Pythonic way to return pandas dataframe with helper function and copy

Question

I was wondering what the most pythonic way to return a pandas dataframe would be in a class. I am curious if I need to include .copy() when returning, and generally would like to understand the pitfalls of not including it. I am using a helper function because the dataframe is called multiple times, and I don't want return from the manipulate_dataframe method.

My questions are:

1. Do I need to place .copy() after df when assigning it to a new object?
2. Do I need to place .copy() after self.final_df when returning it with the get_df helper function?

class df: 

    def __init__(self):
        pass

    def manipulate_dataframe(self, df): 
        """ DATAFRAME MANIPULATION """
        self.final_df = df

    def get_df(self):
        return self.final_df

Answer 1

Question: do you want the thing referred to as self.final_df to directly alter the original data frame, or do you want it to live its own life? Recall Python treats many variables as "views", in the sense that

y = x

creates two links to same information and if you alter y, it also alters x. Example:

>>> x = [3, 4, 6]
>>> y = x
>>> y[0] = 77
>>> x
[77, 4, 6]

The pandas df is just another example of same Python fact.

Making a copy can be time consuming. It will also disconnect your self.final_df from the input data frame. People often do that because Pandas issues warnings about user efforts to assign values into view of data frames. The correct place to start reading on this is Pandas Document https://pandas.pydata.org/docs/user_guide/indexing.html?highlight=copy%20versus%20view. However there seem to be 100 blog posts about it, I prefer the RealPython site https://realpython.com/pandas-settingwithcopywarning but you will find a others, https://www.dataquest.io/blog/settingwithcopywarning). A common effort that careless users take to address that problem is to copy the data frame, which effectively "disconnects" the new copy from the old data frame.

I don't know why you might want to create a class that simply holds a pandas df. Probably you could just create a function that does all of those data manipulations. If those commands are supposed to alter original df, don't make a copy.