Multiply row of pandas dataframe based on column name, using values of another dataframe

Question

I have two DataFrames , as follows:

df1 = 


 name1 name2  name3   ....    nameXXX 
  5.1   1.2    1.1    ...      223.2
  3.22  1.34   1.5    ...      213.2
  4.3   1.32   1.23   ...      523.2
  5.2   1.1    1.543  ...      223.2

df2=

name1     0.2
name2     0.1
name3     0.43
...       ...
nameXXX   0.21

What I need:

df3=

 name1       name2         name3     ...         nameXXX 
5.1 * 0.2   1.2 * 0.1    1.1 * 0.43  ...      223.2 *  0.21
3.22* 0.2   1.34* 0.1    1.5 * 0.43  ...      213.2 * 0.21
4.3 * 0.2   1.32* 0.1    1.2 * 0.43  ...      523.2 * 0.21
5.2 * 0.2   1.1 * 0.1    1.54* 0.43  ...      223.2 *  0.21

• the name s are the column headers

• Basically I want to multiply each column of df1 by the number present in df2 that is in the same row of the column header of df1

I saw the following questions but I couldn't find the solution to my problem:

1) How to select rows from a DataFrame based on column values?

2) Pandas: pairwise multiplication of columns based on column name

3) Multiply columns of a dataframe by getting the column names from a list

4) pandas: Select dataframe columns based on another dataframe's columns

Answer 1

If name column is your index, you can just do

df1.mul(df2.iloc[:,0], axis='columns')

If it's is a normal column, you can set it as an index:

df1.mul(df2.set_index(0).iloc[:,0], axis='columns')

Output:

   name1  name2    name3  nameXXX
0  1.020  0.120  0.47300   46.872
1  0.644  0.134  0.64500   44.772
2  0.860  0.132  0.52890  109.872
3  1.040  0.110  0.66349   46.872

Answer 2

Let us do

df1=df1.mul(df2,axis=1)

Answer 3

I think what you need is to update the column, essentially if you have the same number of columns as rows in your other df, you can try:

cols = df1.columns for i in cols: df1[i] = df1[i] * df2.loc[i].values

Typing from phone sorry for formatting