PEP8 Guidelines Python Clean Coding - Function Annotations: Part 2

The video tutorial explains the concept of function annotations in Python, demonstrating how to create a Fibonacci sequence function with type annotations for parameters and return values. It covers the recursive logic of the function, debugging steps, and the use of the inspect module to retrieve function metadata. The tutorial emphasizes the importance of annotations for code readability and clarity.

What is the primary purpose of using annotations in Python functions?

• To make the function run faster

• To define the types of parameters and return values

• To improve the performance of the function

• To reduce the size of the code

In the Fibonacci function, what type is the parameter 'N' annotated as?

• Integer

• List

• String

• Float

What is the initial value of the 'output' parameter in the Fibonacci function?

• A list with one element

• None

• Zero

• An empty list

What error was encountered when running the Fibonacci function initially?

• Using 'list' instead of 'last'

• Syntax error

• Missing parameter

• Incorrect return type

How can you view the annotations of a function in Python?

• Using the 'annotations' module

• Using the 'type' function

• Using the 'inspect' module

• Using the 'print' function

What does the 'inspect.getfullargspec' function provide?

• The return value of a function

• The execution time of a function

• Complete information about a function's arguments and annotations

• The memory usage of a function

Why is it beneficial to use annotations in Python functions?

• They automatically fix errors in the code

• They reduce the size of the code

• They help in making the code more readable

• They make the code run faster