Multiplying two dataframes returns NaN and wrong shape-CodePudding

I want to multiply two data frames to get a new one. All variables in both data frames are integers, and indexes in both are reset and go from 0:n. These are my data

df1

   11   12   13  14  15  16  17  18  19  20
0  54   87   77  53  30  21   0   0   0   0
1  60  146  171  66  36  34   0   0   0   0
2  49   76   87  57  34  35   0   0   0   0
3  49   76   77  53  30  21   0   0   0   0

df2

    A5   A6   A7   A8 
0  200  300  400  500 
1  201  301  401  501 
2  202  302  402  502 
3  203  303  403  503

If I use .mul method, all results in the data frame are NaN and the shape is all wrong (no matter which axis I use).

df2.mul(df1)

Result

   11  12  13  14  15  16  17  18  19  20  A5  A6  A7  A8
0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
3 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

Expected result is a table like this

   11    12      13      14      15      16  17  18  19  20
72900 129600  140000  79700   45400   38900   0   0   0   0
73112 129985  140412  79929   45530   39011   0   0   0   0
73324 130370  140824  80158   45660   39122   0   0   0   0
73536 130755  141236  80387   45790   39233   0   0   0   0

If I multiply df1 or df2 with scalar (like df1 * 2) the results are ok.

How do I solve this?

CodePudding user response：

This doesn't return the columns with zeros but this will return the two multiplied together. Is this what you are looking for?

df1.drop(columns=df1.columns[len(df2.columns):]).mul(df2)

CodePudding user response：

So, .mul is for element wise multiplication, but I wanted to do matrix multiplication, so .dot was needed. The solution is:

df1.set_index(df2.columns, inplace = True)
df2.dot(df1)