Advanced Python Concepts: Closures, Decorators, Generators, and Scoping

Описание: A decorator in Python is a design pattern that allows you to modify or extend the behavior of a function or a class without explicitly changing its source code. It essentially "wraps" another function or class, adding functionality before or after the wrapped entity's execution.
Key characteristics of Python decorators:
Functions as First-Class Objects:
Python treats functions as first-class objects, meaning they can be passed as arguments to other functions, returned from functions, and assigned to variables. This is fundamental to how decorators work.
Higher-Order Functions:
Decorators are a form of higher-order functions, which are functions that take one or more functions as arguments or return a function as a result.
Syntactic Sugar:
Python provides special @ syntax for applying decorators, making them concise and readable. For example, @my_decorator placed directly above a function definition applies my_decorator to that function.
How they work:
A decorator function takes another function (the one to be decorated) as an argument.
Inside the decorator, a new "wrapper" function is defined. This wrapper function typically contains the added logic and then calls the original function.
The decorator returns this new wrapper function.
When the decorated function is called, it's actually the wrapper function that executes, performing the added logic and then invoking the original function.
Common use cases for decorators:
Logging: Recording function calls, arguments, and return values.
Authentication/Authorization: Checking user permissions before allowing access to a function.
Timing/Profiling: Measuring the execution time of a function.
Caching: Storing results of expensive function calls to avoid re-computation.
Input Validation: Ensuring arguments passed to a function meet certain criteria.