Python Algorithm Lab

Extensive Python and data structures/algorithms learning repository.

This repo has two jobs:

• preserve the original course/practice archive under ITP/, DSA/, and revision/
• provide a clean, tested reference layer under src/dsa/ for optimized implementations

Structure

DSA/                 original DSA practice files
ITP/                 introduction-to-python practice files
revision/            merged revision material
src/dsa/             clean reference implementations
tests/               tests for reference implementations
docs/
  learning-roadmap.md
  problem-index.md

Clean Reference Implementations

Requires Python 3.11+. From the repository root:

python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
python -m pip install -r requirements-dev.txt
python -m pytest

make test runs the same pytest suite. Collection is restricted to tests/: original exercises may read stdin or run demonstrations on import.

The reference suite checks exhaustive small input combinations against Python's built-in search/sort behavior, stable sorting with custom __lt__ objects, non-mutation, and stack/queue lifecycle errors. GitHub Actions exercises Python 3.11–3.13; AWS CodeBuild uses the same tests.

Operation	Time	Extra space	Contract
Linear search	O(n)	O(1)	First matching index, or -1
Binary search	O(log n)	O(1)	Ascending input; any matching index, or -1
Merge sort	O(n log n)	O(n)	Stable new list; mutually orderable elements
Stack push / pop	O(1) amortized	O(n) storage	LIFO; empty pop/peek raises IndexError
Queue enqueue / dequeue	O(1)	O(n) storage	FIFO; empty dequeue raises IndexError

Current reference modules:

• src/dsa/searching.py
• src/dsa/sorting.py
• src/dsa/stack.py
• src/dsa/queue.py

Learning Philosophy

For every topic:

1. Understand the brute-force approach.
2. Write the simple implementation.
3. Optimize time and space complexity.
4. Add tests for edge cases.
5. Document the pattern in docs/problem-index.md.

Status

This is the primary home for Python algorithm, DSA, and revision work.

The archived exercises are historical learning material; the supported, CI-tested surface is src/dsa/.