Pandas DataFrame Transpose and Matrix Multiplication - pandas

Question

I am looking for a way to perform a matrix multiplication on two sets of columns in a dataframe. One set of columns will need to be transposed and then multiplied with the other set. Then I need to take the resulting matrix and do an element wise product with a scalar matrix and add up. Below is an example:
Data for testing:
import pandas as pd
import numpy as np
dftest = pd.DataFrame(data=[['A',0.18,0.25,0.36,0.21,0,0.16,0.16,0.64,0.04,0,0],['B',0,0,0.5,0.5,0,0,0,0.25,0.75,0,0]],columns = ['Ticker','f1','f2','f3','f4','f5','p1','p2','p3','p4','p5','multiplier'])
Starting dataframe with data for Tickers. f1 through f5 represent one set of categories and p1 through p5 represent another.
dftest
Out[276]:
Ticker f1 f2 f3 f4 f5 p1 p2 p3 p4 p5 multiplier
0 A 0.18 0.25 0.36 0.21 0 0.16 0.16 0.64 0.04 0 0
1 B 0.00 0.00 0.50 0.50 0 0.00 0.00 0.25 0.75 0 0
For each row, I need to do transpose columns p1 through p5 and then multiply them to columns f1 through f5. I think I have found the solution using below.
dftest.groupby('Ticker')['f1','f2','f3','f4','f5','p1','p2','p3','p4','p5'].apply(lambda x: x[['p1','p2','p3','p4','p5']].T.dot(x[['f1','f2','f3','f4','f5']]))
Out[408]:
f1 f2 f3 f4 f5
Ticker
A p1 0.0288 0.04 0.0576 0.0336 0.0
p2 0.0288 0.04 0.0576 0.0336 0.0
p3 0.1152 0.16 0.2304 0.1344 0.0
p4 0.0072 0.01 0.0144 0.0084 0.0
p5 0.0000 0.00 0.0000 0.0000 0.0
B p1 0.0000 0.00 0.0000 0.0000 0.0
p2 0.0000 0.00 0.0000 0.0000 0.0
p3 0.0000 0.00 0.1250 0.1250 0.0
p4 0.0000 0.00 0.3750 0.3750 0.0
p5 0.0000 0.00 0.0000 0.0000 0.0
Next I need to do a element wise product of the above matrix against another 5x5 matrix that is in another DataFrame and then add up the columns or rows (you get the same result either way). If I extend the above statement as below, I get the result I want.
dftest.groupby('Ticker')['f1','f2','f3','f4','f5','p1','p2','p3','p4','p5'].apply(lambda x: pd.DataFrame(m.values * x[['p1','p2','p3','p4','p5']].T.dot(x[['f1','f2','f3','f4','f5']]).values, columns = m.columns, index = m.index).sum().sum())
Out[409]:
Ticker
A 2.7476
B 1.6250
dtype: float64
So far so good, I think. Happy to know a better and faster way to do this. The next question and this is where I am stuck.
How do I take this and update the "multiplier" column on my original dataFrame?
if I try to do the following:
dftest['multiplier']=dftest.groupby('Ticker')['f1','f2','f3','f4','f5','p1','p2','p3','p4','p5'].apply(lambda x: pd.DataFrame(m.values * x[['p1','p2','p3','p4','p5']].T.dot(x[['f1','f2','f3','f4','f5']]).values, columns = m.columns, index = m.index).sum().sum())
I get NaNs in the multiplier column.
dftest
Out[407]:
Ticker f1 f2 f3 f4 f5 p1 p2 p3 p4 p5 multiplier
0 A 0.18 0.25 0.36 0.21 0 0.16 0.16 0.64 0.04 0 NaN
1 B 0.00 0.00 0.50 0.50 0 0.00 0.00 0.25 0.75 0 NaN
I suspect it has to do with indexing and whether all the indices after grouping are translating back to the original dataframe. Second, do I need a group by statement for this one? Since it is a row by row solution, can't I just do it without grouping or group by the index? any suggestions on that?
I need to do this without iterating row by row because the whole code will iterate due to some optimization I have to do. So I need to run this whole process, look at the results and if they are outside some constraints, calculate new f1 through f5 and p1 through p5 and run the whole thing again.
I posted a question on this earlier but it was confusing so this a second attempt. Hope it makes sense.
Thanks in advance for all your help.