Pandas update values using loc with repeated indices

All,

I have a dataframe with repeated indices. I’m trying to update the values using the index for all rows with that index. Here is an example of what I have

  name  x
t        
0    A  5
0    B  2
1    A  7
2    A  5
2    B  9
2    C  3

"A" is present at every time. I want to replace "x" with the current value of "x", minus the value of "x" for "A" at that time. The tricky part is to get with an array or dataframe that is, in this case

array([5, 5, 7, 5, 5, 5])

which is the value for "A", but repeated for each timestamp. I can then subtract this from df[‘x’]. My working solution is below.

temp = df[df['name'] == 'A']
d = dict(zip(temp.index, temp['x']))
df['x'] = df['x'] - df.index.to_frame()['t'].replace(d)


  name  x
t        
0    A  0
0    B -3
1    A  0
2    A  0
2    B  4
2    C -2

This works, but feels a bit hacky, and I can’t help but think there is a better (and must faster) solution…

 df['x']=df.groupby((df.name=='A').cumsum())['x'].apply(lambda s:s.sub(s.iloc[0]))


 name  x
t        
0    A  0
0    B -3
1    A  0
2    A  0
2    B  4
2    C -2

df.x-=df.loc[df.name=='A','x'].reindex(df.index).values
df
Out[362]: 
  name  x
t        
0    A  0
0    B -3
1    A  0
2    A  0
2    B  4
2    C -2