Python Learning Labs

A structured collection of Python exercises and small applied programs built while strengthening software engineering fundamentals.

All implementations in this repository were written and iterated by me as part of my learning process. The goal is to document progression from Python fundamentals toward small, organized programs that can be explained, debugged, and extended.

Topics covered

• PY-01 — Types, variables, and operators
• PY-02 — Control flow and loops
• PY-03 — Functions, parameters, returns, and scope
• PY-04 — Lists, tuples, dictionaries, sets, and comprehensions
• PY-05 — Strings, files, and pathlib
• PY-06 — JSON and serialization
• PY-07 — Exceptions and tracebacks
• PY-08 — Modules, imports, and packages
• PY-09 — OOP, classes, and dataclasses
• PY-10 — Typing, Optional, Enum, and Protocol
• PY-11 — Iterators, generators, and context managers
• PY-12 — Logging, configuration, virtual environments, pip, and pyproject.toml

Applied labs

The repository includes small programs such as an expense tracker, API usage cost estimator, model benchmark analyzer, log file inspector, resilient JSON configuration loader, modular AI request package, model run tracker, typed reporter, streaming result processor, and logging/tooling lab.

Learning approach

Each folder focuses on a specific Python concept. Exercises are intentionally small enough to isolate the concept, while applied labs combine previously learned skills when that adds useful practice. The code prioritizes clarity and understanding over production-level architecture.

Next step

With the Python foundation documented here, the next learning block is HTTP/REST and consuming external APIs.