Welcome to learning about type annotations in Python! Understanding type annotations can significantly improve the readability and maintainability of your code. By providing explicit types for your variables and function parameters, you not only help yourself but also others who might be reading your code understand your intentions more clearly. Our goal today is to ensure you are comfortable with the syntax and usage of type annotations in Python.

Type annotations explicitly specify the data types of variables, function parameters, and return values. While Python is dynamically typed, meaning it doesn't require explicit data types, adding type annotations is beneficial:

• Code Clarity: Makes your code more readable.
• Error Prevention: Helps catch errors early with tools like mypy.
• Documentation: Serves as documentation for your code's data types.

Let's break down the basic syntax using the add function.

Without type annotations:

With type annotations:

In the function signature def add(a: int, b: int) -> int::

• a: int indicates a should be an int.
• b: int indicates b should be an int.
• -> int specifies the function returns an int.

You can also annotate variables outside of functions to ensure their expected types:

Here, x and y are explicitly annotated as integers, clarifying their types.

Let's look at another example. Consider a function that greets a user:

• name: str indicates name should be a str.
• -> str specifies the function returns a str.

Example:

Now, let's see an example of a function that takes a list of integers and returns a boolean:

• numbers: list[int] indicates numbers should be a list of integers.
• -> bool specifies the function returns a boolean.

And here is another example of a function that finds sum of numbers from the given list:

The operator | indicates that the variable can be of multiple types (also known as a union type). In this case, elements in the nums list can be either integers or floats, and the function's return type can also be an integer or a float. This flexibility allows the function to handle a mix of numeric types seamlessly.

You learned about the importance and usage of type annotations in Python. We covered:

• What type annotations are and their benefits.
• How to use type annotations for function parameters and return types.
• How to annotate variables.

This knowledge helps you write more readable and maintainable code, making your intentions clear to anyone who reads it.

Let's move on to practice exercises. You'll apply your knowledge of type annotations in various coding tasks, solidifying your understanding and improving your Python skills.