Create a video on Python decorators, I want to understand it fully
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Welcome to Python decorators! Decorators are a powerful feature that lets you modify or extend functions without changing their original code. You'll recognize them by the at symbol above function definitions. Think of decorators as wrappers that add extra functionality to your existing functions. The at syntax is actually just syntactic sugar for function reassignment.
Before understanding decorators, we need to grasp that Python functions are first-class objects. This means functions can be assigned to variables, passed as arguments to other functions, and returned as values. You can store them in lists or dictionaries just like any other object. This flexibility is what makes decorators possible - we can manipulate functions just like any other data.
Now let's create our first decorator! A decorator is simply a function that takes another function as input and returns a modified version. The pattern is always the same: define an outer function that accepts the target function, create an inner wrapper function that adds behavior, then return the wrapper. The at symbol automatically applies this pattern, making your code cleaner and more readable.
Advanced decorators can accept arguments! When you see at repeat with parentheses and a number, that's a parametrized decorator. This requires three layers of functions: the outermost takes the decorator arguments, the middle layer is the actual decorator that receives the function, and the innermost is the wrapper. This pattern creates a decorator factory that can generate customized decorators based on the parameters you provide.
To summarize what we've learned about Python decorators: They're a powerful way to modify or extend functions without changing their original code. Decorators work because functions are first-class objects in Python. The at symbol is just syntactic sugar that makes the code cleaner. Remember to use functools dot wraps to preserve metadata, and explore common decorator patterns like logging, timing, and caching in your own projects.