationships. Efficient for searching and sorting.

Example: Binary search trees.

Heaps:

Specialized tree-based structures. Used for priority queues and heap sort.

Example: Min-heap, max-heap.

Remember, choosing the right data structure depends on the problem you’re solving. Python provides a rich toolkit to handle various scenarios!