If It Is a Duck - It Better Quack Like a Duck

Python's dynamic typing enables rapid prototyping and easier testing, creating smaller, more comprehensible codebases. However, as projects expand, this permissive nature can become problematic—it becomes unclear which types are passed and expected, slowing development as programmers must independently deduce them.

Static languages have adopted type inference features like C#'s var, C++'s auto, and Scala's val, reducing typing burden while preserving benefits. Dynamic languages are not standing still. Tools like TypeScript and Flow add type checking to JavaScript seamlessly.

Three significant PEPs were drafted in 2014-2015: PEP 482 (Literature Overview for Type Hints), PEP 483 (The Theory of Type Hints), and PEP 484 (Type Hints). With Guido von Rossum as co-author, these features were anticipated for Python 3.5. The proposed mechanism relies on function annotations and optional comments, adding no extra syntax and incurring no runtime costs.

Mypy serves as a foundational tool for type checking. It allows optional annotations without disrupting existing code execution, enabling iterative type-checking introduction. Mypy detects type mismatches early and supports stub files for third-party libraries lacking annotations.

PyCharm provides robust IDE-based type checking through docstrings and function annotations. Other notable tools include Jedi for autocomplete with type inference, pysonar2 as a type inferencer, and traditional linters like pylint and pyflakes offering broader code analysis.

Type hinting represents a balanced approach combining dynamic and static typing benefits without requiring extensive refactoring. This feature promises improved maintainability for large projects while maintaining Python's flexibility and rapid development advantages.