Python
The everyday language for data work, backend APIs, automation, and AI — built on a small set of ideas worth understanding deeply.
mutability
scope & closures
decorators
generators
collections
OOP
__slots__
async/await
context managers
type hints
Topic overview
Values and references, scope and closures, decorators, generators, the right collection type, Python's object protocol, memory optimisation with __slots__, the async event loop model, context managers for resource safety, modern type annotations, and graceful error handling.
Core concepts
Mutable vs immutable, LEGB, closures, functools.wraps, comprehensions, yield, Counter/defaultdict/deque, dunder methods, __slots__, GIL, coroutines and tasks, async context managers, __enter__/__exit__, Optional/Protocol, and try/except/finally.
Why it matters
Strong Python fundamentals let you move quickly without introducing subtle bugs — reference aliasing, mutable defaults, late-binding closures, blocking event loops, and GIL misconceptions trip up engineers who learned Python by copying examples rather than understanding the model.
Why it matters
Production Python tests whether you can reason through tricky reference and scope questions, write clean transformations, know the right collection type, explain the async event loop, and design Pythonic resource-safe APIs. The "why" behind each feature makes the knowledge stick.