Column wise multiplication of a set of Dataframe with elements of other Dataframe-CodePudding

I have few pandas DataFrames (say a, b, c) like this:

And another Dataframe (name it x), whose number of rows are equal to the number of dataframes like above:

How can I multiply the entire first column of the first dataframe (a) with x[0][0]?
Then the second column of the dataframe a (bb_11) with x[0][1].
Then the third column of the dataframe a (cc_12) with x[0][2], and so on.

For dataframe b, we should use the second row of x, i.e. x[1][j] (where j varies from 0-3). That is, I need to multiply the entire first column of the second dataframe (b) with x[1][0].
Then the second column of the dataframe b (bb_11) with x[1][1].
Then the third column of the dataframe b (cc_12) with x[1][2], and so on.

N.B. All dataframe column names will be the same. Our dataframes will be read from a file so looping will be an easier option.

Sample Data:

import pandas as pd
  
d = {'aa_10' : pd.Series([np.nan, 2, 3, 4]),
     'bb_11' : pd.Series([6, np.nan, 8, 9]),
     'cc_12' : pd.Series([1, 2, np.nan, 4]),
     'dd_13' : pd.Series([6, 7, 8, np.nan])}
  
# creates Dataframe.
a = pd.DataFrame(d)
  
# print the data.
print (a)

# Initialize data to Dicts of series.
d = {'aa_10' : pd.Series([np.nan, 12, 13, 14]),
     'bb_11' : pd.Series([16, np.nan, 18, 19]),
     'cc_12' : pd.Series([11, 12, np.nan, 14]),
     'dd_13' : pd.Series([16, 17, 18, np.nan])}
  
# creates Dataframe.
b = pd.DataFrame(d)
  
# print the data.
print(b)

# Initialize data to Dicts of series.
d = {'aa_10' : pd.Series([np.nan, 21, 31, 41]),
     'bb_11' : pd.Series([61, np.nan, 81, 91]),
     'cc_12' : pd.Series([11, 21, np.nan, 41]),
     'dd_13' : pd.Series([61, 71, 81, np.nan])}
  
# creates Dataframe.
c = pd.DataFrame(d)
  
# print the data.
print(c)

# Initialize data to Dicts of series.
d = {'aa_10' : pd.Series([1, 2, 3]),
     'bb_11' : pd.Series([6, 7, 8]),
     'cc_12' : pd.Series([10, 11, 12]),
     'dd_13' : pd.Series([13, 14, 15])}
  
# creates Dataframe.
x = pd.DataFrame(d)
  
# print the data.
print(x)

CodePudding user response：

You can do that

a,b,c = [x.mul(y) for x , y in zip([a,b,c],x.values.tolist())]
a
   aa_10  bb_11  cc_12  dd_13
0    NaN   36.0   10.0   78.0
1    2.0    NaN   20.0   91.0
2    3.0   48.0    NaN  104.0
3    4.0   54.0   40.0    NaN