Pandas - cumulative sum of two columns

[michalmonday]

I know it's ugly and I'm not experienced with pandas but it works:p

import pandas as pd

def get_team_names(df):
    return df[0][0], df[1][0]


df  = pd.DataFrame([
["Gothenburg", "Malmo", 2018,"A"],
["Malmo","Gothenburg",  2018, "D"],
["Malmo", "Gothenburg", 2018, "A"],
["Gothenburg", "Malmo", 2018, "H"],
["Gothenburg", "Malmo" ,2018, "D"],
["Gothenburg", "Malmo", 2018, "A"],
["Gothenburg", "Malmo", 2018, "H"],
["Malmo", "Gothenburg", 2018, "A"],
["Gothenburg", "Malmo", 2018, "H"],
["Malmo", "Gothenburg", 2018, "H"],
[ "Malmo","Gothenburg", 2018, "D"],
[ "Malmo", "Gothenburg",2018, "H"],
 
])

team_1, team_2 = get_team_names(df)

df.columns = ['H_team', 'A_team', 'Year', 'Result']

draws = df['Result']=='D'

hh = (df['Result']=='H').where(df['H_team'] == team_1) * 3
ha = (df['Result']=='A').where(df['A_team'] == team_1) * 3

ah = (df['Result']=='H').where(df['H_team'] == team_2) * 3
aa = (df['Result']=='A').where(df['A_team'] == team_2) * 3

team_1_points = hh.add(ha, fill_value=0).add(draws, fill_value=0).cumsum().shift(1)
team_2_points = ah.add(aa, fill_value=0).add(draws, fill_value=0).cumsum().shift(1)

df['H_points'] = team_1_points.where(df['H_team'] == team_1, team_2_points)
df['A_points'] = team_1_points.where(df['A_team'] == team_1, team_2_points)

print(df)

Output:
        H_team      A_team  Year Result  H_points  A_points
0   Gothenburg       Malmo  2018      A       NaN       NaN
1        Malmo  Gothenburg  2018      D       3.0       0.0
2        Malmo  Gothenburg  2018      A       4.0       1.0
3   Gothenburg       Malmo  2018      H       4.0       4.0
4   Gothenburg       Malmo  2018      D       7.0       4.0
5   Gothenburg       Malmo  2018      A       8.0       5.0
6   Gothenburg       Malmo  2018      H       8.0       8.0
7        Malmo  Gothenburg  2018      A       8.0      11.0
8   Gothenburg       Malmo  2018      H      14.0       8.0
9        Malmo  Gothenburg  2018      H       8.0      17.0
10       Malmo  Gothenburg  2018      D      11.0      17.0
11       Malmo  Gothenburg  2018      H      12.0      18.0