I am trying to subtract df A from df B where the column mapping is based on a 3rd mapping data frame. In this example, B should be subtracted from x1 and A should be subtracted from x2.
This can be done with loops and some other dirty methods, but I was wondering if there is a more concise way to do that.
Dataframe a
date | A | B |
---|---|---|
12/31/2019 | 0.1 | 0.4 |
12/31/2020 | 0.3 | 0.6 |
Dataframe b
date | x1 | x2 |
---|---|---|
12/31/2019 | 1.0 | 0.8 |
12/31/2020 | 0.4 | 0.7 |
Dataframe c
From | To |
---|---|
x1 | B |
x2 | A |
Required result
date | x1 | x2 |
---|---|---|
12/31/2019 | 0.6 | 0.7 |
12/31/2020 | -0.2 | 0.4 |
CodePudding user response:
You can use rename
to temporary rename the column and subtract. Assuming the date
is your index:
a - b.rename(columns=c.set_index('From')['To'])
Output:
A B
date
12/31/2019 -0.7 -0.6
12/31/2020 -0.4 0.2
CodePudding user response:
Use merge
before subtracting:
tmp = pd.merge(dfa, dfb, on='date')
dfb[['x1', 'x2']] = tmp[dfc['From']].values - tmp[dfc['To']].values
print(dfb)
# Output:
date x1 x2
0 12/31/2019 0.6 0.7
1 12/31/2020 -0.2 0.4