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How to use sklearn Column Transformer?

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I'm trying to convert categorical value (in my case it is country column) into encoded value using LabelEncoder and then with OneHotEncoder and was able to convert the categorical value. But i'm getting warning like OneHotEncoder 'categorical_features' keyword is deprecated "use the ColumnTransformer instead." So how i can use ColumnTransformer to achieve same result ?

Below is my input data set and the code which i tried

Input Data set

Country Age Salary
France  44  72000
Spain   27  48000
Germany 30  54000
Spain   38  61000
Germany 40  67000
France  35  58000
Spain   26  52000
France  48  79000
Germany 50  83000
France  37  67000


import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder, OneHotEncoder

#X is my dataset variable name

label_encoder = LabelEncoder()
x.iloc[:,0] = label_encoder.fit_transform(x.iloc[:,0]) #LabelEncoder is used to encode the country value
hot_encoder = OneHotEncoder(categorical_features = [0])
x = hot_encoder.fit_transform(x).toarray()

And the output i'm getting as, How can i get the same output with column transformer

0(fran) 1(ger) 2(spain) 3(age)  4(salary)
1         0       0      44        72000
0         0       1      27        48000
0         1       0      30        54000
0         0       1      38        61000
0         1       0      40        67000
1         0       0      35        58000
0         0       1      36        52000
1         0       0      48        79000
0         1       0      50        83000
1         0       0      37        67000

i tried following code

from sklearn.compose import ColumnTransformer, make_column_transformer

preprocess = make_column_transformer(

    ( [0], OneHotEncoder())
)
x = preprocess.fit_transform(x).toarray()

i was able to encode country column with the above code, but missing age and salary column from x varible after transforming

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