Type hints in Python are a way of specifying the expected data types of variables, function arguments, and return values in your code. They were introduced in Python 3.5 as part of the “PEP 484 – Type Hints” proposal and are used to add static typing information to Python code without actually enforcing it at runtime.
Prior to type hints, Python was dynamically typed, which means that variables were not bound to specific data types until runtime. This flexibility is one of Python’s strengths but can sometimes lead to hard-to-spot errors and confusion when dealing with large codebases.
Type hints allow developers to document the expected types more explicitly, which can help in code readability, documentation, and enable better tooling and IDE support for code analysis. However, it’s essential to note that type hints are optional and do not affect the actual behavior of the code during runtime. Python remains a dynamically typed language at its core.
Type Hints Example
Type hints are defined using special notation annotations in the form of
: Type, where
Type represents the expected data type. For example:
def add_numbers(a: int, b: int) -> int: return a + b
In this example,
b are expected to be of type
int, and the function is expected to return an
Several standard types are built into Python, such as
tuple, etc. For more complex types or custom classes, you can import types from the
Keep in mind that type hints are only informative and do not enforce strict type checking. Python will still allow you to pass values of different types to the annotated variables or functions, and the actual type errors will only be revealed at runtime if they occur.
To provide stricter type checking and to benefit from the type hints during development, you can use static type checking tools like Mypy. These tools analyze your code and check if the type hints are consistent with the actual usage of variables and functions.
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