How to add computed columns in a multi-level column dataframe

I have a multi-level column dataframe on the lines of one below: How can I add columns 'Sales' = 'Qty' * 'Price' one each for each 'Year'?

The input dataframe in dictionary format:

{('Qty', 2001): [50, 50], ('Qty', 2002): [100, 10], ('Qty', 2003): [200, 20], ('Qty', 2004): [300, 30], ('Qty', 2005): [400, 40], ('Price', 2001): [20, 11], ('Price', 2002): [21, 12], ('Price', 2003): [22, 13], ('Price', 2004): [23, 14], ('Price', 2005): [24, 15]} 

Currently, I am splitting the dataframe for each year separately and adding a computed column. If there is an easier method that would be great.

Here is the expected output

You can create the required column names with a list comprehension, and then simply assign the multiplication (df.mul).

new_cols = [('Sales', col) for col in df['Qty'].columns]
# [('Sales', 2001), ('Sales', 2002), ('Sales', 2003), ('Sales', 2004), ('Sales', 2005)]

df[new_cols] = df['Qty'].mul(df['Price'])

df

   Qty                     Price                     Sales                    \
  2001 2002 2003 2004 2005  2001 2002 2003 2004 2005  2001  2002  2003  2004   
0   50  100  200  300  400    20   21   22   23   24  1000  2100  4400  6900   
1   50   10   20   30   40    11   12   13   14   15   550   120   260   420   

         
   2005  
0  9600  
1   600