Modular advanced Python learning system updated to the most modern technologies of 2026
This README serves two purposes:
- Entry hub (to quickly understand how the training is structured), and
- Detail document (all original technical content, further below).
If this is your first time here, follow this order:
- Learning Roadmap (Hub)
- Program Structure (Hub)
- Module Summary (Hub)
- Quick Start (Hub)
- Full Documentation (Hub)
PHASE 1: Foundation (4-6 weeks)
01_python_fundamentals → 02_intermediate_python → 03_basic_intermediate_oop
PHASE 2: Technical Core (6-8 weeks)
04_cpython_internals_advanced → 05_modern_concurrency → 06_typing_metaprogramming
PHASE 3: Software Engineering (8-10 weeks)
07_design_patterns → 08_application_architecture → 09_testing_qa → 10_performance_optimization
PHASE 4: Modern Stack 2026 (8-12 weeks)
11_modern_tooling_2026 → 12_fastapi_complete → 13_backend_ecosystem → 14_advanced_python_2026 → 15_basic_data_science → 16_modern_security
Total estimated time: 7-10 months, adaptable to your own pace.
nan-python-engineering-labs/
├── 01...16_*/ → curriculum modules
├── scripts/ → automation tools (includes progress tracking)
├── GETTING_STARTED.md → step-by-step setup
├── STATUS.md → global program status
├── pyproject.toml → tooling/dependencies
└── README.md → general map + full detail
Suggested flow per topic:
topic README → examples → exercise → tests → reflection
| Module | What you learn |
|---|---|
| 01_python_fundamentals | Solid language foundation |
| 02_intermediate_python | Flow, files, exceptions, generators |
| 03_basic_intermediate_oop | Applicable object-oriented design |
| 04_cpython_internals_advanced | Internals, GIL/free-threading, subinterpreters |
| 05_modern_concurrency | Threading, multiprocessing, modern asyncio |
| 06_typing_metaprogramming | Advanced typing and metaprogramming |
| 07_design_patterns | Patterns for robust design |
| 08_application_architecture | Modular and scalable architecture |
| 09_testing_qa | Professional testing and quality |
| 10_performance_optimization | Practical profiling and optimization |
| 11_modern_tooling_2026 | uv, Ruff, modern type checking |
| 12_fastapi_complete | Modern production-ready APIs |
| 13_backend_ecosystem | Backend and infrastructure integration |
| 14_advanced_python_2026 | PyO3 and AI-assisted development |
| 15_basic_data_science | Python data science fundamentals |
| 16_modern_security | Modern software and supply chain security |
curl -LsSf https://astral.sh/uv/install.sh | sh
uv venv
source .venv/bin/activate # Linux/Mac
# .venv\Scripts\activate # Windows
uv pip install -e ".[dev,profiling,ai,pyo3,security,data]"
pre-commit install
uv run scripts/progress.pycd 01_python_fundamentals
cat README.md- GETTING_STARTED.md: installation and setup
- STATUS.md: progress tracking
- scripts/progress.py: automatic progress report
- scripts/run_topic_tests.py: sequential pytest runner for exercise-based topic tests
- pyproject.toml: environment and tooling configuration
This repository is part of the NaNLABS learning lab ecosystem:
- nan-ai-engineering-labs — AI/ML engineering fundamentals and applied techniques
- nan-data-engineering-labs — Data engineering pipelines, warehousing, and orchestration
- nan-ai-native-engineering-labs — AI-native development with prompt engineering and agents
Python Engineering Labs is a structured self-learning project covering Python from fundamentals to advanced topics, including the latest 2026 innovations: free-threading without GIL (PEP 703), Rust-based tooling (uv, Ruff), PyO3 extensions, AI-assisted development, and modern security architecture.
