How to cumulutive count several columns by condition in pandas dataframe

Question

Example DataFrame:

df = pd.DataFrame({'column_1':['a','a','a','a','a','a','b','b','b','b','b','b','b','b','b','c','c','d','d']
                   ,'column_2':[1,1,1,2,2,2,1,1,1,2,2,2,3,3,3,1,2,2,3]
                  })

And I want to count each pair of columns by condition with numeration from 1 to N. I think, I must use apply function, but don't know how to write its realistion.

Using cycles with .loc is very slow (filtering cause)

for column_1 in df.column_1.unique():
    for column_2 in df.column_2.unique():
        condition = (df.column_1 == column_1)&(df.column_2 == column_2)
        max_range = sum(condition)
        df.loc[condition, 'result'] = range(1, max_range + 1)
df.result = df.result.astype(int)

Result:

Answer 1

This is groupby().cumcount():

df['result'] = df.groupby(['column_1','column_2']).cumcount()+1

Output:

   column_1  column_2  result
0         a         1       1
1         a         1       2
2         a         1       3
3         a         2       1
4         a         2       2
5         a         2       3
6         b         1       1
7         b         1       2
8         b         1       3
9         b         2       1
10        b         2       2
11        b         2       3
12        b         3       1
13        b         3       2
14        b         3       3
15        c         1       1
16        c         2       1
17        d         2       1
18        d         3       1