python decorators

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# Decorators
Decorators are quick programming macros that can be used to alter the behavior of a Python object.

```
def add_author(func):
    print('ZED LABS')
    return func
    
@add_author
def get_title():
    return 'Some Info on Using Decorators in Python'

print(get_title())
```
The above add_author(func) function is a simple decorator. It prints the author’s name before the received function runs.
If a function needs to use this decorator, we can add it following an “@” sign on the top of the function.
The use of the “@” sign looks confusing at first, but it’s just a syntax sugar of Python. We can apply the decorator as following as well: `get_title = add_author(get_title)`

A decorator works like a reusable building block, we can apply it to a function when it’s needed without editing the function itself.

[Seven Ways of Using Decorators](https://medium.com/techtofreedom/7-levels-of-using-decorators-in-python-370473fcbe76)

# Some Useful Decorators

https://towardsdatascience.com/10-fabulous-python-decorators-ab674a732871
## @lru_cache

The first decorator on this list comes to us from the functools module. This decorator can be used to speed up consecutive runs of functions and operations using cache. One use is below:

```
@lru_cache
def factorial(n):
    return n * factorial(n-1) if n else 1
    
```

---

## @dataclass

One of the best decorators I utilize all the time in order to save time when writing classes is the dataclass decorator. This decorator can be used to quickly write common standard methods that are typically found in the classes that we write.

Dataclasses in Python are classes that are decorated using a tool from the standard library. The dataclass decorator, @dataclass, can be used to add special methods to user-defined classes. The advantage to this is that this is done automatically, which means that you can focus a bit more on the functions in your class instead of focusing on the class itself.

[Notebook](https://github.com/emmettgb/Emmetts-DS-NoteBooks/blob/master/Python3/Python%20DataClasses.ipynb)

```
@dataclass
class Food:
    name: str
    unit_price: float
    stock: int = 0
        
    def stock_value(self) -> float:
        return(self.stock * self.unit_price)
        
```

Now we can create a new class by calling this constructor, like so:

`carrot = Food("Carrot", .45, 25)
`

https://github.com/emmettgb/Emmetts-DS-NoteBooks/blob/master/Python3/Python%20DataClasses.ipynb

A very valid advantage to using this decorator is also that it avoids a lot of clutter in classes. The example of assigning each individual value inside of a Python class to a member variable is actually incredibly common. There is not really a great reason to write an entire function with a ridiculous amount of `self.x = x` when this is entirely avoidable by simply using a decorator. This can also serve to make a class a lot more readable.

---

## @staticmethod

In some cases, there might be circumstances where one might want to be able to access things that are defined privately as in a more global sense. Sometimes we have functions that are contained within classes that we want to have methodized, and this is exactly what the staticmethod decorator is used for.

Using this decorator, we can make C++ static methods for working with our classes. Normally, methods that are written in the scope of a class will be private to that class, and not accessible unless called as a child. There are circumstances, however, where you might want to take a more functional approach with the way your methods interact with data. Using this decorator, we can create both options without the need for creating more than one function.
class Example:

```   
@staticmethod
    def our_func(stuff):
        print(stuff)
```

We also do not need to explicitly provide our class as an argument. The staticmethod decorator takes care of this for us.

# Functools

https://towardsdatascience.com/functools-an-underrated-python-package-405bbef2dd46

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