- 200+ topics organized in 16 thematic modules — independent and self-contained
- No fixed calendar: learn at your own pace
- Pre-populated templates with curated content
- Progressive exercises (basic → intermediate → advanced) with tests
- 88 design patterns fully documented
- Modern infrastructure: DevContainers, pre-commit hooks, automatic tracking
- Rust ecosystem: uv, Ruff, PyO3
- Python 3.13+: free-threading, subinterpreters
| Module | Completed | Total | Progress | Percentage |
|---|---|---|---|---|
| Python Fundamentals | 0 | 12 | ░░░░░░░░░░░░░░░░░░░░ | 0.0% |
| Intermediate Python | 0 | 15 | ░░░░░░░░░░░░░░░░░░░░ | 0.0% |
| Basic Intermediate Oop | 0 | 12 | ░░░░░░░░░░░░░░░░░░░░ | 0.0% |
| Cpython Internals Advanced | 0 | 27 | ░░░░░░░░░░░░░░░░░░░░ | 0.0% |
| Modern Concurrency | 0 | 25 | ░░░░░░░░░░░░░░░░░░░░ | 0.0% |
| Typing Metaprogramming | 0 | 22 | ░░░░░░░░░░░░░░░░░░░░ | 0.0% |
| Design Patterns | 0 | 88 | ░░░░░░░░░░░░░░░░░░░░ | 0.0% |
| Application Architecture | 0 | 18 | ░░░░░░░░░░░░░░░░░░░░ | 0.0% |
| Testing Qa | 0 | 16 | ░░░░░░░░░░░░░░░░░░░░ | 0.0% |
| Performance Optimization | 0 | 14 | ░░░░░░░░░░░░░░░░░░░░ | 0.0% |
| Modern Tooling 2026 | 0 | 36 | ░░░░░░░░░░░░░░░░░░░░ | 0.0% |
| Fastapi Complete | 0 | 28 | ░░░░░░░░░░░░░░░░░░░░ | 0.0% |
| Backend Ecosystem | 0 | 20 | ░░░░░░░░░░░░░░░░░░░░ | 0.0% |
| Advanced Python 2026 | 0 | 45 | ░░░░░░░░░░░░░░░░░░░░ | 0.0% |
| Basic Data Science | 0 | 10 | ░░░░░░░░░░░░░░░░░░░░ | 0.0% |
| Modern Security | 0 | 40 | ░░░░░░░░░░░░░░░░░░░░ | 0.0% |
| -------- | ----------- | ------- | ---------- | ------------ |
| TOTAL | 0 | 428 | ░░░░░░░░░░░░░░░░░░░░ | 0.0% |
Last updated: nan-python-engineering-labs
Variables, data types, control structures, built-in data structures, basic functions, comprehensions. Topics marked as (optional) for experienced programmers.
Topics: 12 | Estimated time: 15-20 hours
Basic decorators, file handling, exceptions, iterators, generators, important standard modules.
Topics: 15 | Estimated time: 20-25 hours
Classes, inheritance, polymorphism, special methods, properties, descriptors, composition vs inheritance.
Topics: 12 | Estimated time: 18-22 hours
History of the GIL, PEP 703 free-threading, PEP 684 subinterpreters, thread-safety without GIL, object model, reference counting, migration strategies.
Topics: 27 | Estimated time: 40-50 hours
Highlighted topics:
- Free-threading Python 3.13+ (
--disable-gilmode) - Subinterpreters with per-interpreter GIL
- Thread-safety in modern Python code
- Biased reference counting
- Immortal objects (PEP 683)
Threading with/without GIL, subinterpreters for isolation, multiprocessing with shared memory, advanced asyncio, concurrency patterns, concurrent testing.
Topics: 25 | Estimated time: 35-50 hours
Advanced type hints, Protocols, TypeVar, ParamSpec, metaclasses, descriptors, AST manipulation, import hooks.
Topics: 22 | Estimated time: 30-40 hours
88 design patterns organized in 8 subcategories: GoF basics, Pythonic, advanced GoF, architectural, distributed systems, concurrency, messaging, object management.
Patterns: 88 | Estimated time: 60-80 hours
SOLID, DDD, hexagonal architecture, CQRS, Event-Driven, clean architecture with practical Python examples.
Topics: 18 | Estimated time: 25-35 hours
pytest advanced, fixtures, mocking, hypothesis (property-based testing), mutation testing, performance testing.
Topics: 16 | Estimated time: 20-30 hours
Profiling (py-spy, memray, viztracer), algorithmic optimization, Cython, NumPy vectorization, strategic caching.
Topics: 14 | Estimated time: 20-28 hours
uv (Rust-based package manager), Ruff (linter/formatter), BasedPyright/Pylyzer, pre-commit automation, advanced pytest configuration.
Topics: 35 | Estimated time: 25-35 hours
Complete FastAPI framework, JWT authentication, WebSockets, background tasks, deployment on Railway/Fly.io.
Topics: 28 | Estimated time: 40-56 hours
SQLAlchemy 2.0, Redis, RabbitMQ, Kafka, gRPC, distributed observability, service mesh.
Topics: 20 | Estimated time: 28-40 hours
PyO3 (Rust extensions) — 22 topics. AI-Assisted Development — 23 topics. LangChain, LangGraph, autonomous agents.
Topics: 45 | Estimated time: 45-60 hours
NumPy, Pandas, Matplotlib, Polars, practical statistics for data engineering.
Topics: 10 | Estimated time: 15-20 hours
Supply chain security, SBOM, Sigstore (keyless signing), SOPS/Vault (secrets management), runtime hardening.
Topics: 40 | Estimated time: 30-45 hours
This project is licensed under the MIT License. See LICENSE for details.