Iteratively cat rows from one df to cells of another df with conditions

all. I have 2 data frames. My data frames look like this:

# initialize data of lists.
data = {'Name': ['Tom', 'nick', 'krish', 'jack'],
        'Age': [20, 21, 19, 18],
        'nickname': ['T', 'N', 'K','J']}
df1 = pd.DataFrame(data)

data2 = {'Kids': ['child1', 'child2', 'child3', 'child4', 'child1', 'child2', 'child3', 'child4'],
        'Name': ['Tom', 'Tom', 'Tom', 'Tom', 'nick', 'nick', 'nick', 'nick'],
        'nickname': ['T', 'T', 'T', 'T', 'N', 'N', 'N', 'N']}
df2 = pd.DataFrame(data2)

I would. like to append each "name's" children from df2 to the first cell of each "name's" row in df1. Something like this:

Name       Age      nickname
Tom.       20          T
child1     
child2
child3
child4
nick       21          N
child1
child2
child3
child4
krish      19          K
jack       18          J
child1
child2
child3
child4       

I have been able to do this by making each child a row, but that won't work for what I need to do down stream. I don't want each child to be its own row. I just want the text concatenated inside the cell of the corresponding parent if that makes sense. And as you will notice, I need to skip random names. In my real dataset I will need to do this by row index. Thank you for you help in advance.

Harry

You can use GroupBy.apply/concat :

out = (
    df1.groupby("Name", group_keys=False, sort=False)
        .apply(lambda g: pd.concat(
            [g, df2.loc[df2["Name"].eq(g.name),
                        "Kids"].to_frame("Name")]
        ) # with optional ignore_index=True
              )
        # .fillna("") #if needed, uncomment this chain
)

Output :

print(out)

     Name   Age nickname
0     Tom 20.00        T
0  child1   NaN      NaN
1  child2   NaN      NaN
2  child3   NaN      NaN
3  child4   NaN      NaN
1    nick 21.00        N
4  child1   NaN      NaN
5  child2   NaN      NaN
6  child3   NaN      NaN
7  child4   NaN      NaN
2   krish 19.00        K
3    jack 18.00        J

Using concat and sort_values;

out = (
 pd.concat([df1, df2[['Name', 'Kids']]])
   .sort_values(by='Name', kind='stable', ignore_index=True)
   .assign(Name=lambda d: d.pop('Kids').fillna(d['Name']))
)

Output:

      Name   Age nickname
0      Tom  20.0        T
1   child1   NaN      NaN
2   child2   NaN      NaN
3   child3   NaN      NaN
4   child4   NaN      NaN
5     jack  18.0        J
6    krish  19.0        K
7     nick  21.0        N
8   child1   NaN      NaN
9   child2   NaN      NaN
10  child3   NaN      NaN
11  child4   NaN      NaN

If order and empty strings are important:

mapper = pd.Series({k:v for v, k in enumerate(df1['Name'].unique())})

out = (
 pd.concat([df1, df2[['Name', 'Kids']]])
   .sort_values(by='Name', kind='stable',
                key=mapper.get, ignore_index=True)
   .assign(Name=lambda d: d.pop('Kids').fillna(d['Name']))
   .fillna('')
)

Output:

      Name   Age nickname
0      Tom  20.0        T
1   child1               
2   child2               
3   child3               
4   child4               
5     nick  21.0        N
6   child1               
7   child2               
8   child3               
9   child4               
10   krish  19.0        K
11    jack  18.0        J