分别规范化每一行

Question

I would like to apply the normalization function of (first value - the value)/(standard deviation of each row) to each row separately.

For example:

ID	A	B	C
1	20	3	6
2	30	4	7
3	40	5	8
...	...	...	...
1000	300	23	21

so I expect the first value of column "A" in each row will be zero. imagine the rows are more than 1K.

I tried to use this code but does not work

for i in range(len(df)):
new_norm = df((df.iloc[i][0] - df.iloc[i,:])/df.std(axis=1))
df= df.apply(new_norm, axis=1)
df

I used this one also and the same error

df = df((df.iloc[:,0] - df.iloc[:,:]) / df.std(axis=1))

Answer 1

import pandas as pd
df = pd.DataFrame({
    "ID": [1,2,3,10],    
    "A": [20,30,40,300],
    "B": [3,4,5,23],
    "C": [6,7,8,21]
})
vals = df.drop(columns=["ID"]).values
pd.DataFrame((vals[0, :] - vals) / vals.std(axis=0), columns=df.columns[1:])

output:

    A           B           C 
0   0.000000    0.000000    0.000000
1   -0.085377   -0.121101   -0.163846
2   -0.170755   -0.242202   -0.327693
3   -2.390566   -2.422019   -2.457696