How can I multiply a n*m DataFrame with a 1*m DataFrame in pandas?

Question

I have 2 pandas DataFrame that I want to multiply:

frame_score: 
   Score1  Score2
0     100      80
1    -150      20
2    -110      70
3     180      99
4     125      20

frame_weights: 
   Score1  Score2
0     0.6     0.4

I tried:

import pandas as pd
import numpy as np

frame_score = pd.DataFrame({'Score1'  : [100, -150, -110, 180, 125], 
                      'Score2'  : [80,  20, 70, 99, 20]})

frame_weights = pd.DataFrame({'Score1': [0.6], 'Score2' : [0.4]})

print('frame_score: \n{0}'.format(frame_score))
print('\nframe_weights: \n{0}'.format(frame_weights))

# Each of the following alternatives yields the same results
frame_score_weighted = frame_score.mul(frame_weights, axis=0)
frame_score_weighted = frame_score * frame_weights
frame_score_weighted = frame_score.multiply(frame_weights, axis=1)

print('\nframe_score_weighted: \n{0}'.format(frame_score_weighted))

returns:

frame_score_weighted:   
    Score1  Score2
0    60.0    32.0
1     NaN     NaN
2     NaN     NaN
3     NaN     NaN
4     NaN     NaN

The rows 1 to 4 are NaN. How can I avoid that? For example, row 1 should be -90 8 (-90=-150*0.6; 8=20*0.4).

For example, Numpy may broadcast to match dimensions.

Trespass

Answer 1

Edit: for arbitrary dimension, try using values to manipulate the values of the dataframes in an array-like fashion:

# element-wise multiplication
frame_score_weighted = frame_score.values*frame_weights.values

# change to pandas dataframe and rename columns
frame_score_weighted = pd.DataFrame(data=frame_score_weighted, columns=['Score1','Score2'])

#Out: 
   Score1  Score2
0    60.0    32.0
1   -90.0     8.0
2   -66.0    28.0
3   108.0    39.6
4    75.0     8.0

Just use some additional indexing to make sure you extract the desired weights as a scalar when you do the multiplication.

frame_score['Score1'] = frame_score['Score1']*frame_weights['Score1'][0]
frame_score['Score2'] = frame_score['Score2']*frame_weights['Score2'][0]

frame_score
#Out: 
   Score1  Score2
0    60.0    32.0
1   -90.0     8.0
2   -66.0    28.0
3   108.0    39.6
4    75.0     8.0

Totalizator

Answer 2

By default, when pd.DataFrame is multiplied by a pd.Series, pandas aligns the index of the pd.Series with the columns of the pd.DataFrame. So, we get the relevant pd.Series from frame_weights by accessing just the first row.

frame_score * frame_weights.loc[0]

   Score1  Score2
0    60.0    32.0
1   -90.0     8.0
2   -66.0    28.0
3   108.0    39.6
4    75.0     8.0

You can edit frame_score in place with

frame_score *= frame_weights.loc[0]

Pickering