Assessing values ​to a pandas column with conditions depending on other columns

Question

I have a dataframe:

df_test = pd.DataFrame({'col': ['paris', 'paris', 'nantes', 'berlin', 'berlin', 'berlin', 'tokyo'],
                        'id_res': [12, 12, 14, 28, 8, 4, 89]})


     col  id_res
0   paris      12
1   paris      12
2  nantes      14
3  berlin      28
4  berlin       8
5  berlin       4
6   tokyo      89

I want to create a "check" column whose values ​​are as follows:

• If a value in "col" has a duplicate and these duplicates have the same id_res, the value of "check" is False for duplicates
• If a value in "col" has duplicates and the "id_res" of these duplicates are different, assign True in "check" for the largest "id_res" value and False for the smallest
• If a value in "col" has no duplicates, the value of "check" is False.

The output I want is therefore:

    col  id_res  check
0   paris      12  False
1   paris      12  False
2  nantes      14  False
3  berlin      28   True
4  berlin       8  False
5  berlin       4  False
6   tokyo      89  False

I tried with groupby but no satisfactory result. Can anyone help me plz

Answer 1

Create 2 boolean masks then combine them and find the highest id_res value per col:

m1 = df['col'].duplicated(keep=False)
m2 = ~df['id_res'].duplicated(keep=False)
df['check'] = df.index.isin(df[m1 & m2].groupby('col')['id_res'].idxmax())
print(df)

# Output
      col  id_res  check
0   paris      12  False
1   paris      12  False
2  nantes      14  False
3  berlin      28   True
4  berlin       8  False
5  berlin       4  False
6   tokyo      89  False

Details:

>>> pd.concat([df, m1.rename('m1'), m2.rename('m2')])
      col  id_res  check     m1     m2
0   paris      12  False   True  False
1   paris      12  False   True  False
2  nantes      14  False  False   True
3  berlin      28   True   True   True  # <-  group to check
4  berlin       8  False   True   True  # <-     because 
5  berlin       4  False   True   True  # <- m1 and m2 are True
6   tokyo      89  False  False   True