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Type Hints & Annotations

7 Topics
1

Type Hints Basics

Adding optional type annotations to Python — variable annotations, function signatures, return types, and how type hints improve code quality and IDE support.

Modern Python best practice — interviewers expect type-annotated code in production-quality solutions.
2

typing Module

The typing module — Optional, Union, Any, Callable, and modern equivalents in Python 3.9+ and 3.10+ for complex type annotations.

Essential for writing production-quality Python — know both old and modern typing syntax.
3

Generics

TypeVar and Generic — building reusable, type-safe containers and functions that work with any type while maintaining static checking.

Advanced typing — shows ability to design reusable, type-safe abstractions.
4

Type Aliases

Creating reusable type shortcuts — named types for complex annotations, improving readability and reducing repetition in type hints.

Code readability — shows ability to make complex type systems manageable.
5

mypy

Static type checking with mypy — configuring, running, understanding errors, and integrating type checking into your development workflow.

Professional code quality — shows commitment to reliable, maintainable codebases.
6

Protocols

Structural subtyping with Protocol — defining interfaces based on shape rather than inheritance, enabling duck typing with static checking.

Modern Python interface design — Protocol is preferred over ABCs for most use cases.
7

Runtime Type Checking

Runtime type validation with isinstance, type guards, Pydantic, and dataclass validation — enforcing types at runtime when static checking is not enough.

Data validation — knowing when and how to validate types at runtime for API inputs and user data.
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