column with the maximum cost of each group by uid column

"""cost col populated where matched in uid col (greatest value from matches, else remain 0)"""

import pandas as pd
import numpy as np

example_df = {
    'parent_text': ['Pen', 'Pen', 'Pen', 'Pen', 'Pen', 'Pen', 'Pen'],
    'child_desc_text': ['orange' ,'red', 'green', 'brown', 'yellow', 'black', 'silver'],
    'cost': [0 ,1.20, 1.98, 0, 0, 0, 0],
    'uid': [12705024 ,12705087, 12705093, 12705087, 12705093, 12705093, 12705024]}

"""
                cost    uid
0   Pen orange  0.00    12705024
1   Pen red     1.20    12705087
2   Pen green   1.98    12705093
3   Pen brown   0.00    12705087
4   Pen yellow  0.00    12705093
5   Pen black   0.00    12705093
6   Pen silver  0.00    12705024
"""


df['cost'] = df.groupby('uid')['cost'].agg(max).loc[df['uid']].to_numpy()

output_df = {
    'parent_text': ['Pen', 'Pen', 'Pen', 'Pen', 'Pen', 'Pen', 'Pen'],
    'child_desc_text': ['orange' ,'red', 'green', 'brown', 'yellow', 'black', 'silver'],
    'cost': [0 ,1.20, 1.98, 1.20, 1.98, 1.98, 0],
    'uid': [12705024 ,12705087, 12705093, 12705087, 12705093, 12705093, 12705024]
    }

"""
                cost    uid
0   Pen orange  0.00    12705024
1   Pen red     1.20    12705087
2   Pen green   1.98    12705093
3   Pen brown   1.20    12705087
4   Pen yellow  1.98    12705093
5   Pen black   1.98    12705093
6   Pen silver  0.00    12705024
"""