How to check different rows values of a column within the same group and return a specific value?

Question

I have the following code that generates the two columns.

import pandas as pd
  
data = {'Group': ['1', '1', '1', '1', '1', '1',
                  '2', '2', '2', '2', '2', '2',
                  '3', '3', '3', '3', '3', '3',
                  '4', '4', '4', '4', '4', '4',],
        'Test1': ['ABC', 'CDE', 'EFG', 'GHI', 'IJK', 'KLM',
                  'MNO', 'OPQ', 'QRS', 'STU', 'UVW', 'WXYZ',
                  'ABC', 'CDE', 'EFG', 'GHI', 'IJK', 'KLM',
                  'MNO', 'OPQ', 'QRS', 'STU', 'UVW', 'WXYZ',],
        'Test2': ['1234','4567', '8910', '1112', '1314', '1415',
                  '1516', '1718', '1920', '2122', '2324', '2526',
                  '2728', '2930', '3132', '3334', '3536', '3738',
                  '2940', '4142', '4344', '4546', '4748', '4950'],
        'Value': [True, True, False, False, False, True,
                  True, True, True, True, True, True,
                  True, True, True, True, True, False,
                  True, True, True, False, True, True,],
        }
  
df = pd.DataFrame(data)

print(df)

So, by checking the last 2, 3, or 4 rows in each group if they return False, I want to return False. And if all the values are True then, I want to return true for all rows. From the above code, the expected outcome is this. If we check for the last 3 rows in each group

Group | Value
----- | -----  
  1   |   False 
  1   |   False
  1   |   False
  2   |   True
  2   |   True
  2   |   True
  3   |   False
  3   |   False
  3   |   False
  4   |   False
  4   |   False
  4   |   False

Answer 1

Update, per updated question and comments below:

df[['Test1','Test2']].merge(df.groupby('Group')['Value'].apply(lambda x: x.iloc[-3:].mul(x.iloc[-3:].min(), level=0))\
  .reset_index(), left_index=True, right_on='level_1').drop('level_1', axis=1)

Output:

   Test1 Test2 Group  Value
0    GHI  1112     1  False
1    IJK  1314     1  False
2    KLM  1415     1  False
3    STU  2122     2   True
4    UVW  2324     2   True
5   WXYZ  2526     2   True
6    GHI  3334     3  False
7    IJK  3536     3  False
8    KLM  3738     3  False
9    STU  4546     4  False
10   UVW  4748     4  False
11  WXYZ  4950     4  False

IIUC, try this:

df.groupby('Group')['Value'].apply(lambda x: x.iloc[-3:].mul(x.iloc[-3:].min(), level=0))\
  .reset_index()\
  .drop('level_1', axis=1)

Output:

   Group  Value
0      1  False
1      1  False
2      1  False
3      2   True
4      2   True
5      2   True
6      3  False
7      3  False
8      3  False
9      4  False
10     4  False
11     4  False