FilippoBovo/python_decorator.md

## python_decorator.md

      
    Raw
  

              python_decorator.md
            
          
    Decorator Example in Data Science

This example explains Python decorators in the context of data science. The example acts as a quick reminder, rather than a complete guide.
Consider a Pandas DataFrame about posts on a social media. The DataFrame, called posts, contains a column with the number of likes for each post.


post_id
...
likes
...


1
...
43
...


2
...
92
...


3
...
54
...


The following function calculates the average number of likes per post.
def average_likes(data):
    return data['likes'].mean()
We would like to decorate the function with a decorator checking that the likes column has integer type. Such a decorated function may look like the following.
@data_column_has_int_type('likes')
def average_likes(data):
    return data['likes'].mean()
The decorator can be defined in the following way.
def data_column_has_int_type(column):
    def decorator(function):
        def wrapper(*args, **kwargs):
            data = args[0]
            if not pandas.api.types.is_integer_dtype(data[column]):
                raise ValueError(f"Column {column} does not have integer type.")
            return function(*args, **kwargs)
        return wrapper
    return decorator
The decorated function, average_likes, is equivalent to:
data_column_has_int_type('likes')(average_likes)(posts)
This composition of functions unwraps as:


data_column_has_int_type('likes') ⟶ decorator with column set to 'likes', equivalent to:
def decorator(function):
    def wrapper(*args, **kwargs):
        data = args[0]
        if not pandas.api.types.is_integer_dtype(data['likes']):  # <- See change
            raise ValueError(f"Column {column} does not have integer type.") 
        return function(*args, **kwargs)
    return wrapper


decorator(average_likes) ⟶ wrapper with function set to 'average_likes', equivalent to:
def wrapper(*args, **kwargs):
    data = args[0]
    if not pandas.api.types.is_integer_dtype(data['likes']):
        raise ValueError(f"Column {column} does not have integer type.")
    return average_likes(*args, **kwargs)  # <- See change


wrapper(posts) becomes:
if not pandas.api.types.is_integer_dtype(posts['likes']):
        raise ValueError(f"Column {column} does not have integer type.")
average_likes(posts)  # <- See change


This flow can be viewed compactly as,
data_column_has_int_type('likes')(average_likes)(data)
                        decorator(average_likes)(data)  # column='likes'
                                         wrapper(data)  # column='likes', function=average_likes