Rows of dataframe times column of another dataframe

Question

I tried to get the weighted average of each product for each person. So for Tom, it should have 20x1.0+19x2.0+10x3.0, I also hope to have the weights*product by each product as well.

data = {'Name':['Tom', 'nick', 'krish', 'jack'], '1stproducts':[20, 21, 19, 18], '2ndproduct': [19, 28, 10, 10], 
        '3rdproduct': [10, 18, 20, 30]} 
df = pd.DataFrame(data) 
weights = {"weights": [1.0, 2.0, 3.0]}
df2 = pd.DataFrame(weights) 

I have tried pd.DataFrame.multiply(df, df2, axis = 1) , but I got NaN for all values.

Answer 1

Aligning the index in df2 will solve your problem. And referencing the weights column in df2.

df2 = pd.DataFrame(weights, index=['1stproducts', '2ndproduct', '3rdproduct'])
In [26]: df[['1stproducts', '2ndproduct', '3rdproduct']] * df2.weights
Out[26]:
1stproducts  2ndproduct  3rdproduct
0         20.0        38.0        30.0
1         21.0        56.0        54.0
2         19.0        20.0        60.0
3         18.0        20.0        90.0

Similar question here: How to compute weighted sum of all elements in a row in pandas?