创建一个基于 Pandas.DataFrame 的类，使用 pandas.read_csv() 函数进行初始化

Question

My goal is to create an object that behaves the same as a Pandas DataFrame, but with a few extra methods of my own on top of it. As far as I understand, one approach would be to extend the class, which I first tried to do as follows:

class CustomDF(pd.DataFrame):
    def  __init__(self, filename):
        self = pd.read_csv(filename)

But I get errors when trying to view this object, saying: 'CustomDF' object has no attribute '_data' .

My second iteration was to instead not inherit the object, but rather import it as a DataFrame into one of the object attributes, and have the methods work around it, like this:

class CustomDF():

    def  __init__(self, filename):
        self.df = pd.read_csv(filename)

    def custom_method_1(self,a,b,...):
        ...

    def custom_method_2(self,a,b,...):
        ...

This is fine, except that for all custom methods, I need to access the self.df attribute first to do anything on it, but I would prefer that my custom dataframe were just self .

Is there a way that this can be done? Or is this approach not ideal anyway?

Answer 1

The __init__ method is overwritten in your first example.

Use super and then add your custom code

class CustomDF(pd.DataFrame):
    def __init__(self, *args, **kw):
        
        # Your code here

    def custom_method_1(self,a,b,...):
        ...

Answer 2

Is this what you were looking for?

class CustomDF:

    def  __init__(self):
        self.df = pd.read_csv(filename)

    def custom_method_1(self, *args, **kwargs):
        result_1 = do_custom_operations_on(self.df, *args, **kwargs)
        return result_1

    def custom_method_2(self, *args, **kwargs):
        result_2 = do_custom_operations_on(self.df, *args, **kwargs)
        return result_2

    ...

Answer 3

I would probably go with the decorator pattern here. The accepted answer for this post will put you on the right track.

I see that your first iteration would be really cool, but it seem to me you need to know quite a lot of stuff about Pandas' internals, eg, that this _data attribute need to be set in a certain way.

Cheers.

Answer 4

In my project I did something similar and use decorators, like manu suggested. The decorator @property might work for you, it basically turns the method .df() into a property .df . Therefore it will only be read in when it's called specifically. But this only works on instances of the class.

class CustomDF:
    
    @property
    def df(self):
        return pd.read_csv(filename)

    def custom_method_1(self, *args, **kwargs):
        result_1 = do_custom_operations_on(self.df, *args, **kwargs)
        return result_1

    def custom_method_2(self, *args, **kwargs):
        result_2 = do_custom_operations_on(self.df, *args, **kwargs)
        return result_2