I have encoded Gender column by OneHotEncoder. I want to apply log transformation to only Female[0] column but it is applying log to all the columns — why?

My code:

import pandas as p
from sklearn.preprocessing import FunctionTransformer, OneHotEncoder
from sklearn.compose import ColumnTransformer
import numpy as n

customer=p.read_csv('/content/Customers.csv')
customer.drop(['CustomerID','Profession','Family Size','Work Experience'],axis=1,inplace=True)
column=ColumnTransformer(
    [
        ('ohe_gender',OneHotEncoder(sparse=False,dtype=n.int32),[0])
    ],remainder='passthrough'
)
function=ColumnTransformer(
    [
        ('function',FunctionTransformer(n.log1p),[0,1])
    ],remainder='passthrough'
)
s=column.fit_transform(customer)
function.fit_transform(s)

Output:

    array([[0.00000000e+00, 6.93147181e-01, 1.90000000e+01, 1.50000000e+04, 3.90000000e+01],
           [0.00000000e+00, 6.93147181e-01, 2.10000000e+01, 3.50000000e+04, 8.10000000e+01],
           [6.93147181e-01, 0.00000000e+00, 2.00000000e+01, 8.60000000e+04, 6.00000000e+00],
           ...,
           [0.00000000e+00, 6.93147181e-01, 8.70000000e+01, 9.09610000e+04, 1.40000000e+01],
           [0.00000000e+00, 6.93147181e-01, 7.70000000e+01, 1.82109000e+05, 4.00000000e+00],
           [0.00000000e+00, 6.93147181e-01, 9.00000000e+01, 1.10610000e+05, 5.20000000e+01]]

After encoding (OHE) before FunctionTransformer the output was

array([[     0,      1,     19,  15000,     39],
       [     0,      1,     21,  35000,     81],
       [     1,      0,     20,  86000,      6],
       ...,
       [     0,      1,     87,  90961,     14],
       [     0,      1,     77, 182109,      4],
       [     0,      1,     90, 110610,     52]])

I do want to apply log transformation in the 0th index of the above array but as you can see in first output it is applying on all the values although I have specified [0] in column transformer, why? I expect the output with log of only [0] index.