I have some data, that needs to be clusterised into groups. That should be done by a few predifined conditions.
Suppose we have the following table:

d = {'ID': [100, 101, 102, 103, 104, 105],
     'col_1': [12, 3, 7, 13, 19, 25],
     'col_2': [3, 1, 3, 3, 2, 4]
    }

df = pd.DataFrame(data=d)
df.head()

Here, I want to group ID based on the following ranges, conditions, on col_1 and col_2 .
For col_1 I divide values into following groups: [0, 10] , [11, 15] , [16, 20] , [20, +inf]
For col_2 just use the df['col_2'].unique() values: [1] , [2] , [3] , [4] .
The desired groupping is in group_num column:

• notice, that 0 and 3 rows have the same group number and the order, in which group number is assigned*.

For now, I only came up with if-elif function to pre-define all the groups. It's not the solution for now cause in my real task there are far more ranges and confitions.
My code snippet, if it's relevant:

# This logic is not working cause here I have to predefine all the groups configurations, aka numbers,
# but I want to make groups "dymanicly":
# first group created and if the next row is not in that group -> create new one 

def groupping(val_1, val_2):
    
    # not using match case here, cause my Python < 3.10
    if ((val_1 >= 0) and (val_1 <10)) and (val_2 == 1):
        return 1
    elif ((val_1 >= 0) and (val_1 <10)) and (val_2 == 2):
        return 2
    elif ...
    
    ...

df['group_num'] = df.apply(lambda x: groupping(x.col_1, x.col_2), axis=1)