Python Data Structures and Algorithms

A curated Python project implementing data structures and algorithms from scratch, including dynamic arrays, bit vectors, linked lists, hash maps, priority queues, bloom filters, and graph traversal/pathfinding routines.

This repository was built by combining the strongest parts of two university projects and then cleaning, restructuring, and documenting them for portfolio use.

What this project demonstrates

• custom core data structures implemented without relying on Python's built-in equivalents
• algorithmic problem solving using those structures in realistic tasks
• graph traversal and shortest-path search
• probabilistic membership testing with a Bloom filter
• clean modular organisation across structures, algorithms, and applications

Included modules

structures/

• dynamic_array.py — resizable array with sorting support
• bit_vector.py — compact bit storage
• linked_list.py — doubly linked list
• map.py — hash map with separate chaining
• pqueue.py — binary min-heap priority queue
• bloom_filter.py — probabilistic set membership
• graph.py and directed_graph.py — graph representations used by traversal algorithms

algorithms/

• pathfinding.py — BFS, DFS, and Dijkstra traversal
• problems.py — examples using the project data structures in traversal and filtering tasks

applications/

• warmup_challenges.py — selected algorithmic challenge functions adapted into a cleaner module:
  • missing_odds
  • k_cool
  • number_game
  • road_illumination

Example usage

python examples.py

Running tests

python -m unittest discover -s tests

Why this repo is on my GitHub

I wanted one repository that highlights CS fundamentals clearly for graduate software roles: building core data structures, applying them to algorithmic problems, and organising the code like a real project rather than leaving it as raw coursework.