在子类中补充继承的熊猫数据框

Question

Background

I have a skeleton parent pandas dataframe that I generate dummy data for. I have different children where I should have different dummy data for each child in each column, however, they all share the same data of the parent's specific columns.

Workaround attempt

I have created a skeleton base DF to generate the dummy data for the constant columns ( A )

import pandas as pd
import numpy as np

class parent:
    def __init__(self):
        self.df = pd.DataFrame(data=None, columns=['A','B','C'])
    def init_df(self, rows):
        self.df['A'] = np.repeat('ABC',rows)
        return self.df

prnt = parent()
prnt.init_df(2)

    A   B   C
0   ABC NaN NaN
1   ABC NaN NaN

I would like to complement column B and column C in the child class by calling the super() function to have A but complement the rest of B and C .

class child(parent):
    def init_child_df(self, rows):
        child_df = super().init_df(self,rows)
        # child_df['B'] = np.repeat('DEF',rows)
        # child_df['B'] = np.repeat('GHI',rows)
        return child_df
chld = child()
chld.init_child_df(3)

I appreciate explaining and advising the correct standard/professional OOP terms and way to demonstrate this.

Note

Please feel free to refractor my whole code attempt so I can learn from you. It doesn't have to be the way I attempted it if it's not recommended.

Answer 1

I have noticed that I have to remove self from the child class:

class child(parent):
    def init_child_df(self, rows):
        child_df = super().init_df(rows)
        child_df['B'] = np.repeat('DEF',rows)
        child_df['C'] = np.repeat('GHI',rows)
        return child_df
chld = child()
chld.init_child_df(2)


    A   B   C
0   ABC DEF GHI
1   ABC DEF GHI

Please feel free to post a better answer/implementation.