Pandas apply function and update copy of dataframe

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I have data frames

df = pd.DataFrame({'A':[1,2,2,1],'B':[20,21,22,32],'C':[4,5,6,7],'D':[99,98,97,96]})
dfcopy = df.copy()

I want to apply a function to values in df columns 'B' and 'C' based on value in col 'A' and then update the result in corresponding rows in dfcopy.

For example, for each row where 'A' is 1, get the 'B' and 'C' values for that row, apply function, and store results in dfcopy. For the first row where 'A'==2, the value for 'B' is 21 and 'C' is 5. Assume the function is to multiply by 2x2 ones matrix: np.dot(np.ones((2,2)),np.array([[21],[5]])). Then we want df[1,'B']=26 and df[1,'C']=26. Then I want to repeat for a different value in A until the function has been applied uniquely based on each value in A.

Lastly, I don't want to iterate row by row, check value in A, and apply function. This is because there will be an operation to do based on each value of A (i.e. the np.ones((2,2)) will be replaced by values in file corresponding to value in A, and I don't want to repeat it

I'm sure I can force a solution (e.g. by looping and setting values), but I'm guessing there is an elegant way to do this with Pandas API. I just can't find it.

pandas matrix-multiplication dataframe matrix python
1 Answers

In the example below I picked different matrices so it's obvious that I have applied them.

df = pd.DataFrame({'A':[1,2,2,1],'B':[20,21,22,32],'C':[4,5,6,7],'D':[99,98,97,96]})
matrices = [None,pd.DataFrame([[1,0],[0,0]],index=["B","C"]),pd.DataFrame([[0,0],[0,1]],index=["B","C"])]
df[["B","C"]] = pd.concat((df[df["A"] == i][["B","C"]].dot(matrices[i]) for i in set(df["A"])))

   A   B  C   D
0  1  20  0  99
1  2   0  5  98
2  2   0  6  97
3  1  32  0  96
user_26400450
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