Data Structures and Algorithms in Python

This page contains a list of commonly used Data Structures and Algorithms in Python. The source code examples of all the Python Data Structures and Algorithms are up-to-date and are well-tested in our development environment.

Data Structures in Python

A data structure is a specialized format for organizing, processing, retrieving, and storing data. It provides a means to manage vast amounts of data efficiently, and large-scale datasets would be nearly unmanageable without robust data structures.

1. Stack Implementation in Python
2. Queue Implementation in Python
3. Deque Implementation in Python
4. Singly Linked List Implementation in Python
5. Doubly Linked List Implementation in Python
6. Circular Linked List Implementation in Python
7. PriorityQueue Implementation in Python
8. Circular Queue Implementation in Python
9. Binary Search Tree Implementation in Python
10. Stack Implementation Using Linked List in Python
11. Stack Implementation Using Doubly Linked List in Python
12. Binary Tree Implementation in Python 
13. Binary Search Tree Implementation in Python 
14. AVL Tree Implementation in Python

Sorting and Searching Algorithms in Python

An algorithm is a step-by-step procedure or formula for solving a problem. It's a sequence of steps to perform to achieve a particular output. Algorithms can be represented as pseudocode or flowcharts, among other formats.

1. Write a Python program for Linear Search and Binary Search
2. Selection Sort Algorithm in Python
3. Bubble Sort Algorithm in Python
4. Bogo Sort Algorithm in Python
5. Bucket Sort Algorithm in Python
6. Comb Sort Algorithm in Python
7. Counting Sort Algorithm in Python
8. Heap Sort Algorithm in Python
9. Insertion Sort Algorithm in Python
10. Merge Sort Algorithm in Python
11. Quick Sort Algorithm in Python
12. Shell Sort Algorithm in Python
13. Interpolation Search Algorithm in Python
14. Linear Search Algorithm in Python 
15. Binary Search Algorithm in Python

Understanding the right data structure and algorithm to use for a given scenario can greatly improve the efficiency (in terms of runtime and memory usage) of software applications. This knowledge becomes especially vital when dealing with large datasets or when performance is a critical aspect of the application.