使用来自另一个具有条件的数据帧的值更新熊猫数据帧列

Question

I have a dataframe with game matchups by week and a second dataframe with final scores. I want to update the first with scores for each team from the second with condition that game was played in week 1.

    df1 = pd.DataFrame([[1,'aa','hh','',''],
                       [1,'bb','ii','',''],
                       [2,'cc','jj','',''],
                       [1,'dd','kk','',''],
                       [1,'ee','ll','',''],
                       [1,'ff','mm','',''],
                       [2,'gg','nn','','']], columns=['week','team1','team2','score1','score2'])
    df1
Out[3]: 
   week team1 team2 score1 score2
0     1    aa    hh              
1     1    bb    ii              
2     2    cc    jj              
3     1    dd    kk              
4     1    ee    ll              
5     1    ff    mm              
6     2    gg    nn      

    df2 = pd.DataFrame([[1,'aa', 24],
                       [1,'bb', 27],
                       [2,'cc', 20],
                       [1,'dd', 7],
                       [1,'ee', 9],
                       [1,'ff', 20],
                       [2,'gg', 0],
                       [1,'hh', 10],
                       [1,'ii', 3],
                       [2,'jj', 21],
                       [1,'kk', 20],
                       [1,'ll', 13],
                       [1,'mm', 19],
                       [2,'nn', 14]], columns=['week','team','score'])
df2
Out[5]: 
    week team  score
0      1   aa     24
1      1   bb     27
2      2   cc     20
3      1   dd      7
4      1   ee      9
5      1   ff     20
6      2   gg      0
7      1   hh     10
8      1   ii      3
9      2   jj     21
10     1   kk     20
11     1   ll     13
12     1   mm     19
13     2   nn     14

I tried renaming columns to match and used .update

df2.columns = ['week','team1','score1']
df1.update(df2.loc[(df2['week']== 1)])
df1
Out[7]: 
   week team1 team2 score1 score2
0   1.0    aa    hh     24       
1   1.0    bb    ii     27       
2   2.0    cc    jj              
3   1.0    dd    kk      7       
4   1.0    ee    ll      9       
5   1.0    ff    mm     20       
6   2.0    gg    nn              

which gives result I was hoping for but actually doesn't work. When I try to rename again to update score2 I see that it is updating with the first rows from df2 instead of matching them with values in df1. I tried merge but it creates new columns rather than update existing ones. I plan to add new games to df1 and perform this update weekly and I want to update rather than create new columns. What is a way to accomplish this?

my desired output for this example is:

df1
Out[28]: 
   week team1 team2 score1 score2
0     1    aa    hh     24     10
1     1    bb    ii     27      3
2     2    cc    jj              
3     1    dd    kk      7     20
4     1    ee    ll      9     13
5     1    ff    mm     20     19
6     2    gg    nn              

Answer 1

Let's try map instead:

to_map = df2[df2.week==1].set_index('team')['score']

to_update = df1.week==1

df1.loc[to_update, 'score1'] = df1.loc[to_update,'team1'].map(to_map)
df1.loc[to_update, 'score2'] = df1.loc[to_update,'team2'].map(to_map)

Output:

   week team1 team2 score1 score2
0     1    aa    hh     24     10
1     1    bb    ii     27      3
2     2    cc    jj              
3     1    dd    kk      7     20
4     1    ee    ll      9     13
5     1    ff    mm     20     19
6     2    gg    nn              

Answer 2

You can do it with merge:

result=df1.merge(df2, left_on='team1', right_on='team').merge(df2, left_on='team2', right_on='team')[['week_x', 'team1', 'team2', 'score_x', 'score_y']]
result.columns=['week', 'team1', 'team2', 'score1', 'score2']
result.loc[result['week']!=1,['score1', 'score2']]=np.nan
print(result)

Output:

    week team1 team2  score1  score2
0     1    aa    hh      24      10
1     1    bb    ii      27       3
2     2    cc    jj      20      21
3     1    dd    kk       7      20
4     1    ee    ll       9      13
5     1    ff    mm      20      19
6     2    gg    nn       0      14