I have two dataframes, where one dataframe has 2 columns with 11 rows and another dataframe with 2 columns with 2 rows.

print(df)

Output is :

    C1  C2
0   1   1
1   2   2
2   3   3
3   4   4
4   5   5
5   6   6
6   7   7
7   9   9
8   11  13
9   10  11
10  12  11

Second dataframe is

print(df1)

Output is :

    Mean Dev
0   2    0.5
1   1    1.0

I'm trying to subtract each and every value from column 1 of df with 1st column 1st row Mean value and divinding with 2nd column 1st row Dev value. Below is the code

for i in range(0, len(df)):
    print((df['C1'][i] - df1['Mean'][0]) / (df1['Dev'][0]))

Output is :

-2.0
0.0
2.0
4.0
6.0
8.0
10.0
14.0
18.0
16.0
20.0

My question is how to perform the subtraction and dividing for every column with respect to the Mean and Dev columns. For example, i'm trying to write code

for i in range(0, len(df)):
        print((df['C2'][i] - df1['Mean'][1]) / (df1['Dev'][1]))

Followed by

for i in range(0, len(df)):
        print((df['C3'][i] - df1['Mean'][2]) / (df1['Dev'][2]))

Followed by

for i in range(0, len(df)):
        print((df['C4'][i] - df1['Mean'][3]) / (df1['Dev'][3]))

In the above codes, we are looping df values. How to loop the df1 values?

Can anyone help me with this?

CodePudding user response：

You can accomplish this without for loops taking advantage of elementwise subtraction the following way:

import pandas as pd

#Example data
df = pd.DataFrame({'C1': [i for i in range(1, 12)], 'C2': [i for i in range(2, 13)]})
#Example mean and standard deviation
df1 = pd.DataFrame({'Mean': [2, 1], 'Dev': [0.5, 1]})

#Transpose the mean column and subtract from the original dataframe
#Transpose the standard deviation column and divide
df_out = (df - df1['Mean'].to_numpy().T)/df1['dev'].to_numpy().T

This is assuming that the number of rows in the mean/standard deviation matrix is equal to the columns in the data matrix. It also assumes that each row number in the mean/standard deviation matrix corresponds to the same number column number in the data matrix.