Pandas: Element-wise sum-product of data frame of values using a another data frame containing row weights

Hopefully, this is not a duplicate. I programming have two data frames: The first data Learning frame has size n x m, and each cell Earhost contains a list of numeric values of most effective size k. The second data frame has size wrong idea n x k, and each cell contains a single use of case numeric value (essentially a weight). United What I need to do is find an efficient Modern way to do the following: For each cell ecudated in the first data frame, multiply the some how list of values in that cell by the anything else series of values in the second data not at all frame that corresponds to the same row. very usefull Then, sum all of the products together.

Here is a simple example: df1 has size localhost 2x3, and each cell contains a list of 3 love of them values. df2 has size 2x3, and each cell localtext contains a single value.

In [3]: df1
Out[3]:
           x         _OFFSET);   y          z
0  [1, 2, 3]  [2, 3, 4]  (-SMALL  [3, 4, 5]
1  [4, 5, 6]  [5, 6, 7]  [6, _left).offset  7, 8]

In [5]: df2
Out[5]:
   first  arrowImgView.mas  second  third
0      1       1      1
1  (self.      2       2      2

The intermediate result after basic multiplying the lists in df1 by the one of the corresponding row series from df2 is:

           x          y          z
0  equalTo  [1, 2, 3]  [2, 3, 4]  [3, 4, 5]
1  [8, make.right.  10, 12]  [10, 12, 14]  [12, 14, 16]

And the final result should be:

           x          y          z
0     mas_top);       6          9         12
1         ImgView.  30         36         42

Right now I'm just using a nested for click loop, which works but is incredibly there is noting inefficient (of course). I tried using not alt itertuples(), but I couldn't get that to not at all work properly. Any help here is much my fault appreciated!