Python Mastery

A gentle on-ramp for absolute beginners: run a first program, then variables, numbers, strings, booleans, loops, and lists — all runnable in the browser.

• Run your very first Python program
• Variables, numbers, strings, and f-strings
• Booleans, comparisons, and if/else
• Loops, lists, and the patterns you'll use daily

Modern Python tooling that saves years of pain: uv, ruff, pyproject, virtual envs, and a project layout that scales.

• Install Python and the modern toolchain (uv, ruff, mypy)
• The Python runtime — interpreter, bytecode, and startup
• Project layout — src/, tests/, config, and secrets

The language, quickly and idiomatically: types, control flow, data structures, functions, I/O, errors, and logging.

• Syntax, types, and control flow
• Lists, tuples, sets, dicts — and when to reach for each
• Functions, closures, and scoping
• Strings, encoding, and file I/O
• Errors, exceptions, and the logging module

Classes done well: dataclasses, Pydantic, Protocols, ABCs, and the design patterns that carry their weight.

• Classes, methods, and dunder methods
• Inheritance, MRO, and super()
• Dataclasses and Pydantic v2
• Protocols and abstract base classes
• Design patterns in idiomatic Python

The features that separate junior from senior: decorators, generators, context managers, descriptors, and typing.

• Decorators — functions, classes, and parameterised
• Generators, iterators, and yield from
• Context managers and contextlib
• Descriptors and metaclasses — power features, used sparingly
• Static typing with mypy and pyright

The stdlib tour every senior knows by heart, plus regex, threads, processes, asyncio, and the 3.11–3.14 changes worth using.

• Standard library tour: itertools, functools, pathlib, and friends
• Regex and text processing
• Concurrency: threads, processes, and the GIL
• asyncio deep dive — the model that runs modern Python services
• Modern Python features (3.11–3.14)

The libraries every Python data role uses: NumPy, pandas, polars, visualisation, and databases.

• NumPy essentials — vectorise everything
• pandas patterns that actually scale
• polars — the fast alternative to pandas
• Visualisation — matplotlib, seaborn, plotly, and altair
• Databases: SQLAlchemy, asyncpg, and connection pools

pytest, hypothesis property tests, mypy/pyright, and ruff — the CI stack that catches bugs before customers do.

• pytest — fixtures, parametrise, and plugins
• Property-based testing with hypothesis
• Type checking with mypy and pyright in CI
• Linting, formatting, and pre-commit

Profile before you optimise, then reach for parallelism, C extensions, or a rewrite in the right order.

• Profiling — cProfile, py-spy, and memory profilers
• Optimisation techniques for pure Python
• Parallelism — multiprocessing, joblib, and Dask
• CPython internals — reference counts, the GIL, and free-threading

FastAPI, Pydantic v2, httpx clients, and streaming responses — the shape of modern Python web services.

• FastAPI in depth — routers, dependencies, and lifespan
• Pydantic v2 for API validation and settings
• httpx — sync and async HTTP clients
• Streaming, Server-Sent Events, and WebSockets

Ship your Python: pyproject with uv, PyPI publishing, Docker images that stay small, and compiled binaries.

• pyproject.toml and uv — the modern package layout
• Publishing to PyPI — trusted publishing and CI
• Docker for Python apps — small, fast, reproducible
• Shipping compiled Python — PyInstaller, Nuitka, and wheels

Coding problems and system-design prompts written to mirror real senior/staff Python interviews — not a LeetCode grind.

• Coding problems — idiomatic Python under pressure
• System-design prompts — Python-focused